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fatal error C1002: compiler out of heap space in pass 2 when build pip-package for tensorflow 2.9.3 and python 3.9.1 on Windows 10
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[
"My configure:\r\nD:\\NDev\\tensorflow-2.9.3>python ./configure.py\r\n\r\nYou have bazel 5.4.1 installed.\r\n\r\nPlease specify the location of python. [Default is C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python39\\python.exe]:\r\n\r\n\r\n\r\n\r\n\r\nFound possible Python library paths:\r\n\r\n C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\r\n\r\nPlease input the desired Python library path to use. Default is [C:\\Users\\Administrator\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages]\r\n\r\n\r\n\r\nDo you wish to build TensorFlow with ROCm support? [y/N]: N\r\n\r\nNo ROCm support will be enabled for TensorFlow.\r\n\r\n\r\n\r\nDo you wish to build TensorFlow with CUDA support? [y/N]: y\r\n\r\nCUDA support will be enabled for TensorFlow.\r\n\r\n\r\n\r\nDo you wish to build TensorFlow with TensorRT support? [y/N]: n\r\n\r\nNo TensorRT support will be enabled for TensorFlow.\r\n\r\n\r\n\r\nFound CUDA 11.2 in:\r\n\r\n C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.2/lib/x64\r\n\r\n C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.2/include\r\n\r\nFound cuDNN 8 in:\r\n\r\n C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.2/lib/x64\r\n\r\n C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.2/include\r\n\r\n\r\n\r\n\r\n\r\nPlease specify a list of comma-separated CUDA compute capabilities you want to build with.\r\n\r\nYou can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus. Each capability can be specified as \"x.y\" or \"compute_xy\" to include both virtual and binary GPU code, or as \"sm_xy\" to only include the binary code.\r\n\r\nPlease note that each additional compute capability significantly increases your build time and binary size, and that TensorFlow only supports compute capabilities >= 3.5 [Default is: 3.5,7.0]: 8.6\r\n\r\n\r\n\r\n\r\n\r\nPlease specify optimization flags to use during compilation when bazel option \"--config=opt\" is specified [Default is /arch:AVX]:\r\n\r\n\r\n\r\n\r\n\r\nWould you like to override eigen strong inline for some C++ compilation to reduce the compilation time? [Y/n]:\r\n\r\nEigen strong inline overridden.\r\n\r\n\r\n\r\nWould you like to interactively configure ./WORKSPACE for Android builds? [y/N]: n\r\n\r\nNot configuring the WORKSPACE for Android builds.\r\n\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\r\n --config=mkl # Build with MKL support.\r\n\r\n --config=mkl_aarch64 # Build with oneDNN and Compute Library for the Arm Architecture (ACL).\r\n\r\n --config=monolithic # Config for mostly static monolithic build.\r\n\r\n --config=numa # Build with NUMA support.\r\n\r\n --config=dynamic_kernels # (Experimental) Build kernels into separate shared objects.\r\n\r\n --config=v1 # Build with TensorFlow 1 API instead of TF 2 API.\r\n\r\nPreconfigured Bazel build configs to DISABLE default on features:\r\n\r\n --config=nogcp # Disable GCP support.\r\n\r\n --config=nonccl # Disable NVIDIA NCCL support.",
"@tricoffee If you're using an older version of the C++ compiler, consider upgrading to a newer version that may have better memory management. Please delete any existing build files and directories to ensure a fresh start. We recommend you to upgrade the TF version instead of the older version. Kindly let us know the update on this?\r\nThank you!",
"Upgrade the TF version to 2.10.0 with python 3.10.1 build complete successfully @sushreebarsa ",
"@tricoffee Thank you for the update!\r\nCould you please make this issue to closed status if it is resolved?\r\nThank 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/62688\">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/62688\">No</a>\n",
"build tensorflow_cc.dll with tensorflow-2.10.0 with python 3.10.1 Linking tensorflow/tensorflow_cc.dll failed: (Exit 1120): link.exe failed: error executing command @sushreebarsa "
] | 2023-12-24T07:05:17 | 2024-01-02T15:47:22 | 2024-01-02T06:14:05 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.9.3
### OS platform and distribution
Windows 10 Version 22H2 (OS Internal Version 19405.3803)
### Python version
3.9.1
### Bazel version
5.4.1
### GCC/compiler version
MSVC 2019
The Path Environment for compiler is
C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.26.28801\bin\Hostx64\x64
### CUDA/cuDNN version
cuDNN 8.1 for Cuda 11.2
Cuda was installed from cuda_11.2.0_460.89_win10.exe
Cudnn for cuda 11.2.0 is from cudnn-11.2-windows-x64-v8.1.0.77.zip
### GPU model and memory
NVIDIA GeForce RTX 3080 ti 12288MiB
Nvidia Driver Version is 546.33
The driver was installed from 546.33-desktop-win10-win11-64bit-international-nsd-dch-whql.exe
### Current behavior?
**build tensorflow_cc.dll for tensorflow 2.9.3 ** successfully.
but bazel build --config=opt --config=cuda --define=no_tensorflow_py_deps=true --copt=-nvcc_options=disable-warnings --local_ram_resources=2048 //tensorflow:tensorflow_cc.dll
**build tensorflow_cc.lib for tensorflow 2.9.3 ** successfully
but bazel build --config=opt --config=cuda --define=no_tensorflow_py_deps=true --copt=-nvcc_options=disable-warnings --local_ram_resources=2048 //tensorflow:tensorflow_cc_dll_import_lib
**build cpp headers for tensorflow 2.9.3 ** successfully
but bazel build --config=opt --config=cuda --define=no_tensorflow_py_deps=true --copt=-nvcc_options=disable-warnings --local_ram_resources=2048 //tensorflow:install_headers
but bazel build --config=opt --config=cuda --define=no_tensorflow_py_deps=true --copt=-nvcc_options=disable-warnings --local_ram_resources=**2048** //tensorflow/tools/pip_package:build_pip_package failed because compiler out of heap space in pass 2
**try again with more local RAM resources but failed for the same reason**
but bazel build --config=opt --config=cuda --define=no_tensorflow_py_deps=true --copt=-nvcc_options=disable-warnings --local_ram_resources=**4096** //tensorflow/tools/pip_package:build_pip_package failed because compiler out of heap space in pass 2
### Standalone code to reproduce the issue
```shell
but bazel build --config=opt --config=cuda --define=no_tensorflow_py_deps=true --copt=-nvcc_options=disable-warnings --local_ram_resources=4096 //tensorflow/tools/pip_package:build_pip_package
```
### Relevant log output
```shell
**Latest logs are:**
注意: 包含文件: D:\ndev\tf293_build\hu3sahy6\execroot\org_tensorflow\third_party\eigen3\unsupported\Eigen\CXX11\src/FixedPoint/MatVecProduct.h
注意: 包含文件: external/mkl_dnn_v1/include\dnnl.h
注意: 包含文件: D:\ndev\tf293_build\hu3sahy6\execroot\org_tensorflow\external\mkl_dnn_v1\include\oneapi/dnnl/dnnl.h
注意: 包含文件: bazel-out/x64_windows-opt/bin/external/mkl_dnn_v1/include\oneapi/dnnl/dnnl_config.h
注意: 包含文件: D:\ndev\tf293_build\hu3sahy6\execroot\org_tensorflow\external\mkl_dnn_v1\include\oneapi/dnnl/dnnl_types.h
注意: 包含文件: bazel-out/x64_windows-opt/bin/external/mkl_dnn_v1/include\oneapi/dnnl/dnnl_version.h
注意: 包含文件: .\tensorflow/core/platform/dynamic_annotations.h
注意: 包含文件: .\tensorflow/core/platform/platform.h
注意: 包含文件: .\tensorflow/core/platform/default/dynamic_annotations.h
注意: 包含文件: .\tensorflow/compiler/xla/service/cpu/runtime_lightweight_check.h
D:\ndev\tf293_build\hu3sahy6\execroot\org_tensorflow\external\eigen_archive\Eigen\src\Core\products\GeneralBlockPanelKernel.h(2073) : fatal error C1002: 在第 2 遍中编译器的堆空间 不足
Target //tensorflow/tools/pip_package:build_pip_package failed to build
INFO: Elapsed time: 14994.948s, Critical Path: 2927.75s
INFO: 9275 processes: 66 internal, 9209 local.
FAILED: Build did NOT complete successfully
```
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I_kwDOArmXAs56fKKE
| 62,687 |
Numerical precision of tf.math.cumsum operation on float32 vs float64
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[
"Hi **@sapphire008** ,\r\n I was able to reproduce this issue on Colab using TF v2.15, TF-nightly, Please find the [gist](https://colab.research.google.com/gist/Venkat6871/aa5298e814ce7c1652b57c9a3a1d6ea2/62687_2-15-v.ipynb), [gist1](https://colab.research.google.com/gist/Venkat6871/62dc5781e4c6aa38162ed7fec3dc668c/62687_nightly-v.ipynb) here for reference. \r\n\r\nThank you!"
] | 2023-12-24T05:14:53 | 2024-04-18T07:25:25 | null |
NONE
| null | null | null |
### Issue type
Performance
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15.0
### Custom code
Yes
### OS platform and distribution
Linux, MacOS M1 Chip
### 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 would like to report an interesting observation regarding numerical precision of cumsum operations implemented by Tensorflow (`tensorflow==2.15.0`), PyTorch (`torch==2.1.0`), and NumPy (`numpy==1.26.2`).
I have tested the example script (pasted in the Standalone code to reproduce the issue) that compares the cumulative sum of a large array of numbers. On MacOS Sonoma 14.1.1, MacBook Pro 2021 Apple M1 Pro machine, I have obtained the following results:
```text
Float32 results:
torch vs. numpy float32: 4.625
tf vs. numpy float32: 0.0
Float64 results:
torch vs. numpy float64: 0.0
tf vs. numpy float64: 0.0
torch f64 vs. f32: 0.007812499505234882
tf f64 vs. f32: 4.619280484243063
np f64 vs. f32: 4.619280484243063
```
On Colab with CPU (Linux OS) instance using the same versions of the library, I have
```text
Float32 results:
torch vs. numpy float32: 5.15625
tf vs. numpy float32: 0.0
Float64 results:
torch vs. numpy float64: 0.0
tf vs. numpy float64: 0.0
torch f64 vs. f32: 0.007812497526174411
tf f64 vs. f32: 5.155536882346496
np f64 vs. f32: 5.155536882346496
```
Both are saying, on CPU, Tensorflow obtains the same result as NumPy, which loses certain degree of numerical precision in the result of cumsum with float32 inputs. PyTorch, however, does not lose much of the precision.
However, if I run the same code on Colab with GPU (with Linux OS), using identical versions of the library, I have:
```text
Float32 results:
torch vs. numpy float32: 7.328125
tf vs. numpy float32: 7.34375
Float64 results:
torch vs. numpy float64: 0.0
tf vs. numpy float64: 1.4551915228366852e-10
torch f64 vs. f32: 0.007812497817212716
tf f64 vs. f32: 0.1570705088088289
np f64 vs. f32: 7.322426828060998
```
This shows that Tensorflow does not lose much precision on GPU, though slightly more than PyTorch. NumPy still loses precision (since it still uses CPU).
This is an interesting observation. The behavior of PyTorch's cumsum seems more desirable, since it can significantly cut down memory during inference time while retaining certain numerical precision.
### Standalone code to reproduce the issue
Link to [Colab](https://colab.research.google.com/drive/1WBd0FhYacxmak2gVS4GNctRS-IxeJc7M?usp=sharing)
```shell
import numpy as np
import tensorflow as tf
import torch
# float32
X = np.random.rand(16, 500000).astype(np.float32)
np_cumsum_fp32 = np.cumsum(X, axis=1)
tf_cumsum_fp32 = tf.cumsum(tf.constant(X, tf.float32), axis=1)
torch_cumsum_fp32 = torch.cumsum(torch.tensor(X, dtype=torch.float32), 1)
print("Float32 results:")
print("torch vs. numpy float32:", np.abs(torch_cumsum_fp32.numpy() - np_cumsum_fp32).max())
print("tf vs. numpy float32:", np.abs(tf_cumsum_fp32.numpy() - np_cumsum_fp32).max())
# Casting the original value to float64
X = X.astype(np.float64) # same data!
np_cumsum_fp64 = np.cumsum(X, axis=1)
tf_cumsum_fp64 = tf.cumsum(tf.constant(X, tf.float64), axis=1)
torch_cumsum_fp64 = torch.cumsum(torch.tensor(X, dtype=torch.float64), 1)
print("\n")
print("Float64 results:")
print("torch vs. numpy float64:", np.abs(torch_cumsum_fp64.numpy() - np_cumsum_fp64).max())
print("tf vs. numpy float64:", np.abs(tf_cumsum_fp64.numpy() - np_cumsum_fp64).max())
print("torch f64 vs. f32:", np.abs((torch_cumsum_fp32 - torch_cumsum_fp64).numpy()).max())
print("tf f64 vs. f32:", np.abs(tf_cumsum_fp32.numpy() - tf_cumsum_fp64.numpy()).max())
print("np f64 vs. f32:", np.abs(np_cumsum_fp32 - np_cumsum_fp64).max())
```
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_No response_
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| 62,686 |
Tensorflow Java 2.10.1 memory leak issue on constant creation
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[
"@posuhov,\r\nCould you please provide the error log which you are facing the issue, that helps to analyse the issue in deeper. Thank you!",
"@tilakrayal there is no error log. 8Gb of memory consumed within 8 seconds by the code snippet I've attached to the issue description. Once there is no memory anymore device just stop to functioning. \r\nI can attach library that I've compiled if it helps.\r\n[libtensorflow-2.10.1.jar.zip](https://github.com/tensorflow/tensorflow/files/13773517/libtensorflow-2.10.1.jar.zip)\r\n\r\nAlso I attach 2 screenshots where we can see that allocated memory for process on linux is not visible by java. From java's point of view we use only 147Mb, but from OS point of view - 7.2Gb\r\n\r\n\r\n",
"@sachinprasadhs an updates on that?",
" I was able to compile the version 2.15.0 on Ubuntu(20.04) using a command\r\n\r\n```\r\nbazel build --config=elinux_aarch64 \\\r\n --copt=-O2 \\\r\n --config=monolithic \\\r\n --define tensorflow_mkldnn_contraction_kernel=0 \\\r\n --verbose_failures \\\r\n --config=nonccl \\\r\n --config=nogcp \\\r\n //tensorflow/java:tensorflow //tensorflow/java:libtensorflow_jni\r\n```\r\nIt didn't start on the Raspberry Pi with Debian and bullseye, but I was able to make it work on Raspberry with Ubuntu. Unfortunately, the memory leak issue is still reproduceable with 2.15.0 \r\n@sachinprasadhs @tilakrayal can I expect someone to take a look on this issue?",
"Since support here is awful and not able to respond during several months, I will post a solution here, maybe it will be useful for somebody. The problem was that `Ops.create()` creates an internal eager session under the hood and all createdconstants are stored there. To fix it we need to move ops creation inside the loop and create a different session every time for it. like\r\n```\r\nEagerSession eagerSession = EagerSession.create();\r\nOps ops = Ops.create(eagerSession);\r\n...\r\neagerSession.close();\r\n```\r\nwith this approach memory is stable and not growing",
"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/62686\">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/62686\">No</a>\n"
] | 2023-12-23T22:00:44 | 2024-04-04T19:30:32 | 2024-02-25T21:51:41 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.10.1
### Custom code
No
### OS platform and distribution
debian aarch64
### Mobile device
Raspberry pi
### Python version
_No response_
### Bazel version
5.1.1
### GCC/compiler version
11.4.0
### CUDA/cuDNN version
no
### GPU model and memory
no
### Current behavior?
I've built tensorflow java for raspberry and aarch64 architecture with following command on Ubuntu 22.04
```
bazel build --config=elinux_aarch64 \
--copt=-std=gnu11 \
--copt=-O2 \
--config=monolithic \
--define tensorflow_mkldnn_contraction_kernel=0 \
--verbose_failures \
//tensorflow/java:tensorflow //tensorflow/java:libtensorflow_jni
```
When the constant is created in a code, it seems it doesn't free a memory properly. The memory consumption reaches 8Gb in 8 seconds.
I've identified that the problem is in this invocation which is invoked in Constant class
https://github.com/tensorflow/tensorflow/blob/v2.10.1/tensorflow/java/src/main/java/org/tensorflow/op/core/Constant.java#L642-L647
However I stuck here. Unfortunately, I can't build the most recent tensorflow 2.15.0 because it fails with some strange error.
Could you please help me to identify what I'm doing wrong?
### Standalone code to reproduce the issue
```shell
public static void main(String[] args) {
Ops ops = Ops.create();
System.out.println("Ops created");
int capacity = 480 * 640 * 3;
ByteBuffer byteBuffer = ByteBuffer.allocateDirect(capacity);
for (int i = 0; i < capacity; i++) {
byteBuffer.put((byte) 200);
}
byteBuffer.rewind();
while (!Thread.interrupted()) {
byteBuffer.rewind();
long[] imageShape = new long[]{480, 640, 3};
Constant<UInt8> constant = ops.constant(UInt8.class, imageShape, byteBuffer);
System.out.println(constant);
}
}
```
### Relevant log output
_No response_
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TFLite cannot correctly recognize images
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[
"@B-JackMao,\r\nCould you please share a reproducible code that supports your statement and also the tensorflow version you are using, so that the issue can be easily understood? Thank you!",
"I am using TensorFlow Lite version 2.9.0, and I suspect that the context environment has changed. When I put one of the parts into a GPU delegate there is no problem.\r\n\r\n\r\n\r\n------------------ 原始邮件 ------------------\r\n发件人: ***@***.***>; \r\n发送时间: 2023年12月26日(星期二) 晚上8:59\r\n收件人: ***@***.***>; \r\n抄送: ***@***.***>; ***@***.***>; \r\n主题: Re: [tensorflow/tensorflow] TFLite cannot correctly recognize images (Issue #62685)\r\n\r\n\r\n\r\n\r\n\r\n \r\n，\r\n您能否分享一个支持您的陈述以及您正在使用的 tensorflow 版本的可重现代码，以便轻松理解该问题？谢谢！\r\n \r\n-\r\n 直接回复此电子邮件，在 GitHub 上查看或取消订阅。\r\n您收到此邮件是因为有人提到您。Message ID: ***@***.***>",
"Hi @B-JackMao, we can't really help you if you don't provide more context. Can you provide code which shows this problem? i.e. one function which does the left side first, another which does the right side first. Show that the loss or how you are measuring that an image is \"correctly recognized\" is different. Can you upload your .tflite model as well? Thanks for your help.",
"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/62685\">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/62685\">No</a>\n"
] | 2023-12-23T10:34:36 | 2024-01-11T01:49:27 | 2024-01-11T01:49:25 |
NONE
| null | null | null |
When I perform inference on a model with TFLite's CPU backend (without any delegates), the image can be correctly recognized if I run the operators on the left first and then run the operators on the right. However, if I run the operators on the right first and then run the operators on the left, the image cannot be correctly recognized. Is it because the context of operators is determined during initialization? How can I solve this problem? I can ensure that the .tflite model file is not problematic.
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PR_kwDOArmXAs5isVqC
| 62,684 |
Fix protobuf errors when using system protobuf
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[
"This seems to fail API compat test. Can you run the following, please?\r\n\r\n```\r\nbazel run tensorflow/tools/api/tests:api_compatibility_test -- --update_goldens True\r\n```\r\n\r\nThank you"
] | 2023-12-23T06:20:11 | 2024-03-05T06:28:08 | 2024-03-05T06:28:08 |
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```
File "/home/conda/lib/python3.10/site-packages/tensorflow/python/framework/meta_graph.py", line 557, in create_meta_graph_def
meta_graph_def.graph_def.MergeFrom(graph_def)
TypeError: Parameter to MergeFrom() must be instance of same class: expected tensorflow.GraphDef got tensorflow.GraphDef.
```
This error happened in TF 2.4 and 2.5 and was fixed. However, it appeared again in TF 2.15, as `from tensorflow.python import pywrap_tensorflow` was removed in 719f6da42226319a9348f55b86d3103881bae0ee. See previous issues and fixes:
- https://github.com/tensorflow/tensorflow/issues/42596
- bad835f9af3f1e1e04f1ea69db902add1b5076f9
- https://github.com/tensorflow/tensorflow/issues/50545
- https://github.com/tensorflow/tensorflow/pull/51450
This PR adds `from tensorflow.python import pywrap_tensorflow` to `tensorflow/__init__.py` (generated by `api_template.__init__.py`), and it fixes the error when I test it. I am not sure whether there is a better place to add this line. (it seems `tensorflow/python/__init__.py` does not have any import)
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I_kwDOArmXAs56bM98
| 62,683 |
model detects hand as a face. I cant retrieve confidence score from tfjs-models/face-detection
|
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[
"@DiannaShonia Please ensure that you're using a model version that provides confidence scores. Some older versions might not provide the same. Also try to raise the minimum confidence score required for a detection to be considered a face. This can filter out less confident detections, potentially reducing false positives.\r\n\r\nThis issue is related to tfjs so for any further questions , could you please post this issue in [tfjs](https://github.com/tensorflow/tfjs/issues) repository?\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/62683\">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/62683\">No</a>\n"
] | 2023-12-22T13:50:35 | 2024-01-12T01:49:24 | 2024-01-12T01:49:20 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
last version
### 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?
In mediapipe model documentation (see: https://drive.google.com/file/d/1d4-xJP9PVzOvMBDgIjz6NhvpnlG9_i0S/preview) it says that alongside with bounding box coordinates and keypoints, confidence score should be also returned.
But there is no info about that in face-detection github repo and in the code.
In face-detection Demo, you can see for yourself that fists are displayed as faces. I need confidence score to prevent this action.
https://storage.googleapis.com/tfjs-models/demos/face-detection/index.html?model=mediapipe_face_detector
### Standalone code to reproduce the issue
```shell
https://storage.googleapis.com/tfjs-models/demos/face-detection/index.html?model=mediapipe_face_detector
Try to show different hand movements, its being detected as face (mostly fist)
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5ipcu0
| 62,682 |
[AMD-ZENDNN] Enable CPU allocator for plugin
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[
"/cc @penpornk "
] | 2023-12-22T12:22:20 | 2024-01-03T22:44:09 | 2024-01-03T22:44:09 |
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- Higher throughput has been observed for plugin solution with MklCPUAllocator when compared against base CPU allocator.
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Inconsistance tensor shape led to code can't execute properly
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[
"Hi **@xsmilingtoast** ,\r\n\r\nCould you provide a proper link. You already provide one link but it is not opening. In order to expedite the trouble-shooting process, please provide a code snippet to reproduce the issue reported here.\r\nAnd here you are using the old version, Try to update your version.\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/62681\">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/62681\">No</a>\n"
] | 2023-12-22T09:58:37 | 2024-01-10T01:49:17 | 2024-01-10T01:49:13 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.10.0
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
3.10.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
The code led to inconsistance tensor shape addition and can't run properly ,please help
### Standalone code to reproduce the issue
```shell
code in jupyter notebook
file:///G:/Google%20Download/AMD%20bigram.html
```
### Relevant log output
```shell
ValueError Traceback (most recent call last)
Cell In[171], line 138
134 model_on_device = model
137 context = tf.zeros(shape = (1,1) , dtype = tf.int64 )
--> 138 generated_chars = decode(model.generate(context , max_new_tokens=500)[0].numpy().tolist())
139 print(generated_chars)
Cell In[171], line 121, in BigramLanguageModel.generate(self, index, max_new_tokens)
118 def generate(self ,index ,max_new_tokens):
120 for _ in range(max_new_tokens):
--> 121 logits , loss = self.call(index)
123 logits = logits[ : , -1 , : ]
125 probs = tf.nn.softmax(logits , axis=-1)
Cell In[171], line 103, in BigramLanguageModel.call(self, index, targets)
101 pos_emb = self.position_embedding_table(tf.range(T , dtype=tf.float32))
102 x = tok_emb + pos_emb
--> 103 x = self.blocks(x) #decoder cycle
104 x = self.ln_f(x) #normalize
105 logits = self.lm_head(x) #linear transform
File E:\AMD venv for gpt\tensorflow\lib\site-packages\keras\utils\traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
67 filtered_tb = _process_traceback_frames(e.__traceback__)
68 # To get the full stack trace, call:
69 # `tf.debugging.disable_traceback_filtering()`
---> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb
Cell In[171], line 70, in Block.call(self, x, training)
68 y = self.sa(x)
69 x = self.ln1(x + y)
---> 70 y = self.ffwd(x)
71 x = self.ln2(x + y)
72 return x
Cell In[171], line 55, in FeedForward.call(self, x)
53 def call(self , x):
54 ffwd_output = self.net(x)
---> 55 return self.net(ffwd_output)
ValueError: Exception encountered when calling layer "sequential_449" " f"(type Sequential).
Input 0 of layer "dense_5747" is incompatible with the layer: expected axis -1 of input shape to have value 2688, but received input with shape (1, 1536)
Call arguments received by layer "sequential_449" " f"(type Sequential):
• inputs=tf.Tensor(shape=(1, 1536), dtype=float32)
• training=True
• mask=None
```
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I_kwDOArmXAs56ZsPt
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Android C API Select TensorFlow op(s), included in the given model, is(are) not supported by this interpreter
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[
"In my another android project, I run the TFLite benchmark code link the libtensorflowlite_flex.so, and run benchmark with the model(hava select op)automatic use FlexDelegate success. I can not find why this project can success but the first project is fail.\r\n\r\n<img width=\"1287\" alt=\"企业微信20231225-164009@2x\" src=\"https://github.com/tensorflow/tensorflow/assets/30921763/2f4dfd3b-3fca-403d-bbdb-26825ef792af\">\r\nAs a contrast, in this benchmark project AcquireFlexDelegate function will not run in to the default weak define in interpreter_builder.cc !!!!!!!!, It looks like the AcquireFlexDelegate really define in libtensorflowlite_flex.so work success!\r\n\r\nif(BUILD_TFLITE_BENCHMARK_BY_SOURCE)\r\n SET(TNET_TENSORFLOW_LITE_PATH \"${CMAKE_CURRENT_SOURCE_DIR}/../../../3rdparty/TNetTensorflow/tensorflow/lite\")\r\n add_subdirectory(\r\n ${TNET_TENSORFLOW_LITE_PATH}\r\n \"${CMAKE_CURRENT_BINARY_DIR}/tnet_tensorflow_lite\"\r\n )\r\nendif()\r\n\r\n\r\n\r\nif(CMAKE_SYSTEM_NAME MATCHES \"Android\")\r\n add_library(TFLITE_FLEX SHARED IMPORTED)\r\n set_target_properties(TFLITE_FLEX PROPERTIES IMPORTED_LOCATION ${CMAKE_CURRENT_SOURCE_DIR}/libs/android/${ANDROID_ABI}/libtensorflowlite_flex.so)\r\n target_link_libraries(TNET_TFLITE_BENCHMARK\r\n TFLITE_FLEX)\r\n\r\n #依赖的三方库\r\n if(BUILD_TFLITE_BENCHMARK_BY_SOURCE)\r\n target_link_libraries(TNET_TFLITE_BENCHMARK\r\n benchmark_model\r\n )\r\n else()\r\n add_library(TFLITE_BENCHMARK SHARED IMPORTED)\r\n set_target_properties(TFLITE_BENCHMARK PROPERTIES IMPORTED_LOCATION ${CMAKE_CURRENT_SOURCE_DIR}/libs/android/${ANDROID_ABI}/libbenchmark_model.so)\r\n target_link_libraries(TNET_TFLITE_BENCHMARK\r\n TFLITE_BENCHMARK\r\n )\r\n endif()\r\nelseif (CMAKE_SYSTEM_NAME MATCHES \"iOS\")\r\n\r\nelse()\r\n\r\nendif()",
"Hi @Qinlong275, are you able to export your project here? That'll be the easiest, alternatively we can try reproducing with just your model. Feel free to reduce the model to enough to just reproduce the case if you can. Thanks for your help.",
"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/62680\">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/62680\">No</a>\n"
] | 2023-12-22T08:17:45 | 2024-01-17T01:49:36 | 2024-01-17T01:49:33 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tflite 2.12.0
### Custom code
Yes
### OS platform and distribution
mac apple m1; android studio
### Mobile device
HuaWei Nova6
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I run my custom tflite model(have select_op) on android studio use C API(libtensorflowlite_c.so build from sourcecode use android ndk, if i build the /tensorflow/lite dir link tensorflow-lite.so will occur same error !!!!!!!!), and I have linked the libtensorflowlite_flex.so(build follow the https://www.tensorflow.org/lite/guide/ops_select)
But occur these error:
E Select TensorFlow op(s), included in the given model, is(are) not supported by this interpreter. Make sure you apply/link the Flex delegate before inference. For the Android, it can be resolved by adding "org.tensorflow:tensorflow-lite-select-tf-ops" dependency. See instructions: https://www.tensorflow.org/lite/guide/ops_select
2023-12-19 12:44:34.993 3039-5562 tflite com...cent.karaoke.tNetInterpretApp E Node number 622 (FlexDepthwiseConv2dNative) failed to prepare.
2023-12-19 12:44:34.993 3039-5562 tflite com...cent.karaoke.tNetInterpretApp E Failed to allocate tensors!
I think maybe the AcquireFlexDelegate has not work?, I find the source code, the AcquireFlexDelegate looks like a attribute((weak)) symbal. I have loaded the libtensorflowlite_gpu_jni.so before start the interpret.
<img width="1287" alt="企业微信20231225-164009@2x" src="https://github.com/tensorflow/tensorflow/assets/30921763/4c6a83d8-8d74-4170-af39-3675c5f68e72">
<img width="1326" alt="企业微信20231225-164111@2x" src="https://github.com/tensorflow/tensorflow/assets/30921763/c408493c-3513-4034-b7c7-2de3e272d144">
I debug the project, AcquireFlexDelegate function will run in to the default weak define in interpreter_builder.cc. So it may be the really redefine AcquireFlexDelegate not work in libtensorflowlite_flex.so.
### Standalone code to reproduce the issue
```shell
CMakeLists.txt:
if(BUILD_TFLITE_C_BY_SOURCE)
SET(TNET_TENSORFLOW_LITE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../3rdparty/TNetTensorflow/tensorflow/lite/c")
//if i build the /tensorflow/lite dir link tensorflow-lite.so will occur same error !!!!!!!!
add_subdirectory(
${TNET_TENSORFLOW_LITE_PATH}
"${CMAKE_CURRENT_BINARY_DIR}/tnet_tensorflow_lite_c"
)
endif()
add_library(TFLITE_FLEX SHARED IMPORTED)
set_target_properties(TFLITE_FLEX PROPERTIES IMPORTED_LOCATION ${CMAKE_CURRENT_SOURCE_DIR}/libs/android/${ANDROID_ABI}/libtensorflowlite_flex.so)
target_link_libraries(TNET_TFLITE
TFLITE_FLEX)
#依赖的三方库
if(BUILD_TFLITE_C_BY_SOURCE)
target_link_libraries(TNET_TFLITE
tensorflowlite_c
)
else()
add_library(TFLITE_CORE SHARED IMPORTED)
set_target_properties(TFLITE_CORE PROPERTIES IMPORTED_LOCATION ${CMAKE_CURRENT_SOURCE_DIR}/libs/android/${ANDROID_ABI}/libtensorflowlite_c.so)
target_link_libraries(TNET_TFLITE
TFLITE_CORE
)
endif()
Ndk generate configure_command file cmake commad:
/Users/qinlong/Library/Android/sdk/cmake/3.22.1/bin/cmake \
-H/Users/qinlong/aiInterpret/FrontAIFrameworkBack/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/jni \
-DCMAKE_SYSTEM_NAME=Android \
-DCMAKE_EXPORT_COMPILE_COMMANDS=ON \
-DCMAKE_SYSTEM_VERSION=21 \
-DANDROID_ABI=arm64-v8a \
-DCMAKE_ANDROID_ARCH_ABI=arm64-v8a \
-DANDROID_NDK=/Users/qinlong/Library/Android/sdk/ndk/22.1.7171670 \
-DCMAKE_ANDROID_NDK=/Users/qinlong/Library/Android/sdk/ndk/22.1.7171670 \
-DCMAKE_TOOLCHAIN_FILE=/Users/qinlong/Library/Android/sdk/ndk/22.1.7171670/build/cmake/android.toolchain.cmake \
-DCMAKE_MAKE_PROGRAM=/Users/qinlong/Library/Android/sdk/cmake/3.22.1/bin/ninja \
"-DCMAKE_CXX_FLAGS=-std=c++17 -fno-ident -frtti -fexceptions" \
-DCMAKE_LIBRARY_OUTPUT_DIRECTORY=/Users/qinlong/aiInterpret/FrontAIFrameworkBack/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/build/intermediates/cxx/Release/16452w4l/obj/arm64-v8a \
-DCMAKE_RUNTIME_OUTPUT_DIRECTORY=/Users/qinlong/aiInterpret/FrontAIFrameworkBack/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/build/intermediates/cxx/Release/16452w4l/obj/arm64-v8a \
-B/Users/qinlong/aiInterpret/FrontAIFrameworkBack/FrontAIFramework/test/android/lib_tnet_interpret/lib_tnet_interpretsdk/.cxx/Release/16452w4l/arm64-v8a \
-GNinja \
-DANDROID_ARM_NEON=TRUE \
-DANDROID_PLATFORM=android-19 \
-DANDROID_TOOLCHAIN=clang \
-DANDROID_STL=c++_static \
-DCMAKE_BUILD_TYPE=Release \
-DTFLITE_ENABLE_XNNPACK=ON \
-DTFLITE_ENABLE_GPU=ON \
-DTFLITE_ENABLE_BENCHMARK=OFF \
-DBUILD_TFLITE_BENCHMARK_BY_SOURCE=OFF \
-DBUILD_TFLITE_C_BY_SOURCE=ON
```
### Relevant log output
_No response_
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I_kwDOArmXAs56Y2mG
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TensorFlow Lite Inference Crash with `tf.reverse(x, axis=[])`
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[
"Hi @pkgoogle \r\nI have reproduced the issue in both tf-nightly and stable version(tf 2.15) as well. The session has crashed in both versions. Here is the [gist](https://colab.research.google.com/gist/LakshmiKalaKadali/5806e0f8dd5a92e648da714728c91408/-62679.ipynb). Please look into the issue.\r\nThank You\r\n",
"I am able to replicate w/ the same gist, I should note that tf.reverse(x, axis=[]) is effectively a non-op. @ganler, Is that what you intended? I.e. you are saying reverse no axes.",
"Yes the usage leading to a crash is reverse of no axes and it is a non-op. I am reporting it as a bug here as it unexpectedly crashed the Python program leading to inconveniences in an automated model generation pipeline. Maybe it is better to just eliminate the non-op and move on instead of crash without errors. :)",
"No worries, just wanted to ensure you are able to continue w/ your work while we investigate this case, @nutsiepully can you please take a look? Thanks.",
"Hi @ganler , if you are able to access a linux system you may be able to resolve your issue by using [AI-Edge-Torch](https://github.com/google-ai-edge/ai-edge-torch), you can find more information here: [googleblog](https://developers.googleblog.com/en/ai-edge-torch-high-performance-inference-of-pytorch-models-on-mobile-devices/).\r\n\r\nI have actually created a simple script for converting your model here:\r\n\r\n```\r\nimport torch\r\nimport torch.nn as nn\r\nimport ai_edge_torch\r\n\r\nclass Foo(nn.Module):\r\n def __init__(self):\r\n super(Foo, self).__init__()\r\n\r\n def forward(self, x):\r\n return torch.flip(x, dims=[])\r\n\r\nfoo = Foo()\r\n\r\nsample_input = (torch.randn(1),)\r\n\r\n# Convert the model using AI Edge Torch\r\nedge_model = ai_edge_torch.convert(foo.eval(), sample_input)\r\n\r\n# Export the model to TFLite format\r\nedge_model.export('foo_model.tflite')\r\n```\r\n\r\nIf you want to, you can actually try visualizing the result in [model-explorer](https://github.com/google-ai-edge/model-explorer) as well.\r\n\r\nPlease try them out and let us know if this resolves your issue. If you still need further help, feel free to open a new issue at the respective repo."
] | 2023-12-22T03:47:15 | 2024-06-12T08:57:27 | null |
CONTRIBUTOR
| null | null | null |
### 1. System information
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): ubuntu 22
- TensorFlow installation (pip package or built from source): `pip install tf-nightly` where python is 3.9
- TensorFlow library (version, if pip package or github SHA, if built from source):
```
pip show tf-nightly
Name: tf-nightly
Version: 2.16.0.dev20231221
...
```
### 2. Code
Colab link: https://colab.research.google.com/drive/1gAsclHMWEf9in0wkF-y1nIbbFrh1m11V?usp=sharing
```python
import tensorflow as tf
tf.reverse(tf.ones((1,), dtype=tf.float32), []) # no problem
class Foo(tf.Module):
@tf.function(input_signature=[tf.TensorSpec(shape=[None], dtype=tf.float32)])
def reverse(self, x):
# works fine if axis = [0]
# crashes if axis = []
return tf.reverse(x, axis=[])
foo = Foo()
converter = tf.lite.TFLiteConverter.from_concrete_functions(
funcs=[foo.reverse.get_concrete_function()],
trackable_obj=foo,
)
tflite_model = converter.convert()
interpreter = tf.lite.Interpreter(model_content=tflite_model)
# crash
interpreter.get_signature_runner()(x=tf.ones((1,), dtype=tf.float32))
```
<img width="1028" alt="image" src="https://github.com/tensorflow/tensorflow/assets/38074777/33de223a-d703-4f6d-a43e-953d1593b958">
### 3. Failure after conversion
Converted model crash at inference and the model is fully valid.
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I_kwDOArmXAs56X_C2
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LLVM Errors on pre-built tensorflow
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[
"@balloch Could you please review your environment for other libraries that might conflict with LLVM or TensorFlow's build configuration and try to reinstall TensorFlow using `pip install --force-reinstall tensorflow `to ensure clean installation and proper linking.\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.",
"tried with `--force-reinstall` . No change",
"Hi @balloch ,\r\n\r\nFrom the log I see an error related to `ptxas` version compatibility. This is generally associated with cuda toolkit. From Tf2.14v TF provides CUDA packaged tensorflow with the command `pip install tensorflow[and-cuda]==2.14`. Can you try installing cuda package using above command and let us know the outcome. Thanks!\r\n\r\nNote: Please try with fresh installation of tensorflow deleting previously installed packages.",
"I think I figured it out...ish. If I use `conda` to first install the `cuda 11.8` toolkit first then the following tf install works, but its odd that just installing tf doesn't work. for anyone interested in the future, the specific command was:\r\n\r\n```\r\nconda install -c \"nvidia/label/cuda-XX.YY.ZZ\" cuda\r\n```\r\nwhere for the conda channel (`-c`), XX is the major cuda version, YY is the minor cuda version, and ZZ is the patchfix version. For me because of my NDIVIA driver version, I need to work with cuda<12, so for me the command that worked was:\r\n```\r\nconda install -c \"nvidia/label/cuda-11.8.0\" cuda\r\n```\r\n\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/62678\">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/62678\">No</a>\n"
] | 2023-12-21T22:02:00 | 2024-01-13T19:40:43 | 2024-01-13T19:40:39 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.14.0
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.8/8.9.7
### GPU model and memory
NVIDIA RTX3090 (24GB)
### Current behavior?
While trying to train an SAC reinforcement learner on Continual World -- a spinoff of the Farama Foundation MetaWorld -- I am getting what looks like an LLVM build error any time a @tf.function decorator is used. Specifically the error is:
```
: CommandLine Error: Option 'help-list' registered more than once!
LLVM ERROR: inconsistency in registered CommandLine options
```
It seems like this type of error [exists in a lot of other ecosystems](https://discourse.llvm.org/t/can-something-be-done-with-the-inconsistency-in-registered-commandline-options-error/1720), but almost always is an issue with code built from source. Mine is a binary, installed through pip in an environment managed by conda, so I would expect that it would work
### Standalone code to reproduce the issue
```shell
can be found here:
https://github.com/balloch/continual_world/blob/73f63bb4fa0b5d00bda973e20dfb783bfcf1b8aa/run_cl.py
import math
import os
import random
import time
from typing import Callable, Dict, List, Optional, Tuple, Union
import gymnasium as gym
import numpy as np
import tensorflow as tf
from continualworld.sac import models
from continualworld.sac.models import PopArtMlpCritic
from continualworld.sac.replay_buffers import ReplayBuffer, ReservoirReplayBuffer
from continualworld.sac.utils.logx import EpochLogger
from continualworld.utils.enums import BufferType
from continualworld.utils.utils import reset_optimizer, reset_weights, set_seed
class SAC:
def __init__(
self,
env: gym.Env,
test_envs: List[gym.Env],
logger: EpochLogger,
actor_cl: type = models.MlpActor,
actor_kwargs: Dict = None,
critic_cl: type = models.MlpCritic,
critic_kwargs: Dict = None,
seed: int = 0,
steps: int = 1_000_000,
log_every: int = 20_000,
replay_size: int = 1_000_000,
gamma: float = 0.99,
polyak: float = 0.995,
lr: float = 1e-3,
alpha: Union[float, str] = "auto",
batch_size: int = 128,
start_steps: int = 10_000,
update_after: int = 1000,
update_every: int = 50,
num_test_eps_stochastic: int = 10,
num_test_eps_deterministic: int = 1,
max_episode_len: int = 200,
save_freq_epochs: int = 100,
reset_buffer_on_task_change: bool = True,
buffer_type: BufferType = BufferType.FIFO,
reset_optimizer_on_task_change: bool = False,
reset_critic_on_task_change: bool = False,
clipnorm: float = None,
target_output_std: float = None,
agent_policy_exploration: bool = False,
):
"""A class for SAC training, for either single task, continual learning or multi-task learning.
After the instance is created, use run() function to actually run the training.
Args:
env: An environment on which training will be performed.
test_envs: Environments on which evaluation will be periodically performed;
for example, when env is a multi-task environment, test_envs can be a list of individual
task environments.
logger: An object for logging the results.
actor_cl: Class for actor model.
actor_kwargs: Kwargs for actor model.
critic_cl: Class for critic model.
critic_kwargs: Kwargs for critic model.
seed: Seed for randomness.
steps: Number of steps the algorithm will run for.
log_every: Number of steps between subsequent evaluations and logging.
replay_size: Size of the replay buffer.
gamma: Discount factor.
polyak: Interpolation factor in polyak averaging for target
networks. Target networks are updated towards main networks
according to:
target_weights <- polyak * target_weights + (1 - polyak) * weights
(Always between 0 and 1, usually close to 1.)
lr: Learning rate for the optimizer.
alpha: Entropy regularization coefficient. Can be either float value,
or "auto", in which case it is dynamically tuned.
(Equivalent to inverse of reward scale in the original SAC paper.)
batch_size: Minibatch size for the optimization.
start_steps: Number of steps for uniform-random action selection, before running real
policy. Helps exploration.
update_after: Number of env interactions to collect before starting to do gradient
descent updates. Ensures replay buffer is full enough for useful updates.
update_every: Number of env interactions that should elapse between gradient descent
updates.
Note: Regardless of how long you wait between updates, the ratio of env steps to
gradient steps is locked to 1.
num_test_eps_stochastic: Number of episodes to test the stochastic policy in each
evaluation.
num_test_eps_deterministic: Number of episodes to test the deterministic policy in each
evaluation.
max_episode_len: Maximum length of trajectory / episode / rollout.
save_freq_epochs: How often, in epochs, to save the current policy and value function.
(Epoch is defined as time between two subsequent evaluations, lasting log_every steps)
reset_buffer_on_task_change: If True, replay buffer will be cleared after every task
change (in continual learning).
buffer_type: Type of the replay buffer. Either 'fifo' for regular FIFO buffer
or 'reservoir' for reservoir sampling.
reset_optimizer_on_task_change: If True, optimizer will be reset after every task change
(in continual learning).
reset_critic_on_task_change: If True, critic weights are randomly re-initialized after
each task change.
clipnorm: Value for gradient clipping.
target_output_std: If alpha is 'auto', alpha is dynamically tuned so that standard
deviation of the action distribution on every dimension matches target_output_std.
agent_policy_exploration: If True, uniform exploration for start_steps steps is used only
in the first task (in continual learning). Otherwise, it is used in every task.
"""
set_seed(seed, env=env)
if actor_kwargs is None:
actor_kwargs = {}
if critic_kwargs is None:
critic_kwargs = {}
self.env = env
self.num_tasks = env.num_envs
self.test_envs = test_envs
self.logger = logger
self.critic_cl = critic_cl
self.critic_kwargs = critic_kwargs
self.steps = steps
self.log_every = log_every
self.replay_size = replay_size
self.gamma = gamma
self.polyak = polyak
self.alpha = alpha
self.batch_size = batch_size
self.start_steps = start_steps
self.update_after = update_after
self.update_every = update_every
self.num_test_eps_stochastic = num_test_eps_stochastic
self.num_test_eps_deterministic = num_test_eps_deterministic
self.max_episode_len = max_episode_len
self.save_freq_epochs = save_freq_epochs
self.reset_buffer_on_task_change = reset_buffer_on_task_change
self.buffer_type = buffer_type
self.reset_optimizer_on_task_change = reset_optimizer_on_task_change
self.reset_critic_on_task_change = reset_critic_on_task_change
self.clipnorm = clipnorm
self.agent_policy_exploration = agent_policy_exploration
self.use_popart = critic_cl is PopArtMlpCritic
self.obs_dim = env.observation_space.shape[0]
self.act_dim = env.action_space.shape[0]
# This implementation assumes all dimensions share the same bound!
assert np.all(env.action_space.high == env.action_space.high[0])
# Share information about action space with policy architecture
actor_kwargs["action_space"] = env.action_space
actor_kwargs["input_dim"] = self.obs_dim
critic_kwargs["input_dim"] = self.obs_dim + self.act_dim
# Create experience buffer
if buffer_type == BufferType.FIFO:
self.replay_buffer = ReplayBuffer(
obs_dim=self.obs_dim, act_dim=self.act_dim, size=replay_size
)
elif buffer_type == BufferType.RESERVOIR:
self.replay_buffer = ReservoirReplayBuffer(
obs_dim=self.obs_dim, act_dim=self.act_dim, size=replay_size
)
# Create actor and critic networks
self.actor = actor_cl(**actor_kwargs)
self.critic1 = critic_cl(**critic_kwargs)
self.target_critic1 = critic_cl(**critic_kwargs)
self.target_critic1.set_weights(self.critic1.get_weights())
self.critic2 = critic_cl(**critic_kwargs)
self.target_critic2 = critic_cl(**critic_kwargs)
self.target_critic2.set_weights(self.critic2.get_weights())
self.critic_variables = self.critic1.trainable_variables + self.critic2.trainable_variables
self.all_common_variables = (
self.actor.common_variables
+ self.critic1.common_variables
+ self.critic2.common_variables
)
self.optimizer = tf.keras.optimizers.legacy.Adam(learning_rate=lr)
# self.optimizer = tf.keras.optimizers.Adam(learning_rate=lr)
# For reference on automatic alpha tuning, see
# "Automating Entropy Adjustment for Maximum Entropy" section
# in https://arxiv.org/abs/1812.05905
self.auto_alpha = False
if alpha == "auto":
self.auto_alpha = True
self.all_log_alpha = tf.Variable(
np.ones((self.num_tasks, 1), dtype=np.float32), trainable=True
)
if target_output_std is None:
self.target_entropy = -np.prod(env.action_space.shape).astype(np.float32)
else:
target_1d_entropy = np.log(target_output_std * math.sqrt(2 * math.pi * math.e))
self.target_entropy = (
np.prod(env.action_space.shape).astype(np.float32) * target_1d_entropy
)
def adjust_gradients(
self,
actor_gradients: List[tf.Tensor],
critic_gradients: List[tf.Tensor],
alpha_gradient: List[tf.Tensor],
current_task_idx: int,
metrics: dict,
episodic_batch: Dict[str, tf.Tensor] = None,
) -> Tuple[List[tf.Tensor], List[tf.Tensor], List[tf.Tensor]]:
return actor_gradients, critic_gradients, alpha_gradient
def get_auxiliary_loss(self, seq_idx: tf.Tensor) -> tf.Tensor:
return tf.constant(0.0)
def on_test_start(self, seq_idx: tf.Tensor) -> None:
pass
def on_test_end(self, seq_idx: tf.Tensor) -> None:
pass
def on_task_start(self, current_task_idx: int) -> None:
pass
def on_task_end(self, current_task_idx: int) -> None:
pass
def get_episodic_batch(self, current_task_idx: int) -> Optional[Dict[str, tf.Tensor]]:
return None
def get_log_alpha(self, obs: tf.Tensor) -> tf.Tensor:
return tf.squeeze(tf.linalg.matmul(obs[:, -self.num_tasks :], self.all_log_alpha))
@tf.function
def get_action(self, o: tf.Tensor, deterministic: tf.Tensor = tf.constant(False)) -> tf.Tensor:
mu, log_std, pi, logp_pi = self.actor(tf.expand_dims(o, 0))
if deterministic:
return mu[0]
else:
return pi[0]
def get_action_test(
self, o: tf.Tensor, deterministic: tf.Tensor = tf.constant(False)
) -> tf.Tensor:
return self.get_action(o, deterministic)
def get_learn_on_batch(self, current_task_idx: int) -> Callable:
# TODO : decorator causes error:
# : CommandLine Error: Option 'help-list' registered more than once!
# LLVM ERROR: inconsistency in registered CommandLine options
# @tf.function
def learn_on_batch(
seq_idx: tf.Tensor,
batch: Dict[str, tf.Tensor],
episodic_batch: Dict[str, tf.Tensor] = None,
) -> Dict:
gradients, metrics = self.get_gradients(seq_idx, **batch)
# Warning: we refer here to the int task_idx in the parent function, not
# the passed seq_idx.
gradients = self.adjust_gradients(
*gradients,
current_task_idx=current_task_idx,
metrics=metrics,
episodic_batch=episodic_batch,
)
if self.clipnorm is not None:
actor_gradients, critic_gradients, alpha_gradient = gradients
gradients = (
tf.clip_by_global_norm(actor_gradients, self.clipnorm)[0],
tf.clip_by_global_norm(critic_gradients, self.clipnorm)[0],
tf.clip_by_norm(alpha_gradient, self.clipnorm),
)
self.apply_update(*gradients)
return metrics
return learn_on_batch
def get_gradients(
self,
seq_idx: tf.Tensor,
obs: tf.Tensor,
next_obs: tf.Tensor,
actions: tf.Tensor,
rewards: tf.Tensor,
done: tf.Tensor,
) -> Tuple[Tuple[List[tf.Tensor], List[tf.Tensor], List[tf.Tensor]], Dict]:
with tf.GradientTape(persistent=True) as g:
if self.auto_alpha:
log_alpha = self.get_log_alpha(obs)
else:
log_alpha = tf.math.log(self.alpha)
# Main outputs from computation graph
mu, log_std, pi, logp_pi = self.actor(obs)
q1 = self.critic1(obs, actions)
q2 = self.critic2(obs, actions)
# compose q with pi, for pi-learning
q1_pi = self.critic1(obs, pi)
q2_pi = self.critic2(obs, pi)
# get actions and log probs of actions for next states, for Q-learning
_, _, pi_next, logp_pi_next = self.actor(next_obs)
# target q values, using actions from *current* policy
target_q1 = self.target_critic1(next_obs, pi_next)
target_q2 = self.target_critic2(next_obs, pi_next)
# Min Double-Q:
min_q_pi = tf.minimum(q1_pi, q2_pi)
min_target_q = tf.minimum(target_q1, target_q2)
# Entropy-regularized Bellman backup for Q functions, using Clipped Double-Q targets
if self.critic_cl is PopArtMlpCritic:
q_backup = tf.stop_gradient(
self.critic1.normalize(
rewards
+ self.gamma
* (1 - done)
* (
self.critic1.unnormalize(min_target_q, next_obs)
- tf.math.exp(log_alpha) * logp_pi_next
),
obs,
)
)
else:
q_backup = tf.stop_gradient(
rewards
+ self.gamma
* (1 - done)
* (min_target_q - tf.math.exp(log_alpha) * logp_pi_next)
)
# Soft actor-critic losses
pi_loss = tf.reduce_mean(tf.math.exp(log_alpha) * logp_pi - min_q_pi)
q1_loss = 0.5 * tf.reduce_mean((q_backup - q1) ** 2)
q2_loss = 0.5 * tf.reduce_mean((q_backup - q2) ** 2)
value_loss = q1_loss + q2_loss
if self.auto_alpha:
alpha_loss = -tf.reduce_mean(
log_alpha * tf.stop_gradient(logp_pi + self.target_entropy)
)
auxiliary_loss = self.get_auxiliary_loss(seq_idx)
metrics = dict(
pi_loss=pi_loss,
q1_loss=q1_loss,
q2_loss=q2_loss,
q1=q1,
q2=q2,
logp_pi=logp_pi,
reg_loss=auxiliary_loss,
agem_violation=0,
)
pi_loss += auxiliary_loss
value_loss += auxiliary_loss
# Compute gradients
actor_gradients = g.gradient(pi_loss, self.actor.trainable_variables)
critic_gradients = g.gradient(value_loss, self.critic_variables)
if self.auto_alpha:
alpha_gradient = g.gradient(alpha_loss, self.all_log_alpha)
else:
alpha_gradient = None
del g
if self.use_popart:
# Stats are shared between critic1 and critic2.
# We keep them only in critic1.
self.critic1.update_stats(q_backup, obs)
gradients = (actor_gradients, critic_gradients, alpha_gradient)
return gradients, metrics
def apply_update(
self,
actor_gradients: List[tf.Tensor],
critic_gradients: List[tf.Tensor],
alpha_gradient: List[tf.Tensor],
) -> None:
self.optimizer.apply_gradients(zip(actor_gradients, self.actor.trainable_variables))
self.optimizer.apply_gradients(zip(critic_gradients, self.critic_variables))
if self.auto_alpha:
self.optimizer.apply_gradients([(alpha_gradient, self.all_log_alpha)])
# Polyak averaging for target variables
for v, target_v in zip(
self.critic1.trainable_variables, self.target_critic1.trainable_variables
):
target_v.assign(self.polyak * target_v + (1 - self.polyak) * v)
for v, target_v in zip(
self.critic2.trainable_variables, self.target_critic2.trainable_variables
):
target_v.assign(self.polyak * target_v + (1 - self.polyak) * v)
def test_agent(self, deterministic, num_episodes) -> None:
avg_success = []
mode = "deterministic" if deterministic else "stochastic"
for seq_idx, test_env in enumerate(self.test_envs):
key_prefix = f"test/{mode}/{seq_idx}/{test_env.name}/"
self.on_test_start(seq_idx)
for j in range(num_episodes):
obs, info = test_env.reset()
done = False
episode_return = 0
episode_len = 0
while not (done or (episode_len == self.max_episode_len)):
obs, reward, terminated, truncated, info = test_env.step(
self.get_action_test(tf.convert_to_tensor(obs), tf.constant(deterministic))
)
episode_return += reward
episode_len += 1
self.logger.store(
{key_prefix + "return": episode_return, key_prefix + "ep_length": episode_len}
)
self.on_test_end(seq_idx)
self.logger.log_tabular(key_prefix + "return", with_min_and_max=True)
self.logger.log_tabular(key_prefix + "ep_length", average_only=True)
env_success = test_env.pop_successes()
avg_success += env_success
self.logger.log_tabular(key_prefix + "success", np.mean(env_success))
key = f"test/{mode}/average_success"
self.logger.log_tabular(key, np.mean(avg_success))
def _log_after_update(self, results):
self.logger.store(
{
"train/q1_vals": results["q1"],
"train/q2_vals": results["q2"],
"train/log_pi": results["logp_pi"],
"train/loss_pi": results["pi_loss"],
"train/loss_q1": results["q1_loss"],
"train/loss_q2": results["q2_loss"],
"train/loss_reg": results["reg_loss"],
"train/agem_violation": results["agem_violation"],
}
)
for task_idx in range(self.num_tasks):
if self.auto_alpha:
self.logger.store(
{f"train/alpha/{task_idx}": float(tf.math.exp(self.all_log_alpha[task_idx][0]))}
)
if self.use_popart:
self.logger.store(
{
f"train/popart_mean/{task_idx}": self.critic1.moment1[task_idx][0],
f"train/popart_std/{task_idx}": self.critic1.sigma[task_idx][0],
}
)
def _log_after_epoch(self, epoch, current_task_timestep, global_timestep, info):
# Log info about epoch
self.logger.log_tabular("epoch", epoch)
self.logger.log_tabular("train/return", with_min_and_max=True)
self.logger.log_tabular("train/ep_length", average_only=True)
self.logger.log_tabular("total_env_steps", global_timestep + 1)
self.logger.log_tabular("current_task_steps", current_task_timestep + 1)
self.logger.log_tabular("train/q1_vals", with_min_and_max=True)
self.logger.log_tabular("train/q2_vals", with_min_and_max=True)
self.logger.log_tabular("train/log_pi", with_min_and_max=True)
self.logger.log_tabular("train/loss_pi", average_only=True)
self.logger.log_tabular("train/loss_q1", average_only=True)
self.logger.log_tabular("train/loss_q2", average_only=True)
for task_idx in range(self.num_tasks):
if self.auto_alpha:
self.logger.log_tabular(f"train/alpha/{task_idx}", average_only=True)
if self.use_popart:
self.logger.log_tabular(f"train/popart_mean/{task_idx}", average_only=True)
self.logger.log_tabular(f"train/popart_std/{task_idx}", average_only=True)
self.logger.log_tabular("train/loss_reg", average_only=True)
self.logger.log_tabular("train/agem_violation", average_only=True)
avg_success = np.mean(self.env.pop_successes())
self.logger.log_tabular("train/success", avg_success)
if "seq_idx" in info:
self.logger.log_tabular("train/active_env", info["seq_idx"])
self.logger.log_tabular("walltime", time.time() - self.start_time)
self.logger.dump_tabular()
def save_model(self, current_task_idx):
dir_prefixes = []
if current_task_idx == -1:
dir_prefixes.append("./checkpoints")
else:
dir_prefixes.append(f"./checkpoints/task{current_task_idx}")
if current_task_idx == self.num_tasks - 1:
dir_prefixes.append("./checkpoints")
for prefix in dir_prefixes:
self.actor.save_weights(os.path.join(prefix, "actor"))
self.critic1.save_weights(os.path.join(prefix, "critic1"))
self.target_critic1.save_weights(os.path.join(prefix, "target_critic1"))
self.critic2.save_weights(os.path.join(prefix, "critic2"))
self.target_critic2.save_weights(os.path.join(prefix, "target_critic2"))
def _handle_task_change(self, current_task_idx: int):
self.on_task_start(current_task_idx)
if self.reset_buffer_on_task_change:
assert self.buffer_type == BufferType.FIFO
self.replay_buffer = ReplayBuffer(
obs_dim=self.obs_dim, act_dim=self.act_dim, size=self.replay_size
)
if self.reset_critic_on_task_change:
reset_weights(self.critic1, self.critic_cl, self.critic_kwargs)
self.target_critic1.set_weights(self.critic1.get_weights())
reset_weights(self.critic2, self.critic_cl, self.critic_kwargs)
self.target_critic2.set_weights(self.critic2.get_weights())
if self.reset_optimizer_on_task_change:
reset_optimizer(self.optimizer)
# Update variables list and update function in case model changed.
# E.g: For VCL after the first task we set trainable=False for layer
# normalization. We need to recompute the graph in order for TensorFlow
# to notice this change.
self.learn_on_batch = self.get_learn_on_batch(current_task_idx)
self.all_common_variables = (
self.actor.common_variables
+ self.critic1.common_variables
+ self.critic2.common_variables
)
def run(self):
"""A method to run the SAC training, after the object has been created."""
self.start_time = time.time()
obs, info = self.env.reset()
episode_return = 0
episode_len = 0
# Main loop: collect experience in env and update/log each epoch
current_task_timestep = 0
current_task_idx = -1
# self.learn_on_batch = self.get_learn_on_batch(current_task_idx)
for global_timestep in range(self.steps):
# On task change
if current_task_idx != getattr(self.env, "cur_seq_idx", -1):
print("if statement 1")
current_task_timestep = 0
current_task_idx = getattr(self.env, "cur_seq_idx")
self._handle_task_change(current_task_idx)
# Until start_steps have elapsed, randomly sample actions
# from a uniform distribution for better exploration. Afterwards,
# use the learned policy.
if current_task_timestep > self.start_steps or (
self.agent_policy_exploration and current_task_idx > 0
):
print("if not exploring")
action = self.get_action(tf.convert_to_tensor(obs))
else:
action = self.env.action_space.sample()
# Step the env
next_obs, reward, terminated, truncated, info = self.env.step(action)
episode_return += reward
episode_len += 1
# Ignore the "done" signal if it comes from hitting the time
# horizon (that is, when it's an artificial terminal signal
# that isn't based on the agent's state)
done = np.logical_or(terminated,truncated)
done_to_store = done
if episode_len == self.max_episode_len or truncated: # updated for gymnasium
done_to_store = False
# Store experience to replay buffer
self.replay_buffer.store(obs, action, reward, next_obs, done_to_store)
# Super critical, easy to overlook step: make sure to update
# most recent observation!
obs = next_obs
# End of trajectory handling
if done or (episode_len == self.max_episode_len):
self.logger.store({"train/return": episode_return, "train/ep_length": episode_len})
episode_return, episode_len = 0, 0
if global_timestep < self.steps - 1: # This may not work with mujoco anymore
obs, info = self.env.reset()
# Update handling
if (
current_task_timestep >= self.update_after
and current_task_timestep % self.update_every == 0
):
for j in range(self.update_every):
batch = self.replay_buffer.sample_batch(self.batch_size)
episodic_batch = self.get_episodic_batch(current_task_idx)
### TODO LLVM ERROR COMES FROM HERE
results = self.learn_on_batch(
tf.convert_to_tensor(current_task_idx), batch, episodic_batch
)
self._log_after_update(results)
if (
self.env.name == "ContinualLearningEnv"
and current_task_timestep + 1 == self.env.steps_per_env
):
self.on_task_end(current_task_idx)
# End of epoch wrap-up
if ((global_timestep + 1) % self.log_every == 0) or (global_timestep + 1 == self.steps):
epoch = (global_timestep + 1 + self.log_every - 1) // self.log_every
# Save model
if (epoch % self.save_freq_epochs == 0) or (global_timestep + 1 == self.steps):
self.save_model(current_task_idx)
# Test the performance of stochastic and detemi version of the agent.
self.test_agent(deterministic=False, num_episodes=self.num_test_eps_stochastic)
self.test_agent(deterministic=True, num_episodes=self.num_test_eps_deterministic)
self._log_after_epoch(epoch, current_task_timestep, global_timestep, info)
current_task_timestep += 1
def main(
logger: EpochLogger,
tasks: str,
task_list: List[str],
seed: int,
steps_per_task: int,
log_every: int,
replay_size: int,
batch_size: int,
hidden_sizes: Iterable[int],
buffer_type: str,
reset_buffer_on_task_change: bool,
reset_optimizer_on_task_change: bool,
activation: Callable,
use_layer_norm: bool,
lr: float,
gamma: float,
alpha: str,
target_output_std: float,
cl_method: str,
packnet_retrain_steps: int,
regularize_critic: bool,
cl_reg_coef: float,
vcl_first_task_kl: bool,
episodic_mem_per_task: int,
episodic_batch_size: int,
reset_critic_on_task_change: bool,
multihead_archs: bool,
hide_task_id: bool,
clipnorm: float,
agent_policy_exploration: bool,
):
# assert (tasks is None) != (task_list is None)
tasks='CW10' # TODO unhardcore this
if tasks is not None:
tasks = TASK_SEQS[tasks]
else:
tasks = task_list
train_env = get_cl_env(tasks, steps_per_task)
# Consider normalizing test envs in the future.
num_tasks = len(tasks)
test_envs = [
get_single_env(task, one_hot_idx=i, one_hot_len=num_tasks) for i, task in enumerate(tasks)
]
steps = steps_per_task * len(tasks)
num_heads = num_tasks if multihead_archs else 1
actor_kwargs = dict(
hidden_sizes=hidden_sizes,
activation=get_activation_from_str(activation),
use_layer_norm=use_layer_norm,
num_heads=num_heads,
hide_task_id=hide_task_id,
)
critic_kwargs = dict(
hidden_sizes=hidden_sizes,
activation=get_activation_from_str(activation),
use_layer_norm=use_layer_norm,
num_heads=num_heads,
hide_task_id=hide_task_id,
)
if cl_method == "vcl":
actor_cl = VclMlpActor
else:
actor_cl = MlpActor
vanilla_sac_kwargs = {
"env": train_env,
"test_envs": test_envs,
"logger": logger,
"seed": seed,
"steps": steps,
"log_every": log_every,
"replay_size": replay_size,
"batch_size": batch_size,
"actor_cl": actor_cl,
"actor_kwargs": actor_kwargs,
"critic_kwargs": critic_kwargs,
"buffer_type": BufferType(buffer_type),
"reset_buffer_on_task_change": reset_buffer_on_task_change,
"reset_optimizer_on_task_change": reset_optimizer_on_task_change,
"lr": lr,
"alpha": alpha,
"reset_critic_on_task_change": reset_critic_on_task_change,
"clipnorm": clipnorm,
"gamma": gamma,
"target_output_std": target_output_std,
"agent_policy_exploration": agent_policy_exploration,
}
sac = SAC(**vanilla_sac_kwargs)
sac.run()
if __name__ == "__main__":
args = vars(cl_parse_args())
logger = EpochLogger(args["logger_output"], config=args, group_id=args["group_id"])
del args["group_id"]
del args["logger_output"]
main(logger, **args)
```
```
### Relevant log output
```shell
2023-12-21 16:11:30.774908: E tensorflow/compiler/xla/stream_executor/gpu/asm_compiler.cc:114] *** WARNING *** You are using ptxas 10.1.243, which is older than 11.1. ptxas before 11.1 is known to miscompile XLA code, leading to incorrect results or invalid-address errors.
2023-12-21 16:11:30.776085: W tensorflow/compiler/mlir/tools/kernel_gen/transforms/gpu_kernel_to_blob_pass.cc:191] Failed to compile generated PTX with ptxas. Falling back to compilation by driver.
if not exploring
: CommandLine Error: Option 'help-list' registered more than once!
LLVM ERROR: inconsistency in registered CommandLine options
[1] 1542478 abort (core dumped) /home/balloch/miniconda3/envs/meta_world/bin/python
```
|
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| 2,053,086,825 |
I_kwDOArmXAs56X55p
| 62,677 |
How to determine next token indices and slice output tensor in concrete function utilizing model prediction via model(prompt)
|
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[
"Hi **@andrew-lyons** ,\r\n\r\nCould you consider techniques like:Optimizing tensor slicing operations.\r\nUsing XLA (Accelerated Linear Algebra) compilation for faster computations. I hope it will be useful to you.\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/62677\">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/62677\">No</a>\n"
] | 2023-12-21T21:37:18 | 2024-01-17T01:49:39 | 2024-01-17T01:49:35 |
NONE
| null | null | null |
### 1. System information
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- Mac, so running this via Google Colab
- TensorFlow installation (pip package or built from source):
- built via `pip install https://github.com/keras-team/keras-nlp.git@google-io-2023`
- TensorFlow library (version, if pip package or github SHA, if built from source):
- `tensorflow-text==2.12`
### 2. Code
Provide code to help us reproduce your issues using one of the following options:
#### Option B: Paste your code here or provide a link to a custom end-to-end colab
```
import numpy as np
import keras_nlp
import tensorflow as tf
import tensorflow_datasets as tfds
import tensorflow_text as tf_text
from tensorflow import keras
from tensorflow.lite.python import interpreter
import time
gpt2_tokenizer = keras_nlp.models.GPT2Tokenizer.from_preset("gpt2_base_en")
gpt2_preprocessor = keras_nlp.models.GPT2CausalLMPreprocessor.from_preset(
"gpt2_base_en",
sequence_length=256,
add_end_token=True,
)
gpt2_lm = keras_nlp.models.GPT2CausalLM.from_preset("gpt2_base_en", preprocessor=gpt2_preprocessor)
@tf.function
def predict(prompt):
preprocessed = gpt2_preprocessor(prompt)[0]
input_map = {
'token_ids': tf.reshape(preprocessed['token_ids'], [1, 255]),
'padding_mask': tf.reshape(preprocessed['padding_mask'], [1, 255])
}
result = gpt2_lm(input_map)[0] # shape of (1, 255, 50256)
# ideally slice output here?
topk = tf.math.top_k(tf.nn.softmax(result), k=25)
return topk.indices
concrete_func = predict.get_concrete_function(tf.TensorSpec([], tf.string))
```
I haven't included my export/conversion to TFLite but everything there is working fine. In the concrete function I'm topk sampling (25) which means my output of this function is 255*25 tokens long.
My question is to how I could understand where the next token lies in this 255 output sequence and only return that, making my output an int32[] of size 25?
If I was to do this outside of the `tf.function`, I would manually tokenize the input prompt, get the shape of the resulting tensor, and slice the model's output using that shape. However I understand that I cannot due this inside the `tf.function`. Is it possible to perform the same kind of logic using tf builtins?
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[
"Hi @caisq Can you please review this PR ? Thank you!"
] | 2023-12-21T19:40:01 | 2023-12-31T13:40:32 | 2023-12-31T13:40:29 |
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Temporary Directory Comment:
Added a comment explaining the creation of a temporary directory for the session root in the setUp method.
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"@BaseMax Please make sure that you have followed the official [doc ](https://www.tensorflow.org/install/source_windows) and also referred the gpu support [guide](https://www.tensorflow.org/install/pip#windows-wsl2) .\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.",
"# Setting up TensorFlow with GPU on Windows - Step-by-Step Guide\r\n\r\n## Document Formatting for Notepad:\r\n\r\nTo enhance readability, follow these steps in Notepad:\r\n1. Navigate to [FILE EDIT FORMAT VIEW HELP] above tabs.\r\n2. Go to `FORMAT` and select `Word Wrap`.\r\n\r\n## Installation Steps:\r\n\r\n### 1. Download Anaconda:\r\n - Link: [Anaconda](https://www.anaconda.com/download)\r\n\r\n### 2. Download Visual Studio C++ Redistributables:\r\n - Link: [Visual Studio C++ Redistributables](https://aka.ms/vs/17/release/vc_redist.x64.exe)\r\n\r\n### 3. TensorFlow-GPU Installation:\r\n - Visit [TensorFlow Installation Guide](https://www.tensorflow.org/install/source_windows) and go to the TensorFlow-GPU section.\r\n - Check for the latest versions and requirements.\r\n\r\n### 4. Anaconda Environment Setup:\r\n\r\n a. Open Anaconda prompt as administrator.\r\n \r\n b. Create a new environment:\r\n ```\r\n conda create -n tf_gpu python==3.9\r\n ```\r\n\r\n c. Activate the environment:\r\n ```\r\n conda activate tf_gpu\r\n ```\r\n\r\n### 5. Install CUDA and cuDNN:\r\n\r\n a. Check the required versions from the TensorFlow-GPU section (Step 3).\r\n\r\n b. Install CUDA and cuDNN using conda:\r\n ```\r\n conda install cudatoolkit=11.2 cudnn=8.1 -c=conda-forge\r\n ```\r\n\r\n### 6. Install TensorFlow-GPU:\r\n\r\n Install the latest TensorFlow-GPU version (replace 2.10.0 with the latest version):\r\n```\r\npip install --upgrade tensorflow-gpu==2.10.0\r\n```\r\n### Verification:\r\n1. Open Python: via cmd/ powershell.\r\n```python```\r\n\r\n2. Import TensorFlow:\r\n```import tensorflow as tf```\r\n\r\n3. Check GPU availability:\r\n```print(tf.test.is_gpu_available())```\r\n\r\n## Shortcuts:\r\n\r\n- To stop a current process: `Ctrl+C`\r\n\r\n\r\n\r\n### \r\n",
"@BaseMax I hope this clears out the issue . The issue above was mainly due to version conflict.",
"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/62675\">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/62675\">No</a>\n"
] | 2023-12-21T19:11:53 | 2024-01-07T01:51:20 | 2024-01-07T01:51:16 |
NONE
| null | null | null |
```
Microsoft Windows [Version 10.0.22631.2861]
(c) Microsoft Corporation. All rights reserved.
C:\Users\MAX>pip install tensorflow-gpu
Defaulting to user installation because normal site-packages is not writeable
Collecting tensorflow-gpu
Downloading tensorflow-gpu-2.12.0.tar.gz (2.6 kB)
Installing build dependencies ... done
Getting requirements to build wheel ... error
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> [58 lines of output]
Traceback (most recent call last):
File "C:\Users\MAX\AppData\Local\Temp\pip-build-env-jmodbs81\overlay\Lib\site-packages\setuptools\_vendor\packaging\requirements.py", line 35, in __init__
parsed = _parse_requirement(requirement_string)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\MAX\AppData\Local\Temp\pip-build-env-jmodbs81\overlay\Lib\site-packages\setuptools\_vendor\packaging\_parser.py", line 64, in parse_requirement
return _parse_requirement(Tokenizer(source, rules=DEFAULT_RULES))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\MAX\AppData\Local\Temp\pip-build-env-jmodbs81\overlay\Lib\site-packages\setuptools\_vendor\packaging\_parser.py", line 82, in _parse_requirement
url, specifier, marker = _parse_requirement_details(tokenizer)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\MAX\AppData\Local\Temp\pip-build-env-jmodbs81\overlay\Lib\site-packages\setuptools\_vendor\packaging\_parser.py", line 126, in _parse_requirement_details
marker = _parse_requirement_marker(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\MAX\AppData\Local\Temp\pip-build-env-jmodbs81\overlay\Lib\site-packages\setuptools\_vendor\packaging\_parser.py", line 147, in _parse_requirement_marker
tokenizer.raise_syntax_error(
File "C:\Users\MAX\AppData\Local\Temp\pip-build-env-jmodbs81\overlay\Lib\site-packages\setuptools\_vendor\packaging\_tokenizer.py", line 165, in raise_syntax_error
raise ParserSyntaxError(
setuptools.extern.packaging._tokenizer.ParserSyntaxError: Expected end or semicolon (after name and no valid version specifier)
python_version>"3.7"
^
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "C:\Users\MAX\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 353, in <module>
main()
File "C:\Users\MAX\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 335, in main
json_out['return_val'] = hook(**hook_input['kwargs'])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\MAX\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 118, in get_requires_for_build_wheel
return hook(config_settings)
^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\MAX\AppData\Local\Temp\pip-build-env-jmodbs81\overlay\Lib\site-packages\setuptools\build_meta.py", line 325, in get_requires_for_build_wheel
return self._get_build_requires(config_settings, requirements=['wheel'])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\MAX\AppData\Local\Temp\pip-build-env-jmodbs81\overlay\Lib\site-packages\setuptools\build_meta.py", line 295, in _get_build_requires
self.run_setup()
File "C:\Users\MAX\AppData\Local\Temp\pip-build-env-jmodbs81\overlay\Lib\site-packages\setuptools\build_meta.py", line 480, in run_setup
super(_BuildMetaLegacyBackend, self).run_setup(setup_script=setup_script)
File "C:\Users\MAX\AppData\Local\Temp\pip-build-env-jmodbs81\overlay\Lib\site-packages\setuptools\build_meta.py", line 311, in run_setup
exec(code, locals())
File "<string>", line 40, in <module>
File "C:\Users\MAX\AppData\Local\Temp\pip-build-env-jmodbs81\overlay\Lib\site-packages\setuptools\__init__.py", line 102, in setup
_install_setup_requires(attrs)
File "C:\Users\MAX\AppData\Local\Temp\pip-build-env-jmodbs81\overlay\Lib\site-packages\setuptools\__init__.py", line 73, in _install_setup_requires
dist.parse_config_files(ignore_option_errors=True)
File "C:\Users\MAX\AppData\Local\Temp\pip-build-env-jmodbs81\overlay\Lib\site-packages\setuptools\dist.py", line 629, in parse_config_files
self._finalize_requires()
File "C:\Users\MAX\AppData\Local\Temp\pip-build-env-jmodbs81\overlay\Lib\site-packages\setuptools\dist.py", line 364, in _finalize_requires
self._normalize_requires()
File "C:\Users\MAX\AppData\Local\Temp\pip-build-env-jmodbs81\overlay\Lib\site-packages\setuptools\dist.py", line 379, in _normalize_requires
self.install_requires = list(map(str, _reqs.parse(install_requires)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\MAX\AppData\Local\Temp\pip-build-env-jmodbs81\overlay\Lib\site-packages\setuptools\_vendor\packaging\requirements.py", line 37, in __init__
raise InvalidRequirement(str(e)) from e
setuptools.extern.packaging.requirements.InvalidRequirement: Expected end or semicolon (after name and no valid version specifier)
python_version>"3.7"
^
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
```
More details:
```
C:\Users\MAX>python --version
Python 3.12.1
C:\Users\MAX>pip --version
pip 23.3.2 from C:\Users\MAX\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\pip (python 3.12)
C:\Users\MAX>where python
C:\Users\MAX\AppData\Local\Microsoft\WindowsApps\python.exe
```
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I_kwDOArmXAs56WJPV
| 62,674 |
When converting to tflite Input/Output tensor names are present in the signature but not maintained
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[
"Hi @fixedit-dimitris ,\r\nCould you please provide ```reshape_v2_finetuned``` model to reproduce the code.\r\nThank You",
"> Hi @fixedit-dimitris , Could you please provide `reshape_v2_finetuned` model to reproduce the code. Thank You\r\n\r\nHi there, unfortunately i cannot share that. However I managed to solve the issue by converting using a concrete function.\r\n```\r\[email protected](input_signature=[tf.TensorSpec(shape=[None, 244,244,1], dtype=tf.float32)])\r\ndef rename_serve(input_tensor):\r\n out0, out1 = model(input_tensor)\r\n return {\"classifier\": out0, \"regressor\": out1}\r\n\r\nmy_signatures = rename_serve.get_concrete_function()\r\n\r\n# Save the functional model with the signature definition\r\nexport_dir = \"./signature_saved_model/\"\r\ntf.saved_model.save(model, export_dir, signatures= my_signatures)\r\n```\r\n\r\n```# Convert to tflite\r\n# a model different from the one defined in the example!\r\ndef a_representative_datagenerator(n_samples_to_yield=160):\r\n \"\"\" A data generator which produces samples from the model's domain.\r\n Calling this generator should output samples of the same type\r\n and shape as the inputs to the model, similar to those it has been\r\n trained on.\r\n\r\n Args:\r\n n_samples_to_yield (int): The number of samples for this generator\r\n to yield.\r\n\r\n Yields:\r\n np.float32 array: An RGB image from the dataset directory, which has\r\n been processed like the images the model has been\r\n trained on. In this case, this includes\r\n normalization and resizing. The output array has the\r\n shape (1, 244, 244, 1).\r\n \"\"\"\r\n samples = glob.glob(os.path.join('train_samples', '*'))\r\n sample_set = np.random.choice(samples, size=n_samples_to_yield,\r\n replace=False)\r\n for sample_path in sample_set:\r\n sample = Image.open(sample_path)\r\n preprocessed_sample = np.array(sample, dtype=np.float32) / 255.\r\n preprocessed_sample = np.expand_dims(preprocessed_sample, axis=0) # Batch\r\n preprocessed_sample = np.expand_dims(preprocessed_sample, axis=-1) # Channel\r\n yield [preprocessed_sample]\r\n\r\nconc_func = loaded_model.signatures[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY] \r\n\r\n# Create the converter. As the model to convert is of the\r\n# SavedModel format, the from_saved_model function is used\r\n# converter = tf.lite.TFLiteConverter.from_saved_model('reshape_v2_finetuned/', signature_keys=['serving_default'])\r\nconverter = tf.lite.TFLiteConverter.from_concrete_functions([conc_func])\r\n\r\n# Flags which set what optimizations to perform. The DEFAULT flag\r\n# enables quantization of all fixed parameters, such as weights\r\nconverter.optimizations = [tf.lite.Optimize.DEFAULT]\r\n\r\n# Set the converter to use the data generator defined above\r\nconverter.representative_dataset = a_representative_datagenerator\r\n\r\n# Select the set of operators to use\r\nconverter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] \r\nconverter.inference_input_type = tf.uint8\r\n\r\n\r\n# Perform the conversion\r\ntflite_model = converter.convert()\r\n\r\n# Write the converted model to disk\r\nopen('converted_model.tflite', \"wb\").write(tflite_model)```\r\n\r\nThis gives the following outputs: \r\n```Input name is: input_tensor\r\nOutput names are: ['Identity', 'Identity_1']```\r\n"
] | 2023-12-21T15:24:45 | 2023-12-27T13:36:29 | 2023-12-27T13:36:29 |
NONE
| null | null | null |
### 1. System information
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 22.04
- TensorFlow installation (pip package or built from source): pip
- TensorFlow library (version, if pip package or github SHA, if built from source): 2.15
### 2. Code
```
import tensorflow as tf
from tensorflow import keras
import glob
import os
import numpy as np
from PIL import Image
model = keras.models.load_model("reshape_v2_finetuned/")
model.summary()
def a_representative_datagenerator(n_samples_to_yield=160):
samples = glob.glob(os.path.join("train_samples", "*"))
sample_set = np.random.choice(samples, size=n_samples_to_yield,
replace=False)
for sample_path in sample_set:
sample = Image.open(sample_path)
preprocessed_sample = np.array(sample, dtype=np.float32) / 255.
preprocessed_sample = np.expand_dims(preprocessed_sample, axis=0) # Batch
preprocessed_sample = np.expand_dims(preprocessed_sample, axis=-1) # Channel
yield [preprocessed_sample]
converter = tf.lite.TFLiteConverter.from_saved_model('reshape_v2_finetuned/', signature_keys=['serving_default'])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = a_representative_datagenerator
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
tflite_model = converter.convert()
open("converted_model.tflite", "wb").write(tflite_model)
interpreter = tf.lite.Interpreter(model_content=tflite_model)
print('\nInput name is:', interpreter.get_input_details()[0]['name'])
print("Output names are:", [output["name"] for output in interpreter.get_output_details()])
signatures = interpreter.get_signature_list()
print(signatures)
```
### Returns:
```
Input name is: serving_default_input:0
Output names are: [StatefulPartitionedCall:0, StatefulPartitionedCall:1]
{serving_default: {inputs: [input], outputs: [A_regressor_head, B_classifier_head]}}
```
### 5. Ideal result
Input/Output names same as the ones in the signature def. Is this something that can be done (as when converting with the Ambarella toolchain for CV25 this appears to be an issue due to the extra :0 tag) ?
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| 2,052,328,047 |
I_kwDOArmXAs56VApv
| 62,673 |
No tensorflow-cpu meta package
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[
"FYI: @snadampal @penpornk @elfringham ",
"cc: @learning-to-play @MichaelHudgins ",
"cc: @rascani "
] | 2023-12-21T12:23:20 | 2024-04-23T18:20:16 | null |
CONTRIBUTOR
| null | null | null |
### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.14 (latest)
### 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?
The tensorflow-cpu package is for cpu only but has only x86 support. Then there is for example tensorflow-cpu-aws that has aarch64 support. It would be good to have a tensorflow-cpu meta package that could support all platforms.
This could for example solve the following issue: https://github.com/tensorflow/tflite-micro/issues/2367
### Standalone code to reproduce the issue
```shell
There is no tensorflow-cpu meta package.
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5iiLig
| 62,672 |
feat: complete first stage of linking-hell migration.
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Specifically:
1. Break current build (just bringing OSS closer to how things are built internally): Remove `tf_binary_additional_srcs()` and `tf_binary_additional_data_deps()` macros.
2. Remove `tf_binary_pybind_deps()` macros. It will affect following macros: `tf_python_pybind_extension_opensource()`
3. Remove `cc_binary()` inside `tf_kernel_library()` as it is not used (around 500 targets) Clean up most common header-only targets:
4. Remove cc_header_only_library() replace its usage with actual deps (no need to depend on header-only) Remove header-only target creation (and its aliasing) in `tf_proto_library()` macro Migrate to normal `cc_proto_library()` rule, same as used in `protobuf 3.22.x+` - this will unblock updating protobuf to further versions
5. Remove `check_deps()` targets. Are they still needed, would they make sense in new architecture?
6. Remove creation of `libtensorflow_framework` and `libtensorflow_cc` targets and all the stuff facilitating it.
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iOS TensorFlowLiteC App Size impact
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[
"Related: https://github.com/tensorflow/tensorflow/issues/46674",
"Hi @yishuangP, can you please take a look? Thanks.",
"I am not an expert on these topics but I am trying to do more research on them.\r\nI found out that after hiding the symbols, some of them are now `no dead strip`:\r\n\r\nBinary without symbol hiding:\r\n```\r\nnm -m ios-arm64/TensorFlowLiteC.framework/TensorFlowLiteC | grep 'no dead strip' | wc -l\r\n0\r\n```\r\nBinary with symbol hiding:\r\n```\r\nnm -m ios-arm64/TensorFlowLiteC.framework/TensorFlowLiteC | grep 'no dead strip' | wc -l\r\n249\r\n```\r\nMaybe that could be the reason why the app binary size increases, the linker can't dead-strip those symbols.",
"@omarzl, feel free to try out your hypothesis and let us know the results. Feel free to submit a PR for us to review if you found a solution. Thanks."
] | 2023-12-20T20:01:05 | 2024-01-02T19:21:52 | null |
NONE
| null | null | null |
### Issue type
Performance
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15.0
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
6.1.0
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I open this issue to share my findings regarding app size impact of `TensorFlowLiteC` binary in iOS
Currently we are migrating from a really old version to `v2.15.0`:
https://github.com/tensorflow/tensorflow/blob/v2.15.0/tensorflow/lite/ios/TensorFlowLiteC.podspec
And we noticed a notable size increment:
Download size: `+1.08MB`
Install size: `+2.51MB`
We have a large codebase and the app size is a big concern for us, so I tried to find ways to reduce the impact.
I noticed that there is a macro [`tflite_ios_xcframework`](https://github.com/tensorflow/tensorflow/blob/6887368d6d46223f460358323c4b76d61d1558a8/tensorflow/lite/ios/ios.bzl#L93) which creates the `xcframework` and then modfies the object files to hide public symbols.
So I created a new target using the rule `apple_static_xcframework` directly.
_See https://github.com/omarzl/tensorflow/commit/945d7ec241e759586ca30c32221f5b14504169f0 (I can send the pull request if it helps)_
I named this target `TensorFlowLiteC_static_xcframework`, then I compiled it:
```
bazel build --config=ios_fat -c opt --cxxopt=--std=c++17 \
//tensorflow/lite/ios:TensorFlowLiteC_static_xcframework --subcommands
```
By using the xcframework without hidden symbols I noticed that the size increment was notable less `-50%`:
Download size: `+0.50MB`
Install size: `+1.24MB`
Then I changed the clang optimization to `-Os` instead of `-O3` which is the default one used by Bazel in `opt` config:
```
bazel build --config=ios_fat -c opt --cxxopt=--std=c++17 \
//tensorflow/lite/ios:TensorFlowLiteC_static_xcframework --copt=-Os --subcommands
```
There was a minor improvement using it:
Download size: `+0.49MB`
Install size: `+1.23MB`
Finally I tried using the original `tflite_ios_xcframework` target `TensorFlowLiteC_xcframework` but with `-Os` optimization, I was expecting a minor reduction but I was wrong, instead it incremented:
Download size: `+1.30MB`
Install size: `+3.03MB`
Here is a summary of my tests:
| Size | Target `TensorFlowLiteC_xcframework` | Target `TensorFlowLiteC_static_xcframework` | Target `TensorFlowLiteC_xcframework` & `-Os` opt | Target `TensorFlowLiteC_static_xcframework` & `-Os` opt |
|---|---|---|---|---|
| Download | `+1.08MB` | `+0.50MB` | `+0.49MB` | `+1.30MB` |
| Install | `+2.51MB` | `+1.24MB` | `+1.23MB` | `+3.03MB` |
I understand the reasons behind hiding symbols since they can cause a symbol collision ending in unexpected behaviors or crashes in the app but these tests found out that it can cause an increment in app size.
For our case, we don't have any symbol collision with Tensorflow so we can use an un-hided version of the xcframework which benefit us in less app size impact.
_As a side note: The linker `ld` warns you if there is a symbol collision, we opt to make warnings to errors to catch any issue of this kind by adding the flag `-Wl,-fatal_warnings`_
Finally looking at the code in `hide_xcframework_symbols_with_allowlist.sh`
https://github.com/tensorflow/tensorflow/blob/6887368d6d46223f460358323c4b76d61d1558a8/tensorflow/lite/ios/hide_xcframework_symbols_with_allowlist.sh#L120
I understand that by using `xcrun ld -r` you are changing public symbols to private ones to prevent this symbol collision but I don't see you are generating a dynamic executable, I see that the output is still a Mach-O object (`MH_OBJECT`) so I think that this documentation https://www.tensorflow.org/lite/guide/build_ios is wrong since there isn't a dynamic framework, it is still a static framework but with hidden symbols.
In summary, I don't think there is much that can be done without hiding the symbols, I open this issue in case anyone finds a better solution to reduce the size impact and to document what I have found.
### Standalone code to reproduce the issue
```shell
The compilation commands were written above.
```
### Relevant log output
_No response_
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Erro Tensor Flow platform.io no Visual Studio Code ao compilar
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[
"@DevIoTEduardo Could you please ensure the files are located in the correct directory:\r\n`.pio/build/esp32-s3-devkitc-1/lib408/EloquentTinyML/eloquent_tinyml/tensorflow/arm/tensorflow/lite/micro/tools/make/down`\r\nPlease run a clean build which can often resolve issues with missing or outdated dependencies. Could you make sure to use the latest version with all compatibility. Please let us know the update on this. \r\nThank you!\r\n",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62670\">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/62670\">No</a>\n"
] | 2023-12-20T19:00:06 | 2024-01-06T01:48:30 | 2024-01-06T01:48:28 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2
### 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?
lib/EloquentTinyML/src/eloquent_tinyml/tensorflow/arm/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_HWC_q7_fast_nonsquare.c:1: fatal error: opening dependency file .pio/build/esp32-s3-devkitc-1/lib408/EloquentTinyML/eloquent_tinyml/tensorflow/arm/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_HWC_q7_fast_nonsquare.c.d: No such file or directory
#if !defined(ESP32)
compilation terminated.
*** [.pio\build\esp32-s3-devkitc-1\lib408\EloquentTinyML\eloquent_tinyml\tensorflow\arm\tensorflow\lite\micro\tools\make\downloads\cmsis\CMSIS\NN\Source\ConvolutionFunctions\arm_convolve_1x1_HWC_q7_fast_nonsquare.c.o] Error 1
lib/EloquentTinyML/src/eloquent_tinyml/tensorflow/arm/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c:1: fatal error:
opening dependency file .pio/build/esp32-s3-devkitc-1/lib408/EloquentTinyML/eloquent_tinyml/tensorflow/arm/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c.d: No such file or directory
#if !defined(ESP32)
compilation terminated.
*** [.pio\build\esp32-s3-devkitc-1\lib408\EloquentTinyML\eloquent_tinyml\tensorflow\arm\tensorflow\lite\micro\tools\make\downloads\cmsis\CMSIS\NN\Source\ConvolutionFunctions\arm_convolve_HWC_q15_fast_nonsquare.c.o] Error 1
### Standalone code to reproduce the issue
```shell
lib/EloquentTinyML/src/eloquent_tinyml/tensorflow/arm/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_HWC_q7_fast_nonsquare.c:1: fatal error: opening dependency file .pio/build/esp32-s3-devkitc-1/lib408/EloquentTinyML/eloquent_tinyml/tensorflow/arm/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_HWC_q7_fast_nonsquare.c.d: No such file or directory
#if !defined(ESP32)
compilation terminated.
*** [.pio\build\esp32-s3-devkitc-1\lib408\EloquentTinyML\eloquent_tinyml\tensorflow\arm\tensorflow\lite\micro\tools\make\downloads\cmsis\CMSIS\NN\Source\ConvolutionFunctions\arm_convolve_1x1_HWC_q7_fast_nonsquare.c.o] Error 1
lib/EloquentTinyML/src/eloquent_tinyml/tensorflow/arm/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c:1: fatal error:
opening dependency file .pio/build/esp32-s3-devkitc-1/lib408/EloquentTinyML/eloquent_tinyml/tensorflow/arm/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c.d: No such file or directory
#if !defined(ESP32)
compilation terminated.
*** [.pio\build\esp32-s3-devkitc-1\lib408\EloquentTinyML\eloquent_tinyml\tensorflow\arm\tensorflow\lite\micro\tools\make\downloads\cmsis\CMSIS\NN\Source\ConvolutionFunctions\arm_convolve_HWC_q15_fast_nonsquare.c.o] Error 1
```
### Relevant log output
```shell
lib/EloquentTinyML/src/eloquent_tinyml/tensorflow/arm/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_HWC_q7_fast_nonsquare.c:1: fatal error: opening dependency file .pio/build/esp32-s3-devkitc-1/lib408/EloquentTinyML/eloquent_tinyml/tensorflow/arm/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_HWC_q7_fast_nonsquare.c.d: No such file or directory
#if !defined(ESP32)
compilation terminated.
*** [.pio\build\esp32-s3-devkitc-1\lib408\EloquentTinyML\eloquent_tinyml\tensorflow\arm\tensorflow\lite\micro\tools\make\downloads\cmsis\CMSIS\NN\Source\ConvolutionFunctions\arm_convolve_1x1_HWC_q7_fast_nonsquare.c.o] Error 1
lib/EloquentTinyML/src/eloquent_tinyml/tensorflow/arm/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c:1: fatal error:
opening dependency file .pio/build/esp32-s3-devkitc-1/lib408/EloquentTinyML/eloquent_tinyml/tensorflow/arm/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c.d: No such file or directory
#if !defined(ESP32)
compilation terminated.
*** [.pio\build\esp32-s3-devkitc-1\lib408\EloquentTinyML\eloquent_tinyml\tensorflow\arm\tensorflow\lite\micro\tools\make\downloads\cmsis\CMSIS\NN\Source\ConvolutionFunctions\arm_convolve_HWC_q15_fast_nonsquare.c.o] Error 1
```
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[
"Hi @bergentruckung ,\r\n\r\nIf you are adding any optimizations of your own you need to add `--config=opt` to the bazel build command like below.\r\n\r\n\r\n`bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package`\r\n\r\nCould you try above command and let us know outcome. Thanks!",
"Thanks, I tried the above as well. I get the exact same error."
] | 2023-12-20T11:33:33 | 2023-12-26T08:33:09 | 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
No
### OS platform and distribution
Redhat Enterprise Linux 8.9
### Mobile device
-
### Python version
3.11.4
### Bazel version
6.3.2
### GCC/compiler version
clang 16.0.6
### CUDA/cuDNN version
CUDA 12.2, cuDNN 8.9.0
### GPU model and memory
Nvidia A100 80GB PCIe
### Current behavior?
Current: Building from source fails after a while (error is captured in `relevant log output` section).
Expected: Builds to proceed and I get a wheel file that I can install
### Standalone code to reproduce the issue
```shell
* Git clone upstream tensorflow repo from Github and checkout r2.14 branch
* Run `./configure` and apply the following:
Build TF with CUDA: yes
Build TF with ROCm: no
Build TF with TensorRT: no
Specify compute capability: 8.0
Use clang as compiler: yes
Specify optimization flags: -msse4.2 -mavx2 -mfma -march=sandybridge -mtune=broadwell
```
* Run `bazel build //tensorflow/tools/pip_package:build_pip_package` -- this fails after a while
```
### Relevant log output
```shell
Repository rule _tf_http_archive defined at:
/local/tf_tensorflow/third_party/repo.bzl:89:35: in <toplevel>
ERROR: /local/tf_tensorflow/tensorflow/core/kernels/BUILD:7953:18: While resolving toolchains for target //tensorflow/core/kernels:libtfkernel_sobol_op.so: invalid registered toolchain '@local_jdk//:runtime_toolchain_definition': no such package '@local_jdk//': error globbing [lib/**] - [lib/missioncontr
ol/**, lib/visualvm/**] op=FILES: /home/.cache/bazel/_bazel/4167dbe79c65128bd2e3a1505c6f80c3/external/local_jdk/lib/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64
/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/tzdb.dat (Too many levels of symbolic links)
ERROR: Analysis of target '//tensorflow/tools/pip_package:build_pip_package' failed; build aborted:
INFO: Elapsed time: 810.131s
INFO: 0 processes.
FAILED: Build did NOT complete successfully (263 packages loaded, 3936 targets configured)
currently loading: @ml_dtypes//
Fetching repository @pypi_portpicker; Restarting.
Fetching repository @pypi_requests; Restarting.
Fetching repository @pypi_h5py; Restarting.
Fetching repository @pypi_packaging; Restarting.
Fetching repository @pypi_tensorboard; Restarting.
Fetching repository @pypi_keras; Restarting.
Fetching repository @pypi_numpy; Restarting.
Fetching repository @pypi_tensorflow_estimator; Restarting. ... (17 fetches)
```
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CMake Error in linux alpine/Ubuntu
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[
"If I need to install them manually, what all libraries I need to download?",
"@acode-x Include paths shouldn't be relative to the build directory, as this can cause issues when installing or using the library elsewhere. So try to locate the line in tools/cmake/modules/ml_dtypes/CMakeLists.txt setting the INTERFACE_INCLUDE_DIRECTORIES property.\r\nPlease refer to [this](https://www.tensorflow.org/lite/guide/build_cmake) doc to refer to the TensorFlow Lite CMake documentation for specific instructions and examples.\r\nPlease modify it to use absolute or relative paths not tied to the build directory.\r\nThank you!\r\n",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62668\">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/62668\">No</a>\n"
] | 2023-12-20T10:52:11 | 2024-01-06T01:48:33 | 2024-01-06T01:48:29 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15.0
### Custom code
No
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
9.4.0
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When trying to install tflite C++ using CMake, am getting errors such as absl_flags, absl_hash, etc...
However using bazel these libraries are installed automatically internally. How to achieve same using CMake?
Requesting help on same.
Thanks
### Standalone code to reproduce the issue
```shell
git clone --branch v2.15.0 https://github.com/tensorflow/tensorflow.git tensorflow_src
mkdir tflite_build && cd tflite_build
export CFLAGS='-D_FORTIFY_SOURCE=2 -fstack-protector-strong -fPIC'
export CXXFLAGS=${CFLAGS}
export LDFLAGS='-Wl,-z,now'
cmake -DCMAKE_BUILD_TYPE=Release \
-DTFLITE_ENABLE_INSTALL=ON \
-DCMAKE_FIND_PACKAGE_PREFER_CONFIG=ON \
../tensorflow_src/tensorflow/lite
```
### Relevant log output
```shell
CMake Error in tools/cmake/modules/ml_dtypes/CMakeLists.txt:
Target "ml_dtypes" INTERFACE_INCLUDE_DIRECTORIES property contains path:
"/tmp/ci-ObIDKioDjn/tensorflow_src/tflite_build/ml_dtypes"
which is prefixed in the build directory.
CMake Error in tools/cmake/modules/ml_dtypes/CMakeLists.txt:
Target "ml_dtypes" INTERFACE_INCLUDE_DIRECTORIES property contains path:
"/tmp/ci-ObIDKioDjn/tensorflow_src/tflite_build/ml_dtypes/ml_dtypes"
which is prefixed in the build directory.
CMake Error: install(EXPORT "tensorflow-liteTargets" ...) includes target "tensorflow-lite" which requires target "absl_flags" that is not in any export set.
CMake Error: install(EXPORT "tensorflow-liteTargets" ...) includes target "tensorflow-lite" which requires target "absl_hash" that is not in any export set.
CMake Error: install(EXPORT "tensorflow-liteTargets" ...) includes target "tensorflow-lite" which requires target "absl_status" that is not in any export set.
CMake Error: install(EXPORT "tensorflow-liteTargets" ...) includes target "tensorflow-lite" which requires target "absl_strings" that is not in any export set.
CMake Error: install(EXPORT "tensorflow-liteTargets" ...) includes target "tensorflow-lite" which requires target "absl_synchronization" that is not in any export set.
CMake Error: install(EXPORT "tensorflow-liteTargets" ...) includes target "tensorflow-lite" which requires target "absl_variant" that is not in any export set.
CMake Error: install(EXPORT "tensorflow-liteTargets" ...) includes target "tensorflow-lite" which requires target "ruy" that is not in any export set.
CMake Error: install(EXPORT "tensorflow-liteTargets" ...) includes target "tensorflow-lite" which requires target "pthreadpool" that is not in any export set.
CMake Error: install(EXPORT "tensorflow-liteTargets" ...) includes target "tensorflow-lite" which requires target "XNNPACK" that is not in any export set.
-- Generating done
CMake Generate step failed. Build files cannot be regenerated correctly.
```
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I_kwDOArmXAs56L8bD
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Golang TensorFlow2.5.0 have a memory leak on reloading model
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[
"Hi **@LoveVsLike** ,\r\nCould you try to update tensorflow older version to the latest version. After that make sure the latest version of tfgo.Before reloading the model, make sure that any existing TensorFlow sessions are properly closed or deleted. You can explicitly close a session in TensorFlow using sess.Close(). \r\nIf you are facing the same issue after these changes please feel free to ask. And provide code snippet also it is help us to reproduce your issue.\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/62667\">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/62667\">No</a>\n"
] | 2023-12-20T06:58:08 | 2024-01-06T01:48:36 | 2024-01-06T01:48:31 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf2.5.0
### 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?
I am a user of tfgo, and I encountered a bug in tfgo. When the model is reloaded, there is a memory leak, But when I see tfgo source code, I find this bug is from tf, Because I find TF C.TF_DeleteSession is not work
### Standalone code to reproduce the issue
```shell
I find tf lib C.TF_DeleteSession(s.c, status.c) is not work
```
### Relevant log output
_No response_
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[Bug] XLA fail to compile cudnn_fmha by CudnnFusedMHARewriter
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[
"@SuryanarayanaY @AyanmoI @Tixxx topic move from #62661 to here.",
"@sachinprasadhs @akuegel @Cheshire Any progress?",
"If you don't have time for the moment, can you give me an idea to fix this bug? I'm really in a hurry. @akuegel ",
"@MoFHeka apologies for the late response, I have been out of office. I will take a look at this issue.",
"I'm getting this error when running your repro:\r\nValueError: Invalid stage selected: 'hoo'. Valid values are: 'hlo', 'hlo_serialized', 'optimized_hlo', 'optimized_hlo_serialized', 'optimized_hlo_dot'\r\n\r\nIs your TF a custom build?\r\nAlso to make it more efficient, it'd better if you could provide the compiled HLO graph directly. Can you run your repro with these xla flags?\r\n`XLA_FLAGS=\"--xla_gpu_enable_cudnn_fmha=true --xla_dump_hlo_as_text --xla_dump_to=OUTPUT_LOCATION --xla_dump_hlo_pass_re=.*\" `",
"@Tixxx \r\nHere I ran:\r\n```bash\r\nTF_DUMP_GRAPH_PREFIX=generated_test/ TF_XLA_FLAGS=\"--tf_xla_clustering_debug --tf_xla_auto_jit=2\" XLA_FLAGS=\"--xla_gpu_enable_cudnn_fmha --xla_dump_hlo_as_text --xla_dump_hlo_pass_re=.* --xla_dump_to=generated_test/\" python test_xla_mha.py\r\n```\r\nHere is the zip file:\r\n[generated_test_hlo.zip](https://github.com/tensorflow/tensorflow/files/13774793/generated_test_hlo.zip)\r\n\r\nOne more thing, may I ask how could I trigger MHA_Layout::QKV_INTERLEAVED type when using TF XLA with user interface python code? Here is the [example](https://github.com/NVIDIA/cudnn-frontend/blob/9f82dda5c029d15a5f371f0fe003dc0c74a0c987/samples/legacy_samples/helpers.cpp#L250C27-L250C54) showed in cud-frontend repo.\r\nWhen I check tensor slice in HLO, it's a ordinary scheme like:\r\n```python\r\nstrideA[seqlen_dim_idx] = h * d;\r\nstrideA[hidden_dim_idx] = 1;\r\nstrideA[head_dim_idx] = d;\r\nstrideA[batch_dim_idx] = s_q * h * d;\r\n```\r\nNot a QKV 3X form.\r\n\r\n",
"@MoFHeka Hi, right now you can't trigger QKV_INTERLEAVED in XLA since MHA rewriter in XLA does not pattern match this case. This feature will be added in the future.",
"@MoFHeka Also this zip file seems invalid. Could you verify if it is valid?\r\n",
"One thing I noticed in your mlir is the fmha call is not lowered properly to a function call,\r\nin your mlir:\r\n`\"lmhlo_gpu.fMHA\"(%view, %view_1, %view_3, %view_5, %view_6) {......} : (memref<2x4096x16x64xbf16>, memref<2x4096x16x64xbf16>, memref<2x4096x16x64xbf16>, memref<2x4096x16x64xbf16>, memref<0xui8>) -> () \"fmha-bmm-bmm\"`\r\n\r\nthe lowered mlir should look something like:\r\n`call @xla.gpu.fused.attention.bmm.bmm`\r\n\r\nThis would suggest that either the runtime lowering logic has a bug or your xla build doesn't have the runtime lowering code we merged a few months ago.\r\n\r\nwhat's the HEAD of your xla build?\r\n",
"> @MoFHeka Also this zip file seems invalid. Could you verify if it is valid?\r\n\r\nzip file seems work well. I try to upload it again.\r\n[generated_test.zip](https://github.com/tensorflow/tensorflow/files/13782675/generated_test.zip)\r\n",
"> One thing I noticed in your mlir is the fmha call is not lowered properly to a function call, in your mlir: `\"lmhlo_gpu.fMHA\"(%view, %view_1, %view_3, %view_5, %view_6) {......} : (memref<2x4096x16x64xbf16>, memref<2x4096x16x64xbf16>, memref<2x4096x16x64xbf16>, memref<2x4096x16x64xbf16>, memref<0xui8>) -> () \"fmha-bmm-bmm\"`\r\n> \r\n> the lowered mlir should look something like: `call @xla.gpu.fused.attention.bmm.bmm`\r\n> \r\n> This would suggest that either the runtime lowering logic has a bug or your xla build doesn't have the runtime lowering code we merged a few months ago.\r\n> \r\n> what's the HEAD of your xla build?\r\n\r\nI use TF2.4 from image nvcr.io/nvidia/tensorflow:23.11-tf2-py3. When I update TF to latest stable 2.5, it seems all work well. Thank you!\r\n\r\nBut there is another question. Why is FMHA almost no faster than a regular XLA? Also which time we can use FMHA with dim 128 and so on?",
"It should be faster if you have softmax and training. Right now, XLA(cuDNN) has two fmha kernels implemented. The first one is fused attention and the second one is flash attention. Fused attention only support sequence length up to 512 and head dim 64 and it is not likely to support 128 in the future. Flash attention on the other hand, supports all any sequence length and head dim 128. Right now, XLA choose between these 2 kernels based on whether sequence length is larger than 512 or not.",
"@MoFHeka have you tried with softmax and other intermediate nodes to get some perf improvement? if you dont see other errors during compilation, maybe we can close this issue?",
"@Tixxx The improvement is very small, even within 10%, I don't know if it is a measurement error.",
"@Tixxx One more question, when using Keras Attention, query shape is (batch_size, seq_len_q, num_heads * head_dim). But FMHA only support shape (batch_size, seq_len_q, num_heads, head_dim). \r\nBut if use Keras MultiHeadAttention, it use einsum not matmul to calculate.\r\nFMHA will never work.!!!\r\n[Keras Attention]<img width=\"594\" alt=\"Keras Attention\" src=\"https://github.com/tensorflow/tensorflow/assets/90189118/b94826bb-aa80-4804-a28e-9a3c24518bad\">\r\n",
"> @Tixxx One more question, when using Keras Attention, query shape is (batch_size, seq_len_q, num_heads * head_dim). But FMHA only support shape (batch_size, seq_len_q, num_heads, head_dim). But if use Keras MultiHeadAttention, it use einsum not matmul to calculate. FMHA will never work.!!! [Keras Attention]<img alt=\"Keras Attention\" width=\"594\" src=\"https://private-user-images.githubusercontent.com/90189118/294462790-b94826bb-aa80-4804-a28e-9a3c24518bad.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.XwVZyt41uooLS6IZcbgDifVv8SqkUVWZCJ8axgt8cbE\">\r\n\r\nYea i think the tf.keras.layers.Attention is mainly for seq2seq models where one layer can only attend to 1 position, it's similar to an MHA with a single head, CUDNN lib supports single-headed MHAs but without hacking this keras api to give us the correct shape, XLA won't lower it. I think the keras.mha API might be a better option to develop transformer models, https://github.com/keras-team/keras/blob/v2.14.0/keras/layers/attention/multi_head_attention.py#L130-L731\r\nEinsum will be lowered to a gemm eventually in xla so we are able to match it(Jax uses einsum too and we are able to match most of the jax MHA blocks). I checked the source code above for keras MHA, the shape is in the correct form. Maybe give it a try and let us know if that works?",
"@Tixxx Unfortunately, Einsum will be converted to triton kernel instead of cudnn matmul in both TF and Jax(Praxis or Lax)."
] | 2023-12-19T16:04:53 | 2024-01-28T09:28:50 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
tf 2.14
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
CUDA 12.3/cuDNN 8.9.6
### GPU model and memory
A800 80GB
### Current behavior?
set xla_gpu_enable_cudnn_fmha, try using XLA to jit a simple bmm1-bmm2 function by CudnnFusedMHARewriter.
### Standalone code to reproduce the issue
```python
import os
import tensorflow as tf
import numpy as np
batch_size = 16
seq_len_q = 16
num_heads = 256
head_dim = 64
mat_dtypes = tf.bfloat16
# @tf.function(jit_compile=True)
# def build_tf_xla_graph(queries, keys, values):
# keras_attention_layer = tf.keras.layers.Attention(causal=True, use_scale=False, dtype=mat_dtypes)
# flash_attention_output = keras_attention_layer(inputs=[queries, values, keys])
# return flash_attention_output
@tf.function(jit_compile=True)
def build_tf_xla_graph(queries, keys, values):
score = tf.matmul(queries, keys, transpose_b=True)
flash_attention_output = tf.matmul(score, values)
return flash_attention_output
seq_len_v = seq_len_q
queries = tf.random.normal(
(batch_size, seq_len_q, num_heads, head_dim), dtype=mat_dtypes)
keys = tf.random.normal(
(batch_size, seq_len_v, num_heads, head_dim), dtype=mat_dtypes)
values = tf.random.normal(
(batch_size, seq_len_v, num_heads, head_dim), dtype=mat_dtypes)
ir = build_tf_xla_graph.experimental_get_compiler_ir(queries, keys, values)(stage='hoo')
print(ir)
output = build_tf_xla_graph(queries, keys, values)
print(output)
```
and run:
```shell
XLA_FLAGS="--xla_gpu_enable_cudnn_fmha" python test_xla_mha.py
```
### Relevant log output
```shell
2023-12-19 16:03:11.235376: I tensorflow/compiler/xla/service/gpu/cudnn_fused_mha_rewriter.cc:645] Before CudnnFusedMHARewriter:
HloModule a_inference_build_tf_xla_graph_26__XlaMustCompile_true_config_proto_6001324581131673121_executor_type_11160318154034397263_.14, alias_passthrough_params=true, entry_computation_layout={(bf16[16,16,256,64]{3,2,1,0}, bf16[16,16,256,64]{3,2,1,0}, bf16[16,16,256,64]{3,2,1,0})->bf16[16,16,256,64]{3,2,1,0}}
ENTRY %a_inference_build_tf_xla_graph_26__XlaMustCompile_true_config_proto_6001324581131673121_executor_type_11160318154034397263_.14 (arg0.1: bf16[16,16,256,64], arg1.2: bf16[16,16,256,64], arg2.3: bf16[16,16,256,64]) -> bf16[16,16,256,64] {
%arg0.1 = bf16[16,16,256,64]{3,2,1,0} parameter(0), parameter_replication={false}, metadata={op_name="XLA_Args"}
%arg1.2 = bf16[16,16,256,64]{3,2,1,0} parameter(1), parameter_replication={false}, metadata={op_name="XLA_Args"}
%dot.7 = bf16[16,16,256,256]{3,2,1,0} dot(bf16[16,16,256,64]{3,2,1,0} %arg0.1, bf16[16,16,256,64]{3,2,1,0} %arg1.2), lhs_batch_dims={0,1}, lhs_contracting_dims={3}, rhs_batch_dims={0,1}, rhs_contracting_dims={3}, metadata={op_type="BatchMatMulV2" op_name="MatMul" source_file="/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py" source_line=1161}
%arg2.3 = bf16[16,16,256,64]{3,2,1,0} parameter(2), parameter_replication={false}, metadata={op_name="XLA_Args"}
ROOT %dot.9 = bf16[16,16,256,64]{3,2,1,0} dot(bf16[16,16,256,256]{3,2,1,0} %dot.7, bf16[16,16,256,64]{3,2,1,0} %arg2.3), lhs_batch_dims={0,1}, lhs_contracting_dims={3}, rhs_batch_dims={0,1}, rhs_contracting_dims={2}, metadata={op_type="BatchMatMulV2" op_name="MatMul_1" source_file="/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py" source_line=1161}
}
2023-12-19 16:03:11.236008: I tensorflow/compiler/xla/service/gpu/cudnn_fused_mha_rewriter.cc:841] After CudnnFusedMHARewriter:
HloModule a_inference_build_tf_xla_graph_26__XlaMustCompile_true_config_proto_6001324581131673121_executor_type_11160318154034397263_.14, alias_passthrough_params=true, entry_computation_layout={(bf16[16,16,256,64]{3,2,1,0}, bf16[16,16,256,64]{3,2,1,0}, bf16[16,16,256,64]{3,2,1,0})->bf16[16,16,256,64]{3,2,1,0}}
ENTRY %a_inference_build_tf_xla_graph_26__XlaMustCompile_true_config_proto_6001324581131673121_executor_type_11160318154034397263_.14 (arg0.1: bf16[16,16,256,64], arg1.2: bf16[16,16,256,64], arg2.3: bf16[16,16,256,64]) -> bf16[16,16,256,64] {
%arg0.1 = bf16[16,16,256,64]{3,2,1,0} parameter(0), parameter_replication={false}, metadata={op_name="XLA_Args"}
%arg1.2 = bf16[16,16,256,64]{3,2,1,0} parameter(1), parameter_replication={false}, metadata={op_name="XLA_Args"}
%arg2.3 = bf16[16,16,256,64]{3,2,1,0} parameter(2), parameter_replication={false}, metadata={op_name="XLA_Args"}
%fmha-bmm-bmm = (bf16[16,16,256,64]{3,2,1,0}, u8[0]{0}) custom-call(bf16[16,16,256,64]{3,2,1,0} %arg0.1, bf16[16,16,256,64]{3,2,1,0} %arg1.2, bf16[16,16,256,64]{3,2,1,0} %arg2.3), custom_call_target="__cudnn$fhmaBmmBmm", metadata={op_type="BatchMatMulV2" op_name="MatMul" source_file="/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py" source_line=1161}, backend_config={"algorithm":{"algo_id":"0","math_type":"TENSOR_OP_MATH","tuning_knobs":{"17":"1","24":"0"},"is_cudnn_frontend":true,"workspace_size":"0"},"fmha_scale":1,"dropout_rate":0,"bmm1_dot_dimension_numbers":{"lhs_contracting_dimensions":["3"],"rhs_contracting_dimensions":["3"],"lhs_batch_dimensions":["0","1"],"rhs_batch_dimensions":["0","1"]},"bmm2_dot_dimension_numbers":{"lhs_contracting_dimensions":["3"],"rhs_contracting_dimensions":["2"],"lhs_batch_dimensions":["0","1"],"rhs_batch_dimensions":["0","1"]},"intermediate_tensor_shape":{"element_type":"BF16","dimensions":["16","16","256","256"],"tuple_shapes":[],"layout":{"dim_level_types":[],"dim_unique":[],"dim_ordered":[],"minor_to_major":["3","2","1","0"],"tiles":[],"element_size_in_bits":"0","memory_space":"0","index_primitive_type":"PRIMITIVE_TYPE_INVALID","pointer_primitive_type":"PRIMITIVE_TYPE_INVALID","dynamic_shape_metadata_prefix_bytes":"0"},"is_dynamic_dimension":[false,false,false,false]},"seed":"42"}
ROOT %get-tuple-element = bf16[16,16,256,64]{3,2,1,0} get-tuple-element((bf16[16,16,256,64]{3,2,1,0}, u8[0]{0}) %fmha-bmm-bmm), index=0, metadata={op_type="BatchMatMulV2" op_name="MatMul_1" source_file="/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py" source_line=1161}
}
2023-12-19 16:03:11.256692: W tensorflow/core/framework/op_kernel.cc:1839] OP_REQUIRES failed at xla_ops.cc:624 : INTERNAL: Failed to compile XLA Runtime program: failed to run compilation pipeline:
xla.program:5:13: error: failed to legalize operation 'builtin.unrealized_conversion_cast' that was explicitly marked illegal
%view = memref.view %arg0[%c0][] : memref<8388608xi8> to memref<16x16x256x64xbf16>
^
xla.program:5:13: note: see current operation: %70 = "builtin.unrealized_conversion_cast"(%69) : (!llvm.struct<(ptr, ptr, i64, array<4 x i64>, array<4 x i64>)>) -> memref<16x16x256x64xbf16>
Traceback (most recent call last):
File "/code/hej/deepray/test_tf_vmodule.py", line 56, in <module>
output = build_tf_xla_graph(queries, keys, values)
File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/execute.py", line 60, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InternalError: Failed to compile XLA Runtime program: failed to run compilation pipeline:
xla.program:5:13: error: failed to legalize operation 'builtin.unrealized_conversion_cast' that was explicitly marked illegal
%view = memref.view %arg0[%c0][] : memref<8388608xi8> to memref<16x16x256x64xbf16>
^
xla.program:5:13: note: see current operation: %70 = "builtin.unrealized_conversion_cast"(%69) : (!llvm.struct<(ptr, ptr, i64, array<4 x i64>, array<4 x i64>)>) -> memref<16x16x256x64xbf16>
[Op:__inference_build_tf_xla_graph_26]
```
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I_kwDOArmXAs56GYdY
| 62,665 |
Unable to build TF 2.14 from source
|
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[
"Hi @bergentruckung ,\r\n\r\nCould you please submit the ticket in the attached [template](https://github.com/tensorflow/tensorflow/issues/new?assignees=&labels=&projects=&template=tensorflow_issue_template.yaml) with all the required details.\r\n\r\nWe need all the details along with the build steps. Please refer the attached build steps from [here](https://www.tensorflow.org/install/source).",
"Sure, I just created one that follows the template [here](https://github.com/tensorflow/tensorflow/issues/62669).",
"Hi @bergentruckung ,\r\n\r\nCould you please close this issue and track it there?",
"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/62665\">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/62665\">No</a>\n"
] | 2023-12-19T11:44:17 | 2024-01-05T01:48:57 | 2024-01-05T01:48:53 |
NONE
| null | null | null |
Hello,
I'm unable to build TF 2.14 from source for Python 3.11 (3.11.4 to be precise). I'm building with support for CUDA (12.2) and running on an A100 80GB PCIe card. Here's the error that I see:
```
Repository rule _tf_http_archive defined at:
/local/tf_tensorflow/third_party/repo.bzl:89:35: in <toplevel>
ERROR: /local/tf_tensorflow/tensorflow/core/kernels/BUILD:7953:18: While resolving toolchains for target //tensorflow/core/kernels:libtfkernel_sobol_op.so: invalid registered toolchain '@local_jdk//:runtime_toolchain_definition': no such package '@local_jdk//': error globbing [lib/**] - [lib/missioncontr
ol/**, lib/visualvm/**] op=FILES: /home/.cache/bazel/_bazel/4167dbe79c65128bd2e3a1505c6f80c3/external/local_jdk/lib/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64
/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/amd64/tzdb.dat (Too many levels of symbolic links)
ERROR: Analysis of target '//tensorflow/tools/pip_package:build_pip_package' failed; build aborted:
INFO: Elapsed time: 810.131s
INFO: 0 processes.
FAILED: Build did NOT complete successfully (263 packages loaded, 3936 targets configured)
currently loading: @ml_dtypes//
Fetching repository @pypi_portpicker; Restarting.
Fetching repository @pypi_requests; Restarting.
Fetching repository @pypi_h5py; Restarting.
Fetching repository @pypi_packaging; Restarting.
Fetching repository @pypi_tensorboard; Restarting.
Fetching repository @pypi_keras; Restarting.
Fetching repository @pypi_numpy; Restarting.
Fetching repository @pypi_tensorflow_estimator; Restarting. ... (17 fetches)
```
Any recommendations on what I should do to take the build forward?
Thanks in advance.
|
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I_kwDOArmXAs56FgIt
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TFLite conversion (w/ int8 quantization) from ConcreteFunction is broken
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[
"@DLumi Please ensure you provide a representative dataset during conversion for accurate calibration and double-check quantization parameters, especially those related to calibration. Also try different quantization options to find optimal settings. In order to expedite the trouble-shooting process, please provide a code snippet to reproduce the issue reported here.\r\nThank you!",
"@sushreebarsa Can you please explain to me how providing a dataset of actual data instead of randomized dummy would actually help with a conversion error? \r\n\r\nEdit: Apparently, github does not like links that end with an underscore, so I formatted it as a code block, please, try to access the file once again. Please disregard whatever was there regarding you accessing the colab file",
"Hi @DLumi ,\r\n\r\nSorry for the delay. The error with code is due to incorrect representative dataset usage. Please find the corrected [code](https://colab.research.google.com/gist/LakshmiKalaKadali/76b2ccec371ed520c3e946d485ed4070/tflite-from-cf.ipynb#scrollTo=gcu374Ewb-y8). Please let us know if problem persists.\r\n\r\nThank You.",
"@LakshmiKalaKadali Could you please point me out what part was / needs to be changed? \r\nI compared my colab notebook with yours cell by cell and couldn't really spot the corrected part. ",
"Hi @DLumi, \r\n\r\nIn my previous post, code link is not redirecting to corrected code. Please find the corrected code below. Here is the detailed[ gist](https://colab.research.google.com/gist/LakshmiKalaKadali/2ee2f75cf69921c58eff1df85df87f1f/tflite-issue-62664.ipynb).\r\n```import tensorflow as tf\r\nimport numpy as np\r\n\r\nprint('TF version:', tf.__version__)\r\n\r\n# create keras model\r\nkeras_model = tf.keras.applications.MobileNetV2(weights=None)\r\n\r\ninput_names = [x.name for x in keras_model.inputs]\r\n\r\nprint(input_names)\r\n\r\n# retrieve concrete function from it\r\nfull_model = tf.function(lambda x: keras_model(x))\r\ninput_tensors = [tf.TensorSpec(shape=inp.shape, dtype=tf.float32, name=inp.name) for inp in keras_model.inputs]\r\ncf = full_model.get_concrete_function(x=input_tensors)\r\n\r\n# loader with random data for now\r\ndef tflite_loader(input_names):\r\n for _ in range(4):\r\n yield {k: (np.random.random([64, 224, 224, 3])).astype('float32') for k in input_names}\r\n'''\r\nfor d in tflite_loader():\r\n for k, v in d.items():\r\n print(k, v.shape)\r\n break\r\n'''\r\n# Here, I have changed your code with representative_dataset_gen function\r\n# This function converts the data generated by tflite_loader into a suitable format to use as a representative dataset for conversion.\r\ndef representative_dataset_gen():\r\n for data in tflite_loader(input_names):\r\n yield [tf.constant(data[name]) for name in input_names]\r\n \r\nconverter = tf.lite.TFLiteConverter.from_concrete_functions([cf], trackable_obj=keras_model)\r\nconverter.optimizations = [tf.lite.Optimize.DEFAULT]\r\nconverter._experimental_disable_per_channel = True\r\nconverter.target_spec.supported_types = [tf.int8]\r\nconverter.representative_dataset = representative_dataset_gen #added this line of code: notifying the representative_dataset_gen function to converter\r\ntflite_model = converter.convert()\r\n\r\n# To save the tflite model code\r\nwith open('model.tflite', 'wb') as f:\r\n f.write(tflite_model)\r\n```\r\nThank You",
"@LakshmiKalaKadali so, basically, the main change is to yield a list of tensors instead of a dict? But that's more of a workaround than a fix, don't you think? \r\nBecause as far as I'm concerned, TFLite is meant to be fed with dictionaries as well. You can run inference on dictionaries with TFLite, and the converter initialized from keras model can digest representative datasets that yield dictionaries as well. \r\nSo I guess, thanks for the workaround, but can we probably have an actual fix for this so that the behavior of the converter is consistent? \r\n\r\nP.S. I've updated the initial code at `https://colab.research.google.com/drive/1am-t2AeayTFDpZRcUzdROH1Jf7K03gF_` to add cells representing conversion from keras model (just in case you need this context, although, it's just one line of the difference)",
"Hi @DLumi ,\r\n\r\nYes, you are right, the TFlite models initialized from Keras model works with representative datasets that yield dictionaries also. In the code, as the concrete function with signature is used, it is suggestible to pass concrete function to converter rather than a base keras model.\r\n\r\nThank You\r\n",
"@LakshmiKalaKadali I dunno about what's suggestible and what is not, but if there is a public API that helps me do stuff I want - I use it. And if there are multiple APIs for the same thing I assume they are consistent, which is not the case here.\r\nTo achieve consistency I'd propose to either fix the bug preventing calibration process (CF converter) to feed on dictionaries, or to prohibit dictionaries altogether, i.e. disallowing the use of them in `from_keras_model` API and providing a clear understandable error message instead of confusing error that is present now. \r\nIn either case, I'd also correct documentation to explicitly say what data structures are expected to be yielded from representative datasets, as now it's not as clear as it potentially could be. ",
"Hi @pkgoogle,\r\n\r\nPlease look into the issue.\r\n\r\nThank You",
"Hi, @DLumi, is there a resource/documentation which shows that the dictionary workflow is supported? I'm trying to figure out if this a regression or an API change, \r\n\r\nhttps://www.tensorflow.org/lite/models/convert/convert_models#convert_concrete_functions_ is the current way that is supported/recommended.\r\n\r\nThanks for your help.",
"@pkgoogle \r\nActually, now that I remember, it was per documentation, yes.\r\nhttps://www.tensorflow.org/lite/performance/post_training_quantization#full_integer_quantization\r\n\r\n> Since TensorFlow 2.7 version, we recommend using the signature-based approach over the input tensor list-based approach because the input tensor ordering can be easily flipped.\r\n\r\nSo, since documentation on that is a little vague, I assumed that \"signature-based approach\" basically means that my representative dataset is supposed to yield dicts. I kind of forgot that the list option also existed as I spent quite a while setting this whole thing up (given the errors and stuff). ",
"Hi @DLumi, this might be related to https://github.com/tensorflow/tensorflow/issues/62620, the signature seems to get removed in certain situations\r\n\r\nHi @miaout17, can you please take a look? Thanks."
] | 2023-12-19T09:29:22 | 2024-01-16T21:31:00 | null |
NONE
| null | null | null |
### 1. System information
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Win 10 22H2 (but reproducible elsewhere)
- TensorFlow installation (pip package or built from source): pip package
- TensorFlow library (version, if pip package or github SHA, if built from source): 2.14.0
### 2. Code
Provide code to help us reproduce your issues using one of the following options:
`https://colab.research.google.com/drive/1am-t2AeayTFDpZRcUzdROH1Jf7K03gF_`
### 3. Failure after conversion
N/A
### 4. (optional) RNN conversion support
N/A
### 5. (optional) Any other info / logs
TFLite converter fails on calibration step when trying to convert ConcreteFunctions with int8 quantization.
It seems that somewhere during the process it generates a saved model without any signatures, which leads to fail on calibration.
I tried toggling `converter.experimental_lower_to_saved_model`, it does nothing in this case.
As a workaround, `tf.lite.TFLiteConverter.from_keras_model()` does work as intended, but I'd like the dedicated CF conversion pipeline to be fixed.
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-4-24b64f09cd68>](https://localhost:8080/#) in <cell line: 6>()
4 converter.target_spec.supported_types = [tf.int8]
5 converter.representative_dataset = tflite_loader
----> 6 tflite_model = converter.convert()
14 frames
[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/lite.py](https://localhost:8080/#) in convert(self)
2183 Invalid quantization parameters.
2184 """
-> 2185 return super(TFLiteConverterV2, self).convert()
2186
2187
[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/lite.py](https://localhost:8080/#) in wrapper(self, *args, **kwargs)
1137 def wrapper(self, *args, **kwargs):
1138 # pylint: disable=protected-access
-> 1139 return self._convert_and_export_metrics(convert_func, *args, **kwargs)
1140 # pylint: enable=protected-access
1141
[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/lite.py](https://localhost:8080/#) in _convert_and_export_metrics(self, convert_func, *args, **kwargs)
1091 self._save_conversion_params_metric()
1092 start_time = time.process_time()
-> 1093 result = convert_func(self, *args, **kwargs)
1094 elapsed_time_ms = (time.process_time() - start_time) * 1000
1095 if result:
[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/lite.py](https://localhost:8080/#) in convert(self)
1790 )
1791
-> 1792 return super(TFLiteFrozenGraphConverterV2, self).convert(
1793 graph_def, input_tensors, output_tensors
1794 )
[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/lite.py](https://localhost:8080/#) in convert(self, graph_def, input_tensors, output_tensors)
1376 )
1377
-> 1378 return self._optimize_tflite_model(
1379 result, self._quant_mode, quant_io=self.experimental_new_quantizer
1380 )
[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/convert_phase.py](https://localhost:8080/#) in wrapper(*args, **kwargs)
213 except Exception as error:
214 report_error_message(str(error))
--> 215 raise error from None # Re-throws the exception.
216
217 return wrapper
[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/convert_phase.py](https://localhost:8080/#) in wrapper(*args, **kwargs)
203 def wrapper(*args, **kwargs):
204 try:
--> 205 return func(*args, **kwargs)
206 except ConverterError as converter_error:
207 if converter_error.errors:
[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/lite.py](https://localhost:8080/#) in _optimize_tflite_model(self, model, quant_mode, quant_io)
1035 q_allow_float = quant_mode.is_allow_float()
1036 q_variable_quantization = quant_mode.enable_mlir_variable_quantization
-> 1037 model = self._quantize(
1038 model,
1039 q_in_type,
[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/lite.py](https://localhost:8080/#) in _quantize(self, result, input_type, output_type, activations_type, bias_type, allow_float, enable_variable_quantization)
733 )
734 if self._experimental_calibrate_only or self.experimental_new_quantizer:
--> 735 calibrated = calibrate_quantize.calibrate(
736 self.representative_dataset.input_gen
737 )
[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/convert_phase.py](https://localhost:8080/#) in wrapper(*args, **kwargs)
213 except Exception as error:
214 report_error_message(str(error))
--> 215 raise error from None # Re-throws the exception.
216
217 return wrapper
[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/convert_phase.py](https://localhost:8080/#) in wrapper(*args, **kwargs)
203 def wrapper(*args, **kwargs):
204 try:
--> 205 return func(*args, **kwargs)
206 except ConverterError as converter_error:
207 if converter_error.errors:
[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/optimize/calibrator.py](https://localhost:8080/#) in calibrate(self, dataset_gen)
252 dataset_gen: A generator that generates calibration samples.
253 """
--> 254 self._feed_tensors(dataset_gen, resize_input=True)
255 return self._calibrator.Calibrate()
[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/optimize/calibrator.py](https://localhost:8080/#) in _feed_tensors(self, dataset_gen, resize_input)
119 self._interpreter = Interpreter(model_content=self._model_content)
120 signature_key = None
--> 121 input_array = self._create_input_array_from_dict(None, sample)
122 elif isinstance(sample, list):
123 signature_key = None
[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/optimize/calibrator.py](https://localhost:8080/#) in _create_input_array_from_dict(self, signature_key, inputs)
86 def _create_input_array_from_dict(self, signature_key, inputs):
87 input_array = []
---> 88 signature_runner = self._interpreter.get_signature_runner(signature_key)
89 input_details = sorted(
90 signature_runner.get_input_details().items(),
[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/interpreter.py](https://localhost:8080/#) in get_signature_runner(self, signature_key)
851 if signature_key is None:
852 if len(self._signature_defs) != 1:
--> 853 raise ValueError(
854 'SignatureDef signature_key is None and model has {0} Signatures. '
855 'None is only allowed when the model has 1 SignatureDef'.format(
ValueError: SignatureDef signature_key is None and model has 0 Signatures. None is only allowed when the model has 1 SignatureDef
```
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I_kwDOArmXAs56FWfR
| 62,663 |
Failure in building with GPU support from source specifically in 2.7.0
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[
"after some debugging, i found out that it might have something to do with find_cuda_config.py not able to locate `/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcusolver.so.11` so i manually specified `cusolver_version = \"11\"`. Now my cuda config looks like this `struct(compute_capabilities = [\"compute_86\"], config = {\"cublas_include_dir\": \"/usr/local/cuda-11.8/targets/x86_64-linux/include\", \"cublas_library_dir\": \"/usr/local/cuda-11.8/targets/x86_64-linux/lib\", \"cublas_version\": \"11.11.3\", \"cuda_binary_dir\": \"/usr/local/cuda-11.8/bin\", \"cuda_include_dir\": \"/usr/local/cuda-11.8/targets/x86_64-linux/include\", \"cuda_library_dir\": \"/usr/local/cuda-11.8/targets/x86_64-linux/lib\", \"cudnn_include_dir\": \"/usr/include\", \"cudnn_library_dir\": \"/usr/lib/x86_64-linux-gnu\", \"cufft_include_dir\": \"/usr/local/cuda-11.8/targets/x86_64-linux/include\", \"cufft_library_dir\": \"/usr/local/cuda-11.8/targets/x86_64-linux/lib\", \"cufft_version\": \"10.9.0\", \"cupti_include_dir\": \"/usr/local/cuda-11.8/targets/x86_64-linux/include\", \"cupti_library_dir\": \"/usr/local/cuda-11.8/targets/x86_64-linux/lib\", \"curand_include_dir\": \"/usr/local/cuda-11.8/targets/x86_64-linux/include\", \"curand_library_dir\": \"/usr/local/cuda-11.8/targets/x86_64-linux/lib\", \"curand_version\": \"10.3.0\", \"cusolver_include_dir\": \"/usr/local/cuda-11.8/targets/x86_64-linux/include\", \"cusolver_library_dir\": \"/usr/local/cuda-11.8/targets/x86_64-linux/lib\", \"cusolver_version\": \"11\", \"cusparse_include_dir\": \"/usr/local/cuda-11.8/targets/x86_64-linux/include\", \"cusparse_library_dir\": \"/usr/local/cuda-11.8/targets/x86_64-linux/lib\", \"cusparse_version\": \"11.7.5\", \"nvvm_library_dir\": \"/usr/local/cuda-11.8/nvvm/libdevice\"}, cpu_value = \"Linux\", cublas_version = \"11\", cuda_toolkit_path = \"/usr/local/cuda-11.8\", cuda_version = \"11.8\", cuda_version_major = \"11\", cudart_version = \"11.0\", cudnn_version = \"8\", cufft_version = \"10\", curand_version = \"10\", cusolver_version = \"11\", cusparse_version = \"11\")`\r\n\r\nAnd it managed to start compiling but in the end when `executing genrule //tensorflow/python/keras/api:keras_python_api_gen_compat_v1` it gives error as well \r\n```ERROR: /home/haolan/tf-gpu3/tensorflow/python/keras/api/BUILD:133:19: Executing genrule //tensorflow/python/keras/api:keras_python_api_gen_compat_v1 failed (Exit 1): bash failed: error executing command\r\n (cd /home/haolan/.cache/bazel/_bazel_haolan/4841e33fe850472b4b360662c0c7abcc/execroot/org_tensorflow && \\\r\n exec env - \\\r\n CUDA_TOOLKIT_PATH=/usr/local/cuda-11.8 \\\r\n GCC_HOST_COMPILER_PATH=/usr/bin/x86_64-linux-gnu-gcc-11 \\\r\n LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64: \\\r\n PATH=/home/haolan/test/bin:/usr/local/cuda-11.8/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin \\\r\n PYTHON_BIN_PATH=/home/haolan/test/bin/python \\\r\n PYTHON_LIB_PATH=/home/haolan/test/lib/python3.8/site-packages \\\r\n TF2_BEHAVIOR=1 \\\r\n TF_CUDA_COMPUTE_CAPABILITIES=8.6 \\\r\n /bin/bash -c 'source external/bazel_tools/tools/genrule/genrule-setup.sh; bazel-out/k8-dbg/bin/tensorflow/python/keras/api/create_tensorflow.python_api_keras_python_api_gen_compat_v1 --apidir=bazel-out/k8-dbg/bin/tensorflow/python/keras/api_v1/ --apiname=keras --apiversion=1 --loading=default --packages=tensorflow.python,tensorflow.python.keras,tensorflow.python.keras.activations,tensorflow.python.keras.applications.densenet,tensorflow.python.keras.applications.efficientnet,tensorflow.python.keras.applications.imagenet_utils,tensorflow.python.keras.applications.inception_resnet_v2,tensorflow.python.keras.applications.inception_v3,tensorflow.python.keras.applications.mobilenet,tensorflow.python.keras.applications.mobilenet_v2,tensorflow.python.keras.applications.mobilenet_v3,tensorflow.python.keras.applications.nasnet,tensorflow.python.keras.applications.resnet,tensorflow.python.keras.applications.resnet_v2,tensorflow.python.keras.applications.vgg16,tensorflow.python.keras.applications.vgg19,tensorflow.python.keras.applications.xception,tensorflow.python.keras.backend,tensorflow.python.keras.backend_config,tensorflow.python.keras.callbacks,tensorflow.python.keras.callbacks_v1,tensorflow.python.keras.constraints,tensorflow.python.keras.datasets.boston_housing,tensorflow.python.keras.datasets.cifar10,tensorflow.python.keras.datasets.cifar100,tensorflow.python.keras.datasets.fashion_mnist,tensorflow.python.keras.datasets.imdb,tensorflow.python.keras.datasets.mnist,tensorflow.python.keras.datasets.reuters,tensorflow.python.keras.engine.base_layer,tensorflow.python.keras.engine.data_adapter,tensorflow.python.keras.engine.input_layer,tensorflow.python.keras.engine.input_spec,tensorflow.python.keras.engine.sequential,tensorflow.python.keras.engine.training,tensorflow.python.keras.estimator,tensorflow.python.keras.feature_column.sequence_feature_column,tensorflow.python.keras.initializers,tensorflow.python.keras.initializers.initializers_v1,tensorflow.python.keras.initializers.initializers_v2,tensorflow.python.keras.layers.advanced_activations,tensorflow.python.keras.layers.convolutional,tensorflow.python.keras.layers.convolutional_recurrent,tensorflow.python.keras.layers.core,tensorflow.python.keras.layers.cudnn_recurrent,tensorflow.python.keras.layers.dense_attention,tensorflow.python.keras.layers.embeddings,tensorflow.python.keras.layers.legacy_rnn.rnn_cell_impl,tensorflow.python.keras.layers.local,tensorflow.python.keras.layers.merge,tensorflow.python.keras.layers.noise,tensorflow.python.keras.layers.normalization.batch_normalization,tensorflow.python.keras.layers.normalization.batch_normalization_v1,tensorflow.python.keras.layers.normalization.layer_normalization,tensorflow.python.keras.layers.preprocessing,tensorflow.python.keras.layers.pooling,tensorflow.python.keras.layers.recurrent,tensorflow.python.keras.layers.recurrent_v2,tensorflow.python.keras.layers.serialization,tensorflow.python.keras.layers.wrappers,tensorflow.python.keras.legacy_tf_layers.base,tensorflow.python.keras.legacy_tf_layers.convolutional,tensorflow.python.keras.legacy_tf_layers.core,tensorflow.python.keras.legacy_tf_layers.normalization,tensorflow.python.keras.legacy_tf_layers.pooling,tensorflow.python.keras.losses,tensorflow.python.keras.metrics,tensorflow.python.keras.mixed_precision.get_layer_policy,tensorflow.python.keras.mixed_precision.loss_scale_optimizer,tensorflow.python.keras.mixed_precision.policy,tensorflow.python.keras.models,tensorflow.python.keras.optimizer_v2.adadelta,tensorflow.python.keras.optimizer_v2.adagrad,tensorflow.python.keras.optimizer_v2.adam,tensorflow.python.keras.optimizer_v2.adamax,tensorflow.python.keras.optimizer_v2.ftrl,tensorflow.python.keras.optimizer_v2.gradient_descent,tensorflow.python.keras.optimizer_v2.learning_rate_schedule,tensorflow.python.keras.optimizer_v2.nadam,tensorflow.python.keras.optimizer_v2.optimizer_v2,tensorflow.python.keras.optimizer_v2.rmsprop,tensorflow.python.keras.optimizers,tensorflow.python.keras.premade.linear,tensorflow.python.keras.premade.wide_deep,tensorflow.python.keras.preprocessing.image,tensorflow.python.keras.preprocessing.sequence,tensorflow.python.keras.preprocessing.text,tensorflow.python.keras.regularizers,tensorflow.python.keras.saving.model_config,tensorflow.python.keras.saving.save,tensorflow.python.keras.saving.saved_model_experimental,tensorflow.python.keras.utils.data_utils,tensorflow.python.keras.utils.generic_utils,tensorflow.python.keras.utils.io_utils,tensorflow.python.keras.utils.layer_utils,tensorflow.python.keras.utils.losses_utils,tensorflow.python.keras.utils.multi_gpu_utils,tensorflow.python.keras.utils.np_utils,tensorflow.python.keras.utils.tf_utils,tensorflow.python.keras.utils.vis_utils,tensorflow.python.keras.wrappers.scikit_learn --output_package=tensorflow.python.keras.api._v1 --use_relative_imports=True bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/__internal__/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/__internal__/legacy/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/__internal__/legacy/layers/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/__internal__/legacy/layers/experimental/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/__internal__/legacy/rnn_cell/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/activations/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/densenet/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/efficientnet/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/imagenet_utils/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/inception_resnet_v2/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/inception_v3/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/mobilenet/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/mobilenet_v2/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/mobilenet_v3/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/nasnet/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/resnet/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/resnet_v2/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/resnet50/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/vgg16/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/vgg19/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/xception/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/backend/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/callbacks/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/callbacks/experimental/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/constraints/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/datasets/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/datasets/boston_housing/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/datasets/cifar10/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/datasets/cifar100/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/datasets/fashion_mnist/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/datasets/imdb/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/datasets/mnist/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/datasets/reuters/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/estimator/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/experimental/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/initializers/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/layers/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/layers/experimental/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/layers/experimental/preprocessing/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/losses/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/metrics/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/mixed_precision/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/mixed_precision/experimental/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/models/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/optimizers/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/optimizers/schedules/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/premade/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/preprocessing/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/preprocessing/image/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/preprocessing/sequence/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/preprocessing/text/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/regularizers/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/utils/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/wrappers/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/wrappers/scikit_learn/__init__.py')\r\nExecution platform: @local_execution_config_platform//:platform\r\nTraceback (most recent call last):\r\n File \"/home/haolan/.cache/bazel/_bazel_haolan/4841e33fe850472b4b360662c0c7abcc/execroot/org_tensorflow/bazel-out/k8-dbg/bin/tensorflow/python/keras/api/create_tensorflow.python_api_keras_python_api_gen_compat_v1.runfiles/org_tensorflow/tensorflow/python/pywrap_tensorflow.py\", line 64, in <module>\r\n from tensorflow.python._pywrap_tensorflow_internal import *\r\nImportError: /home/haolan/.cache/bazel/_bazel_haolan/4841e33fe850472b4b360662c0c7abcc/execroot/org_tensorflow/bazel-out/k8-dbg/bin/tensorflow/python/keras/api/create_tensorflow.python_api_keras_python_api_gen_compat_v1.runfiles/org_tensorflow/tensorflow/python/_pywrap_tensorflow_internal.so: undefined symbol: _ZN4mlir2TF6detail25ResourceAliasAnalysisInfo18kMaxResourceTypeIdE\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/home/haolan/.cache/bazel/_bazel_haolan/4841e33fe850472b4b360662c0c7abcc/execroot/org_tensorflow/bazel-out/k8-dbg/bin/tensorflow/python/keras/api/create_tensorflow.python_api_keras_python_api_gen_compat_v1.runfiles/org_tensorflow/tensorflow/python/tools/api/generator/create_python_api.py\", line 26, in <module>\r\n from tensorflow.python.tools.api.generator import doc_srcs\r\n File \"/home/haolan/.cache/bazel/_bazel_haolan/4841e33fe850472b4b360662c0c7abcc/execroot/org_tensorflow/bazel-out/k8-dbg/bin/tensorflow/python/keras/api/create_tensorflow.python_api_keras_python_api_gen_compat_v1.runfiles/org_tensorflow/tensorflow/python/__init__.py\", line 40, in <module>\r\n from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow\r\n File \"/home/haolan/.cache/bazel/_bazel_haolan/4841e33fe850472b4b360662c0c7abcc/execroot/org_tensorflow/bazel-out/k8-dbg/bin/tensorflow/python/keras/api/create_tensorflow.python_api_keras_python_api_gen_compat_v1.runfiles/org_tensorflow/tensorflow/python/pywrap_tensorflow.py\", line 79, in <module>\r\n raise ImportError(\r\nImportError: Traceback (most recent call last):\r\n File \"/home/haolan/.cache/bazel/_bazel_haolan/4841e33fe850472b4b360662c0c7abcc/execroot/org_tensorflow/bazel-out/k8-dbg/bin/tensorflow/python/keras/api/create_tensorflow.python_api_keras_python_api_gen_compat_v1.runfiles/org_tensorflow/tensorflow/python/pywrap_tensorflow.py\", line 64, in <module>\r\n from tensorflow.python._pywrap_tensorflow_internal import *\r\nImportError: /home/haolan/.cache/bazel/_bazel_haolan/4841e33fe850472b4b360662c0c7abcc/execroot/org_tensorflow/bazel-out/k8-dbg/bin/tensorflow/python/keras/api/create_tensorflow.python_api_keras_python_api_gen_compat_v1.runfiles/org_tensorflow/tensorflow/python/_pywrap_tensorflow_internal.so: undefined symbol: _ZN4mlir2TF6detail25ResourceAliasAnalysisInfo18kMaxResourceTypeIdE\r\n\r\n\r\nFailed to load the native TensorFlow runtime.\r\nSee https://www.tensorflow.org/install/errors for some common causes and solutions.\r\nIf you need help, create an issue at https://github.com/tensorflow/tensorflow/issues and include the entire stack trace above this error message.\r\nTarget //tensorflow/tools/pip_package:build_pip_package failed to build\r\nERROR: /home/haolan/tf-gpu3/tensorflow/lite/python/BUILD:63:10 Executing genrule //tensorflow/python/keras/api:keras_python_api_gen_compat_v1 failed (Exit 1): bash failed: error executing command\r\n (cd /home/haolan/.cache/bazel/_bazel_haolan/4841e33fe850472b4b360662c0c7abcc/execroot/org_tensorflow && \\\r\n exec env - \\\r\n CUDA_TOOLKIT_PATH=/usr/local/cuda-11.8 \\\r\n GCC_HOST_COMPILER_PATH=/usr/bin/x86_64-linux-gnu-gcc-11 \\\r\n LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64: \\\r\n PATH=/home/haolan/test/bin:/usr/local/cuda-11.8/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin \\\r\n PYTHON_BIN_PATH=/home/haolan/test/bin/python \\\r\n PYTHON_LIB_PATH=/home/haolan/test/lib/python3.8/site-packages \\\r\n TF2_BEHAVIOR=1 \\\r\n TF_CUDA_COMPUTE_CAPABILITIES=8.6 \\\r\n /bin/bash -c 'source external/bazel_tools/tools/genrule/genrule-setup.sh; bazel-out/k8-dbg/bin/tensorflow/python/keras/api/create_tensorflow.python_api_keras_python_api_gen_compat_v1 --apidir=bazel-out/k8-dbg/bin/tensorflow/python/keras/api_v1/ --apiname=keras --apiversion=1 --loading=default --packages=tensorflow.python,tensorflow.python.keras,tensorflow.python.keras.activations,tensorflow.python.keras.applications.densenet,tensorflow.python.keras.applications.efficientnet,tensorflow.python.keras.applications.imagenet_utils,tensorflow.python.keras.applications.inception_resnet_v2,tensorflow.python.keras.applications.inception_v3,tensorflow.python.keras.applications.mobilenet,tensorflow.python.keras.applications.mobilenet_v2,tensorflow.python.keras.applications.mobilenet_v3,tensorflow.python.keras.applications.nasnet,tensorflow.python.keras.applications.resnet,tensorflow.python.keras.applications.resnet_v2,tensorflow.python.keras.applications.vgg16,tensorflow.python.keras.applications.vgg19,tensorflow.python.keras.applications.xception,tensorflow.python.keras.backend,tensorflow.python.keras.backend_config,tensorflow.python.keras.callbacks,tensorflow.python.keras.callbacks_v1,tensorflow.python.keras.constraints,tensorflow.python.keras.datasets.boston_housing,tensorflow.python.keras.datasets.cifar10,tensorflow.python.keras.datasets.cifar100,tensorflow.python.keras.datasets.fashion_mnist,tensorflow.python.keras.datasets.imdb,tensorflow.python.keras.datasets.mnist,tensorflow.python.keras.datasets.reuters,tensorflow.python.keras.engine.base_layer,tensorflow.python.keras.engine.data_adapter,tensorflow.python.keras.engine.input_layer,tensorflow.python.keras.engine.input_spec,tensorflow.python.keras.engine.sequential,tensorflow.python.keras.engine.training,tensorflow.python.keras.estimator,tensorflow.python.keras.feature_column.sequence_feature_column,tensorflow.python.keras.initializers,tensorflow.python.keras.initializers.initializers_v1,tensorflow.python.keras.initializers.initializers_v2,tensorflow.python.keras.layers.advanced_activations,tensorflow.python.keras.layers.convolutional,tensorflow.python.keras.layers.convolutional_recurrent,tensorflow.python.keras.layers.core,tensorflow.python.keras.layers.cudnn_recurrent,tensorflow.python.keras.layers.dense_attention,tensorflow.python.keras.layers.embeddings,tensorflow.python.keras.layers.legacy_rnn.rnn_cell_impl,tensorflow.python.keras.layers.local,tensorflow.python.keras.layers.merge,tensorflow.python.keras.layers.noise,tensorflow.python.keras.layers.normalization.batch_normalization,tensorflow.python.keras.layers.normalization.batch_normalization_v1,tensorflow.python.keras.layers.normalization.layer_normalization,tensorflow.python.keras.layers.preprocessing,tensorflow.python.keras.layers.pooling,tensorflow.python.keras.layers.recurrent,tensorflow.python.keras.layers.recurrent_v2,tensorflow.python.keras.layers.serialization,tensorflow.python.keras.layers.wrappers,tensorflow.python.keras.legacy_tf_layers.base,tensorflow.python.keras.legacy_tf_layers.convolutional,tensorflow.python.keras.legacy_tf_layers.core,tensorflow.python.keras.legacy_tf_layers.normalization,tensorflow.python.keras.legacy_tf_layers.pooling,tensorflow.python.keras.losses,tensorflow.python.keras.metrics,tensorflow.python.keras.mixed_precision.get_layer_policy,tensorflow.python.keras.mixed_precision.loss_scale_optimizer,tensorflow.python.keras.mixed_precision.policy,tensorflow.python.keras.models,tensorflow.python.keras.optimizer_v2.adadelta,tensorflow.python.keras.optimizer_v2.adagrad,tensorflow.python.keras.optimizer_v2.adam,tensorflow.python.keras.optimizer_v2.adamax,tensorflow.python.keras.optimizer_v2.ftrl,tensorflow.python.keras.optimizer_v2.gradient_descent,tensorflow.python.keras.optimizer_v2.learning_rate_schedule,tensorflow.python.keras.optimizer_v2.nadam,tensorflow.python.keras.optimizer_v2.optimizer_v2,tensorflow.python.keras.optimizer_v2.rmsprop,tensorflow.python.keras.optimizers,tensorflow.python.keras.premade.linear,tensorflow.python.keras.premade.wide_deep,tensorflow.python.keras.preprocessing.image,tensorflow.python.keras.preprocessing.sequence,tensorflow.python.keras.preprocessing.text,tensorflow.python.keras.regularizers,tensorflow.python.keras.saving.model_config,tensorflow.python.keras.saving.save,tensorflow.python.keras.saving.saved_model_experimental,tensorflow.python.keras.utils.data_utils,tensorflow.python.keras.utils.generic_utils,tensorflow.python.keras.utils.io_utils,tensorflow.python.keras.utils.layer_utils,tensorflow.python.keras.utils.losses_utils,tensorflow.python.keras.utils.multi_gpu_utils,tensorflow.python.keras.utils.np_utils,tensorflow.python.keras.utils.tf_utils,tensorflow.python.keras.utils.vis_utils,tensorflow.python.keras.wrappers.scikit_learn --output_package=tensorflow.python.keras.api._v1 --use_relative_imports=True bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/__internal__/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/__internal__/legacy/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/__internal__/legacy/layers/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/__internal__/legacy/layers/experimental/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/__internal__/legacy/rnn_cell/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/activations/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/densenet/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/efficientnet/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/imagenet_utils/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/inception_resnet_v2/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/inception_v3/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/mobilenet/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/mobilenet_v2/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/mobilenet_v3/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/nasnet/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/resnet/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/resnet_v2/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/resnet50/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/vgg16/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/vgg19/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/applications/xception/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/backend/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/callbacks/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/callbacks/experimental/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/constraints/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/datasets/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/datasets/boston_housing/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/datasets/cifar10/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/datasets/cifar100/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/datasets/fashion_mnist/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/datasets/imdb/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/datasets/mnist/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/datasets/reuters/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/estimator/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/experimental/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/initializers/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/layers/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/layers/experimental/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/layers/experimental/preprocessing/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/losses/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/metrics/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/mixed_precision/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/mixed_precision/experimental/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/models/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/optimizers/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/optimizers/schedules/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/premade/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/preprocessing/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/preprocessing/image/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/preprocessing/sequence/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/preprocessing/text/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/regularizers/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/utils/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/wrappers/__init__.py bazel-out/k8-dbg/bin/tensorflow/python/keras/api/_v1/keras/wrappers/scikit_learn/__init__.py')\r\nExecution platform: @local_execution_config_platform//:platform```",
"issue resolved by adding flag `--cxxopt=\"-D_GLIBCXX_USE_CXX11_ABI=0\"` during build",
"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/62663\">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/62663\">No</a>\n"
] | 2023-12-19T09:05:06 | 2023-12-21T07:55:20 | 2023-12-21T07:55:16 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.7.0
### Custom code
No
### OS platform and distribution
Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.8
### Bazel version
3.7.2
### GCC/compiler version
11.4.0
### CUDA/cuDNN version
11.8
### GPU model and memory
Nvidia RTX A6000
### Current behavior?
I am trying to build [this custom code ](https://github.com/SamsungLabs/fastflow-tensorflow), but has encountered error when building with GPU support. I have followed the [documentation](https://www.tensorflow.org/install/source) and have set up proper configuration as seen below, setting cuda==Y and confirming the environment path auto detected are indeed valid.
I thought it is an issue from the custom code but i found out that the same issue persists even in the main repository, r2.7 branch. Moreover, after upgrading bazel to 6.1.0 and build the master branch with same configurations, it was successful.
I'm convinced this is likely an issue linked to version 2.7.0, though I'm not sure how to tackle this issue. As i don't see a easy way of bypassing this old version since [this repo](https://github.com/SamsungLabs/FastFlow) that i want to try out is dependent on it.
Any assistance on it is greatly appreciated!
### Standalone code to reproduce the issue
```shell
1. build failure on the custom code producing the output in the log section below
2. build failure on official code r2.7 branch with same configuration and same error
3. upgrade to bazel 6.1.0 and build successfully on official code master branch
```
### Relevant log output
```shell
$ cat .tf_configure.bazelrc
build --action_env PYTHON_BIN_PATH="/usr/bin/python3"
build --action_env PYTHON_LIB_PATH="/usr/lib/python3/dist-packages"
build --python_path="/usr/bin/python3"
build --action_env CUDA_TOOLKIT_PATH="/usr/local/cuda-11.8"
build --action_env TF_CUDA_COMPUTE_CAPABILITIES="8.6"
build --action_env LD_LIBRARY_PATH="/usr/local/cuda-11.8/lib64"
build --action_env GCC_HOST_COMPILER_PATH="/usr/bin/x86_64-linux-gnu-gcc-11"
build --config=cuda
build:opt --copt=-Wno-sign-compare
build:opt --host_copt=-Wno-sign-compare
test --flaky_test_attempts=3
test --test_size_filters=small,medium
test --test_env=LD_LIBRARY_PATH
test:v1 --test_tag_filters=-benchmark-test,-no_oss,-no_gpu,-oss_serial
test:v1 --build_tag_filters=-benchmark-test,-no_oss,-no_gpu
test:v2 --test_tag_filters=-benchmark-test,-no_oss,-no_gpu,-oss_serial,-v1only
test:v2 --build_tag_filters=-benchmark-test,-no_oss,-no_gpu,-v1only
$ bazel build //tensorflow/tools/pip_package:build_pip_package
Starting local Bazel server and connecting to it...
INFO: Options provided by the client:
Inherited 'common' options: --isatty=1 --terminal_columns=98
INFO: Reading rc options for 'build' from /home/haolan/tf-gpu/.bazelrc:
Inherited 'common' options: --experimental_repo_remote_exec
INFO: Reading rc options for 'build' from /home/haolan/tf-gpu/.bazelrc:
'build' options: --define framework_shared_object=true --java_toolchain=@tf_toolchains//toolchains/java:tf_java_toolchain --host_java_toolchain=@tf_toolchains//toolchains/java:tf_java_toolchain --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true
INFO: Reading rc options for 'build' from /home/haolan/tf-gpu/.tf_configure.bazelrc:
'build' options: --action_env PYTHON_BIN_PATH=/usr/bin/python3 --action_env PYTHON_LIB_PATH=/usr/lib/python3/dist-packages --python_path=/usr/bin/python3 --action_env CUDA_TOOLKIT_PATH=/usr/local/cuda-11.8 --action_env TF_CUDA_COMPUTE_CAPABILITIES=8.6 --action_env LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64: --action_env GCC_HOST_COMPILER_PATH=/usr/bin/x86_64-linux-gnu-gcc-11 --config=cuda
INFO: Reading rc options for 'build' from /home/haolan/tf-gpu/.bazelrc:
'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/common,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/fallback,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils
INFO: Found applicable config definition build:short_logs in file /home/haolan/tf-gpu/.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file /home/haolan/tf-gpu/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:cuda in file /home/haolan/tf-gpu/.bazelrc: --repo_env TF_NEED_CUDA=1 --crosstool_top=@local_config_cuda//crosstool:toolchain --@local_config_cuda//:enable_cuda
INFO: Found applicable config definition build:linux in file /home/haolan/tf-gpu/.bazelrc: --copt=-w --host_copt=-w --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++14 --host_cxxopt=-std=c++14 --config=dynamic_kernels --distinct_host_configuration=false --experimental_guard_against_concurrent_changes
INFO: Found applicable config definition build:dynamic_kernels in file /home/haolan/tf-gpu/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS
INFO: Repository local_config_cuda instantiated at:
/home/haolan/tf-gpu/WORKSPACE:15:14: in <toplevel>
/home/haolan/tf-gpu/tensorflow/workspace2.bzl:1079:19: in workspace
/home/haolan/tf-gpu/tensorflow/workspace2.bzl:94:19: in _tf_toolchains
Repository rule cuda_configure defined at:
/home/haolan/tf-gpu/third_party/gpus/cuda_configure.bzl:1448:33: in <toplevel>
ERROR: An error occurred during the fetch of repository 'local_config_cuda':
Traceback (most recent call last):
File "/home/haolan/tf-gpu/third_party/gpus/cuda_configure.bzl", line 1401, column 38, in _cuda_autoconf_impl
_create_local_cuda_repository(repository_ctx)
File "/home/haolan/tf-gpu/third_party/gpus/cuda_configure.bzl", line 1076, column 27, in _create_local_cuda_repository
cuda_libs = _find_libs(repository_ctx, check_cuda_libs_script, cuda_config)
File "/home/haolan/tf-gpu/third_party/gpus/cuda_configure.bzl", line 606, column 21, in _find_libs
_check_cuda_libs(repository_ctx, check_cuda_libs_script, check_cuda_libs_params.values())
File "/home/haolan/tf-gpu/third_party/gpus/cuda_configure.bzl", line 501, column 28, in _check_cuda_libs
checked_paths = execute(repository_ctx, [python_bin, "-c", cmd]).stdout.splitlines()
File "/home/haolan/tf-gpu/third_party/remote_config/common.bzl", line 230, column 13, in execute
fail(
Error in fail: Repository command failed
Expected even number of arguments
INFO: Found applicable config definition build:cuda in file /home/haolan/tf-gpu/.bazelrc: --repo_env TF_NEED_CUDA=1 --crosstool_top=@local_config_cuda//crosstool:toolchain --@local_config_cuda//:enable_cuda
ERROR: @local_config_cuda//:enable_cuda :: Error loading option @local_config_cuda//:enable_cuda: Repository command failed
Expected even number of arguments
```
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| 2,047,920,890 |
I_kwDOArmXAs56EMr6
| 62,662 |
Android C/C++ API Select TensorFlow op(s), included in the given model, is(are) not supported by this interpreter. Make sure you apply/link the Flex delegate before inference
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[
"@Qinlong275,\r\nFor using flex ops for C/C++ we need to enable Flex delegate by linking a TensorFlow Lite Flex delegate shared library. You can build it with Bazel as the following command.\r\n\r\n\r\n> bazel build -c opt --config=monolithic tensorflow/lite/delegates/flex:tensorflowlite_flex\r\n\r\nAlso please take a look at this issue for reference.\r\nhttps://github.com/tensorflow/tensorflow/issues/61495\r\nhttps://github.com/tensorflow/tensorflow/issues/61743\r\n\r\nThank you!\r\n\r\n",
"> @Qinlong275为了 在 C/C++ 中使用 Flex 操作，我们需要通过链接 TensorFlow Lite Flex 委托共享库来启用 Flex 委托。您可以使用 Bazel 构建它，如下命令。\r\n> \r\n> > bazel build -c opt --config=monolithic tensorflow/lite/delegates/flex:tensorflowlite_flex\r\n> \r\n> 还请您看看这个问题并提供参考。 [#61495 ](https://github.com/tensorflow/tensorflow/issues/61495) #61743\r\n> \r\n> 谢谢你！\r\nI want to build the tensorflowlite_flex so to use on android platform.\r\nI build tensorflowlite_flex occur the follow error! (Mac M1 pro, bazel verison 6.3.2-homebrew)\r\nRepository rule _tf_http_archive defined at:\r\n /Users/qinlong/android/tensorflow/third_party/repo.bzl:89:35: in <toplevel>\r\nERROR: /private/var/tmp/_bazel_qinlong/4d0b12327583c8b4e37f0824509a394f/external/llvm-project/llvm/BUILD.bazel:158:11: Illegal ambiguous match on configurable attribute \"defines\" in @llvm-project//llvm:config:\r\n@llvm-project//llvm:macos_arm64\r\n@bazel_tools//src/conditions:darwin\r\nMultiple matches are not allowed unless one is unambiguously more specialized or they resolve to the same value. See https://bazel.build/reference/be/functions#select.\r\nERROR: Analysis of target '//tensorflow/lite/delegates/flex:tensorflowlite_flex' failed; build aborted: \r\nINFO: Elapsed time: 6.111s\r\nINFO: 0 processes.\r\nFAILED: Build did NOT complete successfully (0 packages loaded, 1 target confi\\\r\n\r\n\r\n",
"> @Qinlong275, For using flex ops for C/C++ we need to enable Flex delegate by linking a TensorFlow Lite Flex delegate shared library. You can build it with Bazel as the following command.\r\n> \r\n> > bazel build -c opt --config=monolithic tensorflow/lite/delegates/flex:tensorflowlite_flex\r\n> \r\n> Also please take a look at this issue for reference. #61495 #61743\r\n> \r\n> Thank you!\r\n\r\nFine, I resolve the problem by the follow code:\r\nbazel build -c opt \\\r\n--config=monolithic \\\r\n--config=android_arm64 tensorflow/lite/delegates/flex:tensorflowlite_flex\r\n(add the --config=android_arm64 param, then build libtensorflowlite_flex.so success!!!)",
"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/62662\">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/62662\">No</a>\n"
] | 2023-12-19T04:55:58 | 2023-12-20T11:01:17 | 2023-12-20T08:58:03 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
tf 2.12.0
### Custom code
Yes
### OS platform and distribution
mac apple m1; android studio
### Mobile device
HuaWei Nova6
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I run my custom tflite model on androidstudio use C API(SO library), then occur these error:
E Select TensorFlow op(s), included in the given model, is(are) not supported by this interpreter. Make sure you apply/link the Flex delegate before inference. For the Android, it can be resolved by adding "org.tensorflow:tensorflow-lite-select-tf-ops" dependency. See instructions: https://www.tensorflow.org/lite/guide/ops_select
2023-12-19 12:44:34.993 3039-5562 tflite com...cent.karaoke.tNetInterpretApp E Node number 622 (FlexDepthwiseConv2dNative) failed to prepare.
2023-12-19 12:44:34.993 3039-5562 tflite com...cent.karaoke.tNetInterpretApp E Failed to allocate tensors!
I think maybe the AcquireFlexDelegate has not work?, I find the source code, the AcquireFlexDelegate looks like a __attribute__((weak)) symbal. I have loaded the libtensorflowlite_gpu_jni.so before start the interpret.
### Standalone code to reproduce the issue
```shell
if (is_android)
#依赖的三方库
add_library(TFLITE_CORE SHARED IMPORTED)
set_target_properties(TFLITE_CORE PROPERTIES IMPORTED_LOCATION ${CMAKE_CURRENT_SOURCE_DIR}/tflite/libs/android/${ANDROID_ABI}/libtensorflowlite_jni.so)
add_library(TFLITE_FLEX SHARED IMPORTED)
set_target_properties(TFLITE_FLEX PROPERTIES IMPORTED_LOCATION ${CMAKE_CURRENT_SOURCE_DIR}/tflite/libs/android/${ANDROID_ABI}/libtensorflowlite_flex_jni.so)
add_library(TFLITE_GPU_DELEGATE SHARED IMPORTED)
set_target_properties(TFLITE_GPU_DELEGATE PROPERTIES IMPORTED_LOCATION ${CMAKE_CURRENT_SOURCE_DIR}/tflite/libs/android/${ANDROID_ABI}/libtensorflowlite_gpu_jni.so)
target_link_libraries(TNET_SOURCE
TFLITE_CORE
TFLITE_FLEX
TFLITE_GPU_DELEGATE
)
endif ()
```
### Relevant log output
_No response_
|
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I_kwDOArmXAs56AyyO
| 62,661 |
[Question] How can I make sure Tensorflow has already used cuDNN Flash Fused Attention?
|
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[
"Hi @MoFHeka ,\r\n\r\nTo enable XLA we need to decorate respective function with `@tf.function(jit_compile=True)`. There is this method called [experimental_get_compiler_ir](https://github.com/tensorflow/tensorflow/blob/51cda66dedc2db71709efc6e68998be53c94a533/tensorflow/python/types/core.py#L251) which will give IR for of the input code. Could you check this whether useful for what you are looking for ?",
"@SuryanarayanaY Here is the IR, I can't see any fused kernel but only matmul\r\n```bash\r\nHloModule a_inference_build_tf_xla_graph_104__.107, entry_computation_layout={()->f16[4096,2048,8,16]{3,2,1,0}}\r\n\r\n%region_0.57 (Arg_0.58: f16[], Arg_1.59: f16[]) -> f16[] {\r\n %Arg_0.58 = f16[] parameter(0)\r\n %Arg_1.59 = f16[] parameter(1)\r\n ROOT %maximum.60 = f16[] maximum(f16[] %Arg_0.58, f16[] %Arg_1.59), metadata={op_type=\"Softmax\" op_name=\"attention/Softmax\"}\r\n}\r\n\r\n%region_1.69 (Arg_0.70: f32[], Arg_1.71: f32[]) -> f32[] {\r\n %Arg_0.70 = f32[] parameter(0)\r\n %Arg_1.71 = f32[] parameter(1)\r\n ROOT %add.72 = f32[] add(f32[] %Arg_0.70, f32[] %Arg_1.71), metadata={op_type=\"Softmax\" op_name=\"attention/Softmax\"}\r\n}\r\n\r\nENTRY %a_inference_build_tf_xla_graph_104__.107 () -> f16[4096,2048,8,16] {\r\n %constant.3 = s32[4]{0} constant({4096, 2048, 8, 8}), metadata={op_type=\"StridedSlice\" op_name=\"attention/strided_slice\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %constant.1 = s32[4]{0} constant({4096, 2048, 8, 8}), metadata={op_type=\"StridedSlice\" op_name=\"attention/strided_slice\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %slice.2 = s32[2]{0} slice(s32[4]{0} %constant.1), slice={[0:2]}, metadata={op_type=\"StridedSlice\" op_name=\"attention/strided_slice\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %reshape.4 = s32[2]{0} reshape(s32[2]{0} %slice.2), metadata={op_type=\"StridedSlice\" op_name=\"attention/strided_slice\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %constant.8 = s32[4]{0} constant({4096, 2048, 8, 16}), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %slice.9 = s32[1]{0} slice(s32[4]{0} %constant.8), slice={[0:1]}, metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %reshape.10 = s32[] reshape(s32[1]{0} %slice.9), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %convert.11 = s32[] convert(s32[] %reshape.10), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %slice.12 = s32[1]{0} slice(s32[4]{0} %constant.8), slice={[1:2]}, metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %reshape.13 = s32[] reshape(s32[1]{0} %slice.12), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %convert.14 = s32[] convert(s32[] %reshape.13), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %slice.15 = s32[1]{0} slice(s32[4]{0} %constant.8), slice={[2:3]}, metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %reshape.16 = s32[] reshape(s32[1]{0} %slice.15), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %convert.17 = s32[] convert(s32[] %reshape.16), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %slice.18 = s32[1]{0} slice(s32[4]{0} %constant.8), slice={[3:4]}, metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %reshape.19 = s32[] reshape(s32[1]{0} %slice.18), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %convert.20 = s32[] convert(s32[] %reshape.19), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %constant.30 = s32[4]{0} constant({4096, 2048, 8, 16}), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_1/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %slice.31 = s32[1]{0} slice(s32[4]{0} %constant.30), slice={[0:1]}, metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_1/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %reshape.32 = s32[] reshape(s32[1]{0} %slice.31), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_1/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %convert.33 = s32[] convert(s32[] %reshape.32), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_1/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %slice.34 = s32[1]{0} slice(s32[4]{0} %constant.30), slice={[1:2]}, metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_1/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %reshape.35 = s32[] reshape(s32[1]{0} %slice.34), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_1/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %convert.36 = s32[] convert(s32[] %reshape.35), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_1/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %slice.37 = s32[1]{0} slice(s32[4]{0} %constant.30), slice={[2:3]}, metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_1/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %reshape.38 = s32[] reshape(s32[1]{0} %slice.37), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_1/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %convert.39 = s32[] convert(s32[] %reshape.38), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_1/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %slice.40 = s32[1]{0} slice(s32[4]{0} %constant.30), slice={[3:4]}, metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_1/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %reshape.41 = s32[] reshape(s32[1]{0} %slice.40), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_1/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %convert.42 = s32[] convert(s32[] %reshape.41), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_1/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %constant.83 = s32[4]{0} constant({4096, 2048, 8, 16}), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_2/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %slice.84 = s32[1]{0} slice(s32[4]{0} %constant.83), slice={[0:1]}, metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_2/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %reshape.85 = s32[] reshape(s32[1]{0} %slice.84), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_2/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %convert.86 = s32[] convert(s32[] %reshape.85), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_2/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %slice.87 = s32[1]{0} slice(s32[4]{0} %constant.83), slice={[1:2]}, metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_2/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %reshape.88 = s32[] reshape(s32[1]{0} %slice.87), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_2/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %convert.89 = s32[] convert(s32[] %reshape.88), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_2/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %slice.90 = s32[1]{0} slice(s32[4]{0} %constant.83), slice={[2:3]}, metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_2/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %reshape.91 = s32[] reshape(s32[1]{0} %slice.90), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_2/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %convert.92 = s32[] convert(s32[] %reshape.91), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_2/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %slice.93 = s32[1]{0} slice(s32[4]{0} %constant.83), slice={[3:4]}, metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_2/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %reshape.94 = s32[] reshape(s32[1]{0} %slice.93), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_2/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %convert.95 = s32[] convert(s32[] %reshape.94), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_2/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %constant.6 = f16[] constant(0), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %constant.5 = f16[] constant(1), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %rng.7 = f16[4096,2048,8,16]{3,2,1,0} rng(f16[] %constant.6, f16[] %constant.5), distribution=rng_normal, metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %constant.21 = f16[] constant(1), metadata={op_type=\"Mul\" op_name=\"random_normal/mul\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %broadcast.22 = f16[4096,2048,8,16]{3,2,1,0} broadcast(f16[] %constant.21), dimensions={}, metadata={op_type=\"Mul\" op_name=\"random_normal/mul\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %multiply.23 = f16[4096,2048,8,16]{3,2,1,0} multiply(f16[4096,2048,8,16]{3,2,1,0} %rng.7, f16[4096,2048,8,16]{3,2,1,0} %broadcast.22), metadata={op_type=\"Mul\" op_name=\"random_normal/mul\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %constant.24 = f16[] constant(0), metadata={op_type=\"AddV2\" op_name=\"random_normal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %broadcast.25 = f16[4096,2048,8,16]{3,2,1,0} broadcast(f16[] %constant.24), dimensions={}, metadata={op_type=\"AddV2\" op_name=\"random_normal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %add.26 = f16[4096,2048,8,16]{3,2,1,0} add(f16[4096,2048,8,16]{3,2,1,0} %multiply.23, f16[4096,2048,8,16]{3,2,1,0} %broadcast.25), metadata={op_type=\"AddV2\" op_name=\"random_normal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %constant.28 = f16[] constant(0), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_1/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %constant.27 = f16[] constant(1), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_1/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %rng.29 = f16[4096,2048,8,16]{3,2,1,0} rng(f16[] %constant.28, f16[] %constant.27), distribution=rng_normal, metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_1/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %constant.43 = f16[] constant(1), metadata={op_type=\"Mul\" op_name=\"random_normal_1/mul\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %broadcast.44 = f16[4096,2048,8,16]{3,2,1,0} broadcast(f16[] %constant.43), dimensions={}, metadata={op_type=\"Mul\" op_name=\"random_normal_1/mul\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %multiply.45 = f16[4096,2048,8,16]{3,2,1,0} multiply(f16[4096,2048,8,16]{3,2,1,0} %rng.29, f16[4096,2048,8,16]{3,2,1,0} %broadcast.44), metadata={op_type=\"Mul\" op_name=\"random_normal_1/mul\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %constant.46 = f16[] constant(0), metadata={op_type=\"AddV2\" op_name=\"random_normal_1\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %broadcast.47 = f16[4096,2048,8,16]{3,2,1,0} broadcast(f16[] %constant.46), dimensions={}, metadata={op_type=\"AddV2\" op_name=\"random_normal_1\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %add.48 = f16[4096,2048,8,16]{3,2,1,0} add(f16[4096,2048,8,16]{3,2,1,0} %multiply.45, f16[4096,2048,8,16]{3,2,1,0} %broadcast.47), metadata={op_type=\"AddV2\" op_name=\"random_normal_1\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %dot.49 = f16[4096,2048,8,8]{3,2,1,0} dot(f16[4096,2048,8,16]{3,2,1,0} %add.26, f16[4096,2048,8,16]{3,2,1,0} %add.48), lhs_batch_dims={0,1}, lhs_contracting_dims={3}, rhs_batch_dims={0,1}, rhs_contracting_dims={3}, metadata={op_type=\"BatchMatMulV2\" op_name=\"attention/MatMul\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %transpose.50 = f16[4096,2048,8,8]{3,2,1,0} transpose(f16[4096,2048,8,8]{3,2,1,0} %dot.49), dimensions={0,1,2,3}, metadata={op_type=\"BatchMatMulV2\" op_name=\"attention/MatMul\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %constant.51 = f16[1,1,8,8]{3,2,1,0} constant({...}), metadata={op_type=\"Sub\" op_name=\"attention/sub\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %reshape.52 = f16[8,8]{1,0} reshape(f16[1,1,8,8]{3,2,1,0} %constant.51), metadata={op_type=\"Sub\" op_name=\"attention/sub\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %broadcast.53 = f16[4096,2048,8,8]{3,2,1,0} broadcast(f16[8,8]{1,0} %reshape.52), dimensions={2,3}, metadata={op_type=\"Sub\" op_name=\"attention/sub\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %subtract.54 = f16[4096,2048,8,8]{3,2,1,0} subtract(f16[4096,2048,8,8]{3,2,1,0} %transpose.50, f16[4096,2048,8,8]{3,2,1,0} %broadcast.53), metadata={op_type=\"Sub\" op_name=\"attention/sub\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %constant.56 = f16[] constant(-inf), metadata={op_type=\"Softmax\" op_name=\"attention/Softmax\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %reduce.61 = f16[4096,2048,8]{2,1,0} reduce(f16[4096,2048,8,8]{3,2,1,0} %subtract.54, f16[] %constant.56), dimensions={3}, to_apply=%region_0.57, metadata={op_type=\"Softmax\" op_name=\"attention/Softmax\"}\r\n %reshape.62 = f16[4096,2048,8,1]{3,2,1,0} reshape(f16[4096,2048,8]{2,1,0} %reduce.61), metadata={op_type=\"Softmax\" op_name=\"attention/Softmax\"}\r\n %broadcast.63 = f16[4096,2048,8,1]{3,2,1,0} broadcast(f16[4096,2048,8,1]{3,2,1,0} %reshape.62), dimensions={0,1,2,3}, metadata={op_type=\"Softmax\" op_name=\"attention/Softmax\"}\r\n %reshape.64 = f16[4096,2048,8]{2,1,0} reshape(f16[4096,2048,8,1]{3,2,1,0} %broadcast.63), metadata={op_type=\"Softmax\" op_name=\"attention/Softmax\"}\r\n %broadcast.65 = f16[4096,2048,8,8]{3,2,1,0} broadcast(f16[4096,2048,8]{2,1,0} %reshape.64), dimensions={0,1,2}, metadata={op_type=\"Softmax\" op_name=\"attention/Softmax\"}\r\n %subtract.66 = f16[4096,2048,8,8]{3,2,1,0} subtract(f16[4096,2048,8,8]{3,2,1,0} %subtract.54, f16[4096,2048,8,8]{3,2,1,0} %broadcast.65), metadata={op_type=\"Softmax\" op_name=\"attention/Softmax\"}\r\n %exponential.67 = f16[4096,2048,8,8]{3,2,1,0} exponential(f16[4096,2048,8,8]{3,2,1,0} %subtract.66), metadata={op_type=\"Softmax\" op_name=\"attention/Softmax\"}\r\n %convert.68 = f32[4096,2048,8,8]{3,2,1,0} convert(f16[4096,2048,8,8]{3,2,1,0} %exponential.67), metadata={op_type=\"Softmax\" op_name=\"attention/Softmax\"}\r\n %constant.55 = f32[] constant(-0), metadata={op_type=\"Softmax\" op_name=\"attention/Softmax\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %reduce.73 = f32[4096,2048,8]{2,1,0} reduce(f32[4096,2048,8,8]{3,2,1,0} %convert.68, f32[] %constant.55), dimensions={3}, to_apply=%region_1.69, metadata={op_type=\"Softmax\" op_name=\"attention/Softmax\"}\r\n %convert.74 = f16[4096,2048,8]{2,1,0} convert(f32[4096,2048,8]{2,1,0} %reduce.73), metadata={op_type=\"Softmax\" op_name=\"attention/Softmax\"}\r\n %reshape.75 = f16[4096,2048,8,1]{3,2,1,0} reshape(f16[4096,2048,8]{2,1,0} %convert.74), metadata={op_type=\"Softmax\" op_name=\"attention/Softmax\"}\r\n %broadcast.76 = f16[4096,2048,8,1]{3,2,1,0} broadcast(f16[4096,2048,8,1]{3,2,1,0} %reshape.75), dimensions={0,1,2,3}, metadata={op_type=\"Softmax\" op_name=\"attention/Softmax\"}\r\n %reshape.77 = f16[4096,2048,8]{2,1,0} reshape(f16[4096,2048,8,1]{3,2,1,0} %broadcast.76), metadata={op_type=\"Softmax\" op_name=\"attention/Softmax\"}\r\n %broadcast.78 = f16[4096,2048,8,8]{3,2,1,0} broadcast(f16[4096,2048,8]{2,1,0} %reshape.77), dimensions={0,1,2}, metadata={op_type=\"Softmax\" op_name=\"attention/Softmax\"}\r\n %divide.79 = f16[4096,2048,8,8]{3,2,1,0} divide(f16[4096,2048,8,8]{3,2,1,0} %exponential.67, f16[4096,2048,8,8]{3,2,1,0} %broadcast.78), metadata={op_type=\"Softmax\" op_name=\"attention/Softmax\"}\r\n %constant.81 = f16[] constant(0), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_2/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %constant.80 = f16[] constant(1), metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_2/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %rng.82 = f16[4096,2048,8,16]{3,2,1,0} rng(f16[] %constant.81, f16[] %constant.80), distribution=rng_normal, metadata={op_type=\"RandomStandardNormal\" op_name=\"random_normal_2/RandomStandardNormal\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %constant.96 = f16[] constant(1), metadata={op_type=\"Mul\" op_name=\"random_normal_2/mul\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %broadcast.97 = f16[4096,2048,8,16]{3,2,1,0} broadcast(f16[] %constant.96), dimensions={}, metadata={op_type=\"Mul\" op_name=\"random_normal_2/mul\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %multiply.98 = f16[4096,2048,8,16]{3,2,1,0} multiply(f16[4096,2048,8,16]{3,2,1,0} %rng.82, f16[4096,2048,8,16]{3,2,1,0} %broadcast.97), metadata={op_type=\"Mul\" op_name=\"random_normal_2/mul\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %constant.99 = f16[] constant(0), metadata={op_type=\"AddV2\" op_name=\"random_normal_2\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %broadcast.100 = f16[4096,2048,8,16]{3,2,1,0} broadcast(f16[] %constant.99), dimensions={}, metadata={op_type=\"AddV2\" op_name=\"random_normal_2\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %add.101 = f16[4096,2048,8,16]{3,2,1,0} add(f16[4096,2048,8,16]{3,2,1,0} %multiply.98, f16[4096,2048,8,16]{3,2,1,0} %broadcast.100), metadata={op_type=\"AddV2\" op_name=\"random_normal_2\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %dot.102 = f16[4096,2048,8,16]{3,2,1,0} dot(f16[4096,2048,8,8]{3,2,1,0} %divide.79, f16[4096,2048,8,16]{3,2,1,0} %add.101), lhs_batch_dims={0,1}, lhs_contracting_dims={3}, rhs_batch_dims={0,1}, rhs_contracting_dims={2}, metadata={op_type=\"BatchMatMulV2\" op_name=\"attention/MatMul_1\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %transpose.103 = f16[4096,2048,8,16]{3,2,1,0} transpose(f16[4096,2048,8,16]{3,2,1,0} %dot.102), dimensions={0,1,2,3}, metadata={op_type=\"BatchMatMulV2\" op_name=\"attention/MatMul_1\" source_file=\"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\" source_line=1161}\r\n %reshape.104 = f16[4096,2048,8,16]{3,2,1,0} reshape(f16[4096,2048,8,16]{3,2,1,0} %transpose.103), metadata={op_name=\"XLA_Retvals\"}\r\n %tuple.105 = (f16[4096,2048,8,16]{3,2,1,0}) tuple(f16[4096,2048,8,16]{3,2,1,0} %reshape.104), metadata={op_name=\"XLA_Retvals\"}\r\n ROOT %get-tuple-element.106 = f16[4096,2048,8,16]{3,2,1,0} get-tuple-element((f16[4096,2048,8,16]{3,2,1,0}) %tuple.105), index=0, metadata={op_name=\"XLA_Retvals\"}\r\n}\r\n```",
"@SuryanarayanaY @AyanmoI @Tixxx Here is a little \"fmha-bmm-bmm\" test, which I enable VLOG in cudnn_fused_mha_rewriter. But still, there is nothing log about cudnn_fused_mha.\r\n```python\r\nimport os\r\nos.environ['TF_CPP_VMODULE'] = 'cudnn_fused_mha_rewriter=2'\r\n\r\nimport tensorflow as tf\r\nimport numpy as np\r\n\r\n\r\nbatch_size = 4096\r\nseq_len_q = 2048\r\nnum_heads = 8\r\nhead_dim = 16\r\nmat_dtypes = tf.float16\r\n\r\n\r\n# @tf.function(jit_compile=True)\r\n# def build_tf_xla_graph(queries, keys, values):\r\n# keras_attention_layer = tf.keras.layers.Attention(causal=True, use_scale=False, dtype=mat_dtypes)\r\n# flash_attention_output = keras_attention_layer(inputs=[queries, values, keys])\r\n# return flash_attention_output\r\n\r\[email protected](jit_compile=True)\r\ndef build_tf_xla_graph(queries, keys, values):\r\n score = tf.matmul(queries, keys, transpose_b=True)\r\n flash_attention_output = tf.matmul(score, values)\r\n return flash_attention_output\r\n\r\nseq_len_v = seq_len_q\r\nqueries = tf.random.normal(\r\n (batch_size, seq_len_q, num_heads, head_dim), dtype=mat_dtypes)\r\nkeys = tf.random.normal(\r\n (batch_size, seq_len_v, num_heads, head_dim), dtype=mat_dtypes)\r\nvalues = tf.random.normal(\r\n (batch_size, seq_len_v, num_heads, head_dim), dtype=mat_dtypes)\r\n\r\nir = build_tf_xla_graph.experimental_get_compiler_ir(queries, keys, values)(stage='hoo')\r\nprint(ir)\r\n\r\noutput = build_tf_xla_graph(queries, keys, values)\r\nprint(output)\r\n```",
"Enable XLA CudnnFusedMHARewriter successfully by export XLA_FLAGS=--xla_gpu_enable_cudnn_fmha, but trigger new bugs. #62666 ",
"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/62661\">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/62661\">No</a>\n"
] | 2023-12-18T16:31:08 | 2023-12-19T16:06:56 | 2023-12-19T16:06:52 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
tf 2.14
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
CUDA 12.3/cuDNN 8.9.6
### GPU model and memory
A800 80GB
### Current behavior?
In the [commit](https://github.com/tensorflow/tensorflow/commit/81a1946366e57edd1029153caa8f826c19717b75), we can see Nvidia people have already added the XLA supporting to convert separated kernel into one fused kernel when calculating attention(mha).
So how can I make sure it really happens. For example when I use tf.keras.layers.Attention.
### Standalone code to reproduce the issue
```shell
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Benchmark for FlashAttn and grad of FlashAttn."""
import numpy as np
import math
from tensorflow.core.protobuf import config_pb2
from tensorflow.python.client import session as session_lib
from tensorflow.python.framework import config as framework_config
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import variables
from tensorflow.python.ops import variable_scope
from tensorflow.python.platform import benchmark
from tensorflow.python.platform import test
from tensorflow.python.platform import tf_logging as logging
import tensorflow as tf
def build_origin_graph(batch_size, seq_len_q, num_heads, head_dim, dtype=dtypes.float32):
"""Build a graph containing a sequence of FlashAttn operations.
Args:
Returns:
"""
seq_len_v = seq_len_q
queries = tf.random.normal(
(batch_size, seq_len_q, num_heads, head_dim), dtype=dtype)
keys = tf.random.normal(
(batch_size, seq_len_v, num_heads, head_dim), dtype=dtype)
values = tf.random.normal(
(batch_size, seq_len_v, num_heads, head_dim), dtype=dtype)
scores = tf.einsum('bthd,bshd->bhts', queries / math.sqrt(queries.shape[-1]), keys)
attention = tf.nn.softmax(scores)
outputs = tf.einsum('bhts,bshd->bthd', attention, values)
return outputs
def build_tf_plus_graph(batch_size, seq_len_q, num_heads, head_dim, dtype=dtypes.float32):
"""Build a graph containing a sequence of FlashAttn operations.
Args:
Returns:
"""
seq_len_v = seq_len_q
queries = tf.random.normal(
(batch_size, seq_len_q, num_heads, head_dim), dtype=dtype)
keys = tf.random.normal(
(batch_size, seq_len_v, num_heads, head_dim), dtype=dtype)
values = tf.random.normal(
(batch_size, seq_len_v, num_heads, head_dim), dtype=dtype)
flash_attention_layer = FlashAttentionLayer(
seq_len_q, seq_len_v, num_heads, head_dim)
flash_attention_output = flash_attention_layer(
queries, keys, values)
return flash_attention_output
@tf.function
def build_tf_xla_graph(batch_size, seq_len_q, num_heads, head_dim, dtype=dtypes.float32):
"""Build a graph containing a sequence of FlashAttn operations.
Args:
Returns:
"""
seq_len_v = seq_len_q
with tf.device("/job:localhost/replica:0/task:0/device:XLA_GPU:0"):
queries = tf.random.normal(
(batch_size, seq_len_q, num_heads, head_dim), dtype=dtype)
keys = tf.random.normal(
(batch_size, seq_len_v, num_heads, head_dim), dtype=dtype)
values = tf.random.normal(
(batch_size, seq_len_v, num_heads, head_dim), dtype=dtype)
keras_attention_layer = tf.keras.layers.Attention(use_scale=False)
flash_attention_output = keras_attention_layer(inputs=[queries, values, keys])
return flash_attention_output
class OriginAttnBenchmark(test.Benchmark):
"""Benchmark FlashAttn!"""
_build_graph_fn = staticmethod(build_origin_graph)
def _run_graph(self, batch_size, seq_len_q, num_heads, head_dim, dtype):
"""Run the graph and print its execution time.
Args:
Returns:
"""
graph = ops.Graph()
with graph.as_default():
outputs = self._build_graph_fn(batch_size, seq_len_q, num_heads, head_dim, dtype)
config = config_pb2.ConfigProto(graph_options=config_pb2.GraphOptions(
optimizer_options=config_pb2.OptimizerOptions(
opt_level=config_pb2.OptimizerOptions.ON_2)))
with session_lib.Session(graph=graph, config=config) as session:
logging.set_verbosity(0)
variables.global_variables_initializer().run()
bench = benchmark.TensorFlowBenchmark()
attention_matrix_computation = batch_size * 2 * seq_len_q * seq_len_q * num_heads * head_dim
attention_over_values = batch_size * 2 * seq_len_q * seq_len_q * num_heads * head_dim
dtype_byte = dtype.size
bench.run_op_benchmark(
session,
outputs,
mbs=(attention_matrix_computation + attention_over_values) * dtype_byte * 100 / 1e6,
store_trace=True,
extras={
"batch_size": batch_size,
"seq_len_q": seq_len_q,
"num_heads": num_heads,
"head_dim": batch_size,
"dtype": seq_len_q,
})
def benchmark_FlashAttn(self):
print("Attention Forward")
# batch_size = [4096, 8192]
# seq_len_q = [1, 7, 30]
# num_heads = [2, 4, 8]
# head_dim = [128, 512, 1024]
# dtypes = [dtypes.float32, dtypes.float16]
batch_size = [4096]
seq_len_q = [30]
num_heads = [4]
head_dim = [128]
mat_dtypes = [dtypes.float16]
for b in batch_size:
for s in seq_len_q:
for h in num_heads:
for d in head_dim:
for dtype in mat_dtypes:
self._run_graph(b, s, h, d, dtype)
class TFXLAAttnBenchmark(OriginAttnBenchmark):
_build_graph_fn = staticmethod(build_tf_xla_graph)
if __name__ == "__main__":
physical_devices = framework_config.list_physical_devices('GPU')
if len(physical_devices):
framework_config.set_visible_devices(physical_devices[0], 'GPU')
test.main()
```
### Relevant log output
```shell
2023-12-18 16:20:00.138356: 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`.
2023-12-18 16:20:00.180184: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9360] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2023-12-18 16:20:00.180220: 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
2023-12-18 16:20:00.180265: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1537] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2023-12-18 16:20:00.188748: I tensorflow/core/platform/cpu_feature_guard.cc:183] 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, in other operations, rebuild TensorFlow with the appropriate compiler flags.
Attention Forward
2023-12-18 16:20:02.332430: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x563f6ff5d7c0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2023-12-18 16:20:02.332459: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2023-12-18 16:20:02.332501: E ./tensorflow/compiler/xla/stream_executor/stream_executor_internal.h:124] SetPriority unimplemented for this stream.
2023-12-18 16:20:02.466545: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1883] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 79005 MB memory: -> device: 0, name: NVIDIA A800-SXM4-80GB, pci bus id: 0000:d0:00.0, compute capability: 8.0
2023-12-18 16:20:02.478813: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x563f7158d210 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2023-12-18 16:20:02.478843: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA A800-SXM4-80GB, Compute Capability 8.0
2023-12-18 16:20:02.526593: E ./tensorflow/compiler/xla/stream_executor/stream_executor_internal.h:124] SetPriority unimplemented for this stream.
2023-12-18 16:20:02.532643: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1883] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 79005 MB memory: -> device: 0, name: NVIDIA A800-SXM4-80GB, pci bus id: 0000:d0:00.0, compute capability: 8.0
2023-12-18 16:20:02.542548: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:382] MLIR V1 optimization pass is not enabled
2023-12-18 16:20:02.628241: I tensorflow/compiler/jit/xla_device.cc:460] XLA_GPU and XLA_CPU devices are deprecated and will be removed in subsequent releases. Instead, use either @tf.function(jit_compile=True) for must-compile semantics, or run with TF_XLA_FLAGS=--tf_xla_auto_jit=2 for auto-clustering best-effort compilation.
2023-12-18 16:20:02.692172: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2023-12-18 16:20:02.746939: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8906
2023-12-18 16:20:05.145859: 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.
2023-12-18 16:20:06.211684: I tensorflow/tsl/profiler/lib/profiler_session.cc:104] Profiler session initializing.
2023-12-18 16:20:06.211730: I tensorflow/tsl/profiler/lib/profiler_session.cc:119] Profiler session started.
2023-12-18 16:20:06.211783: I tensorflow/compiler/xla/backends/profiler/gpu/cupti_tracer.cc:1694] Profiler found 1 GPUs
2023-12-18 16:20:06.320320: I tensorflow/tsl/profiler/lib/profiler_session.cc:70] Profiler session collecting data.
2023-12-18 16:20:06.322079: I tensorflow/compiler/xla/backends/profiler/gpu/cupti_tracer.cc:1828] CUPTI activity buffer flushed
2023-12-18 16:20:06.347580: I tensorflow/compiler/xla/backends/profiler/gpu/cupti_collector.cc:541] GpuTracer has collected 24 callback api events and 17 activity events.
2023-12-18 16:20:06.348167: I tensorflow/tsl/profiler/lib/profiler_session.cc:131] Profiler session tear down.
```
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I_kwDOArmXAs56Ah_n
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TF2.15 Tensorboard-Profiler Errors in Jupyter Notebook
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[
"@Te-eMster Could you please verify CUPTI Installation and Compatibility using the following:\r\n`conda list cupti` also please ensure `LD_LIBRARY_PATH `includes the path to CUPTI libraries(e.g.,/usr/local/cuda/extras/CUPTI/lib64).\r\nThank you!\r\n",
"@sushreebarsa Thanks for your help.\r\n\r\nHere are the relevant outputs from terminal:\r\n\r\n**conda list cupti in kernel-environment:**\r\npackages in environment at /home/matai/anaconda3/envs/tf2.15-py3.11: \r\n \r\nName \t\tVersion Build \tChannel \r\ncuda-cupti \t\t12.2.142 0 \tnvidia/label/cuda-12.2.2 \r\ncuda-cupti-static \t\t12.2.142 0 \tnvidia/label/cuda-12.2.2 \r\nnvidia-cuda-cupti-cu12 \t12.2.142 pypi_0 \tpypi \r\n\r\n**\"os.environ.__getitem__('LD_LIBRARY_PATH‘)\" executed in the Notebook:**\r\n'/home/user/anaconda3/envs/tf2.15-py3.11/lib'\r\n\r\n**\"ls | grep libcupti\" executed in the LD_LIBRARY_PATH directory:**\r\nlibcupti.so\r\nlibcupti.so.12\r\nlibcupti.so.2023.2.2\r\nlibcupti_static.a\r\n",
"@Te-eMster Could you please have a look at this colab [gist](https://colab.research.google.com/gist/sushreebarsa/5ad09985df807cf6938748ab83f9d917/62660.ipynb) where I was able to run the provided code successfully ?\r\nPlease create a new environment and try again. 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.",
"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/62660\">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/62660\">No</a>\n",
"I was having the same issue. I had installed tensorlow[and-cuda] using pipenv on a Ubuntu 22.04 machine.\r\nI solved it by adding \"_env_path_/lib/python3.10/site-packages/nvidia/cuda_cupti/lib/\" to LD_LIBRARY_PATH. \r\n\r\nIs there a way to avoid manually setting LD_LIBRARY_PATH ?\r\n\r\n"
] | 2023-12-18T15:51:29 | 2024-01-31T13:46:48 | 2024-01-11T01:49:29 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
TF2.15
### Custom code
No
### OS platform and distribution
Linux Mint 21.2 / Anaconda 23.11
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.2 / 8.9 ("bundled" versions with tensorflow[and-cuda])
### GPU model and memory
RTX 3080 10GB
### Current behavior?
I installed TF2.15 in a fresh Anaconda-Install as described in the "Install TensorFlow with pip" section. Installation runs smoothly and the example model runs smoothly as well.
The only other installations were:
- conda update conda
- conda install -c "nvidia/label/cuda-12.2.2" cuda-toolkit
However on compiling the model with Tensorboard-Callback in Jupyter Notebook and subsequently fitting the model I get multiple errors.
First there are the "cuptiGetTimestamp: error 999" and subsequent errors in the logs. Second, when trying to load the Profile-tab in Tensorboard, I get to select Profile, some data is loaded, but it says "Failed to load libcupti (is it installed and accessible?)"
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
os.environ['TENSORBOARD_BINARY'] = '/home/user/anaconda3/bin/tensorboard'
os.environ['CUDA_PATH'] = '/home/user/anaconda3'
os.environ['LD_LIBRARY_PATH'] = '/home/user/anaconda3/lib'
os.environ['PATH'] = os.environ['PATH']+':/home/user/anaconda3/lib'
%load_ext tensorboard
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
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10)
])
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir='/home/user/project/logs/test/test1', histogram_freq = 0, profile_batch='50,100')
model.compile(optimizer='adam',
loss=loss_fn,
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback])
%tensorboard --load_fast=false --logdir='./logs/test/test1'
```
### Relevant log output
```shell
[When creating the "tensorboard_callback" variable]
2023-12-18 16:29:27.188828: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 7743 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3080, pci bus id: 0000:02:00.0, compute capability: 8.6
2023-12-18 16:29:27.345796: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:104] Profiler session initializing.
2023-12-18 16:29:27.345815: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:119] Profiler session started.
2023-12-18 16:29:27.345840: I external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:1883] Profiler found 1 GPUs
2023-12-18 16:29:27.346062: E external/local_xla/xla/backends/profiler/gpu/cupti_error_manager.cc:137] cuptiGetTimestamp: error 999:
2023-12-18 16:29:27.346073: E external/local_xla/xla/backends/profiler/gpu/cupti_error_manager.cc:186] cuptiSubscribe: ignored due to a previous error.
2023-12-18 16:29:27.346078: E external/local_xla/xla/backends/profiler/gpu/cupti_error_manager.cc:459] cuptiGetResultString: ignored due to a previous error.
2023-12-18 16:29:27.346082: E external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:1935] function cupti_interface_->Subscribe( &subscriber_, (CUpti_CallbackFunc)ApiCallback, this)failed with error
2023-12-18 16:29:27.346132: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:131] Profiler session tear down.
2023-12-18 16:29:27.346144: E external/local_xla/xla/backends/profiler/gpu/cupti_error_manager.cc:142] cuptiFinalize: ignored due to a previous error.
2023-12-18 16:29:27.346148: E external/local_xla/xla/backends/profiler/gpu/cupti_error_manager.cc:459] cuptiGetResultString: ignored due to a previous error.
2023-12-18 16:29:27.346152: E external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:2026] function cupti_interface_->Finalize()failed with error
[When running model.fit]
2023-12-18 16:29:29.504764: I external/local_xla/xla/service/service.cc:168] XLA service 0x7fc4f9fd6150 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2023-12-18 16:29:29.504788: I external/local_xla/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 3080, Compute Capability 8.6
2023-12-18 16:29:29.510351: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2023-12-18 16:29:29.529016: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8904
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1702913369.646503 9396 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
[When reaching the profiled batches]
2023-12-18 16:29:30.228094: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:104] Profiler session initializing.
2023-12-18 16:29:30.228137: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:119] Profiler session started.
2023-12-18 16:29:30.228163: E external/local_xla/xla/backends/profiler/gpu/cupti_error_manager.cc:135] cuptiGetTimestamp: ignored due to a previous error.
2023-12-18 16:29:30.228179: E external/local_xla/xla/backends/profiler/gpu/cupti_error_manager.cc:186] cuptiSubscribe: ignored due to a previous error.
2023-12-18 16:29:30.228190: E external/local_xla/xla/backends/profiler/gpu/cupti_error_manager.cc:459] cuptiGetResultString: ignored due to a previous error.
2023-12-18 16:29:30.228201: E external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:1935] function cupti_interface_->Subscribe( &subscriber_, (CUpti_CallbackFunc)ApiCallback, this)failed with error
2023-12-18 16:29:30.476342: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:70] Profiler session collecting data.
2023-12-18 16:29:30.478398: E external/local_xla/xla/backends/profiler/gpu/cupti_error_manager.cc:142] cuptiFinalize: ignored due to a previous error.
2023-12-18 16:29:30.478426: E external/local_xla/xla/backends/profiler/gpu/cupti_error_manager.cc:459] cuptiGetResultString: ignored due to a previous error.
2023-12-18 16:29:30.478439: E external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:2026] function cupti_interface_->Finalize()failed with error
2023-12-18 16:29:30.517775: E external/local_xla/xla/backends/profiler/gpu/cupti_error_manager.cc:135] cuptiGetTimestamp: ignored due to a previous error.
2023-12-18 16:29:30.517808: E external/local_xla/xla/backends/profiler/gpu/cupti_error_manager.cc:135] cuptiGetTimestamp: ignored due to a previous error.
2023-12-18 16:29:30.517819: I external/local_xla/xla/backends/profiler/gpu/cupti_collector.cc:541] GpuTracer has collected 0 callback api events and 0 activity events.
2023-12-18 16:29:30.531760: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:131] Profiler session tear down.
2023-12-18 16:29:30.533373: I external/local_tsl/tsl/profiler/rpc/client/save_profile.cc:144] Collecting XSpace to repository: /home/user/project/logs/test/test1/plugins/profile/2023_12_18_16_29_30/user.xplane.pb
```
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I_kwDOArmXAs56AP6V
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Build failure on PPC
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[
"Although I have seen this issue with TF v2.14, I think this is a general issue with versions >2.14 because all use the new hermetic python for builds. \r\n\r\nIs there a way by which one can avoid using hermetic python can run build the old way?",
"I also meet this problem, and can not Collecting numpy==1.23.5 when build tf2.14 by source code."
] | 2023-12-18T15:11:46 | 2023-12-21T03:47:22 | null |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.14.0
### Custom code
Yes
### OS platform and distribution
RedHat 8.8, ppc64le
### 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?
Unable to build Tensorflow on ppc64le. Using hermetic python is likely the cause - https://github.com/tensorflow/tensorflow/commit/e85860e8382a460a0dd8547a536e5eaaf9096a9f.
### Standalone code to reproduce the issue
```shell
$ conda create -y -n buildenv gxx_linux-ppc64le
$ conda activate buildenv
$ pip install numpy==1.23.5
```
### Relevant log output
```shell
^[[32mAnalyzing:^[[0m target //tensorflow/tools/pip_package:build_pip_package (630 packages loaded, 36959 targets configured)
^[[32mINFO: ^[[0mRepository pypi_numpy instantiated at:
<mydir>/tensorflow-base_1702527635090/work/WORKSPACE:70:13: in <toplevel>
<mydir2>/bazel/_bazel_builder/1be9e4755d8878d0d0d324f026563f8d/external/pypi/requirements.bzl:49:20: in install_deps
Repository rule whl_library defined at:
<mydir2>/bazel/_bazel_builder/1be9e4755d8878d0d0d324f026563f8d/external/rules_python/python/pip_install/pip_repository.bzl:697:30: in <toplevel>
^[[31m^[[1mERROR: ^[[0mAn error occurred during the fetch of repository 'pypi_numpy':
Traceback (most recent call last):
File "<mydir2>/bazel/_bazel_builder/1be9e4755d8878d0d0d324f026563f8d/external/rules_python/python/pip_install/pip_repository.bzl", line 596, column 13, in _whl_library_impl
fail("whl_library %s failed: %s (%s) error code: '%s'" % (rctx.attr.name, result.stdout, result.stderr, result.return_code))
Error in fail: whl_library pypi_numpy failed: Collecting numpy==1.23.5 (from -r /tmp/tmpb_nvevum (line 1))
Using cached numpy-1.23.5.tar.gz (10.7 MB)
Installing build dependencies: started
Installing build dependencies: finished with status 'done'
Getting requirements to build wheel: started
Getting requirements to build wheel: finished with status 'done'
Preparing metadata (pyproject.toml): started
Preparing metadata (pyproject.toml): finished with status 'done'
Building wheels for collected packages: numpy
Building wheel for numpy (pyproject.toml): started
Building wheel for numpy (pyproject.toml): finished with status 'error'
Failed to build numpy
( error: subprocess-exited-with-error
× Building wheel for numpy (pyproject.toml) did not run successfully.
│ exit code: 1
INFO: powerpc64le-conda-linux-gnu-cc: build/src.linux-ppc64le-3.11/numpy/core/src/umath/loops_hyperbolic.dispatch.vsx4.c
during RTL pass: expand
In file included from build/src.linux-ppc64le-3.11/numpy/core/src/umath/loops_hyperbolic.dispatch.vsx4.c:11:
numpy/core/src/umath/loops_hyperbolic.dispatch.c.src: In function 'FLOAT_tanh_VSX4':
numpy/core/src/umath/loops_hyperbolic.dispatch.c.src:374:9: internal compiler error: in rs6000_sibcall_aix, at config/rs6000/rs6000.c:25670
374 | npy_clear_floatstatus_barrier((char*)dimensions);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Please submit a full bug report,
with preprocessed source if appropriate.
See <https://gcc.gnu.org/bugs/> for instructions.
during RTL pass: expand
In file included from build/src.linux-ppc64le-3.11/numpy/core/src/umath/loops_arithmetic.dispatch.vsx4.c:11:
numpy/core/src/umath/loops_arithmetic.dispatch.c.src: In function 'BYTE_divide_VSX4':
numpy/core/src/umath/loops_arithmetic.dispatch.c.src:76:13: internal compiler error: in rs6000_sibcall_aix, at config/rs6000/rs6000.c:25670
76 | npy_set_floatstatus_overflow();
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Please submit a full bug report,
with preprocessed source if appropriate.
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for numpy
ERROR: Failed to build one or more wheels
Traceback (most recent call last):
```
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I_kwDOArmXAs55_gd3
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TFlite Model Maker installation successfull - but error on import
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[
"I could also reproduce the issue when installing model maker from pip (version 0.4.2)\r\n\r\n```\r\nconda install tensorflow=2.8.2=gpu_py38h75b8afa_0\r\npip install tflite-model-maker\r\npip install numpy==1.23.5\r\npython\r\n>>> from tflite_model_maker.config import QuantizationConfig\r\n```\r\n\r\nThen the error like above: \r\n\r\n```\r\ntensorflow.python.framework.errors_impl.NotFoundError: /home/alex/anaconda3/envs/tf_gpu_env_mm_pip/lib/python3.8/site-packages/scann/scann_ops/cc/_scann_ops.so: undefined symbol: _ZN4absl12lts_2021032420raw_logging_internal21internal_log_functionE\r\n```",
"@alexw92, This issue is unlikely to be resolved soon, Could you please use [mediapipe model maker](https://developers.google.com/mediapipe/solutions/model_maker) instead for now. Here is also a [gist](https://colab.sandbox.google.com/gist/pkgoogle/93fb7581fab1ea14728c61adf584ca13/media_pipe_example.ipynb) that runs through some image classification examples with mediapipe model maker, including quantization: gist. If you can't accomplish your goals with mediapipe-model-maker please let us know and we'll see if there is a way to accomplish your goals.\r\n\r\nhttps://github.com/tensorflow/tensorflow/issues/60431\r\n\r\nThank you!",
"@tilakrayal Hello and thanks for your response. Yes it looks interesting and I thing it will work! But how do I install it? When I try to install mediapipe model maker via pip, pip ignores my already installed conda tensorflow 2.10 environment and loads tf 2.15 from pip which wont work for me since it uses AVX. I think even the old model maker will work if I can find out the reason for the error above.",
"@alexw92 Could you consider creating a new, clean conda environment specifically for the mediapipe-model-maker to avoid conflicts with other packages or TensorFlow versions.\r\nThank you!",
"@sushreebarsa Sure thats what I have been doing for the last couple of days. I always started within a new conda environment and tried so many different configurations and package versions. I was not able to create one single environment where mediapipe-model-maker was using the already installed (installed within conda using conda install of course not globally) tensorflow version. So I wonder if this is even possible. Btw for this I created another issue since this one is referring to the traditional model maker while the other [issue ](https://github.com/google/mediapipe/issues/5033#) is in the mediapipe repo ",
"@alexw92 Could you double-check the import statement for accuracy and ensure you're using the correct path to the model maker module?\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 I checked the import statement and yes it is correct definetly! The import is actually from the samples page of tf lite model maker. \r\nBy the way, is tf lite model maker about to be substituted completly by mediapipe model maker? So is there a recommendation by Google to always use mediapipe model maker instead of the one in this repo?",
"Hi @pkgoogle, \r\n\r\nPlease look into the issue\r\n\r\nThank You",
"Hi @alexw92, you have a couple of options:\r\n\r\n1. Just use [mediapipe model maker](https://developers.google.com/mediapipe/solutions/model_maker) in colab (You can use a GPU/TPU here as well though there are potential limitations) https://research.google.com/colaboratory/faq.html\r\n2. Attempt to fix your current setup, can you try `pip install scann`?\r\n3. Build Tensorflow from source w/o AVX support and with Cuda(Nvidia GPUs)/RocM(AMD GPUs) support https://www.tensorflow.org/install/source (For WSL you would follow linux instructions), then try to simultaneously install all your required packages with the built package so that they have the best chance to play nicely together\r\n4. Use a lower level API and skip tflite-model-maker/mediapipe-model-maker and use [keras](https://keras.io/)/[TF](https://www.tensorflow.org/tutorials) directly\r\n\r\nI think b/c you don't have AVX support and we essentially aren't supporting older versions, these are your best choices, let me know if any of them work and we can try to pursue those choices further.",
"@pkgoogle \r\n1) Yes I am aware of this thanks.\r\n2) I did and this did not work unfortunately. Also uninstalling and reinstalling scann did not work\r\n3) This is what I am afraid of. I have no experience in building from source.\r\n4) That would mean throwing thousands lines of code away I already wrote for working with model-maker\r\n\r\nWhat I did in the end was borrowing another system which an AVX-capable CPU. It is really sad that newer TF versions are build to require AVX even though the majority of users will use their GPU anyway. I will update my system with a new CPU then since it seems to be the quickest solution. \r\n\r\nBy the way could you please tell me:\r\n**Maintenance Status:** Is the Model Maker located in the examples directory of the TensorFlow repository still actively maintained? Or has it been superseded by the MediaPipe Model Maker?\r\n\r\n**Official Recommendation:** For developing models, especially with a focus on mobile and edge devices, does Google currently recommend transitioning to the MediaPipe Model Maker?",
"Hi @alexw92,\r\n\r\nBe careful with motherboard compatibility if you go that route.\r\n\r\n> Maintenance Status: Is the Model Maker located in the examples directory of the TensorFlow repository still actively maintained? Or has it been superseded by the MediaPipe Model Maker?\r\n\r\nModel Maker is currently still active but is running into the problems you have seen, so that's why we are diverting users to MediaPipe Model Maker. Effectively and practically if you want anything done right now, yes ... I can't say if that will continue to be the case in the future.\r\n\r\n> Official Recommendation: For developing models, especially with a focus on mobile and edge devices, does Google currently recommend transitioning to the MediaPipe Model Maker?\r\n\r\nYes or TFLite directly, as always, the lower you go in the API the more customizability you have at the cost of developer experience.\r\n\r\n> 3. This is what I am afraid of. I have no experience in building from source.\r\n\r\nOf course you know the best use of your own time, but the only way to get experience is to try 😄. Your experience here will be smoother on linux/WSL. Building from source is generally the most reliable way to support unique configurations/combinations -- or see why that configuration/combination is not supported.",
"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/62658\">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/62658\">No</a>\n"
] | 2023-12-18T13:41:19 | 2024-02-03T01:47:10 | 2024-02-03T01:47:04 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.8.2
### Custom code
No
### OS platform and distribution
WSL Ubuntu 20.04 on Windows 10
### Mobile device
_No response_
### Python version
3.8
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I thought I have successfully installed TFLite model maker but I can an error when trying to test code which happens on some imports. It seems to be scann related which is installed at version 1.2.6 by pip which should be compatible.
One important thing might be that I installed tensorflow 2.8.2 using anaconda because the pip packages are built requiring AVX which my CPU does not have. I verified that my GPU is used correctly within the anaconda environment with this installed package though.
The following error happens on imports e.g. ```from tflite_model_maker.config import QuantizationConfig```:
```
python
Python 3.8.18 (default, Sep 11 2023, 13:20:55)
[GCC 11.2.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from tflite_model_maker import object_detector
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/alex/examples/tensorflow_examples/lite/model_maker/pip_package/src/tflite_model_maker/__init__.py", line 51, in <module>
from tflite_model_maker import searcher
File "/home/alex/examples/tensorflow_examples/lite/model_maker/pip_package/src/tflite_model_maker/searcher/__init__.py", line 25, in <module>
from tensorflow_examples.lite.model_maker.core.task.searcher import ExportFormat
File "/home/alex/examples/tensorflow_examples/lite/model_maker/pip_package/src/tensorflow_examples/lite/model_maker/core/task/searcher.py", line 30, in <module>
from tensorflow_examples.lite.model_maker.core.utils import ondevice_scann_builder
File "/home/alex/examples/tensorflow_examples/lite/model_maker/pip_package/src/tensorflow_examples/lite/model_maker/core/utils/ondevice_scann_builder.py", line 17, in <module>
from scann.proto import scann_pb2
File "/home/alex/anaconda3/envs/tf_gpu_env/lib/python3.8/site-packages/scann/__init__.py", line 2, in <module>
from scann.scann_ops.py import scann_ops
File "/home/alex/anaconda3/envs/tf_gpu_env/lib/python3.8/site-packages/scann/scann_ops/py/scann_ops.py", line 23, in <module>
_scann_ops_so = tf.load_op_library(
File "/home/alex/anaconda3/envs/tf_gpu_env/lib/python3.8/site-packages/tensorflow/python/framework/load_library.py", line 54, in load_op_library
lib_handle = py_tf.TF_LoadLibrary(library_filename)
tensorflow.python.framework.errors_impl.NotFoundError: /home/alex/anaconda3/envs/tf_gpu_env/lib/python3.8/site-packages/scann/scann_ops/cc/_scann_ops.so: undefined symbol: _ZN4absl12lts_2021032420raw_logging_internal21internal_log_functionE
```
Is it related that I use tensorflow from conda and not from pip?
### Standalone code to reproduce the issue
```shell
This is what i did in WSL
~$ conda install tensorflow=2.8.2=gpu_py38h75b8afa_0
~$ python
>>> import tensorflow as tf
>>> tf.config.list_physical_devices('GPU')
2023-12-18 01:18:17.785271: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:922] could not open file to read NUMA node: /sys/bus/pci/devices/0000:02:00.0/numa_node
Your kernel may have been built without NUMA support.
2023-12-18 01:18:17.799686: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:922] could not open file to read NUMA node: /sys/bus/pci/devices/0000:02:00.0/numa_node
Your kernel may have been built without NUMA support.
2023-12-18 01:18:17.800511: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:922] could not open file to read NUMA node: /sys/bus/pci/devices/0000:02:00.0/numa_node
Your kernel may have been built without NUMA support.
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
~$ pip install -q pycocotools
~$ pip install -q opencv-python-headless==4.1.2.30
~$ sudo apt -y install libportaudio2
~/examples/tensorflow_examples/lite/model_maker/pip_package$ pip install -e .
~/examples/tensorflow_examples/lite/model_maker/pip_package$ python
>>> from tflite_model_maker.config import QuantizationConfig
```
```
### Relevant log output
_No response_
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ERRO AO COMPILAR NO VSCODE PLATFORM.IO cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7_nonsquare.c.d: No such file or directory
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[
"Hi @DevIoTEduardo,\r\n\r\nCould you please submit the ticket in the attached [template](https://github.com/tensorflow/tensorflow/issues/new?assignees=&labels=&projects=&template=tensorflow_issue_template.yaml) with all the required details.\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/62657\">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/62657\">No</a>\n"
] | 2023-12-18T12:50:05 | 2024-01-04T01:48:32 | 2024-01-04T01:48:29 |
NONE
| null | null | null |
dered.c.o
Compiling .pio\build\esp32dev\src\EloquentTinyML-2.4.0\src\tensorflow_arm\tensorflow\lite\micro\tools\make\downloads\cmsis\CMSIS\NN\Source\ConvolutionFunctions\arm_nn_mat_mult_kernel_s8_s16.c.o
src/EloquentTinyML-2.4.0/src/tensorflow_arm/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7_nonsquare.c:1: fatal error:
opening dependency file .pio/build/esp32dev/src/EloquentTinyML-2.4.0/src/tensorflow_arm/tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7_nonsquare.c.d: No such file or directory
#if !defined(ESP32)
compilation terminated.
*** [.pio\build\esp32dev\src\EloquentTinyML-2.4.0\src\tensorflow_arm\tensorflow\lite\micro\tools\make\downloads\cmsis\CMSIS\NN\Source\ConvolutionFunctions\arm_depthwise_separable_conv_HWC_q7_nonsquare.c.o] Error 1
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I_kwDOArmXAs5598C5
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Observer with TFUniformReplayBuffer
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[
"This is part of the report\r\n\r\n\r\n```\r\nelf._env = env\r\n self._policy = policy\r\n self._train_step = train_step\r\n self._observers = observers or []\r\n # Create a copy of the list to avoid modifying the user provided list.\r\n self._observers = list(self._observers)\r\n self._metrics = metrics or []\r\n self._image_metrics = image_metrics or []\r\n self._observers.extend(self._metrics + self._image_metrics)\r\n self._reference_metrics = reference_metrics or []\r\n # Make sure metrics are not repeated.\r\n self._observers = list(set(self._observers))\r\n self._transition_observers = list(transition_observers or [])\r\n self._info_observers = list(info_observers or [])\r\n```\r\n```\r\n\r\nTypeError: unhashable type: 'list'\r\n In call to configurable 'Actor' (<class 'tf_agents.train.actor.Actor'>)\r\n```",
"Hi, I think the issue is that you are passing a list in list to \"observers\" argument of \"initial_collect_actor\". You should either change the line observers=[replay_observer] to observers=replay_observer or replay_observer = [replay_buffer.add_batch] to replay_observer = replay_buffer.add_batch. I am not sure but I hope both versions should work.",
"Yes I changed that and and then it has worked up to this point, but afterwards this line of code then caused problems.\r\nI also agree with you that both variants should work .\r\n\r\ninitial_collect_actor.run()\r\nA ops.Py file opens during debug, which has an error in the following.\r\n\r\ndef raise_from_not_ok_status(e, name) -> NoReturn:\r\n e.message += (\" name: \" + str(name if name is not None else \"\"))\r\n raise core._status_to_exception(e) from None # pylint: disable=protected-access\r\n\r\nIn the Debug Console it says:\r\nraplay_observer: {NameError]NameError(\"name 'replay_observer' is not defined)\r\nBut I already thought that I had defined it, and with: replay_observer = [replay_buffer.add_batch]",
"@MarleneBs \r\nIn order to expedite the trouble-shooting process, please provide a complete code snippet to reproduce the issue and TF version you are using? \r\nCould you please have a look at this TF-Agents [documentation](https://www.tensorflow.org/agents/tutorials/1_dqn_tutorial) for more details and examples?\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."
] | 2023-12-18T10:20:29 | 2024-01-06T01:48:33 | 2024-01-06T01:48:33 |
NONE
| null | null | null |
"Hello everybody,
How can I create a TFUniformReplayBuffer-Observer to use in an actor? I created a buffer as follows:
```
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
data_spec=tf_agent.collect_data_spec,
batch_size=1, #The batch size must be taken into account here
max_length=replay_buffer_capacity)
dataset = replay_buffer.as_dataset(
sample_batch_size=batch_size,
num_steps=2,
single_deterministic_pass=False).prefetch(tf.data.experimental.AUTOTUNE)
experience_dataset_fn = lambda: dataset
```
and then tried to create an observer like this: replay_observer = [replay_buffer.add_batch], which is consistent with the TensorFlow documentation. However, it is not used for an actor there.
I attempted the following:
```
replay_observer = [replay_buffer.add_batch]
initial_collect_actor = actor.Actor(
collect_env,
random_policy,
train_step,
steps_per_run=initial_collect_steps,
observers=[replay_observer])
```
When I debug, there is an error report that I do not know how to handle because I thought my approach should work.
Does anyone have a solution, approach, or inspiration for me to deal with this problem?
I would be extremely grateful, many thanks for your approaches and creative ideas.
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| 62,655 |
How to pass zipped Tensors and RaggedTensors to .fit()?
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[
"Hi **@mihalt** ,\r\nSorry for the delay, Unpack the zipped data in your training loop:\r\nThis is the simplest approach and works well for small datasets or when you need more control over the individual Tensors and RaggedTensors.\r\nInside your training loop, unpack the zipped data into separate variables for your features and labels.\r\n\r\nThank you!",
"> Hi **@mihalt** , Sorry for the delay, Unpack the zipped data in your training loop: This is the simplest approach and works well for small datasets or when you need more control over the individual Tensors and RaggedTensors. Inside your training loop, unpack the zipped data into separate variables for your features and labels.\r\n> \r\n> Thank you!\r\n\r\noh, do you offer to do custom training loop? Really not happy with this. Do you think is it a bug? Do you plan to fix it in near releases of tensorflow? I would better prefer to wait when you do it. ",
"Hi @mihalt ,\r\n\r\nThe `fit` method indeed takes input as `tf.data.Dataset` object which can accept Ragged Tensors as input also.\r\n\r\nFrom the error it seems you are passing `rank1` scalar values as input.Ragged tensors should have atleast rank2.\r\n\r\n\r\n\r\n> oh, do you offer to do custom training loop? Really not happy with this. Do you think is it a bug? Do you plan to fix it in near releases of tensorflow? I would better prefer to wait when you do it.\r\n\r\nFor passing Ragged input there is no need of custom loop. You can pass it directly to model.fit . But the ragged input should be qualified as `>= rank2 `",
"What my colleague is trying to say is that by using the `Dataset` class he gets a set of **input data** and **target**. In this case, the `dataset `has shapes `[batch_size, ragged_rank1, ragged_rank2, embedding_dim]`. According to it, the **targets data** have shapes `[batch_size, ragged_rank1, class_num]`. When we try to call one element for inspection/processing via` .take(1)` we get **input data** shapes `[1, singe_fixed_rank1, ragged_rank2, embedding_dim]`, so there is only one ragged rank. In this case, for the **target data**, the data shapes `[1, singe_fixed_rank1, class_num]`, which is not a ragged tensor.\r\nAfter processing the **input data**, the model returns a RaggedTensor and the target is stay Tensor. An error occurs at the loss processing stage, which says that RaggedTensor and Tensor cannot be compared.\r\nThe simplest thing is to fix the data output in the `Dataset `class so that when `ragged=True`, all data sets are returned in shapes of a RaggedTensor, despite the absence of ragged_rank in any set.\r\nThe second option is to add a check to the loss calculation for the same sizes of RaggedTensor and Tensor before processing. Allow such losses to be calculated for tensors with identical shapes.\r\nAs typed in the start of topic, using `.map()` to fix Tensor to RaggedTensor in target return the error...",
"Yes, and I would not like to do manually something like this, because it will be very temporal solution due to a bag of tensorflow (by my opinion) \r\n> The second option is to add a check to the loss calculation for the same sizes of RaggedTensor and Tensor before processing. Allow such losses to be calculated for tensors with identical shapes.\r\n\r\nSo, I would prefer to wait while this problem will be resolved on tensorflow side. ",
"Hi @mihalt ,\r\n\r\nCould you please submit a minimal code snippet with dummy data to replicate the reported issue? Thanks!",
"```\r\nimport tensorflow as tf\r\n\r\nx = [\r\n [\r\n [0, 1, 2, 3],\r\n ],\r\n [\r\n [0, 1, 2],\r\n [0],\r\n ],\r\n [\r\n [0, 1, 2, 3, 4],\r\n ],\r\n]\r\ny = [\r\n [0],\r\n [1, 1],\r\n [2],\r\n]\r\n\r\ntokens = tf.data.Dataset.from_tensor_slices(tf.ragged.constant(x, dtype=tf.int32))\r\ntags = tf.data.Dataset.from_tensor_slices(tf.ragged.constant(y, dtype=tf.int32))\r\n\r\nds = tf.data.Dataset.zip(tokens, tags)\r\n\r\nprint('--batch--')\r\nprint(ds.take(2))\r\na, b = ds.take(2)\r\nprint(a,b)\r\n\r\nprint('\\n--element_iter--')\r\nfor element in ds:\r\n print(element)\r\n```\r\nconsole gave this^\r\n--batch--\r\n_<_TakeDataset element_spec=(RaggedTensorSpec(TensorShape([None, None]), tf.int32, 1, tf.int64), RaggedTensorSpec(TensorShape([None]), tf.int32, 0, tf.int64))>\r\n(<tf.RaggedTensor [[0, 1, 2, 3]]>, <tf.**Tensor**: shape=(1,), dtype=int32, numpy=array([0])>) (<tf.RaggedTensor [[0, 1, 2], [0]]>, <tf.**Tensor**: shape=(2,), dtype=int32, numpy=array([1, 1])>)\r\n\r\n--element_iter--\r\n(<tf.RaggedTensor [[0, 1, 2, 3]]>, <tf.**Tensor**: shape=(1,), dtype=int32, numpy=array([0])>)\r\n(<tf.RaggedTensor [[0, 1, 2], [0]]>, <tf.**Tensor**: shape=(2,), dtype=int32, numpy=array([1, 1])>)\r\n(<tf.RaggedTensor [[0, 1, 2, 3, 4]]>, <tf.**Tensor**: shape=(1,), dtype=int32, numpy=array([2])>)_\r\n\r\nSo, only X become ragged, all Y is Tensor.",
"Hi @mihalt ,\r\n\r\nThanks for repro snippet. I have replicated the issue and attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/4008a4dadbcb04149da51c472cf432ef/62655.ipynb). Need to check with SME for this behaviour."
] | 2023-12-17T15:12:01 | 2024-01-04T05:58:36 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.14
### 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?
I can't use zipped dataset with Tensors as labels and RaggedTensors as tokens with this error.
TypeError: Some of the inputs are not tf.RaggedTensor. Input received: [<tf.Tensor 'Cast_1:0' shape=(None, None) dtype=float32>, tf.RaggedTensor(values=Tensor("sequential_3/dense_8/Softmax:0", shape=(None, 15), dtype=float32), row_splits=Tensor("sequential_3/rnn_10/RaggedFromTensor/concat:0", shape=(None,), dtype=int64))]
### Standalone code to reproduce the issue
```shell
And I can't convert Tensors in dataset to Ragged through map due to error
ValueError: The rank of a RaggedTensor must be greater than 1, i.e., a list of scalars won't have ragged dimensions. Received argument `tensor` with rank 1.
```
### Relevant log output
_No response_
|
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"gists_url": "https://api.github.com/users/LakshmiKalaKadali/gists{/gist_id}",
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"organizations_url": "https://api.github.com/users/LakshmiKalaKadali/orgs",
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[
"Just tried bumping down gradle version in [gradle-wrapper.properties](https://github.com/tensorflow/examples/blob/master/lite/examples/object_detection/android/gradle/wrapper/gradle-wrapper.properties) from 8.4 to 7.2 (lowest for JAVA 11), and everything worked from there perfectly\r\n",
"Hi @ArnasVysniauskas,\r\n\r\n It's a known [issue ](https://discuss.tensorflow.org/t/object-detection-android-base/20921/4), the error occurs due to upgradation of gradle 8.4 version. As a temporary fix, you can use your solution. Engineering team is working on this issue.\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.",
"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/62654\">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/62654\">No</a>\n",
"I've just bumped into the same issue. As that's the first sample that shows up when try tensorflow life it would be great to fix it",
"Similar issue here, I followed the fix from earlier by replacing the distributionUrl in gradle-wrapper.properties to \r\ndistributionUrl=https\\://services.gradle.org/distributions/gradle-7.3.3-bin.zip\r\n"
] | 2023-12-17T10:20:39 | 2024-05-03T03:40:43 | 2024-01-04T01:48:31 |
NONE
| null | null | null |
Android studio is not able to build object_tracking Example from [TensorFlow/examples](https://github.com/tensorflow/examples/tree/master/lite/examples/object_detection/android). I am very much new with Android app development, java and gradle, so it is more likely that I am doing something wrong. From readme, It should work out of the box, but it doesn't, hence I am writing here. Maybe there is a more detailed description somewhere on how to get this running? Thank you!
What I tried:
- Changing Android Tools version (suggested on [Object Detection Tutorial](https://www.tensorflow.org/lite/android/tutorials/object_detection)); didn't help. The tutorial is for an older version of repo though :/, so I wasn't expecting much.
```
// from: classpath
'com.android.tools.build:gradle:4.2.2'
// to: classpath
'com.android.tools.build:gradle:4.1.2'
```
Android Studio version I am using:
> Android Studio Hedgehog | 2023.1.1
> Build #AI-231.9392.1.2311.11076708, built on November 9, 2023
> Runtime version: 17.0.7+0-17.0.7b1000.6-10550314 x86_64
> VM: OpenJDK 64-Bit Server VM by JetBrains s.r.o.
> macOS 13.1
> GC: G1 Young Generation, G1 Old Generation
> Memory: 2048M
> Cores: 16
> Metal Rendering is ON
> Registry:
> external.system.auto.import.disabled=true
> debugger.new.tool.window.layout=true
> ide.text.editor.with.preview.show.floating.toolbar=false
> ide.experimental.ui=true
Error message I get:
```
A problem occurred configuring project ':app'.
> Could not create task ':app:compileDebugKotlin'.
> Could not create task ':app:dataBindingGenBaseClassesDebug'.
> Cannot use @TaskAction annotation on method DataBindingGenBaseClassesTask.writeBaseClasses() because interface org.gradle.api.tasks.incremental.IncrementalTaskInputs is not a valid parameter to an action method.
* Try:
> Run with --info or --debug option to get more log output.
> Run with --scan to get full insights.
> Get more help at https://help.gradle.org.
* Exception is:
org.gradle.api.ProjectConfigurationException: A problem occurred configuring project ':app'.
at org.gradle.configuration.project.LifecycleProjectEvaluator.wrapException(LifecycleProjectEvaluator.java:84)
at org.gradle.configuration.project.LifecycleProjectEvaluator.addConfigurationFailure(LifecycleProjectEvaluator.java:77)
at org.gradle.configuration.project.LifecycleProjectEvaluator.access$400(LifecycleProjectEvaluator.java:55)
at org.gradle.configuration.project.LifecycleProjectEvaluator$NotifyAfterEvaluate.run(LifecycleProjectEvaluator.java:255)
at org.gradle.internal.operations.DefaultBuildOperationRunner$1.execute(DefaultBuildOperationRunner.java:29)
at org.gradle.internal.operations.DefaultBuildOperationRunner$1.execute(DefaultBuildOperationRunner.java:26)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:66)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:157)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.run(DefaultBuildOperationRunner.java:47)
at org.gradle.internal.operations.DefaultBuildOperationExecutor.run(DefaultBuildOperationExecutor.java:73)
at org.gradle.configuration.project.LifecycleProjectEvaluator$EvaluateProject.lambda$run$0(LifecycleProjectEvaluator.java:114)
at org.gradle.api.internal.project.DefaultProjectStateRegistry$ProjectStateImpl.lambda$applyToMutableState$1(DefaultProjectStateRegistry.java:406)
at org.gradle.api.internal.project.DefaultProjectStateRegistry$ProjectStateImpl.lambda$fromMutableState$2(DefaultProjectStateRegistry.java:429)
at org.gradle.internal.work.DefaultWorkerLeaseService.withReplacedLocks(DefaultWorkerLeaseService.java:360)
at org.gradle.api.internal.project.DefaultProjectStateRegistry$ProjectStateImpl.fromMutableState(DefaultProjectStateRegistry.java:429)
at org.gradle.api.internal.project.DefaultProjectStateRegistry$ProjectStateImpl.applyToMutableState(DefaultProjectStateRegistry.java:405)
at org.gradle.configuration.project.LifecycleProjectEvaluator$EvaluateProject.run(LifecycleProjectEvaluator.java:100)
at org.gradle.internal.operations.DefaultBuildOperationRunner$1.execute(DefaultBuildOperationRunner.java:29)
at org.gradle.internal.operations.DefaultBuildOperationRunner$1.execute(DefaultBuildOperationRunner.java:26)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:66)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:157)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.run(DefaultBuildOperationRunner.java:47)
at org.gradle.internal.operations.DefaultBuildOperationExecutor.run(DefaultBuildOperationExecutor.java:73)
at org.gradle.configuration.project.LifecycleProjectEvaluator.evaluate(LifecycleProjectEvaluator.java:72)
at org.gradle.api.internal.project.DefaultProject.evaluate(DefaultProject.java:788)
at org.gradle.api.internal.project.DefaultProject.evaluate(DefaultProject.java:156)
at org.gradle.api.internal.project.ProjectLifecycleController.lambda$ensureSelfConfigured$2(ProjectLifecycleController.java:84)
at org.gradle.internal.model.StateTransitionController.lambda$doTransition$14(StateTransitionController.java:255)
at org.gradle.internal.model.StateTransitionController.doTransition(StateTransitionController.java:266)
at org.gradle.internal.model.StateTransitionController.doTransition(StateTransitionController.java:254)
at org.gradle.internal.model.StateTransitionController.lambda$maybeTransitionIfNotCurrentlyTransitioning$10(StateTransitionController.java:199)
at org.gradle.internal.work.DefaultSynchronizer.withLock(DefaultSynchronizer.java:34)
at org.gradle.internal.model.StateTransitionController.maybeTransitionIfNotCurrentlyTransitioning(StateTransitionController.java:195)
at org.gradle.api.internal.project.ProjectLifecycleController.ensureSelfConfigured(ProjectLifecycleController.java:84)
at org.gradle.api.internal.project.DefaultProjectStateRegistry$ProjectStateImpl.ensureConfigured(DefaultProjectStateRegistry.java:380)
at org.gradle.execution.TaskPathProjectEvaluator.configure(TaskPathProjectEvaluator.java:34)
at org.gradle.execution.TaskPathProjectEvaluator.configureHierarchy(TaskPathProjectEvaluator.java:50)
at org.gradle.configuration.DefaultProjectsPreparer.prepareProjects(DefaultProjectsPreparer.java:42)
at org.gradle.configuration.BuildTreePreparingProjectsPreparer.prepareProjects(BuildTreePreparingProjectsPreparer.java:65)
at org.gradle.configuration.BuildOperationFiringProjectsPreparer$ConfigureBuild.run(BuildOperationFiringProjectsPreparer.java:52)
at org.gradle.internal.operations.DefaultBuildOperationRunner$1.execute(DefaultBuildOperationRunner.java:29)
at org.gradle.internal.operations.DefaultBuildOperationRunner$1.execute(DefaultBuildOperationRunner.java:26)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:66)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:157)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.run(DefaultBuildOperationRunner.java:47)
at org.gradle.internal.operations.DefaultBuildOperationExecutor.run(DefaultBuildOperationExecutor.java:73)
at org.gradle.configuration.BuildOperationFiringProjectsPreparer.prepareProjects(BuildOperationFiringProjectsPreparer.java:40)
at org.gradle.initialization.VintageBuildModelController.lambda$prepareProjects$2(VintageBuildModelController.java:84)
at org.gradle.internal.model.StateTransitionController.lambda$doTransition$14(StateTransitionController.java:255)
at org.gradle.internal.model.StateTransitionController.doTransition(StateTransitionController.java:266)
at org.gradle.internal.model.StateTransitionController.doTransition(StateTransitionController.java:254)
at org.gradle.internal.model.StateTransitionController.lambda$transitionIfNotPreviously$11(StateTransitionController.java:213)
at org.gradle.internal.work.DefaultSynchronizer.withLock(DefaultSynchronizer.java:34)
at org.gradle.internal.model.StateTransitionController.transitionIfNotPreviously(StateTransitionController.java:209)
at org.gradle.initialization.VintageBuildModelController.prepareProjects(VintageBuildModelController.java:84)
at org.gradle.initialization.VintageBuildModelController.getConfiguredModel(VintageBuildModelController.java:64)
at org.gradle.internal.build.DefaultBuildLifecycleController.lambda$withProjectsConfigured$1(DefaultBuildLifecycleController.java:133)
at org.gradle.internal.model.StateTransitionController.lambda$notInState$3(StateTransitionController.java:132)
at org.gradle.internal.work.DefaultSynchronizer.withLock(DefaultSynchronizer.java:44)
at org.gradle.internal.model.StateTransitionController.notInState(StateTransitionController.java:128)
at org.gradle.internal.build.DefaultBuildLifecycleController.withProjectsConfigured(DefaultBuildLifecycleController.java:133)
at org.gradle.internal.build.DefaultBuildToolingModelController.locateBuilderForTarget(DefaultBuildToolingModelController.java:57)
at org.gradle.internal.buildtree.DefaultBuildTreeModelCreator$DefaultBuildTreeModelController.lambda$locateBuilderForTarget$0(DefaultBuildTreeModelCreator.java:73)
at org.gradle.internal.build.DefaultBuildLifecycleController.withToolingModels(DefaultBuildLifecycleController.java:327)
at org.gradle.internal.build.AbstractBuildState.withToolingModels(AbstractBuildState.java:140)
at org.gradle.internal.buildtree.DefaultBuildTreeModelCreator$DefaultBuildTreeModelController.locateBuilderForTarget(DefaultBuildTreeModelCreator.java:73)
at org.gradle.internal.buildtree.DefaultBuildTreeModelCreator$DefaultBuildTreeModelController.locateBuilderForDefaultTarget(DefaultBuildTreeModelCreator.java:68)
at org.gradle.tooling.internal.provider.runner.DefaultBuildController.getTarget(DefaultBuildController.java:157)
at org.gradle.tooling.internal.provider.runner.DefaultBuildController.getModel(DefaultBuildController.java:101)
at org.gradle.tooling.internal.consumer.connection.ParameterAwareBuildControllerAdapter.getModel(ParameterAwareBuildControllerAdapter.java:39)
at org.gradle.tooling.internal.consumer.connection.UnparameterizedBuildController.getModel(UnparameterizedBuildController.java:113)
at org.gradle.tooling.internal.consumer.connection.NestedActionAwareBuildControllerAdapter.getModel(NestedActionAwareBuildControllerAdapter.java:31)
at org.gradle.tooling.internal.consumer.connection.UnparameterizedBuildController.findModel(UnparameterizedBuildController.java:97)
at org.gradle.tooling.internal.consumer.connection.NestedActionAwareBuildControllerAdapter.findModel(NestedActionAwareBuildControllerAdapter.java:31)
at org.gradle.tooling.internal.consumer.connection.UnparameterizedBuildController.findModel(UnparameterizedBuildController.java:81)
at org.gradle.tooling.internal.consumer.connection.NestedActionAwareBuildControllerAdapter.findModel(NestedActionAwareBuildControllerAdapter.java:31)
at org.gradle.tooling.internal.consumer.connection.UnparameterizedBuildController.findModel(UnparameterizedBuildController.java:66)
at org.gradle.tooling.internal.consumer.connection.NestedActionAwareBuildControllerAdapter.findModel(NestedActionAwareBuildControllerAdapter.java:31)
at org.jetbrains.plugins.gradle.model.ProjectImportAction.execute(ProjectImportAction.java:126)
at org.jetbrains.plugins.gradle.model.ProjectImportAction.execute(ProjectImportAction.java:43)
at org.gradle.tooling.internal.consumer.connection.InternalBuildActionAdapter.execute(InternalBuildActionAdapter.java:64)
at org.gradle.tooling.internal.provider.runner.AbstractClientProvidedBuildActionRunner$ActionAdapter.runAction(AbstractClientProvidedBuildActionRunner.java:131)
at org.gradle.tooling.internal.provider.runner.AbstractClientProvidedBuildActionRunner$ActionAdapter.beforeTasks(AbstractClientProvidedBuildActionRunner.java:99)
at org.gradle.internal.buildtree.DefaultBuildTreeModelCreator.beforeTasks(DefaultBuildTreeModelCreator.java:52)
at org.gradle.internal.buildtree.DefaultBuildTreeLifecycleController.lambda$fromBuildModel$2(DefaultBuildTreeLifecycleController.java:74)
at org.gradle.internal.buildtree.DefaultBuildTreeLifecycleController.lambda$runBuild$4(DefaultBuildTreeLifecycleController.java:98)
at org.gradle.internal.model.StateTransitionController.lambda$transition$6(StateTransitionController.java:169)
at org.gradle.internal.model.StateTransitionController.doTransition(StateTransitionController.java:266)
at org.gradle.internal.model.StateTransitionController.lambda$transition$7(StateTransitionController.java:169)
at org.gradle.internal.work.DefaultSynchronizer.withLock(DefaultSynchronizer.java:44)
at org.gradle.internal.model.StateTransitionController.transition(StateTransitionController.java:169)
at org.gradle.internal.buildtree.DefaultBuildTreeLifecycleController.runBuild(DefaultBuildTreeLifecycleController.java:95)
at org.gradle.internal.buildtree.DefaultBuildTreeLifecycleController.fromBuildModel(DefaultBuildTreeLifecycleController.java:73)
at org.gradle.tooling.internal.provider.runner.AbstractClientProvidedBuildActionRunner.runClientAction(AbstractClientProvidedBuildActionRunner.java:43)
at org.gradle.tooling.internal.provider.runner.ClientProvidedPhasedActionRunner.run(ClientProvidedPhasedActionRunner.java:53)
at org.gradle.launcher.exec.ChainingBuildActionRunner.run(ChainingBuildActionRunner.java:35)
at org.gradle.internal.buildtree.ProblemReportingBuildActionRunner.run(ProblemReportingBuildActionRunner.java:49)
at org.gradle.launcher.exec.BuildOutcomeReportingBuildActionRunner.run(BuildOutcomeReportingBuildActionRunner.java:65)
at org.gradle.tooling.internal.provider.FileSystemWatchingBuildActionRunner.run(FileSystemWatchingBuildActionRunner.java:140)
at org.gradle.launcher.exec.BuildCompletionNotifyingBuildActionRunner.run(BuildCompletionNotifyingBuildActionRunner.java:41)
at org.gradle.launcher.exec.RootBuildLifecycleBuildActionExecutor.lambda$execute$0(RootBuildLifecycleBuildActionExecutor.java:40)
at org.gradle.composite.internal.DefaultRootBuildState.run(DefaultRootBuildState.java:123)
at org.gradle.launcher.exec.RootBuildLifecycleBuildActionExecutor.execute(RootBuildLifecycleBuildActionExecutor.java:40)
at org.gradle.internal.buildtree.InitDeprecationLoggingActionExecutor.execute(InitDeprecationLoggingActionExecutor.java:62)
at org.gradle.internal.buildtree.InitProblems.execute(InitProblems.java:38)
at org.gradle.internal.buildtree.DefaultBuildTreeContext.execute(DefaultBuildTreeContext.java:40)
at org.gradle.launcher.exec.BuildTreeLifecycleBuildActionExecutor.lambda$execute$0(BuildTreeLifecycleBuildActionExecutor.java:65)
at org.gradle.internal.buildtree.BuildTreeState.run(BuildTreeState.java:58)
at org.gradle.launcher.exec.BuildTreeLifecycleBuildActionExecutor.execute(BuildTreeLifecycleBuildActionExecutor.java:65)
at org.gradle.launcher.exec.RunAsBuildOperationBuildActionExecutor$3.call(RunAsBuildOperationBuildActionExecutor.java:61)
at org.gradle.launcher.exec.RunAsBuildOperationBuildActionExecutor$3.call(RunAsBuildOperationBuildActionExecutor.java:57)
at org.gradle.internal.operations.DefaultBuildOperationRunner$CallableBuildOperationWorker.execute(DefaultBuildOperationRunner.java:204)
at org.gradle.internal.operations.DefaultBuildOperationRunner$CallableBuildOperationWorker.execute(DefaultBuildOperationRunner.java:199)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:66)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:157)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.call(DefaultBuildOperationRunner.java:53)
at org.gradle.internal.operations.DefaultBuildOperationExecutor.call(DefaultBuildOperationExecutor.java:78)
at org.gradle.launcher.exec.RunAsBuildOperationBuildActionExecutor.execute(RunAsBuildOperationBuildActionExecutor.java:57)
at org.gradle.launcher.exec.RunAsWorkerThreadBuildActionExecutor.lambda$execute$0(RunAsWorkerThreadBuildActionExecutor.java:36)
at org.gradle.internal.work.DefaultWorkerLeaseService.withLocks(DefaultWorkerLeaseService.java:264)
at org.gradle.internal.work.DefaultWorkerLeaseService.runAsWorkerThread(DefaultWorkerLeaseService.java:128)
at org.gradle.launcher.exec.RunAsWorkerThreadBuildActionExecutor.execute(RunAsWorkerThreadBuildActionExecutor.java:36)
at org.gradle.tooling.internal.provider.continuous.ContinuousBuildActionExecutor.execute(ContinuousBuildActionExecutor.java:110)
at org.gradle.tooling.internal.provider.SubscribableBuildActionExecutor.execute(SubscribableBuildActionExecutor.java:64)
at org.gradle.internal.session.DefaultBuildSessionContext.execute(DefaultBuildSessionContext.java:46)
at org.gradle.tooling.internal.provider.BuildSessionLifecycleBuildActionExecuter$ActionImpl.apply(BuildSessionLifecycleBuildActionExecuter.java:92)
at org.gradle.tooling.internal.provider.BuildSessionLifecycleBuildActionExecuter$ActionImpl.apply(BuildSessionLifecycleBuildActionExecuter.java:80)
at org.gradle.internal.session.BuildSessionState.run(BuildSessionState.java:69)
at org.gradle.tooling.internal.provider.BuildSessionLifecycleBuildActionExecuter.execute(BuildSessionLifecycleBuildActionExecuter.java:62)
at org.gradle.tooling.internal.provider.BuildSessionLifecycleBuildActionExecuter.execute(BuildSessionLifecycleBuildActionExecuter.java:41)
at org.gradle.tooling.internal.provider.StartParamsValidatingActionExecuter.execute(StartParamsValidatingActionExecuter.java:64)
at org.gradle.tooling.internal.provider.StartParamsValidatingActionExecuter.execute(StartParamsValidatingActionExecuter.java:32)
at org.gradle.tooling.internal.provider.SessionFailureReportingActionExecuter.execute(SessionFailureReportingActionExecuter.java:51)
at org.gradle.tooling.internal.provider.SessionFailureReportingActionExecuter.execute(SessionFailureReportingActionExecuter.java:39)
at org.gradle.tooling.internal.provider.SetupLoggingActionExecuter.execute(SetupLoggingActionExecuter.java:47)
at org.gradle.tooling.internal.provider.SetupLoggingActionExecuter.execute(SetupLoggingActionExecuter.java:31)
at org.gradle.launcher.daemon.server.exec.ExecuteBuild.doBuild(ExecuteBuild.java:65)
at org.gradle.launcher.daemon.server.exec.BuildCommandOnly.execute(BuildCommandOnly.java:37)
at org.gradle.launcher.daemon.server.api.DaemonCommandExecution.proceed(DaemonCommandExecution.java:104)
at org.gradle.launcher.daemon.server.exec.WatchForDisconnection.execute(WatchForDisconnection.java:39)
at org.gradle.launcher.daemon.server.api.DaemonCommandExecution.proceed(DaemonCommandExecution.java:104)
at org.gradle.launcher.daemon.server.exec.ResetDeprecationLogger.execute(ResetDeprecationLogger.java:29)
at org.gradle.launcher.daemon.server.api.DaemonCommandExecution.proceed(DaemonCommandExecution.java:104)
at org.gradle.launcher.daemon.server.exec.RequestStopIfSingleUsedDaemon.execute(RequestStopIfSingleUsedDaemon.java:35)
at org.gradle.launcher.daemon.server.api.DaemonCommandExecution.proceed(DaemonCommandExecution.java:104)
at org.gradle.launcher.daemon.server.exec.ForwardClientInput$2.create(ForwardClientInput.java:78)
at org.gradle.launcher.daemon.server.exec.ForwardClientInput$2.create(ForwardClientInput.java:75)
at org.gradle.util.internal.Swapper.swap(Swapper.java:38)
at org.gradle.launcher.daemon.server.exec.ForwardClientInput.execute(ForwardClientInput.java:75)
at org.gradle.launcher.daemon.server.api.DaemonCommandExecution.proceed(DaemonCommandExecution.java:104)
at org.gradle.launcher.daemon.server.exec.LogAndCheckHealth.execute(LogAndCheckHealth.java:64)
at org.gradle.launcher.daemon.server.api.DaemonCommandExecution.proceed(DaemonCommandExecution.java:104)
at org.gradle.launcher.daemon.server.exec.LogToClient.doBuild(LogToClient.java:63)
at org.gradle.launcher.daemon.server.exec.BuildCommandOnly.execute(BuildCommandOnly.java:37)
at org.gradle.launcher.daemon.server.api.DaemonCommandExecution.proceed(DaemonCommandExecution.java:104)
at org.gradle.launcher.daemon.server.exec.EstablishBuildEnvironment.doBuild(EstablishBuildEnvironment.java:84)
at org.gradle.launcher.daemon.server.exec.BuildCommandOnly.execute(BuildCommandOnly.java:37)
at org.gradle.launcher.daemon.server.api.DaemonCommandExecution.proceed(DaemonCommandExecution.java:104)
at org.gradle.launcher.daemon.server.exec.StartBuildOrRespondWithBusy$1.run(StartBuildOrRespondWithBusy.java:52)
at org.gradle.launcher.daemon.server.DaemonStateCoordinator$1.run(DaemonStateCoordinator.java:297)
at org.gradle.internal.concurrent.ExecutorPolicy$CatchAndRecordFailures.onExecute(ExecutorPolicy.java:64)
at org.gradle.internal.concurrent.AbstractManagedExecutor$1.run(AbstractManagedExecutor.java:47)
Caused by: org.gradle.api.internal.tasks.DefaultTaskContainer$TaskCreationException: Could not create task ':app:compileDebugKotlin'.
at org.gradle.api.internal.tasks.DefaultTaskContainer.taskCreationException(DefaultTaskContainer.java:721)
at org.gradle.api.internal.tasks.DefaultTaskContainer.access$600(DefaultTaskContainer.java:77)
at org.gradle.api.internal.tasks.DefaultTaskContainer$TaskCreatingProvider.domainObjectCreationException(DefaultTaskContainer.java:713)
at org.gradle.api.internal.DefaultNamedDomainObjectCollection$AbstractDomainObjectCreatingProvider.tryCreate(DefaultNamedDomainObjectCollection.java:954)
at org.gradle.api.internal.tasks.DefaultTaskContainer$TaskCreatingProvider.access$1401(DefaultTaskContainer.java:660)
at org.gradle.api.internal.tasks.DefaultTaskContainer$TaskCreatingProvider$1.run(DefaultTaskContainer.java:686)
at org.gradle.internal.operations.DefaultBuildOperationRunner$1.execute(DefaultBuildOperationRunner.java:29)
at org.gradle.internal.operations.DefaultBuildOperationRunner$1.execute(DefaultBuildOperationRunner.java:26)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:66)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:157)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.run(DefaultBuildOperationRunner.java:47)
at org.gradle.internal.operations.DefaultBuildOperationExecutor.run(DefaultBuildOperationExecutor.java:73)
at org.gradle.api.internal.tasks.DefaultTaskContainer$TaskCreatingProvider.tryCreate(DefaultTaskContainer.java:682)
at org.gradle.api.internal.DefaultNamedDomainObjectCollection$AbstractDomainObjectCreatingProvider.calculateOwnValue(DefaultNamedDomainObjectCollection.java:935)
at org.gradle.api.internal.provider.AbstractMinimalProvider.calculateOwnPresentValue(AbstractMinimalProvider.java:80)
at org.gradle.api.internal.provider.AbstractMinimalProvider.get(AbstractMinimalProvider.java:100)
at org.gradle.api.internal.DefaultNamedDomainObjectCollection$AbstractDomainObjectCreatingProvider.get(DefaultNamedDomainObjectCollection.java:921)
at org.jetbrains.kotlin.gradle.internal.Kapt3GradleSubplugin$createKaptKotlinTask$kaptTaskProvider$1.invoke(Kapt3KotlinGradleSubplugin.kt:516)
at org.jetbrains.kotlin.gradle.internal.Kapt3GradleSubplugin$createKaptKotlinTask$kaptTaskProvider$1.invoke(Kapt3KotlinGradleSubplugin.kt:513)
at org.jetbrains.kotlin.gradle.tasks.TasksProviderKt$sam$org_gradle_api_Action$0.execute(TasksProvider.kt)
at org.gradle.api.internal.DefaultMutationGuard$1.execute(DefaultMutationGuard.java:45)
at org.gradle.api.internal.DefaultMutationGuard$1.execute(DefaultMutationGuard.java:45)
at org.gradle.configuration.internal.DefaultUserCodeApplicationContext$CurrentApplication$1.execute(DefaultUserCodeApplicationContext.java:123)
at org.gradle.api.internal.DefaultCollectionCallbackActionDecorator$BuildOperationEmittingAction$1.run(DefaultCollectionCallbackActionDecorator.java:110)
at org.gradle.internal.operations.DefaultBuildOperationRunner$1.execute(DefaultBuildOperationRunner.java:29)
at org.gradle.internal.operations.DefaultBuildOperationRunner$1.execute(DefaultBuildOperationRunner.java:26)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:66)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:157)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.run(DefaultBuildOperationRunner.java:47)
at org.gradle.internal.operations.DefaultBuildOperationExecutor.run(DefaultBuildOperationExecutor.java:73)
at org.gradle.api.internal.DefaultCollectionCallbackActionDecorator$BuildOperationEmittingAction.execute(DefaultCollectionCallbackActionDecorator.java:107)
at org.gradle.api.internal.DefaultNamedDomainObjectCollection$AbstractDomainObjectCreatingProvider.configure(DefaultNamedDomainObjectCollection.java:909)
at org.jetbrains.kotlin.gradle.tasks.TasksProviderKt.registerTask(TasksProvider.kt:55)
at org.jetbrains.kotlin.gradle.internal.Kapt3GradleSubplugin.createKaptKotlinTask(Kapt3KotlinGradleSubplugin.kt:513)
at org.jetbrains.kotlin.gradle.internal.Kapt3GradleSubplugin.applyToCompilation(Kapt3KotlinGradleSubplugin.kt:301)
at org.jetbrains.kotlin.gradle.plugin.SubpluginEnvironment.addSubpluginOptions(SubpluginEnvironment.kt:82)
at org.jetbrains.kotlin.gradle.plugin.AbstractAndroidProjectHandler$configureTarget$3$1.invoke(KotlinPlugin.kt:840)
at org.jetbrains.kotlin.gradle.plugin.AbstractAndroidProjectHandler$configureTarget$3$1.invoke(KotlinPlugin.kt:835)
at org.jetbrains.kotlin.gradle.plugin.KotlinPluginKt$sam$org_gradle_api_Action$0.execute(KotlinPlugin.kt)
at org.gradle.configuration.internal.DefaultUserCodeApplicationContext$CurrentApplication$1.execute(DefaultUserCodeApplicationContext.java:123)
at org.gradle.api.internal.DefaultCollectionCallbackActionDecorator$BuildOperationEmittingAction$1.run(DefaultCollectionCallbackActionDecorator.java:110)
at org.gradle.internal.operations.DefaultBuildOperationRunner$1.execute(DefaultBuildOperationRunner.java:29)
at org.gradle.internal.operations.DefaultBuildOperationRunner$1.execute(DefaultBuildOperationRunner.java:26)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:66)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:157)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.run(DefaultBuildOperationRunner.java:47)
at org.gradle.internal.operations.DefaultBuildOperationExecutor.run(DefaultBuildOperationExecutor.java:73)
at org.gradle.api.internal.DefaultCollectionCallbackActionDecorator$BuildOperationEmittingAction.execute(DefaultCollectionCallbackActionDecorator.java:107)
at org.gradle.api.internal.DefaultDomainObjectCollection.all(DefaultDomainObjectCollection.java:161)
at org.jetbrains.kotlin.gradle.plugin.KotlinPluginKt.forEachVariant(KotlinPlugin.kt:1132)
at org.jetbrains.kotlin.gradle.plugin.AbstractAndroidProjectHandler$configureTarget$3.invoke(KotlinPlugin.kt:835)
at org.jetbrains.kotlin.gradle.plugin.AbstractAndroidProjectHandler$configureTarget$3.invoke(KotlinPlugin.kt:834)
at org.jetbrains.kotlin.gradle.plugin.KotlinMultiplatformPluginKt$whenEvaluated$1$1$1.execute(KotlinMultiplatformPlugin.kt:223)
at org.jetbrains.kotlin.gradle.plugin.KotlinMultiplatformPluginKt$whenEvaluated$1$1$1.execute(KotlinMultiplatformPlugin.kt:223)
at org.gradle.configuration.internal.DefaultUserCodeApplicationContext$CurrentApplication$1.execute(DefaultUserCodeApplicationContext.java:123)
at org.gradle.configuration.internal.DefaultListenerBuildOperationDecorator$BuildOperationEmittingAction$1.run(DefaultListenerBuildOperationDecorator.java:171)
at org.gradle.internal.operations.DefaultBuildOperationRunner$1.execute(DefaultBuildOperationRunner.java:29)
at org.gradle.internal.operations.DefaultBuildOperationRunner$1.execute(DefaultBuildOperationRunner.java:26)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:66)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:157)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.run(DefaultBuildOperationRunner.java:47)
at org.gradle.internal.operations.DefaultBuildOperationExecutor.run(DefaultBuildOperationExecutor.java:73)
at org.gradle.configuration.internal.DefaultListenerBuildOperationDecorator$BuildOperationEmittingAction.execute(DefaultListenerBuildOperationDecorator.java:168)
at org.gradle.internal.event.BroadcastDispatch$ActionInvocationHandler.dispatch(BroadcastDispatch.java:99)
at org.gradle.internal.event.BroadcastDispatch$ActionInvocationHandler.dispatch(BroadcastDispatch.java:87)
at org.gradle.internal.event.AbstractBroadcastDispatch.dispatch(AbstractBroadcastDispatch.java:43)
at org.gradle.internal.event.BroadcastDispatch$SingletonDispatch.dispatch(BroadcastDispatch.java:268)
at org.gradle.internal.event.BroadcastDispatch$SingletonDispatch.dispatch(BroadcastDispatch.java:170)
at org.gradle.internal.event.AbstractBroadcastDispatch.dispatch(AbstractBroadcastDispatch.java:83)
at org.gradle.internal.event.AbstractBroadcastDispatch.dispatch(AbstractBroadcastDispatch.java:69)
at org.gradle.internal.event.BroadcastDispatch$CompositeDispatch.dispatch(BroadcastDispatch.java:381)
at org.gradle.internal.event.BroadcastDispatch$CompositeDispatch.dispatch(BroadcastDispatch.java:272)
at org.gradle.internal.event.ListenerBroadcast.dispatch(ListenerBroadcast.java:148)
at org.gradle.internal.event.ListenerBroadcast.dispatch(ListenerBroadcast.java:37)
at org.gradle.internal.dispatch.ProxyDispatchAdapter$DispatchingInvocationHandler.invoke(ProxyDispatchAdapter.java:94)
at jdk.proxy1/jdk.proxy1.$Proxy65.afterEvaluate(Unknown Source)
at org.gradle.configuration.project.LifecycleProjectEvaluator$NotifyAfterEvaluate$1.execute(LifecycleProjectEvaluator.java:247)
at org.gradle.configuration.project.LifecycleProjectEvaluator$NotifyAfterEvaluate$1.execute(LifecycleProjectEvaluator.java:244)
at org.gradle.api.internal.project.DefaultProject.stepEvaluationListener(DefaultProject.java:1495)
at org.gradle.configuration.project.LifecycleProjectEvaluator$NotifyAfterEvaluate.run(LifecycleProjectEvaluator.java:253)
at org.gradle.internal.operations.DefaultBuildOperationRunner$1.execute(DefaultBuildOperationRunner.java:29)
at org.gradle.internal.operations.DefaultBuildOperationRunner$1.execute(DefaultBuildOperationRunner.java:26)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:66)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:157)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.run(DefaultBuildOperationRunner.java:47)
at org.gradle.internal.operations.DefaultBuildOperationExecutor.run(DefaultBuildOperationExecutor.java:73)
at org.gradle.configuration.project.LifecycleProjectEvaluator$EvaluateProject.lambda$run$0(LifecycleProjectEvaluator.java:114)
at org.gradle.api.internal.project.DefaultProjectStateRegistry$ProjectStateImpl.lambda$applyToMutableState$1(DefaultProjectStateRegistry.java:406)
at org.gradle.api.internal.project.DefaultProjectStateRegistry$ProjectStateImpl.lambda$fromMutableState$2(DefaultProjectStateRegistry.java:429)
at org.gradle.internal.work.DefaultWorkerLeaseService.withReplacedLocks(DefaultWorkerLeaseService.java:360)
at org.gradle.api.internal.project.DefaultProjectStateRegistry$ProjectStateImpl.fromMutableState(DefaultProjectStateRegistry.java:429)
at org.gradle.api.internal.project.DefaultProjectStateRegistry$ProjectStateImpl.applyToMutableState(DefaultProjectStateRegistry.java:405)
at org.gradle.configuration.project.LifecycleProjectEvaluator$EvaluateProject.run(LifecycleProjectEvaluator.java:100)
at org.gradle.internal.operations.DefaultBuildOperationRunner$1.execute(DefaultBuildOperationRunner.java:29)
at org.gradle.internal.operations.DefaultBuildOperationRunner$1.execute(DefaultBuildOperationRunner.java:26)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:66)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:157)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.run(DefaultBuildOperationRunner.java:47)
at org.gradle.internal.operations.DefaultBuildOperationExecutor.run(DefaultBuildOperationExecutor.java:73)
at org.gradle.configuration.project.LifecycleProjectEvaluator.evaluate(LifecycleProjectEvaluator.java:72)
at org.gradle.api.internal.project.DefaultProject.evaluate(DefaultProject.java:788)
at org.gradle.api.internal.project.DefaultProject.evaluate(DefaultProject.java:156)
at org.gradle.api.internal.project.ProjectLifecycleController.lambda$ensureSelfConfigured$2(ProjectLifecycleController.java:84)
at org.gradle.internal.model.StateTransitionController.lambda$doTransition$14(StateTransitionController.java:255)
at org.gradle.internal.model.StateTransitionController.doTransition(StateTransitionController.java:266)
at org.gradle.internal.model.StateTransitionController.doTransition(StateTransitionController.java:254)
at org.gradle.internal.model.StateTransitionController.lambda$maybeTransitionIfNotCurrentlyTransitioning$10(StateTransitionController.java:199)
at org.gradle.internal.work.DefaultSynchronizer.withLock(DefaultSynchronizer.java:34)
at org.gradle.internal.model.StateTransitionController.maybeTransitionIfNotCurrentlyTransitioning(StateTransitionController.java:195)
at org.gradle.api.internal.project.ProjectLifecycleController.ensureSelfConfigured(ProjectLifecycleController.java:84)
at org.gradle.api.internal.project.DefaultProjectStateRegistry$ProjectStateImpl.ensureConfigured(DefaultProjectStateRegistry.java:380)
at org.gradle.execution.TaskPathProjectEvaluator.configure(TaskPathProjectEvaluator.java:34)
at org.gradle.execution.TaskPathProjectEvaluator.configureHierarchy(TaskPathProjectEvaluator.java:50)
at org.gradle.configuration.DefaultProjectsPreparer.prepareProjects(DefaultProjectsPreparer.java:42)
at org.gradle.configuration.BuildTreePreparingProjectsPreparer.prepareProjects(BuildTreePreparingProjectsPreparer.java:65)
at org.gradle.configuration.BuildOperationFiringProjectsPreparer$ConfigureBuild.run(BuildOperationFiringProjectsPreparer.java:52)
at org.gradle.internal.operations.DefaultBuildOperationRunner$1.execute(DefaultBuildOperationRunner.java:29)
at org.gradle.internal.operations.DefaultBuildOperationRunner$1.execute(DefaultBuildOperationRunner.java:26)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:66)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:157)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.run(DefaultBuildOperationRunner.java:47)
at org.gradle.internal.operations.DefaultBuildOperationExecutor.run(DefaultBuildOperationExecutor.java:73)
at org.gradle.configuration.BuildOperationFiringProjectsPreparer.prepareProjects(BuildOperationFiringProjectsPreparer.java:40)
at org.gradle.initialization.VintageBuildModelController.lambda$prepareProjects$2(VintageBuildModelController.java:84)
at org.gradle.internal.model.StateTransitionController.lambda$doTransition$14(StateTransitionController.java:255)
at org.gradle.internal.model.StateTransitionController.doTransition(StateTransitionController.java:266)
at org.gradle.internal.model.StateTransitionController.doTransition(StateTransitionController.java:254)
at org.gradle.internal.model.StateTransitionController.lambda$transitionIfNotPreviously$11(StateTransitionController.java:213)
at org.gradle.internal.work.DefaultSynchronizer.withLock(DefaultSynchronizer.java:34)
at org.gradle.internal.model.StateTransitionController.transitionIfNotPreviously(StateTransitionController.java:209)
at org.gradle.initialization.VintageBuildModelController.prepareProjects(VintageBuildModelController.java:84)
at org.gradle.initialization.VintageBuildModelController.getConfiguredModel(VintageBuildModelController.java:64)
at org.gradle.internal.build.DefaultBuildLifecycleController.lambda$withProjectsConfigured$1(DefaultBuildLifecycleController.java:133)
at org.gradle.internal.model.StateTransitionController.lambda$notInState$3(StateTransitionController.java:132)
at org.gradle.internal.work.DefaultSynchronizer.withLock(DefaultSynchronizer.java:44)
at org.gradle.internal.model.StateTransitionController.notInState(StateTransitionController.java:128)
at org.gradle.internal.build.DefaultBuildLifecycleController.withProjectsConfigured(DefaultBuildLifecycleController.java:133)
at org.gradle.internal.build.DefaultBuildToolingModelController.locateBuilderForTarget(DefaultBuildToolingModelController.java:57)
at org.gradle.internal.buildtree.DefaultBuildTreeModelCreator$DefaultBuildTreeModelController.lambda$locateBuilderForTarget$0(DefaultBuildTreeModelCreator.java:73)
at org.gradle.internal.build.DefaultBuildLifecycleController.withToolingModels(DefaultBuildLifecycleController.java:327)
at org.gradle.internal.build.AbstractBuildState.withToolingModels(AbstractBuildState.java:140)
at org.gradle.internal.buildtree.DefaultBuildTreeModelCreator$DefaultBuildTreeModelController.locateBuilderForTarget(DefaultBuildTreeModelCreator.java:73)
at org.gradle.internal.buildtree.DefaultBuildTreeModelCreator$DefaultBuildTreeModelController.locateBuilderForDefaultTarget(DefaultBuildTreeModelCreator.java:68)
at org.gradle.tooling.internal.provider.runner.DefaultBuildController.getTarget(DefaultBuildController.java:157)
at org.gradle.tooling.internal.provider.runner.DefaultBuildController.getModel(DefaultBuildController.java:101)
at org.gradle.tooling.internal.consumer.connection.ParameterAwareBuildControllerAdapter.getModel(ParameterAwareBuildControllerAdapter.java:39)
at org.gradle.tooling.internal.consumer.connection.UnparameterizedBuildController.getModel(UnparameterizedBuildController.java:113)
at org.gradle.tooling.internal.consumer.connection.NestedActionAwareBuildControllerAdapter.getModel(NestedActionAwareBuildControllerAdapter.java:31)
at org.gradle.tooling.internal.consumer.connection.UnparameterizedBuildController.findModel(UnparameterizedBuildController.java:97)
at org.gradle.tooling.internal.consumer.connection.NestedActionAwareBuildControllerAdapter.findModel(NestedActionAwareBuildControllerAdapter.java:31)
at org.gradle.tooling.internal.consumer.connection.UnparameterizedBuildController.findModel(UnparameterizedBuildController.java:81)
at org.gradle.tooling.internal.consumer.connection.NestedActionAwareBuildControllerAdapter.findModel(NestedActionAwareBuildControllerAdapter.java:31)
at org.gradle.tooling.internal.consumer.connection.UnparameterizedBuildController.findModel(UnparameterizedBuildController.java:66)
at org.gradle.tooling.internal.consumer.connection.NestedActionAwareBuildControllerAdapter.findModel(NestedActionAwareBuildControllerAdapter.java:31)
at org.jetbrains.plugins.gradle.model.ProjectImportAction.execute(ProjectImportAction.java:126)
at org.jetbrains.plugins.gradle.model.ProjectImportAction.execute(ProjectImportAction.java:43)
at org.gradle.tooling.internal.consumer.connection.InternalBuildActionAdapter.execute(InternalBuildActionAdapter.java:64)
at org.gradle.tooling.internal.provider.runner.AbstractClientProvidedBuildActionRunner$ActionAdapter.runAction(AbstractClientProvidedBuildActionRunner.java:131)
at org.gradle.tooling.internal.provider.runner.AbstractClientProvidedBuildActionRunner$ActionAdapter.beforeTasks(AbstractClientProvidedBuildActionRunner.java:99)
at org.gradle.internal.buildtree.DefaultBuildTreeModelCreator.beforeTasks(DefaultBuildTreeModelCreator.java:52)
at org.gradle.internal.buildtree.DefaultBuildTreeLifecycleController.lambda$fromBuildModel$2(DefaultBuildTreeLifecycleController.java:74)
at org.gradle.internal.buildtree.DefaultBuildTreeLifecycleController.lambda$runBuild$4(DefaultBuildTreeLifecycleController.java:98)
at org.gradle.internal.model.StateTransitionController.lambda$transition$6(StateTransitionController.java:169)
at org.gradle.internal.model.StateTransitionController.doTransition(StateTransitionController.java:266)
at org.gradle.internal.model.StateTransitionController.lambda$transition$7(StateTransitionController.java:169)
at org.gradle.internal.work.DefaultSynchronizer.withLock(DefaultSynchronizer.java:44)
at org.gradle.internal.model.StateTransitionController.transition(StateTransitionController.java:169)
at org.gradle.internal.buildtree.DefaultBuildTreeLifecycleController.runBuild(DefaultBuildTreeLifecycleController.java:95)
at org.gradle.internal.buildtree.DefaultBuildTreeLifecycleController.fromBuildModel(DefaultBuildTreeLifecycleController.java:73)
at org.gradle.tooling.internal.provider.runner.AbstractClientProvidedBuildActionRunner.runClientAction(AbstractClientProvidedBuildActionRunner.java:43)
at org.gradle.tooling.internal.provider.runner.ClientProvidedPhasedActionRunner.run(ClientProvidedPhasedActionRunner.java:53)
at org.gradle.launcher.exec.ChainingBuildActionRunner.run(ChainingBuildActionRunner.java:35)
at org.gradle.internal.buildtree.ProblemReportingBuildActionRunner.run(ProblemReportingBuildActionRunner.java:49)
at org.gradle.launcher.exec.BuildOutcomeReportingBuildActionRunner.run(BuildOutcomeReportingBuildActionRunner.java:65)
at org.gradle.tooling.internal.provider.FileSystemWatchingBuildActionRunner.run(FileSystemWatchingBuildActionRunner.java:140)
at org.gradle.launcher.exec.BuildCompletionNotifyingBuildActionRunner.run(BuildCompletionNotifyingBuildActionRunner.java:41)
at org.gradle.launcher.exec.RootBuildLifecycleBuildActionExecutor.lambda$execute$0(RootBuildLifecycleBuildActionExecutor.java:40)
at org.gradle.composite.internal.DefaultRootBuildState.run(DefaultRootBuildState.java:123)
at org.gradle.launcher.exec.RootBuildLifecycleBuildActionExecutor.execute(RootBuildLifecycleBuildActionExecutor.java:40)
at org.gradle.internal.buildtree.InitDeprecationLoggingActionExecutor.execute(InitDeprecationLoggingActionExecutor.java:62)
at org.gradle.internal.buildtree.InitProblems.execute(InitProblems.java:38)
at org.gradle.internal.buildtree.DefaultBuildTreeContext.execute(DefaultBuildTreeContext.java:40)
at org.gradle.launcher.exec.BuildTreeLifecycleBuildActionExecutor.lambda$execute$0(BuildTreeLifecycleBuildActionExecutor.java:65)
at org.gradle.internal.buildtree.BuildTreeState.run(BuildTreeState.java:58)
at org.gradle.launcher.exec.BuildTreeLifecycleBuildActionExecutor.execute(BuildTreeLifecycleBuildActionExecutor.java:65)
at org.gradle.launcher.exec.RunAsBuildOperationBuildActionExecutor$3.call(RunAsBuildOperationBuildActionExecutor.java:61)
at org.gradle.launcher.exec.RunAsBuildOperationBuildActionExecutor$3.call(RunAsBuildOperationBuildActionExecutor.java:57)
at org.gradle.internal.operations.DefaultBuildOperationRunner$CallableBuildOperationWorker.execute(DefaultBuildOperationRunner.java:204)
at org.gradle.internal.operations.DefaultBuildOperationRunner$CallableBuildOperationWorker.execute(DefaultBuildOperationRunner.java:199)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:66)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:157)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.call(DefaultBuildOperationRunner.java:53)
at org.gradle.internal.operations.DefaultBuildOperationExecutor.call(DefaultBuildOperationExecutor.java:78)
at org.gradle.launcher.exec.RunAsBuildOperationBuildActionExecutor.execute(RunAsBuildOperationBuildActionExecutor.java:57)
at org.gradle.launcher.exec.RunAsWorkerThreadBuildActionExecutor.lambda$execute$0(RunAsWorkerThreadBuildActionExecutor.java:36)
at org.gradle.internal.work.DefaultWorkerLeaseService.withLocks(DefaultWorkerLeaseService.java:264)
at org.gradle.internal.work.DefaultWorkerLeaseService.runAsWorkerThread(DefaultWorkerLeaseService.java:128)
at org.gradle.launcher.exec.RunAsWorkerThreadBuildActionExecutor.execute(RunAsWorkerThreadBuildActionExecutor.java:36)
at org.gradle.tooling.internal.provider.continuous.ContinuousBuildActionExecutor.execute(ContinuousBuildActionExecutor.java:110)
at org.gradle.tooling.internal.provider.SubscribableBuildActionExecutor.execute(SubscribableBuildActionExecutor.java:64)
at org.gradle.internal.session.DefaultBuildSessionContext.execute(DefaultBuildSessionContext.java:46)
at org.gradle.tooling.internal.provider.BuildSessionLifecycleBuildActionExecuter$ActionImpl.apply(BuildSessionLifecycleBuildActionExecuter.java:92)
at org.gradle.tooling.internal.provider.BuildSessionLifecycleBuildActionExecuter$ActionImpl.apply(BuildSessionLifecycleBuildActionExecuter.java:80)
at org.gradle.internal.session.BuildSessionState.run(BuildSessionState.java:69)
at org.gradle.tooling.internal.provider.BuildSessionLifecycleBuildActionExecuter.execute(BuildSessionLifecycleBuildActionExecuter.java:62)
at org.gradle.tooling.internal.provider.BuildSessionLifecycleBuildActionExecuter.execute(BuildSessionLifecycleBuildActionExecuter.java:41)
at org.gradle.tooling.internal.provider.StartParamsValidatingActionExecuter.execute(StartParamsValidatingActionExecuter.java:64)
at org.gradle.tooling.internal.provider.StartParamsValidatingActionExecuter.execute(StartParamsValidatingActionExecuter.java:32)
at org.gradle.tooling.internal.provider.SessionFailureReportingActionExecuter.execute(SessionFailureReportingActionExecuter.java:51)
at org.gradle.tooling.internal.provider.SessionFailureReportingActionExecuter.execute(SessionFailureReportingActionExecuter.java:39)
at org.gradle.tooling.internal.provider.SetupLoggingActionExecuter.execute(SetupLoggingActionExecuter.java:47)
at org.gradle.tooling.internal.provider.SetupLoggingActionExecuter.execute(SetupLoggingActionExecuter.java:31)
at org.gradle.launcher.daemon.server.exec.ExecuteBuild.doBuild(ExecuteBuild.java:65)
at org.gradle.launcher.daemon.server.exec.BuildCommandOnly.execute(BuildCommandOnly.java:37)
at org.gradle.launcher.daemon.server.api.DaemonCommandExecution.proceed(DaemonCommandExecution.java:104)
at org.gradle.launcher.daemon.server.exec.WatchForDisconnection.execute(WatchForDisconnection.java:39)
at org.gradle.launcher.daemon.server.api.DaemonCommandExecution.proceed(DaemonCommandExecution.java:104)
at org.gradle.launcher.daemon.server.exec.ResetDeprecationLogger.execute(ResetDeprecationLogger.java:29)
at org.gradle.launcher.daemon.server.api.DaemonCommandExecution.proceed(DaemonCommandExecution.java:104)
at org.gradle.launcher.daemon.server.exec.RequestStopIfSingleUsedDaemon.execute(RequestStopIfSingleUsedDaemon.java:35)
at org.gradle.launcher.daemon.server.api.DaemonCommandExecution.proceed(DaemonCommandExecution.java:104)
at org.gradle.launcher.daemon.server.exec.ForwardClientInput$2.create(ForwardClientInput.java:78)
at org.gradle.launcher.daemon.server.exec.ForwardClientInput$2.create(ForwardClientInput.java:75)
at org.gradle.util.internal.Swapper.swap(Swapper.java:38)
at org.gradle.launcher.daemon.server.exec.ForwardClientInput.execute(ForwardClientInput.java:75)
at org.gradle.launcher.daemon.server.api.DaemonCommandExecution.proceed(DaemonCommandExecution.java:104)
at org.gradle.launcher.daemon.server.exec.LogAndCheckHealth.execute(LogAndCheckHealth.java:64)
at org.gradle.launcher.daemon.server.api.DaemonCommandExecution.proceed(DaemonCommandExecution.java:104)
at org.gradle.launcher.daemon.server.exec.LogToClient.doBuild(LogToClient.java:63)
at org.gradle.launcher.daemon.server.exec.BuildCommandOnly.execute(BuildCommandOnly.java:37)
at org.gradle.launcher.daemon.server.api.DaemonCommandExecution.proceed(DaemonCommandExecution.java:104)
at org.gradle.launcher.daemon.server.exec.EstablishBuildEnvironment.doBuild(EstablishBuildEnvironment.java:84)
at org.gradle.launcher.daemon.server.exec.BuildCommandOnly.execute(BuildCommandOnly.java:37)
at org.gradle.launcher.daemon.server.api.DaemonCommandExecution.proceed(DaemonCommandExecution.java:104)
at org.gradle.launcher.daemon.server.exec.StartBuildOrRespondWithBusy$1.run(StartBuildOrRespondWithBusy.java:52)
at org.gradle.launcher.daemon.server.DaemonStateCoordinator$1.run(DaemonStateCoordinator.java:297)
at org.gradle.internal.concurrent.ExecutorPolicy$CatchAndRecordFailures.onExecute(ExecutorPolicy.java:64)
at org.gradle.internal.concurrent.AbstractManagedExecutor$1.run(AbstractManagedExecutor.java:47)
Caused by: org.gradle.api.internal.tasks.DefaultTaskContainer$TaskCreationException: Could not create task ':app:dataBindingGenBaseClassesDebug'.
at org.gradle.api.internal.tasks.DefaultTaskContainer.taskCreationException(DefaultTaskContainer.java:721)
at org.gradle.api.internal.tasks.DefaultTaskContainer.access$600(DefaultTaskContainer.java:77)
at org.gradle.api.internal.tasks.DefaultTaskContainer$TaskCreatingProvider.domainObjectCreationException(DefaultTaskContainer.java:713)
at org.gradle.api.internal.DefaultNamedDomainObjectCollection$AbstractDomainObjectCreatingProvider.tryCreate(DefaultNamedDomainObjectCollection.java:954)
at org.gradle.api.internal.tasks.DefaultTaskContainer$TaskCreatingProvider.access$1401(DefaultTaskContainer.java:660)
at org.gradle.api.internal.tasks.DefaultTaskContainer$TaskCreatingProvider$1.run(DefaultTaskContainer.java:686)
at org.gradle.internal.operations.DefaultBuildOperationRunner$1.execute(DefaultBuildOperationRunner.java:29)
at org.gradle.internal.operations.DefaultBuildOperationRunner$1.execute(DefaultBuildOperationRunner.java:26)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:66)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:157)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.run(DefaultBuildOperationRunner.java:47)
at org.gradle.internal.operations.DefaultBuildOperationExecutor.run(DefaultBuildOperationExecutor.java:73)
at org.gradle.api.internal.tasks.DefaultTaskContainer$TaskCreatingProvider.tryCreate(DefaultTaskContainer.java:682)
at org.gradle.api.internal.DefaultNamedDomainObjectCollection$AbstractDomainObjectCreatingProvider.calculateOwnValue(DefaultNamedDomainObjectCollection.java:935)
at org.gradle.api.internal.provider.AbstractMinimalProvider.calculateValue(AbstractMinimalProvider.java:115)
at org.gradle.api.internal.provider.FlatMapProvider.calculateOwnValue(FlatMapProvider.java:46)
at org.gradle.api.internal.provider.AbstractMinimalProvider.calculateValue(AbstractMinimalProvider.java:115)
at org.gradle.api.internal.provider.TransformBackedProvider.calculateOwnValue(TransformBackedProvider.java:82)
at org.gradle.api.internal.provider.AbstractMinimalProvider.calculateValue(AbstractMinimalProvider.java:115)
at org.gradle.api.internal.provider.DefaultProperty.calculateValueFrom(DefaultProperty.java:128)
at org.gradle.api.internal.provider.DefaultProperty.calculateValueFrom(DefaultProperty.java:26)
at org.gradle.api.internal.provider.AbstractProperty.doCalculateValue(AbstractProperty.java:142)
at org.gradle.api.internal.provider.AbstractProperty.calculateOwnValue(AbstractProperty.java:136)
at org.gradle.api.internal.provider.AbstractMinimalProvider.calculateValue(AbstractMinimalProvider.java:115)
at org.gradle.api.internal.provider.DefaultProperty.calculateValueFrom(DefaultProperty.java:128)
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at org.gradle.api.internal.provider.AbstractProperty.calculateOwnValue(AbstractProperty.java:136)
at org.gradle.api.internal.provider.AbstractMinimalProvider.calculateValue(AbstractMinimalProvider.java:115)
at org.gradle.api.internal.provider.DefaultProperty.calculateValueFrom(DefaultProperty.java:128)
at org.gradle.api.internal.provider.DefaultProperty.calculateValueFrom(DefaultProperty.java:26)
at org.gradle.api.internal.provider.AbstractProperty.doCalculateValue(AbstractProperty.java:142)
at org.gradle.api.internal.provider.AbstractProperty.calculateOwnValue(AbstractProperty.java:136)
at org.gradle.api.internal.provider.AbstractMinimalProvider.calculateOwnPresentValue(AbstractMinimalProvider.java:80)
at org.gradle.api.internal.provider.AbstractMinimalProvider.get(AbstractMinimalProvider.java:100)
at org.gradle.api.internal.provider.ProviderResolutionStrategy$2.resolve(ProviderResolutionStrategy.java:33)
at org.gradle.util.internal.DeferredUtil.unpack(DeferredUtil.java:59)
at org.gradle.util.internal.DeferredUtil.unpack(DeferredUtil.java:38)
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at org.jetbrains.kotlin.gradle.plugin.AbstractAndroidProjectHandler$postprocessVariant$2$1.execute(KotlinPlugin.kt:994)
at org.jetbrains.kotlin.gradle.plugin.AbstractAndroidProjectHandler$postprocessVariant$2$1.execute(KotlinPlugin.kt:994)
at org.gradle.api.internal.DefaultMutationGuard$1.execute(DefaultMutationGuard.java:45)
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at org.gradle.api.internal.DefaultCollectionCallbackActionDecorator$BuildOperationEmittingAction$1.run(DefaultCollectionCallbackActionDecorator.java:110)
at org.gradle.internal.operations.DefaultBuildOperationRunner$1.execute(DefaultBuildOperationRunner.java:29)
at org.gradle.internal.operations.DefaultBuildOperationRunner$1.execute(DefaultBuildOperationRunner.java:26)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:66)
at org.gradle.internal.operations.DefaultBuildOperationRunner$2.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:157)
at org.gradle.internal.operations.DefaultBuildOperationRunner.execute(DefaultBuildOperationRunner.java:59)
at org.gradle.internal.operations.DefaultBuildOperationRunner.run(DefaultBuildOperationRunner.java:47)
at org.gradle.internal.operations.DefaultBuildOperationExecutor.run(DefaultBuildOperationExecutor.java:73)
at org.gradle.api.internal.DefaultCollectionCallbackActionDecorator$BuildOperationEmittingAction.execute(DefaultCollectionCallbackActionDecorator.java:107)
at org.gradle.internal.ImmutableActionSet$SetWithManyActions.execute(ImmutableActionSet.java:329)
at org.gradle.api.internal.DefaultDomainObjectCollection.doAdd(DefaultDomainObjectCollection.java:262)
at org.gradle.api.internal.DefaultNamedDomainObjectCollection.doAdd(DefaultNamedDomainObjectCollection.java:114)
at org.gradle.api.internal.DefaultDomainObjectCollection.add(DefaultDomainObjectCollection.java:256)
at org.gradle.api.internal.DefaultNamedDomainObjectCollection$AbstractDomainObjectCreatingProvider.tryCreate(DefaultNamedDomainObjectCollection.java:950)
... 250 more
Caused by: org.gradle.api.GradleException: Cannot use @TaskAction annotation on method DataBindingGenBaseClassesTask.writeBaseClasses() because interface org.gradle.api.tasks.incremental.IncrementalTaskInputs is not a valid parameter to an action method.
at org.gradle.api.internal.project.taskfactory.DefaultTaskClassInfoStore.createTaskAction(DefaultTaskClassInfoStore.java:126)
at org.gradle.api.internal.project.taskfactory.DefaultTaskClassInfoStore.createTaskClassInfo(DefaultTaskClassInfoStore.java:63)
at org.gradle.api.internal.project.taskfactory.DefaultTaskClassInfoStore.lambda$new$0(DefaultTaskClassInfoStore.java:43)
at org.gradle.cache.internal.DefaultCrossBuildInMemoryCacheFactory$AbstractCrossBuildInMemoryCache.get(DefaultCrossBuildInMemoryCacheFactory.java:130)
at org.gradle.api.internal.project.taskfactory.DefaultTaskClassInfoStore.getTaskClassInfo(DefaultTaskClassInfoStore.java:51)
at org.gradle.api.internal.project.taskfactory.AnnotationProcessingTaskFactory.process(AnnotationProcessingTaskFactory.java:52)
at org.gradle.api.internal.project.taskfactory.AnnotationProcessingTaskFactory.create(AnnotationProcessingTaskFactory.java:48)
at org.gradle.api.internal.tasks.DefaultTaskContainer.createTask(DefaultTaskContainer.java:328)
at org.gradle.api.internal.tasks.DefaultTaskContainer.access$200(DefaultTaskContainer.java:77)
at org.gradle.api.internal.tasks.DefaultTaskContainer$TaskCreatingProvider.createDomainObject(DefaultTaskContainer.java:703)
at org.gradle.api.internal.tasks.DefaultTaskContainer$TaskCreatingProvider.createDomainObject(DefaultTaskContainer.java:660)
at org.gradle.api.internal.DefaultNamedDomainObjectCollection$AbstractDomainObjectCreatingProvider.tryCreate(DefaultNamedDomainObjectCollection.java:947)
... 311 more
Deprecated Gradle features were used in this build, making it incompatible with Gradle 9.0.
You can use '--warning-mode all' to show the individual deprecation warnings and determine if they come from your own scripts or plugins.
For more on this, please refer to https://docs.gradle.org/8.4/userguide/command_line_interface.html#sec:command_line_warnings in the Gradle documentation.
```
|
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I_kwDOArmXAs554OPE
| 62,653 |
TextVectorization layer vocabulary doesn't match custom standardization function
|
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[
"Hi @AndhikaWB ,\r\n\r\nI am not getting your question here. I can verify that `TextVectorization(...,standardize = 'lower_and_strip_punctuation')` layer works fine which removed all punctuations and converted input into lower text as well as per attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/a612b8fae2e4d874290be2f7c8ead8f5/62653.ipynb). \r\n\r\nWhere as the callable `custom_standardization` defined by you not able to remove the punctuations. Is that what you wanted to report ? In that case this is not an issue with Tensorflow. You need to verify the custom callable code correctly and if you are looking for such support please report the issue at [TF-Forum](https://discuss.tensorflow.org/) or **stack overflow** . \r\n\r\nIf you are reporting any Tensorflow bug please let us know. Thanks!",
"@SuryanarayanaY Yes, but I already checked that with debugging, the `custom_standardization` function that I wrote return the text correctly with punctuations and other useless stuff removed.\r\n\r\nIt should have the same behavior as Tensorflow built-in `lower_and_strip_punctuation`, but in this case, if I use the callable function (`custom_standardization`), Tensorflow will use the unprocessed vocabulary (output from `vectorization_layer.get_vocabulary()`), with punctuations and other useless stuff still intact.\r\n\r\n1. Here is the return from Tensorflow `lower_and_strip_punctuation`:\r\n\r\n\r\n Here is the return from my `custom_standardization` function (I removed the `casefold`/`lowercase` temporarily so you can see the case difference, but punctuation removal is still active):\r\n\r\n\r\n2. Here is the vocabulary list from Tensorflow `lower_and_strip_punctuation`:\r\n\r\n\r\n Here is the vocabulary list from my `custom_standardization` (note that the punctuations are still there):\r\n\r\n\r\nAs you can see from the `custom_standardization` vocabulary list (printed output), it still have punctuations, even though I already removed it before, see `return inputs` variable (`rude`) vs the vocabulary list (`rude!!!`). It's not that my code is wrong, it's just Tensorflow ignore my preprocessed words."
] | 2023-12-16T15:11:42 | 2023-12-20T07:58:52 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
v2.15.0-rc1-8-g6887368d6d4 2.15.0
### Custom code
Yes
### OS platform and distribution
Windows 11 22621 x64
### Mobile device
_No response_
### Python version
3.11.5
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When using custom `standardize` function in `TextVectorization` layer, vocabulary still match the unprocessed words. For example if the sentence contains punctuation or mixed case word like:
``` python
vectorization_layer = keras.layers.TextVectorization(
max_tokens = 12000 + 2,
standardize = custom_standardization
)
text = [
'Your company is the best, especially the service you provided 🤩',
'The customer service is so-so, not good but not bad either',
'The service is NOT GOOD at all, the guy I contacted is so rude!!!'
]
vectorization_layer.adapt(text)
print('Vocabulary:', vectorization_layer.get_vocabulary(), '\n')
print('Output vector:', vectorization_layer(text))
```
The vocabulary list will be:
```
['', '[UNK]', 'is', 'the', 'service', 'not', 'The', '🤩', 'you', 'so-so,', 'so', 'rude!!!', 'provided', 'guy', 'good', 'especially', 'either', 'customer', 'contacted', 'company', 'but', 'best,', 'bad', 'at', 'all,', 'Your', 'NOT', 'I', 'GOOD']
```
But the custom standardization model is basically like this:
``` python
def preproc_for_model(value: str, stemmer = None, stop_words = None):
# Remove punctuations (and emoticons)
value = re.sub(f'[{string.punctuation}]', ' ', value)
# Lowercase, but ignore unicode characters
value = value.casefold()
# Remove 1-character word
value = re.sub(r'\b[a-zA-Z]\b', '', value)
# Replace multiple spaces with a single space
value = re.sub(r'\s+', ' ', value)
if stemmer or stop_words:
# Split sentence as words (tokenization)
word_token = value.split()
if stop_words:
# Remove stop words (must be prioritized before stemmer)
word_token = [ word for word in word_token if word not in stop_words ]
if stemmer:
# Stem/lemmatize words
try: word_token = [ stemmer.lemmatize(word) for word in word_token ]
except: word_token = [ stemmer.stem(word) for word in word_token ]
# Join the tokenized words again
# A bit inefficient if we use it with TextVectorization layer
value = ' '.join(word_token)
return value
def custom_standardization(input_data):
# Skip if the layer is just initialized (no data yet)
if tf.is_symbolic_tensor(input_data): return input_data
# Convert tensor to Numpy's array (for easier manipulation)
# The array will have byte as it's data type
input_data = input_data.numpy()
__preproc_for_model = np.vectorize(
lambda sentence: bytes(
# Please change to "preproc_all" instead for unseen data
# Since we did sentiment analysis earlier, "preproc_useless_info" is already applied
# So all that's left is to apply "preproc_for_model" (remove punctuations, etc)
preproc_for_model(sentence.decode(encoding)),
encoding
),
cache = True
)
# Appply preprocessing and convert back Numpy's byte array to tensor
input_data = __preproc_for_model(input_data)
input_data = tf.convert_to_tensor(input_data)
return input_data
```
Which also applies lowercase and remove punctuations.
This doesn't happen when I use the default parameter (`lower_and_strip_punctuation`). The vocabulary is lowered and the punctuations are removed properly:
```
['', '[UNK]', 'the', 'is', 'service', 'not', 'good', '🤩', 'your', 'you', 'soso', 'so', 'rude', 'provided', 'i', 'guy', 'especially', 'either', 'customer', 'contacted', 'company', 'but', 'best', 'bad', 'at', 'all']
```
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import keras
def preproc_for_model(value: str, stemmer = None, stop_words = None):
# Remove punctuations (and emoticons)
value = re.sub(f'[{string.punctuation}]', ' ', value)
# Lowercase, but ignore unicode characters
value = value.casefold()
# Remove 1-character word
value = re.sub(r'\b[a-zA-Z]\b', '', value)
# Replace multiple spaces with a single space
value = re.sub(r'\s+', ' ', value)
if stemmer or stop_words:
# Split sentence as words (tokenization)
word_token = value.split()
if stop_words:
# Remove stop words (must be prioritized before stemmer)
word_token = [ word for word in word_token if word not in stop_words ]
if stemmer:
# Stem/lemmatize words
try: word_token = [ stemmer.lemmatize(word) for word in word_token ]
except: word_token = [ stemmer.stem(word) for word in word_token ]
# Join the tokenized words again
# A bit inefficient if we use it with TextVectorization layer
value = ' '.join(word_token)
return value
# Max words/features/vocabulary to save
# Only the most frequent words will be saved
max_vocab = 12000
# Max words for each sentence
max_len = 50
# Encoding to interpret the text (to be passed to the model)
# Some encodings can remove "unique" characters such as emojis (e.g. latin-1)
encoding = 'utf-8'
# Standardization to apply before doing vectorization
# This is a hacky function to automate text preprocessing as part of the model
# There is no need to apply preprocessing separately (unless you use Tokenizer)
def custom_standardization(input_data):
# Skip if the layer is just initialized (no data yet)
if tf.is_symbolic_tensor(input_data): return input_data
# Convert tensor to Numpy's array (for easier manipulation)
# The array will have byte as it's data type
input_data = input_data.numpy()
""" # TODO Apply vectorization
for i in range(len(input_data)):
# Apply preprocessing
# Convect byte to string and vice versa
input_data[i] = bytes(
preproc_for_model(input_data[i].decode('utf-8')),
'utf-8'
) """
__preproc_for_model = np.vectorize(
lambda sentence: bytes(
# Please change to "preproc_all" instead for unseen data
# Since we did sentiment analysis earlier, "preproc_useless_info" is already applied
# So all that's left is to apply "preproc_for_model" (remove punctuations, etc)
preproc_for_model(sentence.decode(encoding)),
encoding
),
cache = True
)
# Appply preprocessing and convert back Numpy's byte array to tensor
input_data = __preproc_for_model(input_data)
input_data = tf.convert_to_tensor(input_data)
return input_data
# https://www.tensorflow.org/api_docs/python/tf/keras/layers/TextVectorization
# https://www.tensorflow.org/guide/keras/preprocessing_layers
vectorization_layer = keras.layers.TextVectorization(
# Low vocabulary may affect accuracy, while high vocabulary can affect complexity & memory usage
# For comparison, the latest VADER (v3.3.2) has 11090 vocabulary (combined with emojis)
# Note that 2 spaces will be reserved to for empty word and OOV (out-of-vocabulary)
max_tokens = max_vocab + 2,
# "lower_and_strip_punctuation", "lower", "strip_punctuation", None, or custom function
# FIXME Currently, the custom function won't change the output (stopwords not removed, etc)
standardize = custom_standardization,
# Use 2 or higher to possibly detect idioms, negation, slang words, etc
# None means no N-grams when splitting words aka 1-gram
ngrams = None,
# Either "int" (index array), "multi_hot" (0/1 array), "count" (count array), or "tf_idf" (ratio array)
# Use "int" to minimize array size, but may be a burden for computation later (e.g. multiplication)
output_mode = 'int',
# Apply post-padding (all zeros) to each array up to max tokens, ignored if using "int" mode
# It's like using the "pad_sequences" function, though there is no pre-padding
# Regardless it's used or not, all arrays will have the same length
pad_to_max_tokens = True,
# Limit array size for each sample, only applies to "int" mode
# Useful to further reduce memory usage and limit garbage words, but can affect accuracy
# If the array size is less than the specified length, post-padding will be applied
output_sequence_length = None,
# Encoding to interpret the text
# Some encodings can remove "unique" characters such as emojis (e.g. latin-1)
encoding = encoding,
# Layer name (optional)
name = 'text_vectorization'
)
text = [
'Your company is the best, especially the service you provided 🤩',
'The customer service is so-so, not good but not bad either',
'The service is NOT GOOD at all, the guy I contacted is so rude!!!'
]
# Experiment with the hyperparameters above, then test the result
vectorization_layer.adapt(text)
print('Vocabulary:', vectorization_layer.get_vocabulary(), '\n')
print('Output vector:', vectorization_layer(text))
```
### Relevant log output
_No response_
|
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I_kwDOArmXAs553y8C
| 62,652 |
Documentation Aesthetics: TensorFlow > Learn > For Mobile & Edge > Android > Generate model interfaces using metadata - incomplete rendering of all linked imagery from Android Studio screenshots
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[
"Hi @arnoldmashava, it seems the images have been deleted from our repo, I will check to see if we want to restore them or add new ones. Thanks for your help.",
"Heartfeltly acknowledged, @pkgoogle - taking into account the programmed trajectory of evolution in the iterative builds of `Android Studio` (from `Giraffe | 2022.3.1` to `Iguana | 2023.2.1`), there could be variations that might be in existence, with specific reference to the system `User Interface` and the underlying `APIs` that power the interaction with `TFLite`. With such a possibility in mind, the much safer option (according to the Rasta's opinion) would be the generation of new exhibitory imagery from the latest release of `Android Studio` into which new features were incorporated in the system build, i.e. `Hedgehog | 2023.1.1`.\r\n\r\nThank you so much, to you and the rest of the `TensorFlow` Team. And to all the Contributors, that give away parts of their Souls and Spirits for the Mystique that is `TFLite`, herewith some Haze and Computational Levitation:\r\n\r\n```Dart\r\n\r\n>_ sudo ¡Muchas Gracias! --verbose\r\n\r\n```\r\nPaz y Bendición,\r\n\r\nArnold.",
"Hi @arnoldmashava we removed the broken links/images, https://www.tensorflow.org/lite/inference_with_metadata/codegen#import_a_tensorflow_lite_model_in_android_studio, please review and let us know if there is any other issue.",
"Satisfactory @pkgoogle ",
"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/62652\">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/62652\">No</a>\n"
] | 2023-12-16T08:52:37 | 2024-01-04T11:38:59 | 2024-01-04T11:38:55 |
NONE
| null | null | null |
In the previous and latest versions of the [TFLite Documentation for Android](https://www.tensorflow.org/lite/android), there exists a Section pertaining to the [Importation of a TensorFlow Lite model in Android Studio](https://www.tensorflow.org/lite/inference_with_metadata/codegen#import_a_tensorflow_lite_model_in_android_studio), where the rendering of the procedure's exhibitory screenshots from `Android Studio` in Steps 1, 2 and 4 is incomplete. The respective hyperlinks in each of the intended exhibitory screenshots point to inadmissible `*.png` components:
Step 1: https://www.tensorflow.org/lite/images/android/right_click_menu.png?dcb_=0.5735223111350343
Step 2: https://www.tensorflow.org/lite/images/android/import_dialog.png?dcb_=0.38817372495678715
Step 4: https://www.tensorflow.org/lite/images/android/model_details.png?dcb_=0.16164895516490474
Just for purposes of perpetuating the already existing and discernibly superior quality in the [TFLite Documentation](https://www.tensorflow.org/lite/), I am of the conviction that this minor hyperlinking glitch should not be ignored by the Author(s), for the sake of maximum perfectionism in the [TFLite Documentation](https://www.tensorflow.org/lite/) aesthetics.
In its present form, the resultant visual stimulus emanating from the incomplete rendering of exhibitory screenshots from `Android Studio` is **NOT** salacious and alluring enough for the *Wandering Eye* ...
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I_kwDOArmXAs553ylZ
| 62,651 |
Didn't find op for builtin opcode 'PAD' version '1'
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[
"Hi @Hacktiv8or,\r\n\r\n```\r\n resolver.AddCustom(\"PAD\", tflite::ops::custom::Register_PAD(), 1, 2);\r\n // resolver.AddCustom(\"PAD\", tflite::Register_PADV2(), 1, 1);\r\n```\r\n\r\nCould you please confirm you are getting the same error with PADV2() op instead of PAD().\r\n\r\nThank You\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.",
"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/62651\">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/62651\">No</a>\n"
] | 2023-12-16T08:47:12 | 2024-01-04T01:48:37 | 2024-01-04T01:48:33 |
NONE
| null | null | null |
### Issue type
Others
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.8.0
### Custom code
Yes
### OS platform and distribution
Windows, ESP32-cam
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I wrote a code to read a **.tflite model** file from inbuilt SD Card of **ESP32-CAM** and use it to do detection.
### Error--> Didn't find op for builtin opcode 'PAD' version '1'
### **Serial Monitor-**
```
[ 464][I][esp32-hal-psram.c:96] psramInit(): PSRAM enabled
Didn't find op for builtin opcode 'PAD' version '1'
Failed to get registration from op code d
AllocateTensors() failed
Guru Meditation Error: Core 1 panic'ed (LoadProhibited). Exception was unhandled.
Core 1 register dump:
PC : 0x400d2d5c PS : 0x00060830 A0 : 0x800e94f4 A1 : 0x3ffdaf70
A2 : 0x3ffd6234 A3 : 0x00000000 A4 : 0x3ffc3970 A5 : 0x3ffc3978
A6 : 0x3ffbd720 A7 : 0x80000001 A8 : 0x800d2f8c A9 : 0x3ffdaf30
A10 : 0x00000000 A11 : 0x00000060 A12 : 0x00000060 A13 : 0x00000001
A14 : 0x00011800 A15 : 0x3ffc3978 SAR : 0x0000001e EXCCAUSE: 0x0000001c
EXCVADDR: 0x00000004 LBEG : 0x4008a02d LEND : 0x4008a03d LCOUNT : 0xffffffff
Backtrace: 0x400d2d59:0x3ffdaf70 0x400e94f1:0x3ffdaf90
ELF file SHA256: 95a2e245985e3dfa
Rebooting...
```
### **My Code--**
```
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include <TensorFlowLite_ESP32.h>
#include "main_functions.h"
#include "detection_responder.h"
#include "image_provider.h"
#include "model_settings.h"
// #include "person_detect_model_data.h"
#include"SD_MMC.h"
#include "FS.h"
#include "tensorflow/lite/experimental/micro/kernels/micro_ops.h"
#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
#include "tensorflow/lite/experimental/micro/micro_interpreter.h"
// #include "tensorflow/lite/experimental/micro/micro_mutable_op_resolver.h"
#include "tensorflow/lite/experimental/micro/kernels/all_ops_resolver.h"
#include "tensorflow/lite/schema/schema_generated.h"
#include "tensorflow/lite/version.h"
#include "custom_op.h"
// Globals, used for compatibility with Arduino-style sketches.
namespace {
tflite::ErrorReporter* error_reporter = nullptr;
const tflite::Model* model = nullptr;
tflite::MicroInterpreter* interpreter = nullptr;
TfLiteTensor* input = nullptr;
// An area of memory to use for input, output, and intermediate arrays.
constexpr int kTensorArenaSize = 70 * 1024;
static uint8_t tensor_arena[kTensorArenaSize];
} // namespace
// The name of this function is important for Arduino compatibility.
void setup() {
SD_MMC.begin();
// Set up logging. Google style is to avoid globals or statics because of
// lifetime uncertainty, but since this has a trivial destructor it's okay.
// NOLINTNEXTLINE(runtime-global-variables)
static tflite::MicroErrorReporter micro_error_reporter;
error_reporter = µ_error_reporter;
// Map the model into a usable data structure. This doesn't involve any
// copying or parsing, it's a very lightweight operation.
File modelFile = SD_MMC.open("/models/yolov5n-int8.tflite", FILE_READ);
if (!modelFile) { Serial.println("Failed to open model file"); return; }
size_t modelSize = modelFile.size();
uint8_t *modelData = (uint8_t*)malloc(modelSize); // allocates 2.1MB in heap
modelFile.read(modelData, modelSize); // reads the model file
modelFile.close();
model = tflite::GetModel(modelData);
if (model->version() != TFLITE_SCHEMA_VERSION) {
error_reporter->Report(
"Model provided is schema version %d not equal "
"to supported version %d.",
model->version(), TFLITE_SCHEMA_VERSION);
return;
}
// Pull in only the operation implementations we need.
// This relies on a complete list of all the ops needed by this graph.
// An easier approach is to just use the AllOpsResolver, but this will
// incur some penalty in code space for op implementations that are not
// needed by this graph.
//
// tflite::ops::micro::AllOpsResolver resolver;
// NOLINTNEXTLINE(runtime-global-variables)
static tflite::ops::micro::AllOpsResolver resolver;
// static tflite::MicroMutableOpResolver resolver;
// static tflite::MicroMutableOpResolver<10> resolver;
// resolver.AddPad(); // no attr AddPad
// resolver.AddPadV2();
resolver.AddCustom("PAD", tflite::ops::custom::Register_PAD(), 1, 2);
// resolver.AddCustom("PAD", tflite::Register_PADV2(), 1, 1);
// Build an interpreter to run the model with.
static tflite::MicroInterpreter static_interpreter(
model, resolver, tensor_arena, kTensorArenaSize,
error_reporter);
interpreter = &static_interpreter;
// Allocate memory from the tensor_arena for the model's tensors.
TfLiteStatus allocate_status = interpreter->AllocateTensors();
if (allocate_status != kTfLiteOk) {
error_reporter->Report("AllocateTensors() failed");
return;
}
// Get information about the memory area to use for the model's input.
input = interpreter->input(0);
}
// The name of this function is important for Arduino compatibility.
void loop() {
// Get image from provider.
if (kTfLiteOk != GetImage(error_reporter, kNumCols, kNumRows, kNumChannels,
input->data.uint8)) {
error_reporter->Report("Image capture failed.");
}
// Run the model on this input and make sure it succeeds.
if (kTfLiteOk != interpreter->Invoke()) {
error_reporter->Report("Invoke failed.");
}
TfLiteTensor* output = interpreter->output(0);
// Process the inference results.
// error_reporter->Report(String(typeid(output->data.uint8).name()).c_str());
uint8_t person_score = output->data.uint8[kPersonIndex];
uint8_t no_person_score = output->data.uint8[kNotAPersonIndex];
RespondToDetection(error_reporter, person_score, no_person_score);
}
```
### Standalone code to reproduce the issue
```shell
###My Code--
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include <TensorFlowLite_ESP32.h>
#include "main_functions.h"
#include "detection_responder.h"
#include "image_provider.h"
#include "model_settings.h"
// #include "person_detect_model_data.h"
#include"SD_MMC.h"
#include "FS.h"
#include "tensorflow/lite/experimental/micro/kernels/micro_ops.h"
#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
#include "tensorflow/lite/experimental/micro/micro_interpreter.h"
// #include "tensorflow/lite/experimental/micro/micro_mutable_op_resolver.h"
#include "tensorflow/lite/experimental/micro/kernels/all_ops_resolver.h"
#include "tensorflow/lite/schema/schema_generated.h"
#include "tensorflow/lite/version.h"
#include "custom_op.h"
// Globals, used for compatibility with Arduino-style sketches.
namespace {
tflite::ErrorReporter* error_reporter = nullptr;
const tflite::Model* model = nullptr;
tflite::MicroInterpreter* interpreter = nullptr;
TfLiteTensor* input = nullptr;
// An area of memory to use for input, output, and intermediate arrays.
constexpr int kTensorArenaSize = 70 * 1024;
static uint8_t tensor_arena[kTensorArenaSize];
} // namespace
// The name of this function is important for Arduino compatibility.
void setup() {
SD_MMC.begin();
// Set up logging. Google style is to avoid globals or statics because of
// lifetime uncertainty, but since this has a trivial destructor it's okay.
// NOLINTNEXTLINE(runtime-global-variables)
static tflite::MicroErrorReporter micro_error_reporter;
error_reporter = µ_error_reporter;
// Map the model into a usable data structure. This doesn't involve any
// copying or parsing, it's a very lightweight operation.
File modelFile = SD_MMC.open("/models/yolov5n-int8.tflite", FILE_READ);
if (!modelFile) { Serial.println("Failed to open model file"); return; }
size_t modelSize = modelFile.size();
uint8_t *modelData = (uint8_t*)malloc(modelSize); // allocates 2.1MB in heap
modelFile.read(modelData, modelSize); // reads the model file
modelFile.close();
model = tflite::GetModel(modelData);
if (model->version() != TFLITE_SCHEMA_VERSION) {
error_reporter->Report(
"Model provided is schema version %d not equal "
"to supported version %d.",
model->version(), TFLITE_SCHEMA_VERSION);
return;
}
// Pull in only the operation implementations we need.
// This relies on a complete list of all the ops needed by this graph.
// An easier approach is to just use the AllOpsResolver, but this will
// incur some penalty in code space for op implementations that are not
// needed by this graph.
//
// tflite::ops::micro::AllOpsResolver resolver;
// NOLINTNEXTLINE(runtime-global-variables)
static tflite::ops::micro::AllOpsResolver resolver;
// static tflite::MicroMutableOpResolver resolver;
// static tflite::MicroMutableOpResolver<10> resolver;// number of total operators we want to use ... okcan be more than that also ...
// resolver.AddPad(); // no attr AddPad
// resolver.AddPadV2();
resolver.AddCustom("PAD", tflite::ops::custom::Register_PAD(), 1, 2);
// resolver.AddCustom("PAD", tflite::Register_PADV2(), 1, 1);
// Build an interpreter to run the model with.
static tflite::MicroInterpreter static_interpreter(
model, resolver, tensor_arena, kTensorArenaSize,
error_reporter);
interpreter = &static_interpreter;
// Allocate memory from the tensor_arena for the model's tensors.
TfLiteStatus allocate_status = interpreter->AllocateTensors();
if (allocate_status != kTfLiteOk) {
error_reporter->Report("AllocateTensors() failed");
return;
}
// Get information about the memory area to use for the model's input.
input = interpreter->input(0);
}
// The name of this function is important for Arduino compatibility.
void loop() {
// Get image from provider.
if (kTfLiteOk != GetImage(error_reporter, kNumCols, kNumRows, kNumChannels,
input->data.uint8)) {
error_reporter->Report("Image capture failed.");
}
// Run the model on this input and make sure it succeeds.
if (kTfLiteOk != interpreter->Invoke()) {
error_reporter->Report("Invoke failed.");
}
TfLiteTensor* output = interpreter->output(0);
// Process the inference results.
// error_reporter->Report(String(typeid(output->data.uint8).name()).c_str());
uint8_t person_score = output->data.uint8[kPersonIndex];
uint8_t no_person_score = output->data.uint8[kNotAPersonIndex];
RespondToDetection(error_reporter, person_score, no_person_score);
}
```
```
### Relevant log output
```shell
[ 464][I][esp32-hal-psram.c:96] psramInit(): PSRAM enabled
Didn't find op for builtin opcode 'PAD' version '1'
Failed to get registration from op code d
AllocateTensors() failed
Guru Meditation Error: Core 1 panic'ed (LoadProhibited). Exception was unhandled.
Core 1 register dump:
PC : 0x400d2d5c PS : 0x00060830 A0 : 0x800e94f4 A1 : 0x3ffdaf70
A2 : 0x3ffd6234 A3 : 0x00000000 A4 : 0x3ffc3970 A5 : 0x3ffc3978
A6 : 0x3ffbd720 A7 : 0x80000001 A8 : 0x800d2f8c A9 : 0x3ffdaf30
A10 : 0x00000000 A11 : 0x00000060 A12 : 0x00000060 A13 : 0x00000001
A14 : 0x00011800 A15 : 0x3ffc3978 SAR : 0x0000001e EXCCAUSE: 0x0000001c
EXCVADDR: 0x00000004 LBEG : 0x4008a02d LEND : 0x4008a03d LCOUNT : 0xffffffff
Backtrace: 0x400d2d59:0x3ffdaf70 0x400e94f1:0x3ffdaf90
ELF file SHA256: 95a2e245985e3dfa
Rebooting...
```
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PR_kwDOArmXAs5iKNyI
| 62,650 |
Modifications to Debugging Functionality Tests
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**Report on Modifications to Debugging Functionality Tests**
*Objective:*
The aim of the modifications is to extend the existing debugging functionality tests to cover a variety of variable types, specifically tensors with different shapes and dtypes. This ensures that the debugging infrastructure works consistently across diverse data types.
*Modifications:*
1. **Variable and Tensor Declarations:**
- Added new variables and tensors to represent different variable types.
- Introduced variables with different dtypes, including float32, int32, float64, and int64.
- Created tensors with varying shapes, such as scalar, 3x3, and 2x4.
```python
# Variable with float32 dtype
self.v_float32 = variables.Variable(10.0, dtype=dtypes.float32, name="v_float32")
# Variable with int32 dtype
self.v_int32 = variables.Variable(5, dtype=dtypes.int32, name="v_int32")
# Tensor with float64 dtype and shape (3, 3)
self.tensor_float64 = constant_op.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]],
dtype=dtypes.float64, name="tensor_float64")
# Tensor with int64 dtype and shape (2, 4)
self.tensor_int64 = constant_op.constant([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=dtypes.int64, name="tensor_int64")
```
2. **Session Initialization:**
- Extended the session initialization to include the new variables and tensors.
- Initialized variables and tensors before running test operations.
```python
self.sess.run([self.v_float32.initializer, self.v_int32.initializer])
```
3. **Test Methods:**
- Modified the existing test methods (`testWrapperSessionVariableTypes` and `testHookVariableTypes`) to include operations for the new variables and tensors.
- Ensured that each variable type is tested with the debugging wrappers and hooks.
```python
def testWrapperSessionVariableTypes(self):
# ... existing code ...
# Test with a float32 variable
sess.run(self.inc_v_float32)
# Test with an int32 variable
sess.run(self.inc_v_int32)
# Test with a float64 tensor
sess.run(tf.reduce_sum(self.tensor_float64))
# Test with an int64 tensor
sess.run(tf.reduce_sum(self.tensor_int64))
def testHookVariableTypes(self):
# ... existing code ...
# Test with a float32 variable
mon_sess.run(self.inc_v_float32)
# Test with an int32 variable
mon_sess.run(self.inc_v_int32)
# Test with a float64 tensor
mon_sess.run(tf.reduce_sum(self.tensor_float64))
# Test with an int64 tensor
mon_sess.run(tf.reduce_sum(self.tensor_int64))
```
*Conclusion:*
The modifications successfully extended the existing debugging functionality tests to cover a variety of variable types. The inclusion of tensors with different shapes and dtypes ensures a more comprehensive evaluation of the debugging infrastructure's consistency and effectiveness across diverse data scenarios. The modified tests contribute to a more robust and reliable debugging solution, providing confidence in its applicability to various use cases.
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build tflite gpu for android
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[
"@weinixuehao,\r\nCould you please try `bazel clean --expunge` followed by bazel sync and try to run the **bazel build -c opt --config android_arm64** command for the build. 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.",
"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/62649\">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/62649\">No</a>\n"
] | 2023-12-16T07:12:44 | 2024-01-02T01:48:43 | 2024-01-02T01:48:41 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
latest version
### 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?
@s-MacBook-Pro tensorflow % bazel build -c opt --config android_arm64 --copt -Os --copt -DTFLITE_GPU_BINARY_RELEASE --linkopt -s --strip always //tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_delegate.so --verbose_failures
INFO: Reading 'startup' options from /Users//workspace/tensorflow/.bazelrc: --windows_enable_symlinks
INFO: Options provided by the client:
Inherited 'common' options: --isatty=1 --terminal_columns=214
INFO: Reading rc options for 'build' from /Users//workspace/tensorflow/.bazelrc:
Inherited 'common' options: --experimental_repo_remote_exec
INFO: Reading rc options for 'build' from /Users//workspace/tensorflow/.bazelrc:
'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --features=-force_no_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility
INFO: Reading rc options for 'build' from /Users//workspace/tensorflow/.tf_configure.bazelrc:
'build' options: --action_env PYTHON_BIN_PATH=/Users//.pyenv/versions/3.10.4/bin/python3 --action_env PYTHON_LIB_PATH=/Users//.pyenv/versions/3.10.4/lib/python3.10/site-packages --python_path=/Users//.pyenv/versions/3.10.4/bin/python3 --action_env ANDROID_NDK_HOME=/Users//Library/Android/sdk/ndk/25.2.9519653 --action_env ANDROID_NDK_VERSION=25 --action_env ANDROID_NDK_API_LEVEL=26 --action_env ANDROID_BUILD_TOOLS_VERSION=32.0.0 --action_env ANDROID_SDK_API_LEVEL=33 --action_env ANDROID_SDK_HOME=/Users//library/Android/Sdk
INFO: Found applicable config definition build:short_logs in file /Users//workspace/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file /Users//workspace/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:android_arm64 in file /Users//workspace/tensorflow/.bazelrc: --config=android --cpu=arm64-v8a --fat_apk_cpu=arm64-v8a
INFO: Found applicable config definition build:android in file /Users//workspace/tensorflow/.bazelrc: --crosstool_top=//external:android/crosstool --host_crosstool_top=@bazel_tools//tools/cpp:toolchain --dynamic_mode=off --noenable_platform_specific_config --copt=-w --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --define=with_xla_support=false --config=no_tfrt
INFO: Found applicable config definition build:no_tfrt in file /Users//workspace/tensorflow/.bazelrc: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/ir,tensorflow/compiler/mlir/tfrt/ir/mlrt,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ifrt,tensorflow/compiler/mlir/tfrt/tests/mlrt,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/compiler/mlir/tfrt/transforms/mlrt,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/runtime_fallback/test,tensorflow/core/runtime_fallback/test/gpu,tensorflow/core/runtime_fallback/test/saved_model,tensorflow/core/runtime_fallback/test/testdata,tensorflow/core/tfrt/stubs,tensorflow/core/tfrt/tfrt_session,tensorflow/core/tfrt/mlrt,tensorflow/core/tfrt/mlrt/attribute,tensorflow/core/tfrt/mlrt/kernel,tensorflow/core/tfrt/mlrt/bytecode,tensorflow/core/tfrt/mlrt/interpreter,tensorflow/compiler/mlir/tfrt/translate/mlrt,tensorflow/compiler/mlir/tfrt/translate/mlrt/testdata,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils,tensorflow/core/tfrt/utils/debug,tensorflow/core/tfrt/saved_model/python,tensorflow/core/tfrt/graph_executor/python,tensorflow/core/tfrt/saved_model/utils
INFO: Analyzed target //tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_delegate.so (0 packages loaded, 0 targets configured).
INFO: Found 1 target...
ERROR: /Users//workspace/tensorflow/tensorflow/lite/delegates/gpu/BUILD:150:10: Linking tensorflow/lite/delegates/gpu/libtensorflowlite_gpu_delegate.so failed: (Exit 1): clang failed: error executing command (from target //tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_delegate.so)
(cd /private/var/tmp/_bazel_/16bf2a8366a502c280250866d572e3de/execroot/org_tensorflow && \
exec env - \
ANDROID_BUILD_TOOLS_VERSION=32.0.0 \
ANDROID_NDK_API_LEVEL=26 \
ANDROID_NDK_HOME=/Users//Library/Android/sdk/ndk/25.2.9519653 \
ANDROID_NDK_VERSION=25 \
ANDROID_SDK_API_LEVEL=33 \
ANDROID_SDK_HOME=/Users//library/Android/Sdk \
PATH='/Users//Library/Caches/bazelisk/downloads/sha256/672bc71e889658bc6dd1a4433381652771781c572eb49404dea7f44d656a352b/bin:/Users//.nvm/versions/node/v14.19.0/bin:/usr/local/opt/binutils/bin:/Users//.pyenv/shims:/usr/local/opt/node@14/bin:/Users//flutter/bin:/Applications/Blender.app/Contents/MacOS:/usr/local/bin:/Applications/Sublime Text.app/Contents/SharedSupport/bin:/Users//.nvm/versions/node/v14.19.0/bin:/usr/local/opt/binutils/bin:/usr/local/opt/node@14/bin:/Users//flutter/bin:/Applications/Blender.app/Contents/MacOS:/usr/local/bin:/Applications/Sublime Text.app/Contents/SharedSupport/bin:/Users//workspace/tensorflow/.venv/bin:/Users//.nvm/versions/node/v14.19.0/bin:/usr/local/opt/binutils/bin:/usr/local/opt/node@14/bin:/Users//flutter/bin:/Applications/Blender.app/Contents/MacOS:/usr/local/bin:/Applications/Sublime Text.app/Contents/SharedSupport/bin:/opt/homebrew/bin:/opt/homebrew/sbin:/usr/local/bin:/System/Cryptexes/App/usr/bin:/usr/bin:/bin:/usr/sbin:/sbin:/Library/Apple/usr/bin:/Users//.fig/bin:/Users//.local/bin:/Users//Library/Android/sdk/platform-tools:/Users//Library/Android/sdk/build-tools/32.0.0:/Users//Library/Android/sdk/platform-tools:/Users//Library/Android/sdk/build-tools/32.0.0:/Users//Library/Android/sdk/platform-tools:/Users//Library/Android/sdk/build-tools/32.0.0' \
PWD=/proc/self/cwd \
PYTHON_BIN_PATH=/Users//.pyenv/versions/3.10.4/bin/python3 \
PYTHON_LIB_PATH=/Users//.pyenv/versions/3.10.4/lib/python3.10/site-packages \
TF2_BEHAVIOR=1 \
external/androidndk/toolchains/llvm/prebuilt/darwin-x86_64/bin/clang @bazel-out/arm64-v8a-opt/bin/tensorflow/lite/delegates/gpu/libtensorflowlite_gpu_delegate.so-2.params)
# Configuration: fd149282ebc4250a9081ac030532bc1fefed26619b31e69aa038c3c3436db5d6
# Execution platform: @local_execution_config_platform//:platform
ld.lld: error: unknown argument '-framework'
ld.lld: error: cannot open CoreFoundation: No such file or directory
clang: error: linker command failed with exit code 1 (use -v to see invocation)
Target //tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_delegate.so failed to build
INFO: Elapsed time: 0.553s, Critical Path: 0.33s
INFO: 2 processes: 2 internal.
FAILED: Build did NOT complete successfully
### Standalone code to reproduce the issue
```shell
bazel build -c opt --config android_arm64 --copt -Os --copt -DTFLITE_GPU_BINARY_RELEASE --linkopt -s --strip always //tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_delegate.so --verbose_failures
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5iJwze
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[oneDNN]: Added support for fp16 for oneDNN ops
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"@cantonios Thanks for reviewing this PR. I have addressed your review comments. Please take a look. Thanks.",
"@penpornk Thanks for approving this PR! Looks like `feedback/copybara` checks have failed. Can you please share the test failures since I am unable to access it? Thanks."
] | 2023-12-16T00:01:40 | 2024-01-05T22:53:57 | 2024-01-05T22:53:57 |
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This PR adds FP16 support for the following ops: convolution, matmul (mkl-matmul, batch-matmul, fused-matmul), maxpool, batchnorm, layernorm and softmax.
This PR also adds FP16 ISA guards in C++ and Python so that oneDNN related fusions in remapper and node-rewrites in layout pass happen only on platforms with AVX-512 support. Further, BF16 ISA guards in C++ are refactored into a common function that accepts `DT_FLOAT`, `DT_BFLOAT16` and `DT_HALF` inputs. It can be easily extended in the future for new types as well (ex. `float8`).
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[oneDNN] Relaxed constraints for control input/output check in the pattern matcher
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"**Addressing the Corner Case (shown below)**: \r\nWhen the `Const` that is to be fused into `LeakyRelu` has an incoming control dependency. \r\n\r\n- While fusing `LeakyRelu`, the `Const` node is removed as it becomes an attribute of `LeakyRelu` and thus the control dependency will go away, which is incorrect. \r\n- Therefore, we manually add an incoming control edge to `LeakyRelu` in that case to preserve the control dependencies in the graph. \r\n- This is a special case only for `LeakyRelu` as it removes the `Const` node and uses its value as an attribute.\r\n\r\n\r\n\r\nThe following image shows the manually added control edge:\r\n\r\n\r\n",
"Hi @ezhulenev Can you please review this PR ? Thank you!",
"@cantonios I fixed the image link, should be visible now.\r\n\r\nWe initially saw some fusions not happening for GAN model suite (GAN, CGAN, infoGAN, [DRAGAN](https://github.com/kodalinaveen3/DRAGAN), etc.). With this PR, we saw 5-6% improvement for these models on Intel 4th Gen Xeon (SPR) with `bfloat16` precision.",
"@cantonios let me know if any more changes are needed from my end.",
"@cantonios Can you check now to see if it still fails on Windows?"
] | 2023-12-15T22:05:00 | 2024-03-13T15:42:29 | 2024-03-13T15:42:29 |
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One of the steps in the existing approach for checking if a pattern can be fused is to check if there are any control inputs or outputs within the pattern and simply reject the pattern if that is the case. This PR aims to relax that check allowing more patterns to fuse in the remapper.
**THE IDEA**
- If there are input control dependencies to a node in the pattern **AND** the status of the node after the fusion is _Remain_, then the input control dependency can still be an input to the fusion and therefore the fusion must be allowed.
- Similarly, if there are output control dependencies of a node in the pattern **AND** the status of the node after the fusion is NOT _Remove_, then the output control dependency can still be an output of the fusion and therefore the fusion must be allowed.
**EXAMPLE**
In the snapshot of the pattern above, we want to fuse the _Mul-Maximum_ pattern into _LeakyRelu_. The inputs to **Mul** are _Const_ (not in the snapshot) and _Identity_, and the inputs to **Maximum** are _Mul_ and _Identity_.
This PR aims to allow the fusion in the following cases:
- There is an input control dependency on the Identity node, but it should remain unaffected even if the fusion happens.
- Hypothetically, if there were an outgoing control edge from the Maximum node, then it should still allow the fusion to happen and the fused node should have the outgoing control edge.
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PR_kwDOArmXAs5iIus6
| 62,646 |
[oneDNN] Changes to add non-blocked weights
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"Hi @penpornk Can you please review this PR ? Thank you!"
] | 2023-12-15T18:45:01 | 2024-01-23T21:22:30 | 2024-01-23T21:22:29 |
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In case of Saved_Model or non-cached filters (where weights are not frozen with model), Float32 and small batch size:
OneDNN forward_convolution operation can add overhead for converting the weights of convolution form HWIO(height, width, input channels, out channels) into blocked-format.
So for the forward oneDNN conv op, creating the filter memory descriptor with non-blocked format ( corresponding oneDNN format tag hwio) explicitly, will have better execution performance.
Here are some results with experiment I did with TF-hub models ( Ran inference on saved_models with batch size 1):
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TFHub models | Gain on Batch size 1
-- | --
efficientnetv2-b0 | 1.770106493
efficientnetv2-b1 | 1.831099514
efficientnetv2-b2 | 1.620152472
efficientnetv2-b3 | 1.709685313
efficientnet_b0 | 1.688242841
efficientnet_b1 | 1.623995196
efficientnet_b2 | 1.643487455
efficientnet_b3 | 1.599155729
efficientnet_b4 | 1.504763625
efficientnet_b5 | 1.710542764
efficientnet_b6 | 1.718285252
efficientnet_b7 | 1.624044405
inception_v3 | 2.392505641
resnet_v1_50 | 2.401205099
resnet_v1_101 | 2.550063848
resnet_v1_152 | 2.406703722
resnet_v2_50 | 2.10881868
resnet_v2_101 | 2.279171125
resnet_v2_152 | 2.039712633
mobilenet_v2_100_224 | 2.023775482
mobilenet_v2_130_224 | 1.754219532
mobilenet_v2_140_224 | 1.678450062
mobilenet_v3_small_100_224 | 1.95999857
mobilenet_v3_small_075_224 | 2.011820805
mobilenet_v3_large_100_224 | 1.554949273
mobilenet_v3_large_075_224 | 1.698671655
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Currently non-blocked(hwio) format is restricted to batch size 1 as, through experiments, batch size 1 seems to show notable improvement in performance for Saved_Model
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CI build gives a command not found error on /install/install_pip_packages.sh
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"The error message indicates that the Docker build process is failing at the step where it is trying to run the `/install/install_pip_packages.sh` script inside the container. The specific error is:\r\n\r\n```\r\n/install/install_pip_packages.sh: line 21: python3.6: command not found\r\n```\r\n\r\nThis suggests that the Python 3.6 executable is not available in the Docker image at the time this script is being executed.\r\n\r\nTo resolve this issue, you can try the following steps:\r\n\r\n1. **Check Python Version:**\r\n Confirm the available Python version in your Docker image. It seems that Python 3.6 is not installed or is not in the system's PATH.\r\n\r\n2. **Update Install Script:**\r\n Update the `/install/install_pip_packages.sh` script to use the correct Python version available in your Docker image. You might need to replace `python3.6` with the appropriate Python executable name.\r\n\r\n For example, if the available Python executable is `python3`, you can update the script as follows:\r\n\r\n ```bash\r\n # Replace this line in install_pip_packages.sh\r\n python3.6 -m ensurepip\r\n\r\n # With this line\r\n python3 -m ensurepip\r\n ```\r\n\r\n3. **Modify Dockerfile:**\r\n Ensure that the Dockerfile (`Dockerfile.cpu`) specifies the correct version of Python. You may need to install Python 3.6 explicitly if it's not included in the base image.\r\n\r\n Add the following lines to your Dockerfile before the problematic script:\r\n\r\n ```Dockerfile\r\n # Install Python 3.6\r\n RUN apt-get update && apt-get install -y python3.6\r\n ```\r\n\r\n4. **Verify Dockerfile Context:**\r\n Ensure that all the necessary files and dependencies are available in the Docker build context. It's possible that some required files or scripts are missing.\r\n\r\n5. **Review Dependencies:**\r\n Double-check other dependencies and scripts that may affect the Python installation. Sometimes, issues in earlier steps can cascade into problems in later steps.\r\n\r\nAfter making these adjustments, try rebuilding the Docker image and see if the issue is resolved. If you encounter further issues, review the relevant parts of the Dockerfile and installation scripts to identify any missing dependencies or incorrect configurations. @SuryanarayanaY @giuliocn ",
"@AbhisekOmkar, by adding this line to `Dockerfile.cpu`\r\n```\r\n# Install Python 3\r\nRUN apt-get update && apt-get install -y python3.9\r\n```\r\nI got a different python version inside `install_pip_packages.sh`\r\n```\r\n0.975 ERROR: This script does not work on Python 3.5 The minimum supported Python version is 3.7. Please use https://bootstrap.pypa.io/pip/3.5/get-pip.py instead.\r\n```\r\nThe actual version is `Python 3.5.2` \r\n",
"Hi @giuliocn ,\r\n\r\nFor nightly you need to have Python version in the range of 3.9-3.11. You need to install Python in this range.\r\n\r\nFor me it seems even though you are updating the python the python path not set to updated version. Could you try to set the path to newly installed python version. For example for me this [instructions](https://stackoverflow.com/questions/63168301/how-to-change-the-python-version-from-default-3-5-to-3-8-of-google-colab) worked for updating python version in Google colab.",
"@SuryanarayanaY . Leaving other files unchanged, I tried to install a parallel python 3.9 command in multiple ways inside `install_pip_packages.sh`:\r\n```\r\n# Install python3.9\r\napt-get update && apt-get install -y python3.9\r\n```\r\n```\r\n# Install python3.9\r\n#sudo add-apt-repository ppa:deadsnakes/ppa\r\nsudo apt-get update \r\nsudo apt-get install -y python3.9\r\n```\r\nBut when I checked the result, I couldn't find `python3.9` inside `/usr/bin`:\r\n```\r\n# Check parallel python installation\r\nls -la /usr/bin/ | grep python\r\npython3.9 --version\r\n```\r\nHere is the full log of the last attempt:\r\n<details>\r\n```\r\ntensorflow/tools/ci_build/ci_build.sh CPU bazel test //tensorflow/...\r\nWORKSPACE: /workspaces/tensorflow\r\nCI_DOCKER_BUILD_EXTRA_PARAMS: \r\nCI_DOCKER_EXTRA_PARAMS: \r\nCOMMAND: bazel test //tensorflow/...\r\nCI_COMMAND_PREFIX: ./tensorflow/tools/ci_build/builds/with_the_same_user ./tensorflow/tools/ci_build/builds/configured cpu\r\nCONTAINER_TYPE: cpu\r\nBUILD_TAG: tf_ci\r\n (docker container name will be tf_ci.cpu)\r\n\r\nBuilding container (tf_ci.cpu)...\r\n[+] Building 281.4s (11/17) docker:default\r\n => [internal] load .dockerignore 0.1s\r\n => => transferring context: 2B 0.0s\r\n => [internal] load build definition from Dockerfile.cpu 0.1s\r\n => => transferring dockerfile: 654B 0.0s\r\n => [internal] load metadata for docker.io/library/ubuntu:16.04 0.8s\r\n => [auth] library/ubuntu:pull token for registry-1.docker.io 0.0s\r\n => CACHED [ 1/12] FROM docker.io/library/ubuntu:16.04@sha256:1f1a2d56de1d604801a9671f301190704c25d604a416f59e03c04f5c 0.0s\r\n => [internal] load build context 0.1s\r\n => => transferring context: 5.27kB 0.0s\r\n => [ 2/12] COPY install/*.sh /install/ 0.2s\r\n => [ 3/12] RUN /install/install_bootstrap_deb_packages.sh 36.4s\r\n => [ 4/12] RUN add-apt-repository -y ppa:openjdk-r/ppa && add-apt-repository -y ppa:george-edison55/cmake-3.x 1.7s \r\n => [ 5/12] RUN /install/install_deb_packages.sh 204.1s \r\n => ERROR [ 6/12] RUN /install/install_pip_packages.sh 37.6s \r\n------ \r\n > [ 6/12] RUN /install/install_pip_packages.sh: \r\n0.948 Get:1 http://archive.ubuntu.com/ubuntu xenial InRelease [247 kB]\r\n0.949 Get:2 http://ppa.launchpad.net/george-edison55/cmake-3.x/ubuntu xenial InRelease [17.5 kB]\r\n0.968 Get:3 http://ppa.launchpad.net/openjdk-r/ppa/ubuntu xenial InRelease [20.8 kB]\r\n0.998 Get:4 http://archive.ubuntu.com/ubuntu xenial-updates InRelease [99.8 kB]\r\n1.010 Get:5 http://archive.ubuntu.com/ubuntu xenial-backports InRelease [97.4 kB]\r\n1.053 Get:6 http://ppa.launchpad.net/george-edison55/cmake-3.x/ubuntu xenial/main amd64 Packages [2019 B]\r\n1.090 Get:7 http://security.ubuntu.com/ubuntu xenial-security InRelease [99.8 kB]\r\n1.130 Get:8 http://ppa.launchpad.net/openjdk-r/ppa/ubuntu xenial/main amd64 Packages [19.2 kB]\r\n1.225 Get:9 http://archive.ubuntu.com/ubuntu xenial/main amd64 Packages [1558 kB]\r\n1.280 Get:10 http://archive.ubuntu.com/ubuntu xenial/restricted amd64 Packages [14.1 kB]\r\n1.281 Get:11 http://archive.ubuntu.com/ubuntu xenial/universe amd64 Packages [9827 kB]\r\n1.550 Get:12 http://archive.ubuntu.com/ubuntu xenial/multiverse amd64 Packages [176 kB]\r\n1.562 Get:13 http://archive.ubuntu.com/ubuntu xenial-updates/main amd64 Packages [1625 kB]\r\n1.600 Get:14 http://archive.ubuntu.com/ubuntu xenial-updates/restricted amd64 Packages [16.4 kB]\r\n1.601 Get:15 http://archive.ubuntu.com/ubuntu xenial-updates/universe amd64 Packages [1483 kB]\r\n1.620 Get:16 http://security.ubuntu.com/ubuntu xenial-security/main amd64 Packages [1169 kB]\r\n1.627 Get:17 http://archive.ubuntu.com/ubuntu xenial-updates/multiverse amd64 Packages [25.0 kB]\r\n1.645 Get:18 http://archive.ubuntu.com/ubuntu xenial-backports/main amd64 Packages [11.3 kB]\r\n1.655 Get:19 http://archive.ubuntu.com/ubuntu xenial-backports/universe amd64 Packages [12.9 kB]\r\n2.022 Get:20 http://security.ubuntu.com/ubuntu xenial-security/restricted amd64 Packages [15.9 kB]\r\n2.023 Get:21 http://security.ubuntu.com/ubuntu xenial-security/universe amd64 Packages [928 kB]\r\n2.068 Get:22 http://security.ubuntu.com/ubuntu xenial-security/multiverse amd64 Packages [8820 B]\r\n2.524 Fetched 17.5 MB in 1s (11.0 MB/s)\r\n2.524 Reading package lists...\r\n3.167 Reading package lists...\r\n3.847 Building dependency tree...\r\n4.026 Reading state information...\r\n4.186 The following additional packages will be installed:\r\n4.186 cron ifupdown iproute2 isc-dhcp-client isc-dhcp-common libatm1\r\n4.187 libdns-export162 libisc-export160 libmnl0 libpq5 libxslt1.1 libxtables11\r\n4.187 locales logrotate netbase postgresql-9.5 postgresql-client-9.5\r\n4.187 postgresql-client-common postgresql-common postgresql-contrib-9.5 ssl-cert\r\n4.187 sysstat tzdata\r\n4.190 Suggested packages:\r\n4.190 anacron checksecurity exim4 | postfix | mail-transport-agent ppp rdnssd\r\n4.190 iproute2-doc resolvconf avahi-autoipd isc-dhcp-client-ddns apparmor mailx\r\n4.190 locales-all postgresql-doc-9.5 libdbd-pg-perl openssl-blacklist isag\r\n4.190 The following NEW packages will be installed:\r\n4.190 cron ifupdown iproute2 isc-dhcp-client isc-dhcp-common libatm1\r\n4.190 libdns-export162 libisc-export160 libmnl0 libpq5 libxslt1.1 libxtables11\r\n4.190 locales logrotate netbase postgresql-9.5 postgresql-client-9.5\r\n4.190 postgresql-client-common postgresql-common postgresql-contrib-9.5\r\n4.190 postgresql-plpython3-9.5 ssl-cert sysstat tzdata\r\n4.310 0 upgraded, 24 newly installed, 0 to remove and 1 not upgraded.\r\n4.310 Need to get 10.3 MB of archives.\r\n4.310 After this operation, 42.8 MB of additional disk space will be used.\r\n4.310 Get:1 http://archive.ubuntu.com/ubuntu xenial/main amd64 cron amd64 3.0pl1-128ubuntu2 [68.4 kB]\r\n4.336 Get:2 http://archive.ubuntu.com/ubuntu xenial/main amd64 libatm1 amd64 1:2.5.1-1.5 [24.2 kB]\r\n4.338 Get:3 http://archive.ubuntu.com/ubuntu xenial/main amd64 libmnl0 amd64 1.0.3-5 [12.0 kB]\r\n4.340 Get:4 http://archive.ubuntu.com/ubuntu xenial-updates/main amd64 tzdata all 2021a-0ubuntu0.16.04 [167 kB]\r\n4.356 Get:5 http://archive.ubuntu.com/ubuntu xenial-updates/main amd64 iproute2 amd64 4.3.0-1ubuntu3.16.04.5 [523 kB]\r\n4.383 Get:6 http://archive.ubuntu.com/ubuntu xenial-updates/main amd64 ifupdown amd64 0.8.10ubuntu1.4 [54.9 kB]\r\n4.383 Get:7 http://archive.ubuntu.com/ubuntu xenial-updates/main amd64 libisc-export160 amd64 1:9.10.3.dfsg.P4-8ubuntu1.19 [153 kB]\r\n4.383 Get:8 http://archive.ubuntu.com/ubuntu xenial-updates/main amd64 libdns-export162 amd64 1:9.10.3.dfsg.P4-8ubuntu1.19 [665 kB]\r\n4.395 Get:9 http://archive.ubuntu.com/ubuntu xenial-updates/main amd64 isc-dhcp-client amd64 4.3.3-5ubuntu12.10 [224 kB]\r\n4.398 Get:10 http://archive.ubuntu.com/ubuntu xenial-updates/main amd64 isc-dhcp-common amd64 4.3.3-5ubuntu12.10 [105 kB]\r\n4.400 Get:11 http://archive.ubuntu.com/ubuntu xenial/main amd64 libxtables11 amd64 1.6.0-2ubuntu3 [27.2 kB]\r\n4.418 Get:12 http://archive.ubuntu.com/ubuntu xenial-updates/main amd64 locales all 2.23-0ubuntu11.3 [3197 kB]\r\n4.509 Get:13 http://archive.ubuntu.com/ubuntu xenial-updates/main amd64 logrotate amd64 3.8.7-2ubuntu2.16.04.2 [37.7 kB]\r\n4.510 Get:14 http://archive.ubuntu.com/ubuntu xenial/main amd64 netbase all 5.3 [12.9 kB]\r\n4.511 Get:15 http://archive.ubuntu.com/ubuntu xenial-updates/main amd64 libpq5 amd64 9.5.25-0ubuntu0.16.04.1 [79.2 kB]\r\n4.513 Get:16 http://archive.ubuntu.com/ubuntu xenial-updates/main amd64 libxslt1.1 amd64 1.1.28-2.1ubuntu0.3 [146 kB]\r\n4.517 Get:17 http://archive.ubuntu.com/ubuntu xenial-updates/main amd64 postgresql-client-common all 173ubuntu0.3 [28.4 kB]\r\n4.518 Get:18 http://archive.ubuntu.com/ubuntu xenial-updates/main amd64 postgresql-client-9.5 amd64 9.5.25-0ubuntu0.16.04.1 [878 kB]\r\n4.534 Get:19 http://archive.ubuntu.com/ubuntu xenial/main amd64 ssl-cert all 1.0.37 [16.9 kB]\r\n4.534 Get:20 http://archive.ubuntu.com/ubuntu xenial-updates/main amd64 postgresql-common all 173ubuntu0.3 [154 kB]\r\n4.535 Get:21 http://archive.ubuntu.com/ubuntu xenial-updates/main amd64 postgresql-9.5 amd64 9.5.25-0ubuntu0.16.04.1 [3018 kB]\r\n4.579 Get:22 http://archive.ubuntu.com/ubuntu xenial-updates/main amd64 postgresql-contrib-9.5 amd64 9.5.25-0ubuntu0.16.04.1 [451 kB]\r\n4.604 Get:23 http://archive.ubuntu.com/ubuntu xenial-updates/universe amd64 postgresql-plpython3-9.5 amd64 9.5.25-0ubuntu0.16.04.1 [40.6 kB]\r\n4.631 Get:24 http://archive.ubuntu.com/ubuntu xenial-updates/main amd64 sysstat amd64 11.2.0-1ubuntu0.3 [242 kB]\r\n4.894 debconf: delaying package configuration, since apt-utils is not installed\r\n4.971 Fetched 10.3 MB in 0s (27.1 MB/s)\r\n5.170 Selecting previously unselected package cron.\r\n(Reading database ... 21464 files and directories currently installed.)\r\n5.199 Preparing to unpack .../cron_3.0pl1-128ubuntu2_amd64.deb ...\r\n5.234 Unpacking cron (3.0pl1-128ubuntu2) ...\r\n5.551 Selecting previously unselected package libatm1:amd64.\r\n5.553 Preparing to unpack .../libatm1_1%3a2.5.1-1.5_amd64.deb ...\r\n5.587 Unpacking libatm1:amd64 (1:2.5.1-1.5) ...\r\n5.836 Selecting previously unselected package libmnl0:amd64.\r\n5.838 Preparing to unpack .../libmnl0_1.0.3-5_amd64.deb ...\r\n5.872 Unpacking libmnl0:amd64 (1.0.3-5) ...\r\n6.138 Selecting previously unselected package tzdata.\r\n6.141 Preparing to unpack .../tzdata_2021a-0ubuntu0.16.04_all.deb ...\r\n6.182 Unpacking tzdata (2021a-0ubuntu0.16.04) ...\r\n6.710 Selecting previously unselected package iproute2.\r\n6.712 Preparing to unpack .../iproute2_4.3.0-1ubuntu3.16.04.5_amd64.deb ...\r\n6.745 Unpacking iproute2 (4.3.0-1ubuntu3.16.04.5) ...\r\n7.075 Selecting previously unselected package ifupdown.\r\n7.078 Preparing to unpack .../ifupdown_0.8.10ubuntu1.4_amd64.deb ...\r\n7.119 Unpacking ifupdown (0.8.10ubuntu1.4) ...\r\n7.337 Selecting previously unselected package libisc-export160.\r\n7.340 Preparing to unpack .../libisc-export160_1%3a9.10.3.dfsg.P4-8ubuntu1.19_amd64.deb ...\r\n7.412 Unpacking libisc-export160 (1:9.10.3.dfsg.P4-8ubuntu1.19) ...\r\n7.654 Selecting previously unselected package libdns-export162.\r\n7.656 Preparing to unpack .../libdns-export162_1%3a9.10.3.dfsg.P4-8ubuntu1.19_amd64.deb ...\r\n7.693 Unpacking libdns-export162 (1:9.10.3.dfsg.P4-8ubuntu1.19) ...\r\n7.981 Selecting previously unselected package isc-dhcp-client.\r\n7.984 Preparing to unpack .../isc-dhcp-client_4.3.3-5ubuntu12.10_amd64.deb ...\r\n8.017 Unpacking isc-dhcp-client (4.3.3-5ubuntu12.10) ...\r\n8.206 Selecting previously unselected package isc-dhcp-common.\r\n8.209 Preparing to unpack .../isc-dhcp-common_4.3.3-5ubuntu12.10_amd64.deb ...\r\n8.290 Unpacking isc-dhcp-common (4.3.3-5ubuntu12.10) ...\r\n8.532 Selecting previously unselected package libxtables11:amd64.\r\n8.534 Preparing to unpack .../libxtables11_1.6.0-2ubuntu3_amd64.deb ...\r\n8.575 Unpacking libxtables11:amd64 (1.6.0-2ubuntu3) ...\r\n8.877 Selecting previously unselected package locales.\r\n8.880 Preparing to unpack .../locales_2.23-0ubuntu11.3_all.deb ...\r\n8.914 Unpacking locales (2.23-0ubuntu11.3) ...\r\n9.496 Selecting previously unselected package logrotate.\r\n9.498 Preparing to unpack .../logrotate_3.8.7-2ubuntu2.16.04.2_amd64.deb ...\r\n9.534 Unpacking logrotate (3.8.7-2ubuntu2.16.04.2) ...\r\n9.772 Selecting previously unselected package netbase.\r\n9.774 Preparing to unpack .../archives/netbase_5.3_all.deb ...\r\n9.807 Unpacking netbase (5.3) ...\r\n10.06 Selecting previously unselected package libpq5:amd64.\r\n10.06 Preparing to unpack .../libpq5_9.5.25-0ubuntu0.16.04.1_amd64.deb ...\r\n10.11 Unpacking libpq5:amd64 (9.5.25-0ubuntu0.16.04.1) ...\r\n10.38 Selecting previously unselected package libxslt1.1:amd64.\r\n10.38 Preparing to unpack .../libxslt1.1_1.1.28-2.1ubuntu0.3_amd64.deb ...\r\n10.42 Unpacking libxslt1.1:amd64 (1.1.28-2.1ubuntu0.3) ...\r\n10.65 Selecting previously unselected package postgresql-client-common.\r\n10.65 Preparing to unpack .../postgresql-client-common_173ubuntu0.3_all.deb ...\r\n10.68 Unpacking postgresql-client-common (173ubuntu0.3) ...\r\n10.91 Selecting previously unselected package postgresql-client-9.5.\r\n10.91 Preparing to unpack .../postgresql-client-9.5_9.5.25-0ubuntu0.16.04.1_amd64.deb ...\r\n10.94 Unpacking postgresql-client-9.5 (9.5.25-0ubuntu0.16.04.1) ...\r\n11.26 Selecting previously unselected package ssl-cert.\r\n11.27 Preparing to unpack .../ssl-cert_1.0.37_all.deb ...\r\n11.30 Unpacking ssl-cert (1.0.37) ...\r\n11.62 Selecting previously unselected package postgresql-common.\r\n11.62 Preparing to unpack .../postgresql-common_173ubuntu0.3_all.deb ...\r\n11.67 Adding 'diversion of /usr/bin/pg_config to /usr/bin/pg_config.libpq-dev by postgresql-common'\r\n11.70 Unpacking postgresql-common (173ubuntu0.3) ...\r\n12.00 Selecting previously unselected package postgresql-9.5.\r\n12.00 Preparing to unpack .../postgresql-9.5_9.5.25-0ubuntu0.16.04.1_amd64.deb ...\r\n12.04 Unpacking postgresql-9.5 (9.5.25-0ubuntu0.16.04.1) ...\r\n12.53 Selecting previously unselected package postgresql-contrib-9.5.\r\n12.53 Preparing to unpack .../postgresql-contrib-9.5_9.5.25-0ubuntu0.16.04.1_amd64.deb ...\r\n12.57 Unpacking postgresql-contrib-9.5 (9.5.25-0ubuntu0.16.04.1) ...\r\n12.81 Selecting previously unselected package postgresql-plpython3-9.5.\r\n12.82 Preparing to unpack .../postgresql-plpython3-9.5_9.5.25-0ubuntu0.16.04.1_amd64.deb ...\r\n12.85 Unpacking postgresql-plpython3-9.5 (9.5.25-0ubuntu0.16.04.1) ...\r\n13.14 Selecting previously unselected package sysstat.\r\n13.14 Preparing to unpack .../sysstat_11.2.0-1ubuntu0.3_amd64.deb ...\r\n13.18 Unpacking sysstat (11.2.0-1ubuntu0.3) ...\r\n13.33 Processing triggers for systemd (229-4ubuntu21.31) ...\r\n13.39 Processing triggers for libc-bin (2.23-0ubuntu11.3) ...\r\n13.58 Setting up cron (3.0pl1-128ubuntu2) ...\r\n14.06 Adding group `crontab' (GID 107) ...\r\n14.19 Done.\r\n14.47 update-rc.d: warning: start and stop actions are no longer supported; falling back to defaults\r\n14.47 update-rc.d: warning: stop runlevel arguments (1) do not match cron Default-Stop values (none)\r\n14.50 invoke-rc.d: could not determine current runlevel\r\n14.50 invoke-rc.d: policy-rc.d denied execution of start.\r\n14.54 Setting up libatm1:amd64 (1:2.5.1-1.5) ...\r\n14.71 Setting up libmnl0:amd64 (1.0.3-5) ...\r\n14.87 Setting up tzdata (2021a-0ubuntu0.16.04) ...\r\n15.00 debconf: unable to initialize frontend: Dialog\r\n15.00 debconf: (Dialog frontend will not work on a dumb terminal, an emacs shell buffer, or without a controlling terminal.)\r\n15.00 debconf: falling back to frontend: Readline\r\n15.10 \r\n15.10 Current default time zone: 'Etc/UTC'\r\n15.10 Local time is now: Wed Dec 20 16:01:07 UTC 2023.\r\n15.10 Universal Time is now: Wed Dec 20 16:01:07 UTC 2023.\r\n15.10 Run 'dpkg-reconfigure tzdata' if you wish to change it.\r\n15.10 \r\n15.19 Setting up iproute2 (4.3.0-1ubuntu3.16.04.5) ...\r\n15.56 Setting up ifupdown (0.8.10ubuntu1.4) ...\r\n16.00 Creating /etc/network/interfaces.\r\n16.17 Setting up libisc-export160 (1:9.10.3.dfsg.P4-8ubuntu1.19) ...\r\n16.28 Setting up libdns-export162 (1:9.10.3.dfsg.P4-8ubuntu1.19) ...\r\n16.38 Setting up isc-dhcp-client (4.3.3-5ubuntu12.10) ...\r\n16.64 Setting up isc-dhcp-common (4.3.3-5ubuntu12.10) ...\r\n16.74 Setting up libxtables11:amd64 (1.6.0-2ubuntu3) ...\r\n16.85 Setting up locales (2.23-0ubuntu11.3) ...\r\n17.00 debconf: unable to initialize frontend: Dialog\r\n17.00 debconf: (Dialog frontend will not work on a dumb terminal, an emacs shell buffer, or without a controlling terminal.)\r\n17.00 debconf: falling back to frontend: Readline\r\n17.55 Generating locales (this might take a while)...\r\n17.56 Generation complete.\r\n17.66 Setting up logrotate (3.8.7-2ubuntu2.16.04.2) ...\r\n17.83 Setting up netbase (5.3) ...\r\n18.02 Setting up libpq5:amd64 (9.5.25-0ubuntu0.16.04.1) ...\r\n18.12 Setting up libxslt1.1:amd64 (1.1.28-2.1ubuntu0.3) ...\r\n18.22 Setting up postgresql-client-common (173ubuntu0.3) ...\r\n18.35 Setting up postgresql-client-9.5 (9.5.25-0ubuntu0.16.04.1) ...\r\n18.88 update-alternatives: using /usr/share/postgresql/9.5/man/man1/psql.1.gz to provide /usr/share/man/man1/psql.1.gz (psql.1.gz) in auto mode\r\n18.97 Setting up ssl-cert (1.0.37) ...\r\n19.09 debconf: unable to initialize frontend: Dialog\r\n19.09 debconf: (Dialog frontend will not work on a dumb terminal, an emacs shell buffer, or without a controlling terminal.)\r\n19.09 debconf: falling back to frontend: Readline\r\n19.37 Setting up postgresql-common (173ubuntu0.3) ...\r\n19.59 debconf: unable to initialize frontend: Dialog\r\n19.59 debconf: (Dialog frontend will not work on a dumb terminal, an emacs shell buffer, or without a controlling terminal.)\r\n19.59 debconf: falling back to frontend: Readline\r\n20.28 Adding user postgres to group ssl-cert\r\n20.47 \r\n20.47 Creating config file /etc/postgresql-common/createcluster.conf with new version\r\n20.58 \r\n20.58 Creating config file /etc/logrotate.d/postgresql-common with new version\r\n20.65 Building PostgreSQL dictionaries from installed myspell/hunspell packages...\r\n20.65 Removing obsolete dictionary files:\r\n20.77 invoke-rc.d: could not determine current runlevel\r\n20.78 invoke-rc.d: policy-rc.d denied execution of start.\r\n20.87 Setting up postgresql-9.5 (9.5.25-0ubuntu0.16.04.1) ...\r\n21.00 debconf: unable to initialize frontend: Dialog\r\n21.00 debconf: (Dialog frontend will not work on a dumb terminal, an emacs shell buffer, or without a controlling terminal.)\r\n21.00 debconf: falling back to frontend: Readline\r\n21.14 Creating new cluster 9.5/main ...\r\n21.14 config /etc/postgresql/9.5/main\r\n21.14 data /var/lib/postgresql/9.5/main\r\n21.14 locale C\r\n35.76 socket /var/run/postgresql\r\n35.76 port 5432\r\n35.82 update-alternatives: using /usr/share/postgresql/9.5/man/man1/postmaster.1.gz to provide /usr/share/man/man1/postmaster.1.gz (postmaster.1.gz) in auto mode\r\n35.85 invoke-rc.d: could not determine current runlevel\r\n35.86 invoke-rc.d: policy-rc.d denied execution of start.\r\n35.90 Setting up postgresql-contrib-9.5 (9.5.25-0ubuntu0.16.04.1) ...\r\n36.08 Setting up postgresql-plpython3-9.5 (9.5.25-0ubuntu0.16.04.1) ...\r\n36.18 Setting up sysstat (11.2.0-1ubuntu0.3) ...\r\n36.49 debconf: unable to initialize frontend: Dialog\r\n36.49 debconf: (Dialog frontend will not work on a dumb terminal, an emacs shell buffer, or without a controlling terminal.)\r\n36.49 debconf: falling back to frontend: Readline\r\n36.56 \r\n36.56 Creating config file /etc/default/sysstat with new version\r\n36.60 update-alternatives: using /usr/bin/sar.sysstat to provide /usr/bin/sar (sar) in auto mode\r\n36.85 Processing triggers for systemd (229-4ubuntu21.31) ...\r\n36.91 Processing triggers for libc-bin (2.23-0ubuntu11.3) ...\r\n37.18 lrwxrwxrwx 1 root root 26 Aug 16 2019 dh_pypy -> ../share/dh-python/dh_pypy\r\n37.18 -rwxr-xr-x 1 root root 1056 Nov 24 2017 dh_python2\r\n37.18 lrwxrwxrwx 1 root root 29 Aug 16 2019 dh_python3 -> ../share/dh-python/dh_python3\r\n37.18 lrwxrwxrwx 1 root root 23 Mar 1 2021 pdb2.7 -> ../lib/python2.7/pdb.py\r\n37.18 lrwxrwxrwx 1 root root 23 Jan 26 2021 pdb3.5 -> ../lib/python3.5/pdb.py\r\n37.18 lrwxrwxrwx 1 root root 31 Mar 23 2016 py3versions -> ../share/python3/py3versions.py\r\n37.18 lrwxrwxrwx 1 root root 26 Aug 16 2019 pybuild -> ../share/dh-python/pybuild\r\n37.18 lrwxrwxrwx 1 root root 9 Nov 24 2017 python -> python2.7\r\n37.18 lrwxrwxrwx 1 root root 16 Nov 24 2017 python-config -> python2.7-config\r\n37.18 lrwxrwxrwx 1 root root 9 Nov 24 2017 python2 -> python2.7\r\n37.18 lrwxrwxrwx 1 root root 16 Nov 24 2017 python2-config -> python2.7-config\r\n37.18 -rwxr-xr-x 1 root root 3492624 Mar 1 2021 python2.7\r\n37.18 lrwxrwxrwx 1 root root 33 Mar 1 2021 python2.7-config -> x86_64-linux-gnu-python2.7-config\r\n37.18 lrwxrwxrwx 1 root root 9 Mar 23 2016 python3 -> python3.5\r\n37.18 lrwxrwxrwx 1 root root 16 Mar 23 2016 python3-config -> python3.5-config\r\n37.18 -rwxr-xr-x 2 root root 4456208 Jan 26 2021 python3.5\r\n37.18 lrwxrwxrwx 1 root root 33 Jan 26 2021 python3.5-config -> x86_64-linux-gnu-python3.5-config\r\n37.18 -rwxr-xr-x 2 root root 4456208 Jan 26 2021 python3.5m\r\n37.18 lrwxrwxrwx 1 root root 34 Jan 26 2021 python3.5m-config -> x86_64-linux-gnu-python3.5m-config\r\n37.18 lrwxrwxrwx 1 root root 10 Mar 23 2016 python3m -> python3.5m\r\n37.18 lrwxrwxrwx 1 root root 17 Mar 23 2016 python3m-config -> python3.5m-config\r\n37.18 lrwxrwxrwx 1 root root 29 Nov 24 2017 pyversions -> ../share/python/pyversions.py\r\n37.18 lrwxrwxrwx 1 root root 33 Nov 24 2017 x86_64-linux-gnu-python-config -> x86_64-linux-gnu-python2.7-config\r\n37.18 -rwxr-xr-x 1 root root 2909 Mar 1 2021 x86_64-linux-gnu-python2.7-config\r\n37.18 lrwxrwxrwx 1 root root 33 Mar 23 2016 x86_64-linux-gnu-python3-config -> x86_64-linux-gnu-python3.5-config\r\n37.18 lrwxrwxrwx 1 root root 34 Jan 26 2021 x86_64-linux-gnu-python3.5-config -> x86_64-linux-gnu-python3.5m-config\r\n37.18 -rwxr-xr-x 1 root root 3185 Jan 26 2021 x86_64-linux-gnu-python3.5m-config\r\n37.18 lrwxrwxrwx 1 root root 34 Mar 23 2016 x86_64-linux-gnu-python3m-config -> x86_64-linux-gnu-python3.5m-config\r\n37.18 /install/install_pip_packages.sh: line 25: python3.9: command not found\r\n------\r\nDockerfile.cpu:11\r\n--------------------\r\n 9 | add-apt-repository -y ppa:george-edison55/cmake-3.x\r\n 10 | RUN /install/install_deb_packages.sh\r\n 11 | >>> RUN /install/install_pip_packages.sh\r\n 12 | RUN /install/install_bazel.sh\r\n 13 | RUN /install/install_proto3.sh\r\n--------------------\r\nERROR: failed to solve: process \"/bin/sh -c /install/install_pip_packages.sh\" did not complete successfully: exit code: 127\r\nERROR: docker build failed. Dockerfile is at /workspaces/tensorflow/tensorflow/tools/ci_build/Dockerfile.cpu\r\n```\r\n</details>",
"CC: @angerson , Could you have any pointers here!",
"@giuliocn Are you able to resolve this issue? \r\nI am trying to perform the sanity check on a sample file using docker and got the same error:\r\n`ERROR: failed to solve: process \"/bin/sh -c /install/install_pip_packages.sh\" did not complete successfully: exit code: 127`\r\n\r\nI have also added `RUN apt-get update && apt-get install -y python3.9` before the error line in `Dockerfile.cpu` but it's not picking up on this version either",
"@mahrukhS I am not sure yet. Until now, I have tried the following :\r\n\r\n1. Installing `python3.9` before the error line in `Dockerfile.cpu`, which doesn't work because `python3.9` doesn't exist inside `/usr/bin` after installing the package. Instead it finds `python3.5` through a symbolic link, you can check this by running:\r\n```\r\n# Check parallel python installation\r\nls -la /usr/bin/ | grep python\r\npython3 --version\r\n``` \r\n2. Installing `python3.9` before the error line in `install_pip_packages.sh`, which doesn't work because `python3.9` doesn't exist inside `/usr/bin` after installing the package. Instead it finds `python3.5` through a symbolic link, you can check this by running the same snippet.\r\n\r\n3. Changing the script used to install pip to `https://bootstrap.pypa.io/pip/3.5/get-pip.py`. But this approach creates several issues later in the script because `python3.5` is no longer supported.\r\n\r\nIn conclusion, I believe that a parallel python installation would certainly solve the issue. In order to proceed I would like to know if `python3.9` was correctly installed by reading the corresponding system logs. Have you any idea of how I can access them? Bard suggests this command `journalctl` ...\r\n\r\nUPDATE: @SuryanarayanaY I managed to build and install a newer version of python from source, exactly `3.11.7`, by modifying `/install/build_and_install_python.sh`. But, when I run `python3.11 get-pip.py` it raises a SSL error...\r\n```\r\n13.06 Could not fetch URL https://pypi.org/simple/pip/: There was a problem confirming the ssl certificate: HTTPSConnectionPool(host='pypi.org', port=443): Max retries exceeded with url: /simple/pip/ (Caused by SSLError(\"Can't connect to HTTPS URL because the SSL module is not available.\")) - skipping\r\n------\r\nDockerfile.cpu:12\r\n--------------------\r\n 10 | RUN /install/install_deb_packages.sh\r\n 11 | RUN /install/build_and_install_python__cpu_tests.sh 3.11.7\r\n 12 | >>> RUN /install/install_pip_packages.sh\r\n 13 | RUN /install/install_bazel.sh\r\n 14 | RUN /install/install_proto3.sh\r\n--------------------\r\n``` \r\n\r\n",
"Solved issue and opened a draft pull request. @SuryanarayanaY Would you like to review my changes? #62898 ",
"@giuliocn,\r\nCould you please confirm whether the issue is resolved in the latest TensorFlow version and for the PR raised which is draft stage would be cleared and then it will assign the reviewer for the review.\r\n\r\nhttps://github.com/tensorflow/tensorflow/pull/62720\r\n\r\nhttps://github.com/tensorflow/tensorflow/pull/62898\r\n\r\n\r\nThank you!\r\n"
] | 2023-12-15T17:13:09 | 2024-06-12T11:22:23 | null |
CONTRIBUTOR
| null | null | null |
Steps to error:
1. I forked Tensorflow `master` branch
2. WITH a Github workspace I created a new branch for editing.
3. I tried to run CI tests from the `/workspaces/tensorflow/` directory with command:
```
tensorflow/tools/ci_build/ci_build.sh CPU bazel test //tensorflow/...
```
4. This led me to the following error:
<details>
```
/...
WORKSPACE: /workspaces/tensorflow
CI_DOCKER_BUILD_EXTRA_PARAMS:
CI_DOCKER_EXTRA_PARAMS:
COMMAND: bazel test //tensorflow/...
CI_COMMAND_PREFIX: ./tensorflow/tools/ci_build/builds/with_the_same_user ./tensorflow/tools/ci_build/builds/configured cpu
CONTAINER_TYPE: cpu
BUILD_TAG: tf_ci
(docker container name will be tf_ci.cpu)
Building container (tf_ci.cpu)...
[+] Building 1.7s (11/17) docker:default
=> [internal] load .dockerignore 0.1s
=> => transferring context: 2B 0.0s
=> [internal] load build definition from Dockerfile.cpu 0.1s
=> => transferring dockerfile: 654B 0.0s
=> [internal] load metadata for docker.io/library/ubuntu:16.04 0.8s
=> [auth] library/ubuntu:pull token for registry-1.docker.io 0.0s
=> [ 1/12] FROM docker.io/library/ubuntu:16.04@sha256:1f1a2d56de1d604801a9671f301190704c25d604a416f59e03c04f5c6ffee0d 0.0s
=> [internal] load build context 0.1s
=> => transferring context: 1.73kB 0.0s
=> CACHED [ 2/12] COPY install/*.sh /install/ 0.0s
=> CACHED [ 3/12] RUN /install/install_bootstrap_deb_packages.sh 0.0s
=> CACHED [ 4/12] RUN add-apt-repository -y ppa:openjdk-r/ppa && add-apt-repository -y ppa:george-edison55/cmake- 0.0s
=> CACHED [ 5/12] RUN /install/install_deb_packages.sh 0.0s
=> ERROR [ 6/12] RUN /install/install_pip_packages.sh 0.6s
------
> [ 6/12] RUN /install/install_pip_packages.sh:
0.359 --2023-12-15 16:46:42-- https://bootstrap.pypa.io/get-pip.py
0.359 Resolving bootstrap.pypa.io (bootstrap.pypa.io)... 151.101.36.175, 2a04:4e42:9::175
0.383 Connecting to bootstrap.pypa.io (bootstrap.pypa.io)|151.101.36.175|:443... connected.
0.393 HTTP request sent, awaiting response... 200 OK
0.399 Length: 2632263 (2.5M) [text/x-python]
0.399 Saving to: 'get-pip.py'
0.399
0.399 0K .......... .......... .......... .......... .......... 1% 35.2M 0s
[...]
0.430 2500K .......... .......... .......... .......... .......... 99% 120M 0s
0.430 2550K .......... .......... 100% 130M=0.03s
0.430
0.430 2023-12-15 16:46:42 (81.1 MB/s) - 'get-pip.py' saved [2632263/2632263]
0.430
0.432 /install/install_pip_packages.sh: line 21: python3.6: command not found
------
Dockerfile.cpu:11
--------------------
9 | add-apt-repository -y ppa:george-edison55/cmake-3.x
10 | RUN /install/install_deb_packages.sh
11 | >>> RUN /install/install_pip_packages.sh
12 | RUN /install/install_bazel.sh
13 | RUN /install/install_proto3.sh
--------------------
ERROR: failed to solve: process "/bin/sh -c /install/install_pip_packages.sh" did not complete successfully: exit code: 127
ERROR: docker build failed. Dockerfile is at /workspaces/tensorflow/tensorflow/tools/ci_build/Dockerfile.cpu
```
</details>
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| 62,644 |
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"or is it also possible to omit the collect steps function in the training and simply enter the following as observer in the actors: \r\n`replay_observer = [replay_buffer.add_batch]`\r\ndebug has failed here \r\n\r\n\r\nif I try to debug this: \r\n```\r\ninitial_collect_actor = actor.Actor(\r\n collect_env,\r\n random_policy,\r\n train_step,\r\n steps_per_run=initial_collect_steps)\r\n #observers=[replay_observer]) #do I need a replacement?\r\n\r\nprint(f\"Initial Collect Actor\")\r\ninitial_collect_actor.run()\r\nprint(f\"Initial Collect Actor finished\")\r\n\r\n#smaller collect metric, no summary_dir leading to no collect metric both did not make a significant runtime improvement\r\nenv_step_metric = py_metrics.EnvironmentSteps()\r\ncollect_actor = actor.Actor(\r\n collect_env,\r\n collect_policy,\r\n train_step,\r\n steps_per_run=1,\r\n metrics=actor.collect_metrics(10),\r\n summary_dir=os.path.join(tempdir, learner.TRAIN_DIR))\r\n #observers=[replay_observer , env_step_metric])\r\n```\r\nit takes so so long and doesnt make anything ",
"@MarleneBs \r\nIn order to reproduce the issue reported here, could you please provide the complete code and tensorflow version you are using. Thank you!",
"```\r\nimport os\r\nimport tempfile\r\n\r\nimport tensorflow as tf\r\n\r\nfrom tf_agents.agents.ddpg import critic_network\r\nfrom tf_agents.agents.ddpg import actor_network\r\nfrom tf_agents.agents.sac import sac_agent\r\nfrom tf_agents.environments import tf_py_environment\r\nfrom tf_agents.environments import utils\r\nfrom tf_agents.metrics import py_metrics\r\nfrom tf_agents.networks import actor_distribution_network\r\nfrom tf_agents.policies import greedy_policy\r\nfrom tf_agents.policies import py_tf_eager_policy\r\nfrom tf_agents.policies import random_py_policy\r\nfrom tf_agents.policies import PolicySaver\r\nfrom tf_agents.replay_buffers import reverb_replay_buffer\r\nfrom tf_agents.replay_buffers import reverb_utils\r\n\r\nfrom tf_agents.trajectories import trajectory #hinzugefügt\r\n\r\nfrom tf_agents.replay_buffers import tf_uniform_replay_buffer #hinzugefügt\r\nfrom tf_agents.train import actor\r\nfrom tf_agents.train import learner\r\nfrom tf_agents.train import triggers\r\nfrom tf_agents.train.utils import spec_utils\r\nfrom tf_agents.train.utils import strategy_utils\r\nfrom tf_agents.train.utils import train_utils\r\n\r\nimport shutil\r\n\r\nfrom cigre_pv_wind_PPEnv import CigrePPEnv\r\n\r\n\r\n#This file is mainly copied from Tensorflow SAC Minitaur example\r\n#https://www.tensorflow.org/agents/tutorials/7_SAC_minitaur_tutorial\r\n\r\ntempdir = tempfile.gettempdir()\r\n\r\neval_save_dir = os.path.join(tempdir, 'eval')\r\n\r\nif os.path.exists(eval_save_dir):\r\n shutil.rmtree(eval_save_dir)\r\n\r\n\r\n##########################################################\r\n# Hyperparamertes\r\n##########################################################\r\n\r\n\r\nnum_iterations = 1500000 # @param {type:\"integer\"} number of train steps\r\n\r\ninitial_collect_steps = 50000 # @param {type:\"integer\"} number of random steps in the beginning\r\nreplay_buffer_capacity = 1000000 # @param {type:\"integer\"} #Size of Replay Buffer\r\n#smaller replay buffer capacity than num_iterations can lead to big mistakes in later part of the training if there are no more bad experiences in buffer anymore. In this training it is partly avoided by deleting old replays uniformly and not by fifo\r\n\r\nbatch_size = 1000 # @param {type:\"integer\"} #number of fetched steps from replay buffer per training dataset. Can be varied quiet a bit but smaller batch_sizes lead to longer training time\r\n\r\n\r\ncritic_learning_rate = 3e-4 # @param {type:\"number\"} learning rate for critic NN\r\nactor_learning_rate = 3e-4 # @param {type:\"number\"} learning rate for actor NN\r\nalpha_learning_rate = 3e-4 # @param {type:\"number\"} learning rate for alpha factor which regulates entropy which is the regulating factor for exploration/exploitation tradeoff in SAC\r\n#more on SAC here https://spinningup.openai.com/en/latest/algorithms/sac.html\r\n\r\n#haven't changed these factors from the example\r\ntarget_update_tau = 0.005 # @param {type:\"number\"}\r\ntarget_update_period = 1 # @param {type:\"number\"}\r\ngamma = 0.99 # @param {type:\"number\"}\r\n\r\nreward_scale_factor = 1.0 # @param {type:\"number\"} at some point I set the factor to two but since reward is also scaled in env small changes should not matter to much\r\n\r\n#fully connected layer describtion of NN\r\nactor_fc_layer_params = (100, 100)\r\ncritic_joint_fc_layer_params = (100, 100)\r\n\r\nnum_eval_episodes = 1 # @param {type:\"integer\"}\r\ntrain_episodes_per_eval_episode = 2 # @param {type:\"integer\"}\r\n\r\n\r\n##########################################################\r\n# Environment\r\n##########################################################\r\n#env = CigrePPEnv(False, \"\", [1], 0)\r\n#env.reset()\r\n\r\n#print('Observation Spec:')\r\n#print(env.time_step_spec().observation)\r\n#print('Action Spec:')\r\n#print(env.action_spec())\r\n\r\n#utils.validate_py_environment(env, episodes=2)\r\n\r\n\r\n\r\ncollect_env = CigrePPEnv(False, \"collect_env_log.db\",[1,3,5,7,9,11], 1, \"collect\") #to gather experience\r\neval_env = CigrePPEnv(True, \"eval_env_log.db\", [2,4,6,8,10,12], 0, \"evaluation\") #Environment for evaluating the agents\r\n\r\n\r\n\r\n##########################################################\r\n# Distribution Strategy\r\n##########################################################\r\n#strategy describes the hardware usesage which depends on the training platform\r\nuse_gpu = False\r\n\r\nstrategy = strategy_utils.get_strategy(tpu=False, use_gpu=use_gpu)\r\n\r\n\r\n\r\n##########################################################\r\n# Agent\r\n##########################################################\r\nobservation_spec, action_spec, time_step_spec = (\r\n spec_utils.get_tensor_specs(collect_env))\r\n\r\n\r\nwith strategy.scope():\r\n critic_net = critic_network.CriticNetwork(\r\n (observation_spec, action_spec),\r\n observation_fc_layer_params=None,\r\n action_fc_layer_params=None,\r\n joint_fc_layer_params=critic_joint_fc_layer_params,\r\n kernel_initializer='glorot_uniform',\r\n last_kernel_initializer='glorot_uniform')\r\n\r\n\r\n#used ActorNetwork instead of ActorDistributionNetwork to get deterministic results\r\n#set activation layer to None if negative value should be trained although input/output normalization is advised\r\nwith strategy.scope():\r\n actor_net = actor_network.ActorNetwork(\r\n observation_spec,\r\n action_spec,\r\n fc_layer_params=actor_fc_layer_params,\r\n activation_fn=tf.keras.activations.relu)\r\n\r\n\r\nwith strategy.scope():\r\n train_step = train_utils.create_train_step()\r\n\r\n tf_agent = sac_agent.SacAgent(\r\n time_step_spec,\r\n action_spec,\r\n actor_network=actor_net,\r\n critic_network=critic_net,\r\n actor_optimizer=tf.keras.optimizers.Adam(\r\n learning_rate=actor_learning_rate),\r\n critic_optimizer=tf.keras.optimizers.Adam(\r\n learning_rate=critic_learning_rate),\r\n alpha_optimizer=tf.keras.optimizers.Adam(\r\n learning_rate=alpha_learning_rate),\r\n target_update_tau=target_update_tau,\r\n target_update_period=target_update_period,\r\n td_errors_loss_fn=tf.math.squared_difference,\r\n gamma=gamma,\r\n reward_scale_factor=reward_scale_factor,\r\n train_step_counter=train_step)\r\n\r\n tf_agent.initialize()\r\n\r\n\r\n\r\n##########################################################\r\n# Replay Buffer\r\n##########################################################\r\n#not really changed these variables from example, test trainings of different values 2-100 seemed not to make a big difference\r\n'''rate_limiter=reverb.rate_limiters.SampleToInsertRatio(samples_per_insert=3.0, min_size_to_sample=3, error_buffer=3.0)\r\n\r\n#mainly as in example, just changed remover to Uniform to avoid bad results if the buffer starts deleting the first experiences\r\ntable_name = 'uniform_table'\r\ntable = reverb.Table(\r\n table_name,\r\n max_size=replay_buffer_capacity,\r\n sampler=reverb.selectors.Uniform(),\r\n remover=reverb.selectors.Uniform(),\r\n rate_limiter=reverb.rate_limiters.MinSize(1))\r\n\r\nreverb_server = reverb.Server([table])\r\n\r\nreverb_replay = reverb_replay_buffer.ReverbReplayBuffer(\r\n tf_agent.collect_data_spec,\r\n sequence_length=2,\r\n table_name=table_name,\r\n local_server=reverb_server)\r\n\r\n\r\n#preftch(tf.data.AUTOTUNE) did not seem to make a runtime improvement\r\ndataset = reverb_replay.as_dataset(\r\n sample_batch_size=batch_size, num_steps=2).prefetch(1)\r\nexperience_dataset_fn = lambda: dataset\r\n'''\r\n\r\n#create TF Replay Buffer new\r\n#first omit a rate limiter\r\n#no server connection\r\nreplay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(\r\n data_spec=tf_agent.collect_data_spec,\r\n batch_size=1, #The batch size must be taken into account here\r\n max_length=replay_buffer_capacity)\r\n\r\ndataset = replay_buffer.as_dataset(\r\n sample_batch_size=batch_size,\r\n num_steps=2,\r\n single_deterministic_pass=False).prefetch(tf.data.experimental.AUTOTUNE)\r\nexperience_dataset_fn = lambda: dataset\r\n\r\n#iterator = iter(dataset) #new\r\n\r\n\r\n#copied from this https://github.com/tensorflow/agents/blob/master/docs/tutorials/9_c51_tutorial.ipynb\r\n'''def collect_step(environment, policy):\r\n time_step = environment.current_time_step()\r\n action_step = policy.action(time_step)\r\n next_time_step = environment.step(action_step.action)\r\n traj = trajectory.from_transition(time_step, action_step, next_time_step)\r\n\r\n # Add trajectory to the replay buffer\r\n replay_buffer.add_batch(traj)'''\r\n\r\n\r\n##########################################################\r\n# Policies\r\n##########################################################\r\n#eager polcies just accelerate the training\r\ntf_eval_policy = tf_agent.policy\r\neval_policy = py_tf_eager_policy.PyTFEagerPolicy(\r\n tf_eval_policy, use_tf_function=True)\r\n\r\ntf_collect_policy = tf_agent.collect_policy\r\ncollect_policy = py_tf_eager_policy.PyTFEagerPolicy(\r\n tf_collect_policy, use_tf_function=True)\r\n\r\n\r\nrandom_policy = random_py_policy.RandomPyPolicy(\r\n collect_env.time_step_spec(), collect_env.action_spec())\r\n\r\n\r\n\r\n\r\n\r\n##########################################################\r\n# Actors\r\n##########################################################\r\n'''reverb_observer = reverb_utils.ReverbAddTrajectoryObserver(\r\n reverb_replay.py_client,\r\n table_name,\r\n sequence_length=2,\r\n stride_length=1)'''\r\n\r\nreplay_observer = replay_buffer.add_batch\r\n\r\ninitial_collect_actor = actor.Actor(\r\n collect_env,\r\n random_policy,\r\n train_step,\r\n steps_per_run=initial_collect_steps,\r\n observers=[replay_observer]) #do I need a replacement? # habe die klammern entfernt\r\n\r\n\r\n\r\nprint(f\"Initial Collect Actor\")\r\ninitial_collect_actor.run()\r\nprint(f\"Initial Collect Actor finished\")\r\n\r\n#smaller collect metric, no summary_dir leading to no collect metric both did not make a significant runtime improvement\r\nenv_step_metric = py_metrics.EnvironmentSteps()\r\ncollect_actor = actor.Actor(\r\n collect_env,\r\n collect_policy,\r\n train_step,\r\n steps_per_run=1,\r\n metrics=actor.collect_metrics(10),\r\n summary_dir=os.path.join(tempdir, learner.TRAIN_DIR),\r\n observers=[replay_observer,env_step_metric]) #hier könnte noch Problem sein\r\n\r\n\r\neval_actor = actor.Actor(\r\n eval_env,\r\n eval_policy,\r\n train_step,\r\n episodes_per_run=num_eval_episodes,\r\n metrics=actor.eval_metrics(num_eval_episodes),\r\n summary_dir=eval_save_dir,\r\n)\r\n\r\nsaver = PolicySaver(tf_agent.policy)\r\n\r\n##########################################################\r\n#Start initialization of the Raplay buffer, created new\r\n##########################################################\r\n#copied from https://github.com/tensorflow/agents/blob/master/docs/tutorials/9_c51_tutorial.ipynb\r\n\r\n'''for _ in range(initial_collect_steps):\r\n collect_step(collect_env, random_policy) \r\n\r\niterator = iter(dataset)''' #don't need this\r\n\r\n##########################################################\r\n# Learners\r\n##########################################################\r\n\r\nagent_learner = learner.Learner(\r\n tempdir,\r\n train_step,\r\n tf_agent,\r\n experience_dataset_fn, #generated from replay buffer\r\n strategy=strategy)\r\n\r\n\r\n##########################################################\r\n# Metrics and Evaluation\r\n##########################################################\r\ndef get_eval_metrics():\r\n eval_actor.run()\r\n results = {}\r\n for metric in eval_actor.metrics:\r\n results[metric.name] = metric.result()\r\n return results\r\n\r\n#metrics = get_eval_metrics()\r\n\r\n#logs AverageReturn as sum of rewards per Episode\r\ndef log_eval_metrics(step, metrics):\r\n eval_results = (', ').join(\r\n '{} = {:.6f}'.format(name, result) for name, result in metrics.items())\r\n print('step = {0}: {1}'.format(step, eval_results))\r\n with open(\"training_log.txt\", \"a\") as logfile:\r\n logfile.write('step = {0}: {1}\\n'.format(step, eval_results))\r\n\r\n#log_eval_metrics(0, metrics)\r\n\r\n\r\n##########################################################\r\n# Training\r\n##########################################################\r\n#try:\r\n# %%time\r\n#except:\r\n# pass\r\n\r\n# Reset the train step\r\ntf_agent.train_step_counter.assign(0)\r\n\r\n# Evaluate the agent's policy once before training.\r\navg_return = get_eval_metrics()[\"AverageReturn\"]\r\nmax_return = avg_return\r\nprint(f\"Initial max return set to {max_return}\")\r\n\r\n\r\nprint(f\"Training\")\r\n\r\ntrain_episode_counter = 0\r\n\r\naverage_total_loss = 0\r\naverage_actor_loss = 0\r\naverage_critic_loss = 0\r\naverage_alpha_loss = 0\r\nepisode_step_counter = 0\r\n\r\nfor _ in range(num_iterations):\r\n# Training, collect one step, train one step from replay buffer\r\n #collect_step(collect_env, tf_collect_policy, replay_buffer) #new\r\n collect_actor.run()\r\n loss_info = agent_learner.run(iterations=1)\r\n episode_step_counter += 1\r\n \r\n average_total_loss += loss_info.loss.numpy()\r\n average_actor_loss += loss_info.extra.actor_loss.numpy()\r\n average_critic_loss += loss_info.extra.critic_loss.numpy()\r\n average_alpha_loss += loss_info.extra.alpha_loss.numpy()\r\n \r\n #print(f\"actor_loss: {loss_info.extra.actor_loss.numpy()} ; average_critic_loss:{loss_info.extra.critic_loss.numpy()} ; alpha_loss:{loss_info.extra.alpha_loss.numpy()}\")\r\n \r\n #print(loss_info)\r\n\r\n # Evaluating.\r\n step = agent_learner.train_step_numpy\r\n\r\n if collect_env.is_episode_finished():\r\n train_episode_counter += 1\r\n \r\n average_total_loss /= episode_step_counter\r\n average_actor_loss /= episode_step_counter\r\n average_critic_loss /= episode_step_counter\r\n average_alpha_loss /= episode_step_counter\r\n \r\n \r\n print('step = {0}: loss = {1}'.format(step, average_total_loss))\r\n print('average_actor_loss = {0} : average_critic_loss = {1} : average_alpha_loss={2}\\n'.format(average_actor_loss, average_critic_loss, average_alpha_loss))\r\n with open(\"training_log.txt\", \"a\") as logfile:\r\n logfile.write('step = {0}: loss = {1}\\n'.format(step, average_total_loss))\r\n logfile.write('average_actor_loss = {0} : average_critic_loss = {1} : average_alpha_loss={2}\\n'.format(average_actor_loss, average_critic_loss, average_alpha_loss))\r\n \r\n average_total_loss = 0\r\n average_actor_loss = 0\r\n average_critic_loss = 0\r\n average_alpha_loss = 0\r\n episode_step_counter = 0\r\n \r\n if train_episode_counter == train_episodes_per_eval_episode:\r\n print(f\"eval episode at step {step}\")\r\n metrics = get_eval_metrics()\r\n log_eval_metrics(step, metrics)\r\n train_episode_counter = 0\r\n \r\n #export best actor network, prevents overfitting\r\n if (metrics[\"AverageReturn\"] > max_return):\r\n print(f\"New best eval, saving policy\")\r\n max_return = metrics[\"AverageReturn\"]\r\n saver.save('actor_policy')\r\n #saver.save_checkpoint('actor_policy_checkpoint')\r\n\r\n\r\n#rb_observer.close()\r\n#reverb_server.stop()\r\n\r\nshutil.rmtree(eval_save_dir)\r\nshutil.rmtree(tempdir)\r\n\r\n```\r\n\r\n\r\nAnd tensorflow 2.15.0 \r\nThank you very very much!"
] | 2023-12-15T10:46:05 | 2024-01-04T23:39:52 | null |
NONE
| null | null | null |
Have a nice day,
I have two questions. I am currently trying to adapt the code from this example so that it can also run on a Windows PC: https://www.tensorflow.org/agents/tutorials/7_SAC_minitaur_tutorial In this code example, reverb is used, but it only runs on Linux. I have now decided to use the TF uniform buffer as a replacement, is that a good decision, but I haven't got it 100% right yet.
Ich habe den replay Buffer einfach beim Kreieren wie folgt ersetzt:
```
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
data_spec=tf_agent.collect_data_spec,
batch_size=1, #Die Stapelgröße muss hier berücksichtigt werden
max_length=replay_buffer_capacity)
dataset = replay_buffer.as_dataset(
sample_batch_size=batch_size,
num_steps=2,
single_deterministic_pass=False).prefetch(tf.data.experimental.AUTOTUNE)
experience_dataset_fn = lambda: dataset
```
Does that make sense? But I wonder how the dataset is always kept up to date? The experience_dataset_fn is called in the Learner, but where is it kept up to date?
Then I used a collect function from this example: https://github.com/tensorflow/agents/blob/master/docs/tutorials/9_c51_tutorial.ipynb
```
def collect_step(environment, policy):
time_step = environment.current_time_step()
action_step = policy.action(time_step)
next_time_step = environment.step(action_step.action)
traj = trajectory.from_transition(time_step, action_step, next_time_step)
# Add trajectory to the replay buffer
replay_buffer.add_batch(traj)
```
This is called up during training to store experience, isn't it? As this:
```
for _ in range(num_iterations):
# Training, collect one step, train one step from replay buffer
collect_step(collect_env, tf_collect_policy, replay_buffer) #new
collect_actor.run()
loss_info = agent_learner.run(iterations=1)
episode_step_counter += 1
```
But there are definitely two problems left: I do not know how to change the way how the actors are created, i just commented the connection to reverb out and think the interaction between the actor an the replay buffer is ow just made with the collect_step-Function:
```
initial_collect_actor = actor.Actor(
collect_env,
random_policy,
train_step,
steps_per_run=initial_collect_steps)
#observers=[rb_observer ]) #do I need a replacement?
print(f"Initial Collect Actor")
initial_collect_actor.run()
print(f"Initial Collect Actor finished")
#smaller collect metric, no summary_dir leading to no collect metric both did not make a significant runtime improvement
env_step_metric = py_metrics.EnvironmentSteps()
collect_actor = actor.Actor(
collect_env,
collect_policy,
train_step,
steps_per_run=1,
metrics=actor.collect_metrics(10),
summary_dir=os.path.join(tempdir, learner.TRAIN_DIR))
#observers=[rb_observer , env_step_metric])
```
And the learner accesses the dataset as follows and the learner is also called up in the training:
```
agent_learner = learner.Learner(
tempdir,
train_step,
tf_agent,
experience_dataset_fn, #generated from replay buffer
strategy=strategy)
```
I am now wondering whether the dataset that is used for learning within the training process is always the current one and whether my settings of Reverb with TFUniform are now good and sufficient? The problem is also that I should not make any major changes to the basic code. I would be infinitely grateful if someone could take a look at this and point out errors, omissions or something like that or give me a solution to the problem or a tip.
Kind regards
I also used this for creating: https://github.com/jtscs/DRL_volt-var_control/blob/main/Centralized_Training/main.py
|
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I_kwDOArmXAs55yhgq
| 62,643 |
Custom callback log values are not accurately reflected in the training progress bar
|
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[
"gi",
"Hi **@TimKoornstra** ,\r\nI was able to reproduce the issue on Colab using TF v2.15, and TF-nightly. Please find the [gist](https://colab.research.google.com/gist/Venkat6871/29190e14db8cbbbe8311849fb1be9b43/62643_2-15-nightly-v.ipynb) here for reference.\r\n\r\nThank you!"
] | 2023-12-15T09:31:05 | 2024-03-11T17:43:00 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.14.1
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.9, 3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I implemented a custom Learning Rate schedule that inherits from the `tf.keras.optimizers.schedules.LearningRateSchedule` class. I then created a custom callback to add it to the training log. However, the values shown in the progress bar do not seem to match the "actual" values. This goes for the learning rate, but also another changing variable, such as the batch number. I expected this to update correctly, but it does not seem to do so.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1iH6AxXjVXiaWYGyTnBbpEc_FUjjCbLE4?usp=sharing
```
### Relevant log output
```shell
Epoch 1/10
End of batch 0, LR: 9.375000445288606e-06
1/32 ━━━━━━━━━━━━━━━━━━━━ 2:16 4s/step - loss: 0.0615 - lr: 9.3750e-06 - batch: 0.0000e+00
End of batch 1, LR: 1.8750000890577212e-05
End of batch 2, LR: 2.8125001335865818e-05
End of batch 3, LR: 3.7500001781154424e-05
End of batch 4, LR: 4.6875000407453626e-05
End of batch 5, LR: 5.6250002671731636e-05
6/32 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - loss: 0.0786 - lr: 3.2813e-05 - batch: 2.5000
End of batch 6, LR: 6.562500493600965e-05
End of batch 7, LR: 7.500000356230885e-05
End of batch 8, LR: 8.437500218860805e-05
End of batch 9, LR: 9.375000081490725e-05
End of batch 10, LR: 0.00010312500671716407
End of batch 11, LR: 0.00011250000534346327
12/32 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - loss: 0.0834 - lr: 6.0938e-05 - batch: 5.5000
End of batch 12, LR: 0.00012187500396976247
End of batch 13, LR: 0.0001312500098720193
End of batch 14, LR: 0.0001406250084983185
End of batch 15, LR: 0.0001500000071246177
End of batch 16, LR: 0.0001593750057509169
End of batch 17, LR: 0.0001687500043772161
18/32 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - loss: 0.0867 - lr: 8.9063e-05 - batch: 8.5000
End of batch 18, LR: 0.0001781250030035153
End of batch 19, LR: 0.0001875000016298145
End of batch 20, LR: 0.00019687501480802894
End of batch 21, LR: 0.00020625001343432814
End of batch 22, LR: 0.00021562501206062734
23/32 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - loss: 0.0882 - lr: 1.1250e-04 - batch: 11.0000
End of batch 23, LR: 0.00022500001068692654
End of batch 24, LR: 0.00023437500931322575
End of batch 25, LR: 0.00024375000793952495
End of batch 26, LR: 0.00025312500656582415
End of batch 27, LR: 0.0002625000197440386
28/32 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - loss: 0.0890 - lr: 1.3594e-04 - batch: 13.5000
End of batch 28, LR: 0.00027187500381842256
End of batch 29, LR: 0.000281250016996637
End of batch 30, LR: 0.00029062500107102096
End of batch 31, LR: 0.0003000000142492354
32/32 ━━━━━━━━━━━━━━━━━━━━ 5s 11ms/step - loss: 0.0893 - lr: 1.5469e-04 - batch: 15.5000
```
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Performance Discrepancy Between pip install tensorflow and Building from Source
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[
"@vishwascm,\r\nThe difference in code execution time between a TensorFlow installation from **source** and a pre-built binary installed using **pip install tensorflow** can be influenced by several factors. \r\n\r\nWhen you are trying to build TensorFlow from source, there is an option to customize compiler flags and optimizations. If the compilation is not configured optimally for your hardware, it might result in performance. Also the speed of the storage device where TensorFlow is installed can affect the loading and execution times. If the source build is on a slower storage medium compared to the system where the pre-built binary is installed, it can contribute to differences in execution time.\r\n\r\nTo improve the execution time for TensorFlow built from source, consider the following:\r\n\r\n```\r\n- Use appropriate compiler flags for your CPU architecture.\r\n- Check and customize the build configuration to match your requirements.\r\n- Ensure that GPU support is configured correctly if applicable.\r\n- Monitor the build logs for any errors or warnings that might indicate issues.\r\n```\r\n\r\nTensorFlow build from source can sometimes lead to increased code execution time compared to using pip install tensorflow. While building from source offers certain advantages like customization and access to the new features, it can also introduce performance drawbacks if not done correctly. Thank you!",
"Hi @tilakrayal,\r\nIs there any place where I can see the build command along with all configuration used and flags used for official tf v2.15.0 wheel file available in pypi.org for aarch64 machine (graviton) with sve_256?",
"Hi @tilakrayal ,\r\nIs there any place where I can see the build command along with all configuration used and flags used for official tf v2.15.0 wheel file available in pypi.org for aarch64 machine (graviton) with sve_256?",
"@vishwascm,\r\nApologies for the delay.\r\nYou can directly install the tensorflow v2.15 on aarch64 using **pip install tensorflow-aarch64** where the flags are not required. \r\nhttps://pypi.org/project/tensorflow-aarch64/\r\n\r\nIf you are trying to install the tensorflow using a build source, then we will use the flags based upon the requirement.\r\nhttps://www.tensorflow.org/install/source#build_the_package\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.",
"Hi @tilakrayal,\r\nThanks for the reply. I am looking for some more detail about building tensorflow from source, specific to aarch64 architecture with vector length 256 (Graviton) machine, including clang version, ubuntu version, build tags to use aarch64 etc. Just like how official wheel files are built. Are these details available in tensorflow git repository itself? If available how to use it.\r\n\r\nThanks,\r\nVishwas",
"@vishwascm,\r\nCould you please take a look at this official build from the source document where you can find the clang version, flags required to install the tensorflow.\r\nhttps://www.tensorflow.org/install/source\r\n\r\n`sudo apt-get update && sudo apt-get install -y llvm-16 clang-16`\r\n\r\nYou can try to specify the flags for aarch64 as per the requirement to install the tensorflow from build.\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.",
"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/62642\">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/62642\">No</a>\n"
] | 2023-12-15T06:10:36 | 2024-02-22T01:46:45 | 2024-02-22T01:46:42 |
NONE
| null | null | null |
### Issue type
Performance
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf v2.15.0
### Custom code
No
### OS platform and distribution
ubuntu 22.04.1 on aarch64
### Mobile device
_No response_
### Python version
Python 3.11.0
### Bazel version
6.1.0
### GCC/compiler version
clang version 16.0.6
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I have observed a notable difference in the execution time between TensorFlow installed via pip install tensorflow and TensorFlow built from source. The source-built TensorFlow appears to take longer to run hugging face models like resnet50, bert etc, compared to the pip-installed version. The build commands used are given below:
bazel build -s --config=mkl_aarch64 --features=-layering_check --copt=-O3 --copt=-march=armv8-a+sve --copt=-msve-vector-bits=256 --copt=-Wno-gnu-offsetof-extensions --copt=-Wno-unused-but-set-variable --local_cpu_resources=16 //tensorflow/tools/pip_package:build_pip_package --verbose_failures
./bazel-bin/tensorflow/tools/pip_package/build_pip_package /home/vishwas/Graviton/tensorflow
pip install /home/vishwas/Graviton/tensorflow/tensorflow_wheelfile.whl
It will be great if people help me to provide actual build commands used for aarch64 machine with sve_256.
### Standalone code to reproduce the issue
```shell
cd tensorflow
git checkout v2.15.0
export CC="/usr/local/bin/clang"
export CXX="/usr/local/bin/clang++"
export TF_PYTHON_VERSION=3.11
pip install -U pip numpy wheel packaging requests opt_einsum
bazel build -s --config=mkl_aarch64 --features=-layering_check --copt=-O3 --copt=-march=armv8-a+sve --copt=-msve-vector-bits=256 --copt=-Wno-gnu-offsetof-extensions --copt=-Wno-unused-but-set-variable --local_cpu_resources=16 //tensorflow/tools/pip_package:build_pip_package --verbose_failures
./bazel-bin/tensorflow/tools/pip_package/build_pip_package /home/vishwas/Graviton/tensorflow
pip install /home/vishwas/Graviton/tensorflow/tensorflow_wheelfile.whl
```
### Relevant log output
```shell
Following are the timing differences:
Bert Model Inference: 2237.73 ms with build from source and 349.63 ms for pip install
Resnet50: 411.6 ms with build from source and 139.4 ms for pip install
These are average run timings.
No changes made for code built from source.
```
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MatMul transformed to FullyConnected
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[
"Hi @zxros10,\r\n\r\nYou can set the ```converter.experimental_use_mlir_converter``` to FALSE in the converter to control certain operations like \"MatMul, Add, and Relu\" without getting fused. Please try the following.\r\n```\r\nconverter = tf.lite.TFLiteConverter.from_saved_model('convpart2/saved_model/')\r\nconverter.experimental_use_mlir_converter = False # Use this line of code \r\ntflite_model_quant = converter.convert()\r\n\r\n```\r\nIf not resolved please provide a saved model towards issue resolution.\r\n\r\nThank You",
"The saved_model is generated by onnx2tf, the code convert model is https://github.com/PINTO0309/onnx2tf/blob/main/onnx2tf/onnx2tf.py:1249 \r\n tflite_model = converter.convert()\r\nIn this convet, the matmul convert to fullyconnected already. \r\nI add \r\n converter.experimental_use_mlir_converter = False\r\nbefor this line, but unuseless. So I modify tensorflow/lite/python/convert.py：931\r\n enable_mlir_converter = kwargs.get(\"enable_mlir_converter\", True)\r\nto \r\n enable_mlir_converter = False\r\nThen execute onnx2tf, has error:\r\n\r\nsaved_model output started ==========================================================\r\nsaved_model output complete!\r\nTraceback (most recent call last):\r\n File \"/usr/local/bin/onnx2tf\", line 8, in <module>\r\n sys.exit(main())\r\n File \"/usr/local/lib/python3.10/dist-packages/onnx2tf/onnx2tf.py\", line 2321, in main\r\n model = convert(\r\n File \"/usr/local/lib/python3.10/dist-packages/onnx2tf/onnx2tf.py\", line 1247, in convert\r\n tflite_model = converter.convert()\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/lite.py\", line 2185, in convert\r\n return super(TFLiteConverterV2, self).convert()\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/lite.py\", line 1139, in wrapper\r\n return self._convert_and_export_metrics(convert_func, *args, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/lite.py\", line 1093, in _convert_and_export_metrics\r\n result = convert_func(self, *args, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/lite.py\", line 1792, in convert\r\n return super(TFLiteFrozenGraphConverterV2, self).convert(\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/lite.py\", line 1371, in convert\r\n result = _convert_graphdef(\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/convert_phase.py\", line 212, in wrapper\r\n raise converter_error from None # Re-throws the exception.\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/convert_phase.py\", line 205, in wrapper\r\n return func(*args, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/convert.py\", line 985, in convert_graphdef\r\n data = convert(\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/convert.py\", line 368, in convert\r\n return _run_deprecated_conversion_binary(\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/convert_phase.py\", line 212, in wrapper\r\n raise converter_error from None # Re-throws the exception.\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/convert_phase.py\", line 205, in wrapper\r\n return func(*args, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/convert.py\", line 482, in _run_deprecated_conversion_binary\r\n raise ConverterError(\"See console for info.\\n%s\\n%s\\n\" % (stdout, stderr))\r\ntensorflow.lite.python.convert_phase.ConverterError: See console for info.\r\n2023-12-19 13:16:03.380213: F tensorflow/lite/toco/graph_transformations/propagate_fixed_sizes.cc:1677] Check failed: op->start_indices.size() <= num_input_axes (6 vs. 3)StridedSlice op with output \"model_14/tf.strided_slice_1/StridedSlice\", requires no more than 3 start indices\r\nFatal Python error: Aborted\r\n\r\nThere are slice operators in my model:\r\n\r\nBut if I don't modify the convert.py, has no this error. This perhaps a bug, for my slice node start indices parameter is a one dimension tensor, and input feature map is 3-dim.\r\nThen I remove th Slice node from my model, the convert execute successful, but the MatMul still convert to FullyConnected, even I set unfold_batch_matmul to false in onnx2tf.py\r\nso, converter.experimental_use_mlir_converter = False is not work\r\n",
"Hi @pkgoogle,\r\n\r\nPlease look into the issue.\r\n\r\nThank You\r\n\r\n",
"Hi @zxros10, currently there is no way to do the conversion w/o this optimization, why do you say that your device doesn't support fully connected well?",
"> Hi @zxros10, currently there is no way to do the conversion w/o this optimization, why do you say that your device doesn't support fully connected well?\r\n\r\nIn my device, the FullyConnected will bring in reshap、transpose and expanddim operator node, which exhaust time close to FullyConnected compute time. I resolve in other way, so close this"
] | 2023-12-15T05:14:19 | 2023-12-27T03:41:09 | 2023-12-27T03:41:08 |
NONE
| null | null | null |
### 1. System information
ubuntu22.04
tensorflow 2.15
### 2. Code
import tensorflow as tf
import numpy as np
def representative_data_gen():
input_value2 = np.random.normal(size=(1, 40, 20, 2048)).astype(np.float32)
yield {
"input_0": input_value2,
}
converter = tf.lite.TFLiteConverter.from_saved_model('convpart2/saved_model/')
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_data_gen
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_type = tf.int8
tflite_model_quant = converter.convert()
with open("convpart2_int8.tflite", 'wb') as f:
f.write(tflite_model_quant)
### 3. Failure after conversion
In my model, The MatMul or MatMul+Add or MatMul+Add+Relu are fusion and transformed to FullyConnected. My device doesn't support FullyConnected very well. So I want keep the origin operator(MatMul, Add, Relu), how can I stop this fusion optimization ?
Thanks
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The problem with input data after quantifying the model with Int8
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"@panhu Before quantization, could you try to scale your int16 data to a smaller range centered around 0. This will ensure a wider distribution within the limited int8 range, reducing the risk of values becoming 0 or 127 after conversion. You can use techniques like min-max scaling or standard normalization. Please save the scaling factors and apply them in reverse during inference to recover the original data values.\r\n\r\nThank you!",
"Thanks，I have an important question about the convolution calculation method used by tflite. The following is the convolution calculation method I used, but with the same weights and biases, the results obtained are different? Can you help me take a look？\r\n\r\nvoid convolve_HWC_q7_nonsquare(const q7_t *Im_in, // input image\r\n\tconst uint16_t dim_im_in_x, // input image dimention x\r\n\tconst uint16_t dim_im_in_y, // input image dimention y\r\n\tconst uint16_t ch_im_in, // number of input image channels\r\n\tconst q7_t *wt, // kernel weights\r\n\tconst uint16_t ch_im_out, // number of filters, i.e., output image channels\r\n\tconst uint16_t dim_kernel_x, // filter kernel size x\r\n\tconst uint16_t dim_kernel_y, // filter kernel size y\r\n\tconst uint16_t padding_x, // padding sizes x\r\n\tconst uint16_t padding_y, // padding sizes y\r\n\tconst uint16_t stride_x, // stride x\r\n\tconst uint16_t stride_y, // stride y\r\n const uint16_t dilation_x, // dilation x\r\n\tconst uint16_t dilation_y, // dilation y\r\n\tconst q31_t *bias, // bias\r\n\tconst nnom_qformat_param_t *bias_shift, // bias shifts\r\n const nnom_qformat_param_t *out_shift, // output shift\r\n const nnom_qtype_t q_type, // per channel or per tensor\r\n q7_t *Im_out, // output image\r\n\tconst uint16_t dim_im_out_x, // output image dimension x\r\n\tconst uint16_t dim_im_out_y, // output image dimension y\r\n\tq15_t *bufferA, //buffer space for input\r\n\tq7_t *bufferB //buffer space for output\r\n)\r\n{\r\n int i, j, k, l, m, n;\r\n int conv_out;\r\n int in_row, in_col;\r\n int in_pix_loc, wt_loc;\r\n int shift_idx, shift_steps;\r\n if(q_type == NNOM_QTYPE_PER_AXIS)\r\n shift_steps = 1;\r\n else\r\n shift_steps = 0;\r\n\r\n for (i = 0, shift_idx = 0; i < ch_im_out; i++, shift_idx += shift_steps)\r\n {\r\n for (j = 0; j < dim_im_out_y; j++)\r\n {\r\n int32_t base_idx_y = stride_y * j - padding_y;\r\n for (k = 0; k < dim_im_out_x; k++)\r\n {\r\n\t\t\t\tint32_t base_idx_x = stride_x * k - padding_x;\r\n int32_t ker_y_start = MAX(0, -(base_idx_y-(dilation_y-1))/dilation_y);\r\n int32_t ker_x_start = MAX(0, -(base_idx_x-(dilation_x-1))/dilation_x);\r\n int32_t ker_y_end = MIN(dim_kernel_y, (dim_im_in_y - base_idx_y + (dilation_y-1))/dilation_y);\r\n int32_t ker_x_end = MIN(dim_kernel_x, (dim_im_in_x - base_idx_x + (dilation_x-1))/dilation_x);\r\n\r\n if(bias){\r\n // printf(\"2222\\n\\n\");\r\n conv_out = bias[i];//((q31_t)(bias[i]) << bias_shift[shift_idx]) + NNOM_ROUND(out_shift[shift_idx]);\r\n }\r\n else{\r\n printf(\"1111\\n\\n\");\r\n conv_out = (q31_t) NNOM_ROUND(out_shift[shift_idx]);\r\n }\r\n for (m = ker_y_start; m < ker_y_end; m++)\r\n {\r\n for (n = ker_x_start; n < ker_x_end; n++)\r\n {\r\n in_row = stride_y * j + m * dilation_y - padding_y;\r\n in_col = stride_x * k + n * dilation_x - padding_x;\r\n\r\n // pre-calculate the pixel location and weight location to improve the performance.\r\n in_pix_loc = (in_row * dim_im_in_x + in_col) * ch_im_in;\r\n wt_loc = i * ch_im_in * dim_kernel_y * dim_kernel_x + (m * dim_kernel_x + n) * ch_im_in;\r\n \r\n for (l = 0; l < ch_im_in; l++)\r\n { \r\n conv_out += Im_in[in_pix_loc + l] * wt[wt_loc + l];\r\n } \r\n }\r\n }\r\n\r\n Im_out[i + (j * dim_im_out_x + k) * ch_im_out] =conv_out; //(q7_t)conv_out;//(q7_t)__NNOM_SSAT((conv_out >> out_shift[shift_idx]), 8);\r\n }\r\n }\r\n }\r\n}\r\n",
"@panhu Please refer to the official TFLite [documentation](https://www.tensorflow.org/lite/guide/ops_version) for detailed descriptions of its convolution operations .\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."
] | 2023-12-15T04:01:08 | 2024-01-05T01:48:56 | 2024-01-05T01:48:56 |
NONE
| null | null | null |
### 1. System information
- Linux Ubuntu 16.04
- TensorFlow installation (pip package or built from source):
- TensorFlow library
#### Option A: Reference colab notebooks
I would like to ask how to process my model as input after quantifying it with int8 and setting the input-output of tflite to int8, when my input is int16 data and it has undergone rfft. Because if only simple int8 type conversion is used, a large amount of data will become 127 or 0.
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PR_kwDOArmXAs5iDlHW
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Refactor
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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/62639/checks?check_run_id=19662107059) 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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[oneDNN] Add fusion for dilated Conv2D and DepthwiseConv2dNative
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[
"Hi @ezhulenev, Can you please review this PR ? Thank you!",
"Hi @ezhulenev, Can you please review this PR ? Thank you!",
"Hi @yimeisun123 Can you please rebase your branch and resolve the conflicts? Thank you!",
"Hi @ezhulenev, Can you please review this PR ? Thank you!"
] | 2023-12-14T22:51:39 | 2024-06-07T16:39:39 | null |
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Fuse Conv2D or DepthwiseConv2dNative wrapped by SpaceToBatchND and BatchToSpaceND to respective Conv2D or DepthwiseConv2dNative with dilation attribute.
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PR_kwDOArmXAs5iCQK8
| 62,637 |
[NVIDIA] Update the config file for new CUDNN release
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This PR updates the config file to load the updated cudnn headers, preparing for the next release.
cc @reedwm @nluehr
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PR_kwDOArmXAs5h_Pb3
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cmake: Disable the use of mmap on Windows
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Disable the use of `mmap` on Windows, which doesn't support it.
This fixes an issue introduced by 959b6127cffb9afe846824e26d9f5c3af56f1f6c and reported in #62228.
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I_kwDOArmXAs55qjyQ
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Compiling mlir/lib/Dialect/SPIRV/IR/SPIRVDialect.cpp failed
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[
"@yangy996 Please make sure you have all the necessary dependencies installed for building MLIR, including SPIRV libraries and tools. Also verify that your compiler and build tools are up-to-date. Please try to use TF v2.15 which is the latest one and let us know?\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/62635\">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/62635\">No</a>\n"
] | 2023-12-14T08:32:25 | 2023-12-30T01:47:44 | 2023-12-30T01:47:40 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.12
### Custom code
Yes
### OS platform and distribution
loongarch64
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
5.3.1
### GCC/compiler version
8.3.0
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
INFO: Found applicable config definition build:short_logs in file /home/sunway-cloud-dam-py/tensorflow-2.12/.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file /home/sunway-cloud-dam-py/tensorflow-2.12/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:linux in file /home/sunway-cloud-dam-py/tensorflow-2.12/.bazelrc: --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wno-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-array-bounds --copt=-Wunused-result --copt=-Werror=unused-result --copt=-Wswitch --copt=-Werror=switch --copt=-Wno-error=unused-but-set-variable --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --distinct_host_configuration=false --experimental_guard_against_concurrent_changes
INFO: Found applicable config definition build:dynamic_kernels in file /home/sunway-cloud-dam-py/tensorflow-2.12/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS
INFO: Analyzed target //tensorflow/tools/pip_package:build_pip_package (0 packages loaded, 0 targets configured).
INFO: Found 1 target...
ERROR: /root/.cache/bazel/_bazel_root/33674268a488da9eae565916a4e9aec6/external/llvm-project/mlir/BUILD.bazel:5034:11: Compiling mlir/lib/Dialect/SPIRV/IR/SPIRVDialect.cpp failed: (Exit 1): gcc failed: error executing command
(cd /root/.cache/bazel/_bazel_root/33674268a488da9eae565916a4e9aec6/execroot/org_tensorflow && \
exec env - \
PATH=/root/.cargo/bin:/usr/local/python3.8/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/root/bin:/root/bin \
PWD=/proc/self/cwd \
PYTHON_BIN_PATH=/usr/local/python3.8/bin/python3 \
PYTHON_LIB_PATH=/usr/local/python3.8/lib/python3.8/site-packages \
TF2_BEHAVIOR=1 \
/usr/bin/gcc -U_FORTIFY_SOURCE -fstack-protector -Wall -Wunused-but-set-parameter -Wno-free-nonheap-object -fno-omit-frame-pointer -g0 -O2 '-D_FORTIFY_SOURCE=1' -DNDEBUG -ffunction-sections -fdata-sections '-std=c++0x' -MD -MF bazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_objs/SPIRVDialect/SPIRVDialect.pic.d '-frandom-seed=bazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_objs/SPIRVDialect/SPIRVDialect.pic.o' -fPIC '-DLLVM_ON_UNIX=1' '-DHAVE_BACKTRACE=1' '-DBACKTRACE_HEADER=<execinfo.h>' '-DLTDL_SHLIB_EXT=".so"' '-DLLVM_PLUGIN_EXT=".so"' '-DLLVM_ENABLE_THREADS=1' '-DHAVE_DEREGISTER_FRAME=1' '-DHAVE_LIBPTHREAD=1' '-DHAVE_PTHREAD_GETNAME_NP=1' '-DHAVE_PTHREAD_H=1' '-DHAVE_PTHREAD_SETNAME_NP=1' '-DHAVE_REGISTER_FRAME=1' '-DHAVE_SETENV_R=1' '-DHAVE_STRERROR_R=1' '-DHAVE_SYSEXITS_H=1' '-DHAVE_UNISTD_H=1' -D_GNU_SOURCE '-DHAVE_LINK_H=1' '-DHAVE_LSEEK64=1' '-DHAVE_MALLINFO=1' '-DHAVE_SBRK=1' '-DHAVE_STRUCT_STAT_ST_MTIM_TV_NSEC=1' '-DLLVM_NATIVE_ARCH="X86"' 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bazel-out/loongarch64-opt/bin/external/llvm_zlib -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/BuiltinAttributeInterfacesIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/BuiltinAttributesIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/BuiltinDialectIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/BuiltinLocationAttributesIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/BuiltinOpsIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/BuiltinTypeInterfacesIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/BuiltinTypesIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/CallOpInterfacesIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/CastOpInterfacesIncGen 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-Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/InferIntRangeInterfaceIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/VectorInterfacesIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/LoopLikeInterfaceIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/DialectUtilsIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/ViewLikeInterfaceIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/PDLOpsIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/PDLTypesIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/PDLInterpOpsIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/ConversionPassIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/RuntimeVerifiableOpInterfaceIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/TransformsPassIncGen -isystem external/llvm-project/mlir/include -isystem bazel-out/loongarch64-opt/bin/external/llvm-project/mlir/include -isystem external/llvm-project/llvm/include -isystem bazel-out/loongarch64-opt/bin/external/llvm-project/llvm/include -Wno-all -Wno-extra -Wno-deprecated -Wno-deprecated-declarations -Wno-ignored-attributes -Wno-array-bounds -Wunused-result '-Werror=unused-result' -Wswitch '-Werror=switch' '-Wno-error=unused-but-set-variable' -DAUTOLOAD_DYNAMIC_KERNELS '-std=c++17' -fno-canonical-system-headers -Wno-builtin-macro-redefined '-D__DATE__="redacted"' '-D__TIMESTAMP__="redacted"' '-D__TIME__="redacted"' -c external/llvm-project/mlir/lib/Dialect/SPIRV/IR/SPIRVDialect.cpp -o bazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_objs/SPIRVDialect/SPIRVDialect.pic.o)
# Configuration: 3ec2dc0185454e6fd520117e2a54f1a29859947b741479e8f01823640d46a256
# Execution platform: @local_execution_config_platform//:platform
/tmp/ccNy17dT.s: Assembler messages:
/tmp/ccNy17dT.s:247016: Internal error (Bus error).
Please report this bug.
Target //tensorflow/tools/pip_package:build_pip_package failed to build
ERROR: /home/sunway-cloud-dam-py/tensorflow-2.12/tensorflow/lite/python/BUILD:69:10 Middleman _middlemen/tensorflow_Slite_Spython_Stflite_Uconvert-runfiles failed: (Exit 1): gcc failed: error executing command
(cd /root/.cache/bazel/_bazel_root/33674268a488da9eae565916a4e9aec6/execroot/org_tensorflow && \
exec env - \
PATH=/root/.cargo/bin:/usr/local/python3.8/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/root/bin:/root/bin \
PWD=/proc/self/cwd \
PYTHON_BIN_PATH=/usr/local/python3.8/bin/python3 \
PYTHON_LIB_PATH=/usr/local/python3.8/lib/python3.8/site-packages \
TF2_BEHAVIOR=1 \
/usr/bin/gcc -U_FORTIFY_SOURCE -fstack-protector -Wall -Wunused-but-set-parameter -Wno-free-nonheap-object -fno-omit-frame-pointer -g0 -O2 '-D_FORTIFY_SOURCE=1' -DNDEBUG -ffunction-sections -fdata-sections '-std=c++0x' -MD -MF bazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_objs/SPIRVDialect/SPIRVDialect.pic.d '-frandom-seed=bazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_objs/SPIRVDialect/SPIRVDialect.pic.o' -fPIC '-DLLVM_ON_UNIX=1' '-DHAVE_BACKTRACE=1' '-DBACKTRACE_HEADER=<execinfo.h>' '-DLTDL_SHLIB_EXT=".so"' '-DLLVM_PLUGIN_EXT=".so"' '-DLLVM_ENABLE_THREADS=1' '-DHAVE_DEREGISTER_FRAME=1' '-DHAVE_LIBPTHREAD=1' '-DHAVE_PTHREAD_GETNAME_NP=1' '-DHAVE_PTHREAD_H=1' '-DHAVE_PTHREAD_SETNAME_NP=1' '-DHAVE_REGISTER_FRAME=1' '-DHAVE_SETENV_R=1' '-DHAVE_STRERROR_R=1' '-DHAVE_SYSEXITS_H=1' '-DHAVE_UNISTD_H=1' -D_GNU_SOURCE '-DHAVE_LINK_H=1' '-DHAVE_LSEEK64=1' '-DHAVE_MALLINFO=1' '-DHAVE_SBRK=1' '-DHAVE_STRUCT_STAT_ST_MTIM_TV_NSEC=1' '-DLLVM_NATIVE_ARCH="X86"' 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bazel-out/loongarch64-opt/bin/external/llvm_zlib -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/BuiltinAttributeInterfacesIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/BuiltinAttributesIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/BuiltinDialectIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/BuiltinLocationAttributesIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/BuiltinOpsIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/BuiltinTypeInterfacesIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/BuiltinTypesIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/CallOpInterfacesIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/CastOpInterfacesIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/FunctionInterfacesIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/InferTypeOpInterfaceIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/OpAsmInterfaceIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/RegionKindInterfaceIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/SideEffectInterfacesIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/SymbolInterfacesIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/TensorEncodingIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/ControlFlowInterfacesIncGen -Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/AsmParserTokenKinds 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-Ibazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_virtual_includes/TransformsPassIncGen -isystem external/llvm-project/mlir/include -isystem bazel-out/loongarch64-opt/bin/external/llvm-project/mlir/include -isystem external/llvm-project/llvm/include -isystem bazel-out/loongarch64-opt/bin/external/llvm-project/llvm/include -Wno-all -Wno-extra -Wno-deprecated -Wno-deprecated-declarations -Wno-ignored-attributes -Wno-array-bounds -Wunused-result '-Werror=unused-result' -Wswitch '-Werror=switch' '-Wno-error=unused-but-set-variable' -DAUTOLOAD_DYNAMIC_KERNELS '-std=c++17' -fno-canonical-system-headers -Wno-builtin-macro-redefined '-D__DATE__="redacted"' '-D__TIMESTAMP__="redacted"' '-D__TIME__="redacted"' -c external/llvm-project/mlir/lib/Dialect/SPIRV/IR/SPIRVDialect.cpp -o bazel-out/loongarch64-opt/bin/external/llvm-project/mlir/_objs/SPIRVDialect/SPIRVDialect.pic.o)
# Configuration: 3ec2dc0185454e6fd520117e2a54f1a29859947b741479e8f01823640d46a256
# Execution platform: @local_execution_config_platform//:platform
INFO: Elapsed time: 192.173s, Critical Path: 113.91s
INFO: 1029 processes: 325 internal, 704 local.
FAILED: Build did NOT complete successfully
### Standalone code to reproduce the issue
```shell
bazel build --define=tflite_with_xnnpack=false --verbose_failures //tensorflow/tools/pip_package:build_pip_package
```
### Relevant log output
_No response_
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I_kwDOArmXAs55qYTw
| 62,634 |
App crash when update app from playstore because of tenserflow library.
|
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[
"@ChetanAlmelkar,\r\nThe error which you are facing is due to the **tflite** file which you are accessing may not be properly added to the assets folder. Could you please try to add and let us know if it resolves the error.\r\nhttps://github.com/tensorflow/tensorflow/issues/8233.\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/62634\">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/62634\">No</a>\n"
] | 2023-12-14T08:00:38 | 2023-12-30T01:47:46 | 2023-12-30T01:47:42 |
NONE
| null | null | null |
when i updated app from playstore it is going to crash
Fatal Exception: java.lang.NullPointerException
Attempt to read from null array
com.optick_employee_mobile_app.FaceRecognitionPythonModule.findNearest (FaceRecognitionPythonModule.java:571)
com.optick_employee_mobile_app.FaceRecognitionPythonModule.recognizeImage (FaceRecognitionPythonModule.java:508)
com.optick_employee_mobile_app.FaceRecognitionPythonModule$2.onSuccess (FaceRecognitionPythonModule.java:144)
com.optick_employee_mobile_app.FaceRecognitionPythonModule$2.onSuccess (FaceRecognitionPythonModule.java:127)
I found this message from crashlytics. this is happening only after update app from playstore.
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I_kwDOArmXAs55pcZu
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During trainning model with gpu, randomly stucked and got windows bluescreen crash
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[
"The memory and video memory did not overflow, Windows Task Manager shows that the trend of memory and video memory is very stable.(about 60% of the max, I guess? it shows a straight line.)",
"I checked the dump file with WinDbg.\r\nKEY_VALUES_STRING: 1\r\n\r\n Key : Analysis.CPU.mSec\r\n Value: 3015\r\n\r\n Key : Analysis.DebugAnalysisManager\r\n Value: Create\r\n\r\n Key : Analysis.Elapsed.mSec\r\n Value: 124272\r\n\r\n Key : Analysis.Init.CPU.mSec\r\n Value: 406\r\n\r\n Key : Analysis.Init.Elapsed.mSec\r\n Value: 56593\r\n\r\n Key : Analysis.Memory.CommitPeak.Mb\r\n Value: 82\r\n\r\n Key : WER.OS.Branch\r\n Value: vb_release\r\n\r\n Key : WER.OS.Timestamp\r\n Value: 2019-12-06T14:06:00Z\r\n\r\n Key : WER.OS.Version\r\n Value: 10.0.19041.1\r\n\r\n\r\nBUGCHECK_CODE: 133\r\n\r\nBUGCHECK_P1: 1\r\n\r\nBUGCHECK_P2: 1e00\r\n\r\nBUGCHECK_P3: fffff8024c8fb320\r\n\r\nBUGCHECK_P4: 0\r\n\r\nDPC_TIMEOUT_TYPE: DPC_QUEUE_EXECUTION_TIMEOUT_EXCEEDED\r\n\r\nTRAP_FRAME: fffff80251b19310 -- (.trap 0xfffff80251b19310)\r\nNOTE: The trap frame does not contain all registers.\r\nSome register values may be zeroed or incorrect.\r\nrax=0000000000000000 rbx=0000000000000000 rcx=fffff8026cadbab8\r\nrdx=fffff80251b195e0 rsi=0000000000000000 rdi=0000000000000000\r\nrip=fffff8026bfa2b28 rsp=fffff80251b194a8 rbp=fffff80251b195e0\r\n r8=fffff80251b19630 r9=000000000000001f r10=00000000ffffffff\r\nr11=000000000000000c r12=0000000000000000 r13=0000000000000000\r\nr14=0000000000000000 r15=0000000000000000\r\niopl=0 nv up ei ng nz na pe nc\r\nnvlddmkm+0x192b28:\r\nfffff802`6bfa2b28 4053 push rbx\r\nResetting default scope\r\n\r\nCUSTOMER_CRASH_COUNT: 1\r\n\r\nPROCESS_NAME: System\r\n\r\nSTACK_TEXT: \r\nfffff802`51b28e18 fffff802`4c037996 : 00000000`00000133 00000000`00000001 00000000`00001e00 fffff802`4c8fb320 : nt!KeBugCheckEx\r\nfffff802`51b28e20 fffff802`4be539e3 : 0000024f`dd929daf fffff802`490b4180 00000000`00000000 fffff802`490b4180 : nt!KeAccumulateTicks+0x1e1756\r\nfffff802`51b28e80 fffff802`4be534ca : fffff802`4c8f3940 fffff802`51b19390 fffff802`4ab81500 00000000`00008101 : nt!KeClockInterruptNotify+0x453\r\nfffff802`51b28f30 fffff802`4bf00825 : fffff802`4c8f3940 fffff802`51b28f40 00000000`00000010 ffff6fa3`9b8ce98a : nt!HalpTimerClockIpiRoutine+0x1a\r\nfffff802`51b28f60 fffff802`4bfff6da : fffff802`51b19390 fffff802`4c8f3940 fffff802`51b195e0 00000000`00000000 : nt!KiCallInterruptServiceRoutine+0xa5\r\nfffff802`51b28fb0 fffff802`4bfffee7 : 00000000`00000000 fffff802`6cadbac0 fffff802`51b19650 fffff802`6cadbac0 : nt!KiInterruptSubDispatchNoLockNoEtw+0xfa\r\nfffff802`51b19310 fffff802`6bfa2b28 : fffff802`6bebf451 fffff802`51b19630 ffff980c`cd530000 ffffc7ad`1671fa95 : nt!KiInterruptDispatchNoLockNoEtw+0x37\r\nfffff802`51b194a8 fffff802`6bebf451 : fffff802`51b19630 ffff980c`cd530000 ffffc7ad`1671fa95 ffff980c`cd530000 : nvlddmkm+0x192b28\r\nfffff802`51b194b0 fffff802`51b19630 : ffff980c`cd530000 ffffc7ad`1671fa95 ffff980c`cd530000 fffff802`490b4180 : nvlddmkm+0xaf451\r\nfffff802`51b194b8 ffff980c`cd530000 : ffffc7ad`1671fa95 ffff980c`cd530000 fffff802`490b4180 fffff802`6beabf05 : 0xfffff802`51b19630\r\nfffff802`51b194c0 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000000ff`00000100 00000000`00000001 : 0xffff980c`cd430000\r\nfffff802`51b19500 00000000`00000004 : 00000000`000100ee 000000ff`00000100 00000000`00000001 fffff802`4c927a00 : 0xffff980c`cd530000\r\nfffff802`51b19508 00000000`000100ee : 000000ff`00000100 00000000`00000001 fffff802`4c927a00 fffff802`4bf8dea9 : 0x4\r\nfffff802`51b19510 000000ff`00000100 : 00000000`00000001 fffff802`4c927a00 fffff802`4bf8dea9 fffff802`4c927a00 : 0x100ee\r\nfffff802`51b19518 00000000`00000001 : fffff802`4c927a00 fffff802`4bf8dea9 fffff802`4c927a00 00000000`0c5792a5 : 0x000000ff`00000100\r\nfffff802`51b19520 fffff802`4c927a00 : fffff802`4bf8dea9 fffff802`4c927a00 00000000`0c5792a5 00000096`5b8fdfd0 : 0x1\r\nfffff802`51b19528 fffff802`4bf8dea9 : fffff802`4c927a00 00000000`0c5792a5 00000096`5b8fdfd0 00000096`795d44d0 : nt!KiInitialThread\r\nfffff802`51b19530 00000096`d2c573d0 : 00000000`00000000 00000000`00000000 00000000`00000000 00000022`00000000 : nt!HvlGetReferenceTime+0x21\r\nfffff802`51b19560 00000000`00000000 : 00000000`00000000 00000000`00000000 00000022`00000000 00000000`00000000 : 0x00000096`d2c573d0\r\n\r\n\r\nSYMBOL_NAME: nvlddmkm+192b28\r\n\r\nMODULE_NAME: nvlddmkm\r\n\r\nIMAGE_NAME: nvlddmkm.sys\r\n\r\nSTACK_COMMAND: .thread ; .cxr ; kb\r\n\r\nBUCKET_ID_FUNC_OFFSET: 192b28\r\n\r\nFAILURE_BUCKET_ID: 0x133_ISR_nvlddmkm!unknown_function\r\n\r\nOS_VERSION: 10.0.19041.1\r\n\r\nBUILDLAB_STR: vb_release\r\n\r\nOSPLATFORM_TYPE: x64\r\n\r\nOSNAME: Windows 10\r\n\r\nFAILURE_ID_HASH: {f97493a5-ea2b-23ca-a808-8602773c2a86}\r\n\r\nFollowup: MachineOwner",
"Hi @Map1e0823 ,\r\n\r\nIf you are using TF2.10v then supported CUDA and cuDNN are 11.2 and 8.1 respectively. Please refer [source](https://www.tensorflow.org/install/source#gpu).\r\n\r\nIt seems your environment have higher versions and it may raise compatibility errors. Please downgrade them to suitable tested versions.\r\n\r\nThanks!\r\n",
"Hi I just tried lowering the version of CUDA11.2.2 and cuDNN8.1.1 but the program still crashes. ;_;",
"Anyone normally uses A4000 to train tf? ;_;",
"Hi @Map1e0823 ,\r\n\r\nSince the model is migrated from TF1.x which we are not supporting now. I would recommend to Debug the model code using this guide on [migration_debugging](https://www.tensorflow.org/guide/migrate/migration_debugging). ? 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/62633\">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/62633\">No</a>\n"
] | 2023-12-14T05:01:48 | 2024-01-20T01:49:02 | 2024-01-20T01:48:59 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
tf 2.10.1 git_version v2.10.0-76-gfdfc646704c
### Custom code
Yes
### OS platform and distribution
Windows 10 22H2
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
CUDA 11.8.0, cuDNN8.9.6.50
### GPU model and memory
RTXA4000 16G Memory
### Current behavior?
I was trying to train a StegaStamp model, which is coded by TF1. I modified it into the code of TF2(with compat.v1, disable eager excution and disable v2 behavior etc.) and the code can run in CPU mode (tens of thousands of epochs). But when I try to use the GPU for training, the program will get stuck when running for a few hundred or less than 2,000 epochs, and Windows will restart with a blue screen after dozens of seconds.
I also tried downgrade the version of tf cuda cudnn etc. like tf2.7.0 cuda11.6 cudnn8.3.2(Available combinations given on the web). but the program just exits with "Process finished with exit code -1073740791 (0xC0000409)" and didn't raise any exception.
### Standalone code to reproduce the issue
```shell
StegaStamp on Github
```
### Relevant log output
WARNING:tensorflow:From D:\work\project\StegaStamp\venv\lib\site-packages\tensorflow\python\compat\v2_compat.py:107: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
Instructions for updating:
non-resource variables are not supported in the long term
2023-12-14 13:26:10.184254: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-12-14 13:26:11.931143: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 12588 MB memory: -> device: 0, name: NVIDIA RTX A4000, pci bus id: 0000:01:00.0, compute capability: 8.6
WARNING:tensorflow:From D:\work\project\StegaStamp\venv\lib\site-packages\tensorflow\python\util\dispatch.py:1176: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
WARNING:tensorflow:From D:\work\project\StegaStamp\venv\lib\site-packages\tensorflow\python\util\dispatch.py:1176: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
2023-12-14 13:26:13.575786: W tensorflow/core/common_runtime/graph_constructor.cc:1526] Importing a graph with a lower producer version 27 into an existing graph with producer version 1205. Shape inference will have run different parts of the graph with different producer versions.
WARNING:tensorflow:From D:\work\project\StegaStamp\venv\lib\site-packages\tensorflow\python\training\rmsprop.py:188: calling Ones.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
2023-12-14 13:26:16.487836: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:354] MLIR V1 optimization pass is not enabled
2023-12-14 13:26:19.443420: I tensorflow/core/util/cuda_solvers.cc:179] Creating GpuSolver handles for stream 0000020FC8436850
2023-12-14 13:26:21.158282: I tensorflow/stream_executor/cuda/cuda_blas.cc:1614] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.
2023-12-14 13:26:22.260385: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8906
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Deleting this file as it has been overwritten by https://github.com/tensorflow/tensorflow/tree/master/.github/ISSUE_TEMPLATE.
Also, it seems people are confused by it, so deleting it makes more sense. See #62627
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[
"Hello, @hmc-cs-mdrissi!\r\nThank you for the suggestions!\r\nWe need to thoroughly test any modifications to ensure correct functionality and address potential edge cases.\r\nPlease let us know that if you want to modify the logic to include dtypes in the shared_name calculation.\r\nThis ensures initializers with different dtypes get distinct shared names, preventing conflicts.\r\n`Example: shared_name = f\"{vocab_filename}_{key_dtype}_{value_dtype}\"`\r\n\r\nPlease let us know the exact changes you are suggesting for documentations which would be helpful to fix it.\r\nThank you!",
"Yes I think modifying logic to include dtypes in shared names would be a good fix and is enough. I should have time tomorrow and can make a small PR."
] | 2023-12-13T19:51:16 | 2024-01-04T23:39:06 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.15
### 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?
The logic here for TextFileInitializer [sharing](https://github.com/tensorflow/tensorflow/blob/059b23d0dfd34e0d6cdf1f4a65dbc8ed1dfdf54a/tensorflow/python/ops/lookup_ops.py#L790) does not consider key/value dtype. If two text file initializers are made for same vocab file but with different dtypes (tf.int64/tf.string), then sharing will cause one of them to fail and crash.
The fix should be small change to add dtypes to shared_name.
### Standalone code to reproduce the issue
```shell
import os
import tempfile
import tensorflow as tf
from tensorflow.lookup import TextFileIndex
with tempfile.TemporaryDirectory("w") as tmp_dir:
vocab_file = os.path.join(tmp_dir, "vocab.txt")
with open(vocab_file, "w") as f:
f.write("\n".join(["1", "2", "3"]))
initializer1 = tf.lookup.TextFileInitializer(
vocab_file, tf.string, TextFileIndex.WHOLE_LINE, tf.int64, TextFileIndex.LINE_NUMBER
)
table1 = tf.lookup.StaticHashTable(initializer1, -1)
initializer2 = tf.lookup.TextFileInitializer(
vocab_file, tf.int64, TextFileIndex.WHOLE_LINE, tf.int64, TextFileIndex.LINE_NUMBER
)
table2 = tf.lookup.StaticHashTable(initializer2, -1)
_ = table1.lookup(tf.constant(["1", "2", "3"], dtype=tf.string))
_ = table2.lookup(tf.constant([1, 2, 3], dtype=tf.int64))
```
### Relevant log output
```shell
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-12-13 13:50:41.113925: W tensorflow/core/framework/op_kernel.cc:1745] OP_REQUIRES failed at lookup_table_op.h:94 : INVALID_ARGUMENT: Conflicting key/value dtypes int64->int64 with string-int64 for table hash_table_/var/folders/3g/0v44vhyd05q6n_77rl_jnh6r0000gn/T/tmpiimi4gc8w/vocab.txt_-2_-1
Traceback (most recent call last):
File "/Users/pa-loaner/.pyenv/versions/3.9.16/lib/python3.9/pdb.py", line 1726, in main
pdb._runscript(mainpyfile)
File "/Users/pa-loaner/.pyenv/versions/3.9.16/lib/python3.9/pdb.py", line 1586, in _runscript
self.run(statement)
File "/Users/pa-loaner/.pyenv/versions/3.9.16/lib/python3.9/bdb.py", line 580, in run
exec(cmd, globals, locals)
File "<string>", line 1, in <module>
File "/Users/pa-loaner/Snapchat/Dev/training-platform/scratch/text_file_bug.py", line 1, in <module>
import os
File "/Users/pa-loaner/Snapchat/Dev/.venvs/bento/lib/python3.9/site-packages/tensorflow/python/training/tracking/resource.py", line 104, in __call__
return previous_getter(*args, **kwargs)
File "/Users/pa-loaner/Snapchat/Dev/.venvs/bento/lib/python3.9/site-packages/tensorflow/python/training/tracking/resource.py", line 99, in <lambda>
previous_getter = lambda *a, **kw: default_resource_creator(None, *a, **kw)
File "/Users/pa-loaner/Snapchat/Dev/.venvs/bento/lib/python3.9/site-packages/tensorflow/python/training/tracking/resource.py", line 96, in default_resource_creator
obj.__init__(*a, **kw)
File "/Users/pa-loaner/Snapchat/Dev/.venvs/bento/lib/python3.9/site-packages/tensorflow/python/ops/lookup_ops.py", line 347, in __init__
super(StaticHashTable, self).__init__(default_value, initializer)
File "/Users/pa-loaner/Snapchat/Dev/.venvs/bento/lib/python3.9/site-packages/tensorflow/python/ops/lookup_ops.py", line 198, in __init__
self._resource_handle = self._create_resource()
File "/Users/pa-loaner/Snapchat/Dev/.venvs/bento/lib/python3.9/site-packages/tensorflow/python/ops/lookup_ops.py", line 360, in _create_resource
table_ref = gen_lookup_ops.hash_table_v2(
File "/Users/pa-loaner/Snapchat/Dev/.venvs/bento/lib/python3.9/site-packages/tensorflow/python/ops/gen_lookup_ops.py", line 466, in hash_table_v2
_ops.raise_from_not_ok_status(e, name)
File "/Users/pa-loaner/Snapchat/Dev/.venvs/bento/lib/python3.9/site-packages/tensorflow/python/framework/ops.py", line 7164, in raise_from_not_ok_status
raise core._status_to_exception(e) from None # pylint: disable=protected-access
tensorflow.python.framework.errors_impl.InvalidArgumentError: Conflicting key/value dtypes int64->int64 with string-int64 for table hash_table_/var/folders/3g/0v44vhyd05q6n_77rl_jnh6r0000gn/T/tmpiimi4gc8w/vocab.txt_-2_-1 [Op:HashTableV2] name: hash_table
Uncaught exception. Entering post mortem debugging
Running 'cont' or 'step' will restart the program
> /Users/pa-loaner/Snapchat/Dev/.venvs/bento/lib/python3.9/site-packages/tensorflow/python/framework/ops.py(7164)raise_from_not_ok_status()
-> raise core._status_to_exception(e) from None # pylint: disable=protected-access
```
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[
"@jyingl3, Can you help to have a review? thanks",
"> @jyingl3, Can you help to have a review? thanks\r\n\r\nThanks so much for making this change! LGTM. I will be taking time off, so will let other reviewer(s) to take a look as well. ",
"Hi @jzhoulon Can you please check @swachhandl's [comments](https://github.com/tensorflow/tensorflow/pull/62629#discussion_r1436579736) and keep us posted ? 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.",
"@gbaned can you reopen this pr？ I am missing the reopen button. thanks",
"> @gbaned can you reopen this pr？ I am missing the reopen button. thanks\r\n\r\nHi @jzhoulon I have reopened the PR. Thank you!",
"> Could you please add relevant unit tests where possible for the changes added?\r\n\r\nHi @jzhoulon, could you please add unit tests for the changes where possible? Thanks!",
"Hi @jzhoulon Can you please check @swachhandl's [comments](https://github.com/tensorflow/tensorflow/pull/62629#issuecomment-1932539762) and resolve conflicts?. Thank you!",
"> > Could you please add relevant unit tests where possible for the changes added?\r\n> \r\n> Hi @jzhoulon, could you please add unit tests for the changes where possible? Thanks!\r\n\r\nsure, I will take it",
"Hi @jzhoulon Can you please rebase your branch and resolve the 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."
] | 2023-12-13T08:48:19 | 2024-05-25T01:48:59 | 2024-05-25T01:48:50 |
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This PR is enabling PjRtTensorBuffer feature for NextPluggableDevice
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tf.nn.embedding_lookup works fine in CPU mode, but lacks constraint checking in GPU mode
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[
"Hi @EgodPrime ,\r\n\r\nThis is due to the `tf.gather` Op, which is called internally by `embedding_lookup`. `tf.gather` Op never validate indices on GPU and on CPU indices always be validated.Hence on GPU there is no exception as raised by CPU. This is known issue and also documented in the tf.gather doc [here](https://www.tensorflow.org/api_docs/python/tf/gather#:~:text=The%20validate_indices%20argument%20has%20no%20effect.%20Indices%20are%20always%20validated%20on%20CPU%20and%20never%20validated%20on%20GPU.).\r\n\r\nPlease refer to the argument description of validate_indices under args section of [tf.gather](https://www.tensorflow.org/api_docs/python/tf/gather#:~:text=Caution%3A%20On%20CPU%2C%20if%20an%20out%20of%20bound%20index%20is%20found%2C%20an%20error%20is%20raised.%20On%20GPU%2C%20if%20an%20out%20of%20bound%20index%20is%20found%2C%20a%200%20is%20stored%20in%20the%20corresponding%20output%20value.) which is below.\r\n\r\n> Caution: On CPU, if an out of bound index is found, an error is raised. On GPU, if an out of bound index is found, a 0 is stored in the corresponding output value.\r\n\r\nPlease also refer Developer [comment](https://github.com/tensorflow/tensorflow/issues/59724#issuecomment-1466806376) on same.",
"> Hi @EgodPrime ,\r\n> \r\n> This is due to the `tf.gather` Op, which is called internally by `embedding_lookup`. `tf.gather` Op never validate indices on GPU and on CPU indices always be validated.Hence on GPU there is no exception as raised by CPU. This is known issue and also documented in the tf.gather doc [here](https://www.tensorflow.org/api_docs/python/tf/gather#:~:text=The%20validate_indices%20argument%20has%20no%20effect.%20Indices%20are%20always%20validated%20on%20CPU%20and%20never%20validated%20on%20GPU.).\r\n> \r\n> Please refer to the argument description of validate_indices under args section of [tf.gather](https://www.tensorflow.org/api_docs/python/tf/gather#:~:text=Caution%3A%20On%20CPU%2C%20if%20an%20out%20of%20bound%20index%20is%20found%2C%20an%20error%20is%20raised.%20On%20GPU%2C%20if%20an%20out%20of%20bound%20index%20is%20found%2C%20a%200%20is%20stored%20in%20the%20corresponding%20output%20value.) which is below.\r\n> \r\n> > Caution: On CPU, if an out of bound index is found, an error is raised. On GPU, if an out of bound index is found, a 0 is stored in the corresponding output value.\r\n> \r\n> Please also refer Developer [comment](https://github.com/tensorflow/tensorflow/issues/59724#issuecomment-1466806376) on same.\r\n\r\nThanks for your response. I appreciate the points you've mentioned. However, it would be beneficial if you could directly include some explanations about the constraints related to tf.nn.embedding_lookup to enhance clarity :)",
"This is true for most ops that use indicies - it's too expensive to do constraint checking over the input on GPU, so we always just ignore bad indices. It's still an invalid model that will result in undefined behavior.",
"@EgodPrime,\r\nThe related [PR](https://github.com/tensorflow/tensorflow/pull/62734) which was raised for adding the caution not for the current behaviour has been merged and also please take a look at the above comment from the Developer for the same.\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/embedding_ops.py#L310\r\n\r\nhttps://github.com/tensorflow/tensorflow/pull/62734\r\nThank you!"
] | 2023-12-13T03:21:03 | 2024-06-12T10:49:38 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
tf2.14.0
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.8
### GPU model and memory
_No response_
### Current behavior?
When calling tf.nn.embedding_lookup with an input that does not meet the constraints, the input error will be correctly determined in CPU mode, but the calculation will be completed normally in GPU mode and a result of all 0s will be obtained.
```python
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
import tensorflow as tf
import random
import numpy as np
print(f"TensorFlow git version is {tf.version.GIT_VERSION}")
print(f"TensorFlow version is {tf.version.VERSION}")
result={}
random.seed(4399)
np.random.seed(4399)
tf.random.set_seed(4399)
try:
with tf.device('/CPU:0'):
unamed_0 = tf.random.uniform([4, 3], dtype=tf.float32)
unamed_1 = tf.random.uniform([3], minval=0, maxval=256, dtype=tf.int32)
result["CPU_res"] = tf.nn.embedding_lookup(unamed_0, unamed_1, ) # this should results into error, and it does so
except Exception as e:
result["CPU_err"] = str(e)
print(f"CPU input 1 is {unamed_0}")
print(f"CPU inptu 2 is {unamed_1}")
print(f"CPU result is {result}")
result = {}
random.seed(4399)
np.random.seed(4399)
tf.random.set_seed(4399)
try:
with tf.device('/GPU:0'):
unamed_0 = tf.random.uniform([4, 3], dtype=tf.float32)
unamed_1 = tf.random.uniform([3], minval=0, maxval=256, dtype=tf.int32)
result["GPU_res"] = tf.nn.embedding_lookup(unamed_0, unamed_1, ) # this should results into error, but it does not
except Exception as e:
result["GPU_err"] = str(e)
print(f"GPU input 1 is {unamed_0}")
print(f"GPU inptu 2 is {unamed_1}")
print(f"GPU result is {result}")
```
I have also reproduced this bug in colab.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1YqG7jHpMCLjrE8VqQlBSZtz_lRNgT4rU?usp=sharing
```
### Relevant log output
```shell
TensorFlow git version is v2.14.0-rc1-21-g4dacf3f368e
TensorFlow version is 2.14.0
CPU input 1 is [[0.35562682 0.13057125 0.9950352 ]
[0.27489936 0.9225844 0.87167037]
[0.44606853 0.45545673 0.3351872 ]
[0.6091548 0.7782723 0.10762429]]
CPU inptu 2 is [144 161 30]
CPU result is {'CPU_err': '{{function_node __wrapped__GatherV2_device_/job:localhost/replica:0/task:0/device:CPU:0}} indices[0] = 144 is not in [0, 4) [Op:GatherV2] name: '}
GPU input 1 is [[0.35562682 0.13057125 0.9950352 ]
[0.27489936 0.9225844 0.87167037]
[0.44606853 0.45545673 0.3351872 ]
[0.6091548 0.7782723 0.10762429]]
GPU inptu 2 is [144 161 30]
GPU result is {'GPU_res': <tf.Tensor: shape=(3, 3), dtype=float32, numpy=
array([[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]], dtype=float32)>}
```
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Patch/documentation
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[
"I think we would close this as abandoned",
"Hi @treyboo3 Can you please check @mihaimaruseac's comments and keep us posted? Thank you!"
] | 2023-12-12T19:11:44 | 2024-03-07T16:48:48 | 2024-03-07T16:48:42 |
NONE
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Added an improved version of documentation. I was running into an issue while testing which is why the $PWD was bracketed. I made a small change to the ISSUES.md for better guidance. I also tried to be more descriptive in the README.md for beginners looking to understand or get into Tensorflow. Thank you!
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"Build from source" cuDNN documented version is not correct for TF 2.15.0
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[
"Hi @mcourteaux ,\r\n\r\nThanks for reporting the issue. I have cross checked the setyp.py and its mentioned cudnn required for Tf2.15v as `8.9.4` version.\r\nhttps://github.com/tensorflow/tensorflow/blob/0b15fdfcb3fddcb4b8a095f26d0f786e316163d7/tensorflow/tools/pip_package/setup.py#L171\r\n\r\nWe will update it to `8.9` version",
"Hi @mcourteaux ,\r\n\r\nThe proposed fix has been merged and document also updated. Could you please verify and close the issue. 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/62626\">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/62626\">No</a>\n"
] | 2023-12-12T15:59:51 | 2024-01-04T12:38:21 | 2024-01-04T12:38:18 |
NONE
| null | null | null |
### Issue type
Documentation Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.15.0.post1
### Custom code
No
### OS platform and distribution
Ubuntu 22.04
### 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?
https://www.tensorflow.org/install/source#gpu_support_2 reports cudnn version 8.8 is needed. However, after installing that, and running TensorFlow, it complains:
> 2023-12-12 16:48:40.306928: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:447] Loaded runtime CuDNN library: 8.8.0 but source was compiled with: 8.9.4. CuDNN library needs to have matching major version and equal or higher minor version. If using a binary install, upgrade your CuDNN library. If building from sources, make sure the library loaded at runtime is compatible with the version specified during compile .
So it claims to use 8.9.4 instead of the documented 8.8.0. Either this is a documentation bug, or a CI bug that builds this pip package with the wrong cuDNN version.
I installed with pip:
```
❯ pip3 show tensorflow
Name: tensorflow
Version: 2.15.0.post1
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/martijn/.local/lib/python3.10/site-packages
Requires: absl-py, astunparse, flatbuffers, gast, google-pasta, grpcio, h5py, keras, libclang, ml-dtypes, numpy, opt-einsum, packaging, protobuf, setuptools, six, tensorboard, tensorflow-estimator, tensorflow-io-gcs-filesystem, termcolor, typing-extensions, wrapt
Required-by:
```
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TensorFlow Lite in Play Services issue
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[
"@Allan1974 Could you please make sure that the TensorFlow Lite modules are installed at the same time your application is installed or updated from the Play Store?\r\nIn order to expedite the trouble-shooting process here,Could you please fill the issue [template](https://github.com/tensorflow/tensorflow/issues/new/choose),\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."
] | 2023-12-12T13:43:57 | 2023-12-30T01:47:44 | 2023-12-30T01:47:43 |
NONE
| null | null | null |
[**System](**url**) information**
- Android Device information (use `adb shell getprop ro.build.fingerprint`
if possible):
- TensorFlow Lite in Play Services SDK version (found in `build.gradle`):
- Google Play Services version
(`Settings` > `Apps` > `Google Play Services` > `App details`):
**Standalone code to reproduce the issue**
Provide a reproducible test case that is the bare minimum necessary to generate
the problem. If possible, please share a link to or attach code demonstrating
the problem.
**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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PR_kwDOArmXAs5hxnBe
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Fixed typo in text_searcher tflite docs
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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/62624/checks?check_run_id=19551168936) 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.",
"Check out this pull request on <a href=\"https://app.reviewnb.com/tensorflow/tensorflow/pull/62624\"><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>"
] | 2023-12-12T10:12:29 | 2024-04-03T06:44:48 | 2024-04-03T06:44:48 |
CONTRIBUTOR
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"Sema**n**tic Search" rather than "Sematic Search"
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PR_kwDOArmXAs5hwrbs
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Validate mask argument of tf.boolean_mask
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"It looks like several users rely on the current behavior, so this would be a breaking change."
] | 2023-12-12T07:24:00 | 2023-12-27T23:00:06 | 2023-12-12T17:46:25 |
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As per `tf.boolean_mask` documentation the argument mask must be:
> K-D boolean Tensor, K <= N and K must be known statically.
Currently this API is not validating the `mask` argument. If we pass any numeric values irrespective of `positives` or `negatives` all will be considered as `True` except for `0`. This may not produce desirable results. IMO, it's better to enforce the user to pass the mask as boolean tensor. Hence proposing the validation.
Fixes #61820
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[oneDNN] Add BMMV2 + Mul fusion and test
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[
"Hi @kanvi-nervana Can you please rebase your branch and resolve the conflicts? 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!"
] | 2023-12-12T00:44:28 | 2024-06-07T16:37:38 | null |
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This PR adds BMMV2+Mul pattern for fusion and the required tests.
This pattern is seen for FP16 ViT. The original pattern is BMMV2+Mul+Add but for FP16 the Add is constant folded.
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Remove ARM CI from PR Trigger in arm-ci.yml
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Remove the arm ci trigger from arm-ci.yml in favor of the newer arm64 presubmit.
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"Is there any solution/comment on this ? @tensorflower-gardener @mihaimaruseac",
"Hi @ pkgoogle,\r\nPlease look into the issue.\r\n\r\nThank You",
"Hi @pucha48, can you either upload your model file or show us the code that produces the model? Feel free to simplify or reduce the model so that it just shows the issue. Thanks for your help.",
"@pkgoogle I have added a sample code in zip, showing the sample issue. Please have a look.\r\n\r\n[Archive.zip](https://github.com/tensorflow/tensorflow/files/13781408/Archive.zip)\r\n",
"I was able to reproduce in this [gist](https://colab.sandbox.google.com/gist/pkgoogle/8dc470cb1c235a5831dc592eb9d844d7/62620.ipynb).\r\n\r\nIf I try with tf-nightly, it crashes in the 2nd cell\r\nIf I try with tflite-support (without the specific version) there is a name/package collision when importing flatbuffers\r\n\r\nIt appears to remove the signatures and writes to the file during this call:\r\n```\r\npopulator = _metadata.MetadataPopulator.with_model_file(model_path)\r\n```\r\n\r\nHi @miaout17, can you please take a look? Thanks."
] | 2023-12-11T21:30:02 | 2024-06-12T10:57:35 | null |
NONE
| null | null | null |
### 1. System information
- Mac OS 14.1.2
- TensorFlow mac 2.13.0
- Tflite support version '0.1.0a1'
### 2. Code
```
def write_metadata(model_path, run_name):
model_meta = _metadata_fb.ModelMetadataT()
model_meta.name = "test model"
model_meta.description = run_name
model_meta.version = datetime.now().strftime("%Y.%m.%d")
model_meta.author = "Test"
model_meta.license = f"test"\
"All rights reserved - test"
input_meta_image = _metadata_fb.TensorMetadataT()
input_meta_image.description = "Input image for which to score doneness."
input_meta_image.content = _metadata_fb.ContentT()
input_meta_image.content.contentProperties = _metadata_fb.ImagePropertiesT()
input_meta_image.content.contentProperties.colorSpace = _metadata_fb.ColorSpaceType.RGB
input_meta_image.content.contentPropertiesType = _metadata_fb.ContentProperties.ImageProperties
input_meta_state = _metadata_fb.TensorMetadataT()
input_meta_state.description = "Input state."
input_meta_state.name = "Input state."
# Creates output info.
output_meta_doneness = _metadata_fb.TensorMetadataT()
output_meta_doneness.description = "Output doneness score between 0 and 1."
output_meta_doneness.name = "Output Doneness"
output_meta_state = _metadata_fb.TensorMetadataT()
output_meta_state.description = "Output state."
output_meta_state.name = "Output state."
subgraph = _metadata_fb.SubGraphMetadataT()
interpreter = tf.lite.Interpreter(model_path)
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
state_first_input = input_details[0]['shape'][-1] != 3
state_first_output = output_details[0]['shape'][-1] != 1
input_meta = [input_meta_state, input_meta_image]
output_meta = [output_meta_state, output_meta_doneness]
subgraph.inputTensorMetadata = input_meta if state_first_input else input_meta[::-1]
subgraph.outputTensorMetadata = output_meta if state_first_output else output_meta[::-1]
model_meta.subgraphMetadata = [subgraph]
b = flatbuffers.Builder(0)
b.Finish(
model_meta.Pack(b),
_metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
metadata_buf = b.Output()
populator = _metadata.MetadataPopulator.with_model_file(model_path)
populator.load_metadata_buffer(metadata_buf)
populator.populate()
```
I am using above function to populate the metadata. without running above function getting signature output like
```
interpreter.get_signature_list()
{'serving_default': {'inputs': ['input_image', 'input_state'], 'outputs': ['output_1', 'output_2']}}
```
after running metadata function
```
interpreter.get_signature_list()
{}
```
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PR_kwDOArmXAs5huhan
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[oneDNN]: Added support for quantized types in Enter and Exit ops on CPU
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[
"Hi @penpornk Can you please review this PR ? Thank you!",
"@cantonios Thanks for reviewing this PR. I have addressed your review comments. Please take a look."
] | 2023-12-11T20:52:07 | 2024-01-26T16:31:38 | 2024-01-26T16:31:37 |
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Fixes https://github.com/tensorflow/tensorflow/issues/61761.
TF removed support for quantized types in `Enter` and `Exit` ops for CPU from r2.13. This causes issues when quantizing models such as Transformer-MLPerf using [Intel(R) Neural Compressor](https://github.com/intel/neural-compressor).
An example workflow for using these ops is as follows:
`Const (float32) -> Identity -> Enter (float32) -> MatMul` would be quantized into
`Const (qint8) -> Enter (qint8) -> QuantizedMatMul`
Here, the `Const` node refers to the weights input of `MatMul`.
This PR fixes the above issue by adding back registrations for quantized types in `Enter` and `Exit` ops.
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I_kwDOArmXAs55Yntj
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Why does my full integer quantized tflite model crash when loaded?
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[
"I have attached the model here (I had to zip it since github doesn't accept .tflite files). \r\n[full_integer_quant_model.zip](https://github.com/tensorflow/tensorflow/files/13640900/full_integer_quant_model.zip)\r\n",
"I have a suspicion that this [PR](https://github.com/tensorflow/tensorflow/pull/61698) might fix it. Any idea how I can mitigate this before a fix is merged? How can I ensure real_output_multiplier is under 1?",
"Hi @pkgoogle ,\r\n\r\nI have reproduced the issue in colab on both CPU and GPU. The session has crashed. Please look into the issue\r\n\r\nThank You\r\n",
"Hi @spacycoder, you'll have to get TF source code, make the same modifications as the PR, rebuild TF from source: https://www.tensorflow.org/install/source, install the newly built TF package then try again on your system. Let us know if that resolves the issue or not so that we can prioritize that PR further if it solves more issues. Thanks for your help!\r\n\r\nTo \"make the same modifications as the PR\" to your local repository please apply this patch:\r\n```\r\n<inside TF source root directory>\r\ngit apply 61698.patch\r\n```\r\n[61698.patch](https://github.com/tensorflow/tensorflow/files/13679196/61698.patch)\r\n",
"I applied the patch and built tensorflow. However, it still doesn't work.\r\n\r\n```\r\n(gdb) bt\r\n#0 __pthread_kill_implementation (no_tid=0, signo=6, threadid=140737348053888) at ./nptl/pthread_kill.c:44\r\n#1 __pthread_kill_internal (signo=6, threadid=140737348053888) at ./nptl/pthread_kill.c:78\r\n#2 __GI___pthread_kill (threadid=140737348053888, signo=signo@entry=6) at ./nptl/pthread_kill.c:89\r\n#3 0x00007ffff7a90476 in __GI_raise (sig=sig@entry=6) at ../sysdeps/posix/raise.c:26\r\n#4 0x00007ffff7a767f3 in __GI_abort () at ./stdlib/abort.c:79\r\n#5 0x00007fffa9363ade in tflite::QuantizeMultiplierSmallerThanOneExp(double, int*, int*) ()\r\n from <path>/lib/python3.11/site-packages/tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper.so\r\n#6 0x00007fffa8fab409 in tflite::ops::builtin::add::Prepare(TfLiteContext*, TfLiteNode*) ()\r\n from <path>/lib/python3.11/site-packages/tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper.so\r\n#7 0x00007fffa939aadb in tflite::Subgraph::PrepareOpsStartingAt(int, std::vector<int, std::allocator<int> > const&, int*) ()\r\n from <path>/lib/python3.11/site-packages/tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper.so\r\n#8 0x00007fffa939c208 in tflite::Subgraph::ModifyGraphWithDelegateImpl(TfLiteDelegate*) ()\r\n from <path>/lib/python3.11/site-packages/tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper\r\n```",
"Hi @spacycoder, can you ensure you have installed a TF-cuda package? i.e.\r\n\r\n```\r\npython3 -m pip install tensorflow[and-cuda]\r\n```\r\nand\r\n```\r\npython3 -m pip install tf-nightly[and-cuda]\r\n```\r\nalso please ensure it is working properly:\r\n```\r\n# should have at least one element in the list\r\npython3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\r\n```\r\n\r\nDoes the above occur with `tensorflow[and-cuda]`?",
"It doesn't work with \"tensorflow[and-cuda]\" at least.\r\n\r\nI'm currently having some issues installing tf-nightly with GPU support. So I haven't been able to test that yet.",
"I am able to replicate with tensorflow[and-cuda], @LukeBoyer, can you please take a look? 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/62618\">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/62618\">No</a>\n",
"Closed this issue since it seems to have been an issue with onnx2tf and my specific environment"
] | 2023-12-11T20:45:48 | 2024-01-11T14:52:37 | 2024-01-11T14:51:37 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.16.0-dev20231211
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04
### Mobile device
Linux Ubuntu 22.04
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
CUDA Version: 11.7
### GPU model and memory
NVIDIA GeForce 3080
### Current behavior?
It should not crash when loading the model
### Standalone code to reproduce the issue
```shell
Running this code causes "Abort (core dumped)" after `interpreter.allocate_tensors()`:
interpreter = tf.lite.Interpreter(
model_path="full_integer_quant_model.tflite"
)
interpreter.allocate_tensors()
```
```
### Relevant log output
Aborted (core dumped)
Running gdb with `bt` gives this output:
```
(gdb) bt
#0 __pthread_kill_implementation (no_tid=0, signo=6, threadid=140737352685376) at ./nptl/pthread_kill.c:44
#1 __pthread_kill_internal (signo=6, threadid=140737352685376) at ./nptl/pthread_kill.c:78
#2 __GI___pthread_kill (threadid=140737352685376, signo=signo@entry=6) at ./nptl/pthread_kill.c:89
#3 0x00007ffff7c42476 in __GI_raise (sig=sig@entry=6) at ../sysdeps/posix/raise.c:26
#4 0x00007ffff7c287f3 in __GI_abort () at ./stdlib/abort.c:79
#5 0x00007fff7cc99961 in tflite::QuantizeMultiplierSmallerThanOneExp(double, int*, int*) () from /home/huddly/anaconda3/envs/onnx2tf/lib/python3.11/site-packages/tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper.so
#6 0x00007fff7c8e3439 in tflite::ops::builtin::add::Prepare(TfLiteContext*, TfLiteNode*) () from /home/huddly/anaconda3/envs/onnx2tf/lib/python3.11/site-packages/tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper.so
#7 0x00007fff7ccb030e in tflite::Subgraph::PrepareOpsStartingAt(int, std::vector<int, std::allocator<int> > const&, int*) () from /home/huddly/anaconda3/envs/onnx2tf/lib/python3.11/site-packages/tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper.so
#8 0x00007fff7ccb19d8 in tflite::Subgraph::ModifyGraphWithDelegateImpl(TfLiteDelegate*) () from /home/huddly/anaconda3/envs/onnx2tf/lib/python3.11/site-packages/tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper.so
#9 0x00007fff7ccb219f in tflite::Subgraph::ModifyGraphWithDelegate(TfLiteDelegate*) () from /home/huddly/anaconda3/envs/onnx2tf/lib/python3.11/site-packages/tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper.so
#10 0x00007fff7cca30f3 in tflite::impl::Interpreter::ModifyGraphWithDelegateImpl(TfLiteDelegate*) () from /home/huddly/anaconda3/envs/onnx2tf/lib/python3.11/site-packages/tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper.so
#11 0x00007fff7cca2d91 in tflite::impl::Interpreter::ApplyLazyDelegateProviders() () from /home/huddly/anaconda3/envs/onnx2tf/lib/python3.11/site-packages/tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper.so
#12 0x00007fff7cca2bfe in tflite::impl::Interpreter::AllocateTensors() () from /home/huddly/anaconda3/envs/onnx2tf/lib/python3.11/site-packages/tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper.so
#13 0x00007fff7c8a3e21 in tflite::interpreter_wrapper::InterpreterWrapper::AllocateTensors(int) () from /home/huddly/anaconda3/envs/onnx2tf/lib/python3.11/site-packages/tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper.so
#14 0x00007fff7c8a069b in pybind11::cpp_function::initialize<pybind11_init__pywrap_tensorflow_interpreter_wrapper(pybind11::module_&)::$_4, pybind11::object, tflite::interpreter_wrapper::InterpreterWrapper&, int, pybind11::name, pybind11::is_method, pybind11::sibling, pybind11::arg_v>(pybind11_init__pywrap_tensorflow_interpreter_wrapper(pybind11::module_&)::$_4&&, pybind11::object (*)(tflite::interpreter_wrapper::InterpreterWrapper&, int), pybind11::name const&, pybind11::is_method const&, pybind11::sibling const&, pybind11::arg_v const&)::{lambda(pybind11::detail::function_call&)#1}::__invoke(pybind11::detail::function_call&) () from /home/huddly/anaconda3/envs/onnx2tf/lib/python3.11/site-packages/tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper.so
#15 0x00007fff7c89365f in pybind11::cpp_function::dispatcher(_object*, _object*, _object*) () from /home/huddly/anaconda3/envs/onnx2tf/lib/python3.11/site-packages/tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper.so
#16 0x0000000000528527 in cfunction_call (func=0x7fff7d14b920, args=<optimized out>, kwargs=<optimized out>) at /usr/local/src/conda/python-3.11.0/Objects/methodobject.c:542
#17 0x0000000000504f04 in _PyObject_MakeTpCall (tstate=0x8a4e38 <_PyRuntime+166328>, callable=0x7fff7d14b920, args=<optimized out>, nargs=<optimized out>, keywords=0x0) at /usr/local/src/conda/python-3.11.0/Objects/call.c:214
#18 0x00000000005111d3 in _PyEval_EvalFrameDefault (tstate=<optimized out>, frame=<optimized out>, throwflag=<optimized out>) at /usr/local/src/conda/python-3.11.0/Python/ceval.c:4772
#19 0x00000000005caeae in _PyEval_EvalFrame (throwflag=0, frame=0x7ffff7fb0020, tstate=0x8a4e38 <_PyRuntime+166328>) at /usr/local/src/conda/python-3.11.0/Include/internal/pycore_ceval.h:73
#20 _PyEval_Vector (tstate=0x8a4e38 <_PyRuntime+166328>, func=0x7ffff7b984a0, locals=0x7ffff7bf65c0, args=<optimized out>, argcount=<optimized out>, kwnames=<optimized out>) at /usr/local/src/conda/python-3.11.0/Python/ceval.c:6428
#21 0x00000000005ca4ef in PyEval_EvalCode (co=<optimized out>, globals=0x7ffff7bf65c0, locals=<optimized out>) at /usr/local/src/conda/python-3.11.0/Python/ceval.c:1154
#22 0x00000000005ec747 in run_eval_code_obj (tstate=0x8a4e38 <_PyRuntime+166328>, co=0x7ffff7b71790, globals=0x7ffff7bf65c0, locals=0x7ffff7bf65c0) at /usr/local/src/conda/python-3.11.0/Python/pythonrun.c:1714
#23 0x00000000005e8af0 in run_mod (mod=<optimized out>, filename=<optimized out>, globals=0x7ffff7bf65c0, locals=0x7ffff7bf65c0, flags=<optimized out>, arena=<optimized out>) at /usr/local/src/conda/python-3.11.0/Python/pythonrun.c:1735
#24 0x00000000005fcd22 in pyrun_file (fp=fp@entry=0x90e360, filename=filename@entry=0x7ffff7b5c810, start=start@entry=257, globals=globals@entry=0x7ffff7bf65c0, locals=locals@entry=0x7ffff7bf65c0, closeit=closeit@entry=1, flags=0x7fffffffd758) at /usr/local/src/conda/python-3.11.0/Python/pythonrun.c:1630
#25 0x00000000005fc2ef in _PyRun_SimpleFileObject (fp=0x90e360, filename=0x7ffff7b5c810, closeit=1, flags=0x7fffffffd758) at /usr/local/src/conda/python-3.11.0/Python/pythonrun.c:440
#26 0x00000000005fc0a3 in _PyRun_AnyFileObject (fp=0x90e360, filename=0x7ffff7b5c810, closeit=1, flags=0x7fffffffd758) at /usr/local/src/conda/python-3.11.0/Python/pythonrun.c:79
#27 0x00000000005f6bde in pymain_run_file_obj (skip_source_first_line=0, filename=0x7ffff7b5c810, program_name=0x7ffff7118990) at /usr/local/src/conda/python-3.11.0/Modules/main.c:360
#28 pymain_run_file (config=0x88ae80 <_PyRuntime+59904>) at /usr/local/src/conda/python-3.11.0/Modules/main.c:379
#29 pymain_run_python (exitcode=0x7fffffffd750) at /usr/local/src/conda/python-3.11.0/Modules/main.c:601
#30 Py_RunMain () at /usr/local/src/conda/python-3.11.0/Modules/main.c:680
#31 0x00000000005b9a79 in Py_BytesMain (argc=<optimized out>, argv=<optimized out>) at /usr/local/src/conda/python-3.11.0/Modules/main.c:734
#32 0x00007ffff7c29d90 in __libc_start_call_main (main=main@entry=0x5b99d0 <main>, argc=argc@entry=2, argv=argv@entry=0x7fffffffd9a8) at ../sysdeps/nptl/libc_start_call_main.h:58
#33 0x00007ffff7c29e40 in __libc_start_main_impl (main=0x5b99d0 <main>, argc=2, argv=0x7fffffffd9a8, init=<optimized out>, fini=<optimized out>, rtld_fini=<optimized out>, stack_end=0x7fffffffd998) at ../csu/libc-start.c:392
#34 0x00000000005b98ce in _start ()
```
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PR_kwDOArmXAs5huD4t
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Simplify local reproduction instructions
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[
"Ok, this is ready for review again. I fixed a few issues, including one bug with including TFCI variables from the environment, and added more documentation -- mostly about docker. I have verified that (on my Linux machine), these all appeared to be working:\r\n\r\n- local_multicache\r\n- local_rbe\r\n- local_nodocker\r\n- pycpp.sh\r\n- any.sh\r\n- wheel.sh\r\n\r\nI don't have a good way to verify bisect.sh yet but I think it's fixed. I'll check on it again after this gets merged."
] | 2023-12-11T19:21:56 | 2023-12-19T01:20:25 | 2023-12-19T01:20:25 |
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This change is a big simplification of reproducing a build locally ([see it here](https://github.com/angerson/tensorflow/tree/master/ci/official)):
- Moved a lot of configurable behavior into source-able local_* env files
- Deleted a lot of extraneous instructions
- Added "how do I choose..." instructions for clarification
- Fixed a bug preventing multiple invocations of rename_and_verify_wheels
- Made any.sh and bisect.sh use the cleaner method of sourcing local_ envs
- Cleaned up ci_default and added "how to see an overview of env variables"
One of the big things is that I added some logic in local_default that resets the list of Bazel common args to remove things like --config=rbe. This way a user can choose to run a "release" env configuration without being blocked by permission errors, or by needing to manually amend their configuration. As a result, gathering the correct set of config values is quite easy (see the new instructions). The alternative would be to split "auth-related" flags into a separate variable, which I don't like, as I think it's more confusing for future maintainers to have to continuously decide which options go where.
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TFLite Mobile Benchmarking compilation fails because of the hexagon delegate
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[
"@hello-fri-end Could you manually add dependency declarations for the missing header files in the BUILD file for //tensorflow/lite/profiling:time. This would involve specifying the paths to these files relative to the Android NDK root directory. \r\n\r\nYou can try using a prebuilt Android NDK version that already has the necessary dependencies included in the toolchain. \r\nPlease make sure to use the latest TF version.\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/62616\">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/62616\">No</a>\n"
] | 2023-12-11T15:39:35 | 2023-12-30T01:47:49 | 2023-12-30T01:47:45 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
NIL
### Custom code
Yes
### OS platform and distribution
Arch Linux
### Mobile device
Samsung S22 Plus
### Python version
3.9
### Bazel version
6.1.0
### GCC/compiler version
clang 16.0.6
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
```
INFO: Reading 'startup' options from /home/hellofriend/Documents/testTF/tensorflow/.bazelrc: --windows_enable_symlinks
INFO: Options provided by the client:
Inherited 'common' options: --isatty=1 --terminal_columns=191
INFO: Reading rc options for 'build' from /home/hellofriend/Documents/testTF/tensorflow/.bazelrc:
Inherited 'common' options: --experimental_repo_remote_exec
INFO: Reading rc options for 'build' from /home/hellofriend/Documents/testTF/tensorflow/.bazelrc:
'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --features=-force_no_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility
INFO: Reading rc options for 'build' from /home/hellofriend/Documents/testTF/tensorflow/.tf_configure.bazelrc:
'build' options: --action_env PYTHON_BIN_PATH=/home/hellofriend/miniconda3/bin/python3 --action_env PYTHON_LIB_PATH=/home/hellofriend/miniconda3/lib/python3.9/site-packages --python_path=/home/hellofriend/miniconda3/bin/python3 --action_env CLANG_COMPILER_PATH=/usr/bin/clang-16 --repo_env=CC=/usr/bin/clang-16 --repo_env=BAZEL_COMPILER=/usr/bin/clang-16 --copt=-Wno-gnu-offsetof-extensions --action_env ANDROID_NDK_HOME=/opt/android-ndk --action_env ANDROID_NDK_VERSION=26 --action_env ANDROID_NDK_API_LEVEL=21 --action_env ANDROID_BUILD_TOOLS_VERSION=34.0.0 --action_env ANDROID_SDK_API_LEVEL=34 --action_env ANDROID_SDK_HOME=/home/hellofriend/Android/Sdk
INFO: Found applicable config definition build:short_logs in file /home/hellofriend/Documents/testTF/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file /home/hellofriend/Documents/testTF/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:android_arm64 in file /home/hellofriend/Documents/testTF/tensorflow/.bazelrc: --config=android --cpu=arm64-v8a --fat_apk_cpu=arm64-v8a
INFO: Found applicable config definition build:android in file /home/hellofriend/Documents/testTF/tensorflow/.bazelrc: --crosstool_top=//external:android/crosstool --host_crosstool_top=@bazel_tools//tools/cpp:toolchain --dynamic_mode=off --noenable_platform_specific_config --copt=-w --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --define=with_xla_support=false --config=no_tfrt
INFO: Found applicable config definition build:no_tfrt in file /home/hellofriend/Documents/testTF/tensorflow/.bazelrc: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/ir,tensorflow/compiler/mlir/tfrt/ir/mlrt,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ifrt,tensorflow/compiler/mlir/tfrt/tests/mlrt,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/compiler/mlir/tfrt/transforms/mlrt,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/runtime_fallback/test,tensorflow/core/runtime_fallback/test/gpu,tensorflow/core/runtime_fallback/test/saved_model,tensorflow/core/runtime_fallback/test/testdata,tensorflow/core/tfrt/stubs,tensorflow/core/tfrt/tfrt_session,tensorflow/core/tfrt/mlrt,tensorflow/core/tfrt/mlrt/attribute,tensorflow/core/tfrt/mlrt/kernel,tensorflow/core/tfrt/mlrt/bytecode,tensorflow/core/tfrt/mlrt/interpreter,tensorflow/compiler/mlir/tfrt/translate/mlrt,tensorflow/compiler/mlir/tfrt/translate/mlrt/testdata,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils,tensorflow/core/tfrt/utils/debug,tensorflow/core/tfrt/saved_model/python,tensorflow/core/tfrt/graph_executor/python,tensorflow/core/tfrt/saved_model/utils
INFO: Analyzed target //tensorflow/lite/tools/benchmark/android:benchmark_model (0 packages loaded, 0 targets configured).
INFO: Found 1 target...
ERROR: /home/hellofriend/Documents/testTF/tensorflow/tensorflow/lite/profiling/BUILD:122:11: Compiling tensorflow/lite/profiling/time.cc failed: undeclared inclusion(s) in rule '//tensorflow/lite/profiling:time':
this rule is missing dependency declarations for the following files included by 'tensorflow/lite/profiling/time.cc':
'external/androidndk/toolchains/llvm/prebuilt/linux-x86_64/lib/clang/17/include/stdint.h'
'external/androidndk/toolchains/llvm/prebuilt/linux-x86_64/lib/clang/17/include/stddef.h'
'external/androidndk/toolchains/llvm/prebuilt/linux-x86_64/lib/clang/17/include/__stddef_max_align_t.h'
'external/androidndk/toolchains/llvm/prebuilt/linux-x86_64/lib/clang/17/include/limits.h'
'external/androidndk/toolchains/llvm/prebuilt/linux-x86_64/lib/clang/17/include/float.h'
Target //tensorflow/lite/tools/benchmark/android:benchmark_model failed to build
INFO: Elapsed time: 0.262s, Critical Path: 0.10s
INFO: 13 processes: 13 internal.
FAILED: Build did NOT complete successfully
```
### Standalone code to reproduce the issue
```shell
sudo bazel build -c opt \
--config=android_arm64 --verbose_failures \
tensorflow/lite/tools/benchmark/android:benchmark_model
```
```
### Relevant log output
```shell
`.tf_configure.bazelrc`
build --action_env PYTHON_BIN_PATH="/home/hellofriend/miniconda3/bin/python3"
build --action_env PYTHON_LIB_PATH="/home/hellofriend/miniconda3/lib/python3.9/site-packages"
build --python_path="/home/hellofriend/miniconda3/bin/python3"
build --action_env CLANG_COMPILER_PATH="/usr/bin/clang-16"
build --repo_env=CC=/usr/bin/clang-16
build --repo_env=BAZEL_COMPILER=/usr/bin/clang-16
build --copt=-Wno-gnu-offsetof-extensions
build:opt --copt=-Wno-sign-compare
build:opt --host_copt=-Wno-sign-compare
build --action_env ANDROID_NDK_HOME="/opt/android-ndk"
build --action_env ANDROID_NDK_VERSION="26"
build --action_env ANDROID_NDK_API_LEVEL="21"
build --action_env ANDROID_BUILD_TOOLS_VERSION="34.0.0"
build --action_env ANDROID_SDK_API_LEVEL="34"
build --action_env ANDROID_SDK_HOME="/home/hellofriend/Android/Sdk"
test --test_size_filters=small,medium
test:v1 --test_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-gpu,-oss_serial
test:v1 --build_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-gpu
test:v2 --test_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-gpu,-oss_serial,-v1only
test:v2 --build_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-gpu,-v1only
```
I tried `bazel clean --expunge` but that didn't help. According to the docs, the hexagon delegate can be turned off using `use_hexagon=false` but this throws an error that there's no option `use_hexagon`.
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Slower inference since TensorFlow Lite 2.12
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[
"Hi @pkgoogle,\r\nCould you please look into the issue\r\nThank you",
"Hi @Qheb, I'm looking into this, do you know what Android SDK and API Level you are using?",
"Hi @pkgoogle , thanks for looking into this.\r\nI reproduced the issue while compiling & targeting the Android SDK 34.",
"@Qheb, Thanks!, by API version I meant which version of Android are you using?\r\n\r\n\r\n\r\nThanks for your help.\r\n",
"Oh, sorry!\r\n\r\nI tested it on two devices:\r\n- Samsung Galaxy s23 Ultra (`samsung/dm3qxeea/dm3q:14/UP1A.231005.007/S918BXXS3BWK5:user/release-keys`) running Android 14 (API level 34)\r\n- Samsung Galaxy A51 (`samsung/a51nseea/a51:13/TP1A.220624.014/A515FXXU8HWI1:user/release-keys`) running Android 13 (API level 33)",
"Hi @Qheb, have you tried Google play services versions 2.12 through 2.14? I don't see it above. Additionally how are you making these measurements? Custom code to measure? A benchmarking tool of some sort? Something else in Android Studio? As I currently see this, it seems like there is no issue with version 2.15 which is the latest... as such while it does seem peculiar, I don't really see this as an issue worth investigating.",
"Hi @pkgoogle,\r\n\r\nIs there a way to choose which TFLite version I can use through the Google Play Services ? I seems to me that I can only use the one currently available on the device. The week before I opened this issue, it was `2.13.0`, right now it is `2.15.0`. And with both I have \"normal\" inference times.\r\n\r\nAbout the measurements, I am using the measurements [logged into the example app](https://github.com/tensorflow/examples/blob/master/lite/examples/pose_estimation/android/app/src/main/java/org/tensorflow/lite/examples/poseestimation/ml/PoseNet.kt#L100):\r\n```kotlin\r\nval inferenceStartTimeNanos = SystemClock.elapsedRealtimeNanos()\r\ninterpreter.runForMultipleInputsOutputs(inputArray, outputMap)\r\nlastInferenceTimeNanos = SystemClock.elapsedRealtimeNanos() - inferenceStartTimeNanos\r\nLog.i(\r\n TAG,\r\n String.format(\"Interpreter took %.2f ms\", 1.0f * lastInferenceTimeNanos / 1_000_000)\r\n)\r\n```\r\n\r\nWere you able to test it on a bundled `2.15` version ? I can only have up to the `2.14` on [Maven Central](https://repo.maven.apache.org/maven2/org/tensorflow/tensorflow-lite/). But I tried the nightly build, which is a `2.16` and it still haves the issue:\r\n\r\nTFLite version: `2.16.0-nightly` (bundled)\r\nAverage inference time: `24.09ms`\r\n\r\nIt seems to me that there is a difference between the Google Play Services builds, and the one from the bundled version. Is this possible ?\r\n\r\nAbout Maven Central, is this normal that the `2.15.0` hasn't been published on it ?\r\n\r\nThank you for your time.",
"Hi @Qheb, just to make sure we're talking about the same environment, how are you bundling 2.16.0-nightly? As you said, 2.15.0 is not currently published in Maven Central. Are you building an .aar and bundling that with your project? Like here? https://www.tensorflow.org/lite/android/lite_build#use_nightly_snapshots or are you doing something else? If that is performing poorly then we can look into it, as it's currently underperforming. Thanks for your help and any additional information you can provide.",
"Hello @pkgoogle,\r\nI am **not** building the nightly .aar myself.\r\nInstead I use, the `ossrh-snapshot` Maven repository, like in the link you provided, and I set the tflite dependencies to:\r\n\r\n```groovy\r\nimplementation 'org.tensorflow:tensorflow-lite:0.0.0-nightly-SNAPSHOT'\r\nimplementation 'org.tensorflow:tensorflow-lite-gpu:0.0.0-nightly-SNAPSHOT'\r\nimplementation 'org.tensorflow:tensorflow-lite-support:0.0.0-nightly-SNAPSHOT'\r\n```",
"Hi @Qheb, I was able to test on a Galaxy S23 Ultra, with the nightly snapshot:\r\n\r\n```\r\n2024-01-04 11:27:08.997 21876-22863 Posenet org....lite.examples.poseestimation I Interpreter took 36.65 ms\r\n2024-01-04 11:27:09.051 21876-22863 Posenet org....lite.examples.poseestimation I Interpreter took 36.60 ms\r\n2024-01-04 11:27:09.106 21876-22863 Posenet org....lite.examples.poseestimation I Interpreter took 36.49 ms\r\n2024-01-04 11:27:09.161 21876-22863 Posenet org....lite.examples.poseestimation I Interpreter took 36.39 ms\r\n2024-01-04 11:27:09.215 21876-22863 Posenet org....lite.examples.poseestimation I Interpreter took 36.82 ms\r\n```\r\n\r\nTo use Google Play Services, it seems I have to change some code in the demo, do you have the changes you made to use Google Play Services? so that I can replicate your environment exactly? (or did you change to GPS some other way?) Thanks for your help.",
"Hi @pkgoogle, here are the two commits I used for running the Google Play Services:\r\n\r\n1. [1-update-everything.txt](https://github.com/tensorflow/tensorflow/files/13841289/1-update-everything.txt): Blindly update dependencies so I can run the project (I suppose you've already done it on your side)\r\n2. [2-TfLite-gps.txt](https://github.com/tensorflow/tensorflow/files/13841290/2-TfLite-gps.txt): GMS dependency + Initialize TFLite before creating the pose estimator + set TFLite runtime to FROM_SYSTEM_ONLY\r\n\r\n",
"Thanks @Qheb, I was able to apply your patches directly and am seeing the same performance improvement:\r\n\r\n```\r\n2024-01-05 14:12:58.689 2724-3081 Posenet org....lite.examples.poseestimation I Interpreter took 14.93 ms\r\n2024-01-05 14:12:58.722 2724-3081 Posenet org....lite.examples.poseestimation I Interpreter took 12.08 ms\r\n2024-01-05 14:12:58.763 2724-3081 Posenet org....lite.examples.poseestimation I Interpreter took 14.98 ms\r\n2024-01-05 14:12:58.796 2724-3081 Posenet org....lite.examples.poseestimation I Interpreter took 13.62 ms\r\n2024-01-05 14:12:58.820 2724-3081 Posenet org....lite.examples.poseestimation I Interpreter took 11.32 ms\r\n```\r\n\r\nAt least 2, sometimes 3x the speed on the same device,\r\n\r\n@arfaian, can you please take a look? Thanks.",
"Hello, sorry to insist, but is there any news about this ?"
] | 2023-12-11T14:13:35 | 2024-02-23T10:32:08 | null |
NONE
| null | null | null |
**System information**
- Android Device information: `samsung/dm3qxeea/dm3q:14/UP1A.231005.007/S918BXXS3BWK5:user/release-keys` (reproduced on any device I tested)
- Bundled TFLite version from [Maven](https://repo.maven.apache.org/maven2/org/tensorflow/tensorflow-lite/): `2.12.0` and later
- TensorFlow Lite in Play Services SDK version: `16.1.0`
- Google Play Services version: `23.45.23 (190400-587848529)`
**Standalone code to reproduce the issue**
I used the [TensorFlow Lite Pose Estimation Android Demo](https://github.com/tensorflow/examples/tree/master/lite/examples/pose_estimation/android) where I changed the TensorFlow Lite version
**Any other info / logs**
With any bundled version of TFLite >= 2.12.0, the inference time of some model is twice the inference time with TFLite v2.11.0. This issue is **not** reproduced with the TFLite from the Play Services.
Especially when I compared last week the bundled 2.13.0 against the 2.13.0 from the Play Services. The bundled version inference times were twice the ones of the Play Services.
(I can't test it anymore since the version from the Play Services has been updated to 2.15.0 and this one is not available on Maven)
For example, on a Samsung Galaxy S23 ultra, the PoseNet model from the example app:
TFLite version: `2.11.0 (bundled)`
Average inference time: `12.28ms`
TFLite version: `2.12.0 (bundled)`
Average inference time: `25.93ms`
TFLite version: `2.13.0 (bundled)`
Average inference time: `26.03ms`
TFLite version: `2.14.0 (bundled)`
Average inference time: `25.86ms`
TFLite version: `2.15.0 (Google Play Services)`
Average inference time: `12.12ms`
Any idea where that could come from ?
|
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Profiling hangs in cuda/cupti .so
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[
"@annaa-ka,\r\nTensorflow-2.6.0\tis compatible with python 3.6-3.9, compiler GCC 7.3.1, Bazel 3.7.2, cuDNN 8.1 and CUDA 11.2 versions. In your case, most of the configurations are on the higher side. Could you please try to follow the tested build configurations and install the tensorflow for the smooth execution.\r\nhttps://www.tensorflow.org/install/source#gpu\r\n\r\nThank you!",
"> @annaa-ka, Tensorflow-2.6.0 is compatible with python 3.6-3.9, compiler GCC 7.3.1, Bazel 3.7.2, cuDNN 8.1 and CUDA 11.2 versions. In your case, most of the configurations are on the higher side. Could you please try to follow the tested build configurations and install the tensorflow for the smooth execution. https://www.tensorflow.org/install/source#gpu\r\n> \r\n> Thank you!\r\n\r\nI do understand your concern about the versions. But there are two main issues\r\n\r\nFirstly, simple example from here works fine (https://pastebin.com/r1Nz1qeS).\r\n\r\nSecondly, if i try to profile my model not from the first batch (earlier I put profile_batch=(1, 2), now changed to (2, 3)), I get another trace at all (https://pastebin.com/dfsgcAKY)\r\nSo, I looked for same issues and found out (https://github.com/tensorflow/tensorflow/issues/12667#issuecomment-328724178)\r\n\r\nCan it be still relevant and why do i have such different traces in both cases?\r\n",
"Hi @annaa-ka,\r\n\r\nThanks for filing this. \r\n\r\nJust to confirm if it is a tensorboard profiler issue: When does the stack trace get dumped? Does enabling tensorboard profiler throws a SIGABRT or SIGSEGV? Or the hang is followed by any external signal to kill the job?",
"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/62614\">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/62614\">No</a>\n"
] | 2023-12-11T09:57:00 | 2024-01-27T01:46:25 | 2024-01-27T01:46:23 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.6.2
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04.6 LTS
### Mobile device
_No response_
### Python version
Python 3.8.10
### Bazel version
bazel 5.2.0-1
### GCC/compiler version
gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
### CUDA/cuDNN version
CUDA Version: 12.0
### GPU model and memory
Ubuntu 18.04.6 LTS
### Current behavior?
Hi, i am using tensorflow profiler to profile train of my model. Before the train starts I get the following lines
```
2023-12-10 16:31:15.256811: I tensorflow/core/profiler/lib/profiler_session.cc:146] Profiler session started.
2023-12-10 16:31:15.257177: I tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1614] Profiler found 8 GPUs
2023-12-10 16:31:15.669753: I tensorflow/core/profiler/lib/profiler_session.cc:164] Profiler session tear down.
2023-12-10 16:31:15.670454: I tensorflow/core/profiler/internal/gpu/cupti_tracer.cc:1748] CUPTI activity buffer flushed
[2023-12-10 16:31:15,691 | trajectory_predictor.neural.models | 33727 | INFO] Tensorboard logs will be available in /tmp/tmp9vfwctuu_tb_logs
```
However when the real training starts it hangs.
```
2023-12-10 16:31:18.057702: I tensorflow/core/profiler/lib/profiler_session.cc:131] Profiler session initializing.
2023-12-10 16:31:18.058010: I tensorflow/core/profiler/lib/profiler_session.cc:146] Profiler session started.
```
I’ve got such backtrace
```
#0 futex_abstimed_wait (private=0, abstime=0x0, clockid=0, expected=2, futex_word=<optimized out>) at ../sysdeps/nptl/futex-internal.h:284
#1 __pthread_rwlock_wrlock_full (abstime=0x0, clockid=0, rwlock=0x895a2a0) at pthread_rwlock_common.c:830
#2 __GI___pthread_rwlock_wrlock (rwlock=0x895a2a0) at pthread_rwlock_wrlock.c:27
#3 0x00007fdbd52fa258 in ?? () from /lib/x86_64-linux-gnu/libcuda.so.1
#4 0x00007fdbd523fcc1 in ?? () from /lib/x86_64-linux-gnu/libcuda.so.1
#5 0x00007fd95a8bc01a in ?? () from /usr/local/cuda-11.1/targets/x86_64-linux/lib/libcupti.so.11.1
#6 0x00007fd95a8ba35c in ?? () from /usr/local/cuda-11.1/targets/x86_64-linux/lib/libcupti.so.11.1
#7 0x00007fd95a89ae62 in ?? () from /usr/local/cuda-11.1/targets/x86_64-linux/lib/libcupti.so.11.1
#8 0x00007fd95a8979b2 in ?? () from /usr/local/cuda-11.1/targets/x86_64-linux/lib/libcupti.so.11.1
#9 0x00007fd95a89891b in ?? () from /usr/local/cuda-11.1/targets/x86_64-linux/lib/libcupti.so.11.1
#10 0x00007fd95a86aa86 in ?? () from /usr/local/cuda-11.1/targets/x86_64-linux/lib/libcupti.so.11.1
#11 0x00007fd95a86acf8 in ?? () from /usr/local/cuda-11.1/targets/x86_64-linux/lib/libcupti.so.11.1
#12 0x00007fd95a86be6c in ?? () from /usr/local/cuda-11.1/targets/x86_64-linux/lib/libcupti.so.11.1
#13 0x00007fdbd5058b5b in ?? () from /lib/x86_64-linux-gnu/libcuda.so.1
#14 0x00007fdbd52ff6a0 in ?? () from /lib/x86_64-linux-gnu/libcuda.so.1
#15 0x00007fdbd502c7a6 in ?? () from /lib/x86_64-linux-gnu/libcuda.so.1
#16 0x00007fdbd502e792 in ?? () from /lib/x86_64-linux-gnu/libcuda.so.1
#17 0x00007fdbd512f2ca in ?? () from /lib/x86_64-linux-gnu/libcuda.so.1
#18 0x00007fdc6a1281cb in ?? () from /usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudart.so.11.0
#19 0x00007fdc6a16b7e6 in cudaLaunchKernel () from /usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudart.so.11.0
#20 0x00007fdc7f7b6987 in ?? () from /usr/lib/python3/dist-packages/tensorflow/python/_pywrap_tensorflow_internal.so
#21 0x00007fdc7f7b8715 in tensorflow::functor::FillFunctor<Eigen::GpuDevice, float>::operator()(Eigen::GpuDevice const&, Eigen::TensorMap<Eigen::Tensor<float, 1, 1, long>, 16, Eigen::MakePointer>, Eigen::TensorMap<Eigen::TensorFixedSize<float const, Eigen::Sizes<>, 1, long>, 16, Eigen::MakePointer>) () from /usr/lib/python3/dist-packages/tensorflow/python/_pywrap_tensorflow_internal.so
```
UPD. i tried using strace -f -p my_pid
I found a lot of lines like
```
poll([{fd=20, events=POLLIN}, {fd=22, events=POLLIN}, {fd=24, events=POLLIN}, {fd=26, events=POLLIN}, {fd=28, events=POLLIN}, {fd=29, events=POLLI
```
lsof -p showed that this fd refer to /dev/nvidia0
It seems that libcuda.so.1 and libcupti.so.11.1 were created without debug symbols and are NVIDIA property, so are there any ways to find out what happens?
### Standalone code to reproduce the issue
```shell
The main problem is that when I am running profiling with simple model from the Internet everything works, but we have our model, which I currently do not fully understand and want with backtraces understand what happens there
```
### Relevant log output
_No response_
|
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Issue in instally Tensorflow-gpu
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[
"@jatindarkumar12 Make sure you are using the latest version of pip. You can update pip by running the following command:\r\n```\r\npython -m pip install --upgrade pip\r\n\r\n```\r\nYou can install TensorFlow with the following command:\r\n```\r\npython -m pip install tensorflow\r\n```\r\nYou may need to install additional build dependencies for TensorFlow. The specific dependencies will vary depending on your operating system and environment. You can find instructions for installing build dependencies on the TensorFlow website: https://www.tensorflow.org/install\r\n\r\nThank you!",
"Following up on the above. We do not publish to the `tensorflow-gpu` pypi repository since 2.12. The base `tensorflow` package has had NVIDIA GPU support since 2.1.0. so you likely should be pip installing using `tensorflow` and not `tensorflow-gpu`. See https://pypi.org/project/tensorflow-gpu/ for more info on this modification. ",
"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/62613\">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/62613\">No</a>\n"
] | 2023-12-11T06:35:16 | 2023-12-30T01:47:52 | 2023-12-30T01:47:46 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
latest
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
windows
### Python version
3.11.5
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.3 cuda
### GPU model and memory
nvidia gtx 1650
### Current behavior?
Actually sir , i want to train my tensorflow model on my gpu
but when i setup my gpu
so every setup is done
but in last when i run this command " pip install tensorflow-gpu"
it give me error
why?
### Standalone code to reproduce the issue
```shell
see above
```
### Relevant log output
```shell
Collecting tensorflow-gpu
Using cached tensorflow-gpu-2.12.0.tar.gz (2.6 kB)
Preparing metadata (setup.py) ... error
error: subprocess-exited-with-error
× python setup.py egg_info did not run successfully.
│ exit code: 1
╰─> [44 lines of output]
Traceback (most recent call last):
File "C:\ProgramData\anaconda3\Lib\site-packages\setuptools\_vendor\packaging\requirements.py", line 35, in __init__
parsed = _parse_requirement(requirement_string)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\Lib\site-packages\setuptools\_vendor\packaging\_parser.py", line 64, in parse_requirement
return _parse_requirement(Tokenizer(source, rules=DEFAULT_RULES))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\Lib\site-packages\setuptools\_vendor\packaging\_parser.py", line 82, in _parse_requirement
url, specifier, marker = _parse_requirement_details(tokenizer)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\Lib\site-packages\setuptools\_vendor\packaging\_parser.py", line 126, in _parse_requirement_details
marker = _parse_requirement_marker(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\Lib\site-packages\setuptools\_vendor\packaging\_parser.py", line 147, in _parse_requirement_marker
tokenizer.raise_syntax_error(
File "C:\ProgramData\anaconda3\Lib\site-packages\setuptools\_vendor\packaging\_tokenizer.py", line 165, in raise_syntax_error
raise ParserSyntaxError(
setuptools.extern.packaging._tokenizer.ParserSyntaxError: Expected end or semicolon (after name and no valid version specifier)
python_version>"3.7"
^
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<string>", line 2, in <module>
File "<pip-setuptools-caller>", line 34, in <module>
File "C:\Users\Jatindar Kumar\AppData\Local\Temp\pip-install-6mm7r8qr\tensorflow-gpu_1cfe51ebc0264a7b8bb3ba70f4f234d8\setup.py", line 40, in <module>
setuptools.setup()
File "C:\ProgramData\anaconda3\Lib\site-packages\setuptools\__init__.py", line 106, in setup
_install_setup_requires(attrs)
File "C:\ProgramData\anaconda3\Lib\site-packages\setuptools\__init__.py", line 77, in _install_setup_requires
dist.parse_config_files(ignore_option_errors=True)
File "C:\ProgramData\anaconda3\Lib\site-packages\setuptools\dist.py", line 900, in parse_config_files
self._finalize_requires()
File "C:\ProgramData\anaconda3\Lib\site-packages\setuptools\dist.py", line 597, in _finalize_requires
self._move_install_requirements_markers()
File "C:\ProgramData\anaconda3\Lib\site-packages\setuptools\dist.py", line 637, in _move_install_requirements_markers
inst_reqs = list(_reqs.parse(spec_inst_reqs))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ProgramData\anaconda3\Lib\site-packages\setuptools\_vendor\packaging\requirements.py", line 37, in __init__
raise InvalidRequirement(str(e)) from e
setuptools.extern.packaging.requirements.InvalidRequirement: Expected end or semicolon (after name and no valid version specifier)
python_version>"3.7"
^
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed
× Encountered error while generating package metadata.
╰─> See above for output.
note: This is an issue with the package mentioned above, not pip.
hint: See above for details.
```
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[
"In order to expedite the trouble-shooting process, please provide all the dependencies to reproduce the issue reported here. I have faced a different [issue](https://colab.research.google.com/gist/sushreebarsa/840f8f7a4d9d45d74db62b848ec264e5/62612.ipynb) for replicating the same as reported,please have a look. 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.",
"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/62612\">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/62612\">No</a>\n"
] | 2023-12-11T02:42:20 | 2023-12-27T01:47:56 | 2023-12-27T01:47:52 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15
### Custom code
Yes
### OS platform and distribution
windows 11 64 biit
### Mobile device
windows 11
### 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?
### Standalone code to reproduce the issue
```shell
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
from tensorflow.python import pywrap_tensorflow
from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
# Load the pre-trained model
base_model = tf.keras.applications.InceptionV3(include_top=False, weights='imagenet')
# Freeze the initial layers of the pre-trained model
for layer in base_model.layers:
layer.trainable = False
# Add new layers for fine-tuning
x = base_model.output
x = layers.GlobalAveragePooling2D()(x)
x = layers.Dense(128, activation='relu')(x)
x = layers.Dense(64, activation='relu')(x)
predictions = layers.Dense(3, activation='softmax')(x)
# Create the fine-tuned model
model = tf.keras.Model(inputs=base_model.input, outputs=predictions)
# Compile the model
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
# Prepare the data generators
train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
val_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
'training_data',
target_size=(224, 224),
batch_size=32,
class_mode='categorical')
validation_generator = val_datagen.flow_from_directory(
'validation_data',
target_size=(224, 224),
batch_size=32,
class_mode='categorical')
# Train the model
history = model.fit(
train_generator,
steps_per_epoch=len(train_generator),
epochs=10,
validation_data=validation_generator,
validation_steps=len(validation_generator))
# Save the trained model
model.save('ceramic_classifier.h5')
```
### Relevant log output
```shell
Traceback (most recent call last):
File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\IPython\core\interactiveshell.py:3550 in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
Cell In[8], line 3
import tensorflow as tf
File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\tensorflow\__init__.py:24
from tensorflow.python import pywrap_tensorflow # pylint: disable=unused-import
File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\tensorflow\python\__init__.py:49
from tensorflow.python import pywrap_tensorflow
File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\tensorflow\python\pywrap_tensorflow.py:58
from tensorflow.python.pywrap_tensorflow_internal import *
File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py:114
def TFE_ContextOptionsSetAsync(arg1, async):
^
SyntaxError: invalid syntax
```
it always show when i run the code
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[
"Hi @TaliBojdakYates ,\r\n\r\nAs per this [document](https://www.tensorflow.org/install/source#docker_linux_builds), you can build with tensorflow 2.14 and python 3.9(Compatible).\r\n\r\nThank You \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.",
"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/62611\">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/62611\">No</a>\n"
] | 2023-12-11T00:23:31 | 2023-12-30T01:47:56 | 2023-12-30T01:47:48 |
NONE
| null | null | null |
### Issue type
Support
Hi, I'm currently facing challenges running TensorFlow Lite on my AWS Lambda function due to the specific requirements of the Amazon Linux platform. To fix this I am attempting to build TensorFlow Lite for Amazon Linux using Docker, inspired by an older version I found. Here is the older docker version to complete this:
FROM amazonlinux
WORKDIR /tflite
RUN yum groupinstall -y development
RUN yum install -y python3.7
RUN yum install -y python3-devel
RUN pip3 install numpy wheel pybind11
RUN git clone --branch v2.3.0 https://github.com/tensorflow/tensorflow.git
RUN sh ./tensorflow/tensorflow/lite/tools/make/download_dependencies.sh
RUN sh ./tensorflow/tensorflow/lite/tools/pip_package/build_pip_package.sh
RUN pip3 install tensorflow/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/dist/tflite_runtime-2.3.0-cp37-cp37m-linux_x86_64.whl
CMD tail -f /dev/null
Is there a way to accomplish the same thing with tensorflow 2.14 and with python 3.9?
I appreitate any help or adivce. Thanks
|
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Problem converting from saved model to tflite model
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[
"Hi @NotPjoker05,\r\n\r\nCould you please fill the [template](https://github.com/tensorflow/tensorflow/issues/new/choose) to resolve the issue.\r\nAs well try the code with tensorflow 2.15 version if not tried.\r\n\r\nThank You",
"I'm already using tensorflow 2.15.\r\n### 1. System information\r\n\r\n- Windows 10:\r\n- Tensorflow installed with pycharm gui:\r\n- TensorFlow 2.15 :\r\n\r\n### 2. Code\r\nHere are my 3 files, Training for training my model, utils provides a list of methods used in training and test is the class where I use my model (predictions work in tensorflow but the method for converting from saved model to tflite doesn't work)\r\n\r\n[Training.txt](https://github.com/tensorflow/tensorflow/files/13665686/Training.txt)\r\n[utils.txt](https://github.com/tensorflow/tensorflow/files/13665687/utils.txt)\r\n[Test.txt](https://github.com/tensorflow/tensorflow/files/13665685/Test.txt)\r\n\r\n\r\n\r\n### 3. Failure after conversion\r\nWhen I try to convert my model with the code in Test class I receive this error:\r\nloc(fused[\"ReadVariableOp:\", \"sequential_1/conv2d_1/ReadVariableOp@__inference_serving_default_285\"]): error: missing attribute 'value'\r\nLLVM ERROR: Failed to infer result type(s).\r\n\r\nPlease help me, I've been stuck on this error for weeks...",
"@NotPjoker05 , \r\nPlease provide the input data to reproduce the error.\r\nThank You",
"Sure, this is my dataset:\r\nhttps://mega.nz/file/UrVUEDIK#mHkxTofcMjTWiBMzbALDDbnh1CJkZgpsKTezd5zgXTc \r\n\r\nThank you very much!\r\n",
"Hi @NotPjoker05 ,\r\n\r\nSorry for the delay, I have executed the code in colab 2.15. It's working as expected. The saved model inference and tflite inference(both ```model.tflite``` and ```converted_model.tflite```) are the same as expected. Please find the [gist.](https://colab.research.google.com/gist/LakshmiKalaKadali/879c701a8a66ea624071ecc0c2ddb923/-62610.ipynb)\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/62610\">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/62610\">No</a>\n",
"Same issue. Colab crash without any error..",
"Just in case it helps others like me struggling with this issue. I realized I was using TensorFlow 2.16.1 , from docker image tensorflow:latest-gpu and that was the source of the problem.\r\nI switched to docker image tensorflow:2.15.0-gpu and the problem went away, all working fine now.",
"@adamantivm Thanks for helping with my project, good idea! Solved this problem.",
"Same issue."
] | 2023-12-10T22:08:57 | 2024-05-17T10:41:22 | 2024-01-06T01:48:34 |
NONE
| null | null | null |
Hi, I'm trying to convert my model (saved in 'saved model' format) to a tflite model but I get an error, this is my code:
`converter = tf.lite.TFLiteConverter.from_saved_model('saved_model')
tflite_model = converter.convert()
with open('model.tflite', 'wb') as f:
f.write(tflite_model)`
The error is this:
_**loc(fused["ReadVariableOp:", "sequential_1/conv2d_1/ReadVariableOp@__inference_serving_default_285"]): error: missing attribute 'value'
LLVM ERROR: Failed to infer result type(s).**_
I read the tensorflow page related to the topic and it explains that a refactoring of my model is probably necessary, so I tried to follow the indication but the error I get is the same (my other code is this:)
`
import tensorflow as tf
converter = tf.lite.TFLiteConverter.from_saved_model('saved_model')
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.
tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.
]
tflite_model = converter.convert()
open("converted_model.tflite", "wb").write(tflite_model)
`
I hope someone is able to help me, thanks in advance
|
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Tensorflow-Lite CMake Build Fails On Windows With VS Build Generator
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[
"@embeddetech Could you ensure that you're not building with options related to Python bindings or Unix-specific features if you don't need them. Please make sure the appropriate requirements for the required dependencies like pybind11 are added to your project settings.\r\nThank you!",
"Hi @sushreebarsa , \r\nI didn't specify any settings, as shown above, so it would be just building with the default settings. I would expect that building with the default settings should work. \r\n\r\nI have used Tensorflow Lite for many years, and it has never built properly on WIndows. I have had to spend months to get it working. Now I'm getting back to it again, and am much more familiar with CMake, more understanding of what's happening when I follow the build instructions. I followed these instructions:\r\n\r\nhttps://www.tensorflow.org/lite/guide/build_cmake\r\n\r\n",
"@embeddetech Could you double-check your CMake command line. Ensure you're passing the necessary flags for your platform and desired build configurations.\r\nUse the -DCMAKE_VERBOSE_MAKEFILE=ON flag to see more detailed information about the build process and identify potential missing dependencies.\r\nPlease let us know the outcome?\r\nThank you!",
"Hi @sushreebarsa ,\r\nAs shown above and reproduced in the video below, I don't have any necessary flags for my platform. Visual Studio is my default platform, and that is the desired build system. I would expect the build to not fail when using the default configuration. I believe the video below should be reproducible, and represents a build problem with Tensorflow that definitely needs to be resolved. Thanks!\r\n\r\n\r\nhttps://github.com/tensorflow/tensorflow/assets/13501663/1e185200-ae09-4ecf-a4fb-4a790efc7317\r\n\r\n",
"Hi @pkgoogle,\r\n\r\nPlease look into the issue\r\n\r\nThank You",
"Hi @embeddetech,\r\n\r\nI was able to build the \"tensorflow-lite\" solution in VS 2022 community:\r\n\r\n\r\nThis was on master, can you try again with master or nightly?",
"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 @pkgoogle,\r\nI tried again and got the same results:\r\n\r\n\r\n\r\nThe exact steps are below. \r\n\r\nCurrent working directory is C:\\tensorflow_test:\r\ngit clone https://github.com/tensorflow/tensorflow\r\nmkdir tensorflow_build\r\ncd tensorflow_build\r\ncmake ..\\tensorflow\\tensorflow\\lite\r\ncmake --build .\r\n\r\nAfter running the CMake build command, it ran to completion, and didn't produce any obvious errors, but also doesn't clearly indicate that it built successfully. When I open it in VS2022 and build it, it fails with the same errors as before.\r\n\r\n\r\n\r\n",
"\r\nhttps://github.com/tensorflow/tensorflow/assets/13501663/010d6f5e-bad6-44cd-85b2-e826385e76b9\r\n\r\n",
"Hi @embeddetech, I suspect our difference is due to environmental issues, can you check a couple of things for me:\r\n\r\nCheck your workloads in VS (Tools -> Get Tools and Features...), here's mine:\r\n\r\n\r\nFrom your error message, it seems python related so ensure you have the Python development one, but it might not hurt to just match mine for now.\r\n\r\nI am using a Windows Server 2022 Datacenter OS so that's a possibility... also what is your cmake version? mine is 3.28.1, lastly are you using command prompt or powershell? Thanks for your help.",
"Hi @pkgoogle ,\r\nI just tried with Powershell and got the same results. I had not installed the Python workload or Data Science workload, I installed those and got the same results without regenerating the build system (I'm going to try that now). I am using CMake 3.24.2.",
"@pkgoogle Update: I installed the latest version of CMake, 3.28.1, did a clean build generation and build from PowerShell, and got the same results:\r\n\r\n\r\n",
"@pkgoogle I also installed the Python and Data Science workloads for the above test.",
"Hi @embeddetech, have you tried with the \"Linux & embedded development with C++\" workload, reason being some of your missing headers are GNU C headers/functions/libraries, perhaps that will install it, which solution/project are you opening exactly? Also give me the exact steps for what you are doing in VS ... like:\r\n\r\n1. Open <path/to/(project|solution)/from/tf-root>\r\n2. Set to debug\r\n3. Click Green Run Button\r\n\r\nPlease adjust to what you are actually doing. Thanks for your help.",
"Hi @pkgoogle ,\r\nSorry, I forgot to mention that my \"Linux & embedded development with C++\" workload is also installed. Please see below for my VS2022 installation config.\r\n\r\n\r\n\r\nMy working test folder begins with C:\\tensorflow_test, and I clone the tensorflow with git clone https://github.com/tensorflow/tensorflow as outlined above. My build folder is located at C:\\tensorflow_test\\tensorflow_build. After running the CMake build generation and build, my build system/Visual Studio solution is located at \"C:\\tensorflow_test\\tensorflow_build\\tensorflow-lite.sln\". \r\n\r\n1) Open Visual Studio 2022 (in admin mode, for kicks).\r\n2) Open the solution at C:\\tensorflow_test\\tensorflow_build\\tensorflow-lite.sln\r\n3) Confirm that the \"ALL_BUILD\" project is \"Set As Startup Project\".\r\n4) Confirm that the build configuration is set to \"Debug\", platform \"x64\". \r\n4) Click \"Build->Build Solution\".\r\n\r\nThe build fails as shown by the errors above. I'll dump the build outputs in the message to follow.\r\n\r\n",
"Build started at 3:20 PM...\r\n1>------ Skipped Build: Project: uninstall, Configuration: Debug x64 ------\r\n1>Project not selected to build for this solution configuration \r\n2>------ Skipped Build: Project: flatbuffers-flatc, Configuration: Debug x64 ------\r\n2>Project not selected to build for this solution configuration \r\n3>------ Skipped Build: Project: NightlyMemoryCheck, Configuration: Debug x64 ------\r\n3>Project not selected to build for this solution configuration \r\n4>------ Skipped Build: Project: Nightly, Configuration: Debug x64 ------\r\n4>Project not selected to build for this solution configuration \r\n5>------ Skipped Build: Project: Experimental, Configuration: Debug x64 ------\r\n5>Project not selected to build for this solution configuration \r\n6>------ Skipped Build: Project: Continuous, Configuration: Debug x64 ------\r\n6>Project not selected to build for this solution configuration \r\n7>------ Build started: Project: label_image, Configuration: Debug x64 ------\r\n8>------ Build started: Project: benchmark_model, Configuration: Debug x64 ------\r\n9>------ Build started: Project: _pywrap_tensorflow_interpreter_wrapper, Configuration: Debug x64 ------\r\n10>------ Skipped Build: Project: INSTALL, Configuration: Debug x64 ------\r\n10>Project not selected to build for this solution configuration \r\n9>interpreter_wrapper.cc\r\n7>bitmap_helpers.cc\r\n8>register_custom_op.obj : error LNK2005: \"void __cdecl RegisterSelectedOps(class tflite::MutableOpResolver *)\" (?RegisterSelectedOps@@YAXPEAVMutableOpResolver@tflite@@@Z) already defined in benchmark_tflite_model.obj\r\n9>C:\\tensorflow_test\\tensorflow\\tensorflow\\lite\\python\\interpreter_wrapper\\interpreter_wrapper.h(28,10): error C1083: Cannot open include file: 'Python.h': No such file or directory\r\n9>(compiling source file '../tensorflow/tensorflow/lite/python/interpreter_wrapper/interpreter_wrapper.cc')\r\n9>interpreter_wrapper_pybind11.cc\r\n9>C:\\tensorflow_test\\tensorflow\\tensorflow\\lite\\python\\interpreter_wrapper\\interpreter_wrapper_pybind11.cc(19,10): error C1083: Cannot open include file: 'pybind11/functional.h': No such file or directory\r\n9>numpy.cc\r\n9>C:\\tensorflow_test\\tensorflow\\tensorflow\\lite\\python\\interpreter_wrapper\\numpy.h(49,10): error C1083: Cannot open include file: 'Python.h': No such file or directory\r\n9>(compiling source file '../tensorflow/tensorflow/lite/python/interpreter_wrapper/numpy.cc')\r\n9>python_error_reporter.cc\r\n9>C:\\tensorflow_test\\tensorflow\\tensorflow\\lite\\python\\interpreter_wrapper\\python_error_reporter.h(19,10): error C1083: Cannot open include file: 'Python.h': No such file or directory\r\n9>(compiling source file '../tensorflow/tensorflow/lite/python/interpreter_wrapper/python_error_reporter.cc')\r\n9>python_utils.cc\r\n9>C:\\tensorflow_test\\tensorflow\\tensorflow\\lite\\python\\interpreter_wrapper\\python_utils.h(19,10): error C1083: Cannot open include file: 'Python.h': No such file or directory\r\n9>(compiling source file '../tensorflow/tensorflow/lite/python/interpreter_wrapper/python_utils.cc')\r\n9>Generating Code...\r\n9>Done building project \"_pywrap_tensorflow_interpreter_wrapper.vcxproj\" -- FAILED.\r\n7>label_image.cc\r\n7>C:\\tensorflow_test\\tensorflow\\tensorflow\\lite\\examples\\label_image\\bitmap_helpers.cc(18,10): error C1083: Cannot open include file: 'unistd.h': No such file or directory\r\n8>C:\\tensorflow_test\\tensorflow_build\\tools\\benchmark\\Debug\\benchmark_model.exe : fatal error LNK1169: one or more multiply defined symbols found\r\n8>Done building project \"benchmark_model.vcxproj\" -- FAILED.\r\n7>C:\\tensorflow_test\\tensorflow\\tensorflow\\lite\\examples\\label_image\\label_image.cc(19,10): error C1083: Cannot open include file: 'getopt.h': No such file or directory\r\n7>stats_calculator.cc\r\n7>memory_info.cc\r\n7>profile_summarizer.cc\r\n7>profile_summary_formatter.cc\r\n7>time.cc\r\n7>command_line_flags.cc\r\n7>default_execution_provider.cc\r\n7>delegate_provider.cc\r\n7>utils.cc\r\n7>tool_params.cc\r\n7>xnnpack_delegate_provider.cc\r\n7>xnnpack_plugin.cc\r\n7>Generating Code...\r\n7>Done building project \"label_image.vcxproj\" -- FAILED.\r\n========== Build: 0 succeeded, 3 failed, 152 up-to-date, 7 skipped ==========\r\n========== Build completed at 3:20 PM and took 19.594 seconds ==========\r\n",
"@pkgoogle If you can provide your path to the python.h file on your system, that might help me see wha I'm missing...?",
"Hi @embeddetech, I was actually able to replicate with your exact instructions ... I think I built a different file or built it a different way, for convenience I will summarize the steps here to replicate:\r\n\r\n1. (in powershell)\r\n```\r\ngit clone https://github.com/tensorflow/tensorflow\r\nmkdir tensorflow_build\r\ncd tensorflow_build\r\ncmake ..\\tensorflow\\tensorflow\\lite\r\ncmake --build .\r\n```\r\n\r\n2. Open tensorflow_build\\tensorflow-lite.sln in VS Community 2022 with Admin privileges\r\n3. Confirm that the build configuration is set to \"Debug\", platform \"x64\".\r\n4. Click \"Build->Build Solution\".\r\n\r\nHi @terryheo, can you please take a look? Thanks."
] | 2023-12-10T17:39:22 | 2024-01-10T19:50:15 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15.0 (latest)
### Custom code
No
### OS platform and distribution
Windows 11
### 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-lite build fails on Windows using CMake with Visual Studio 2022. In my experience, Tensorflow lite has never properly built with Visual Studio projects, and this is a major oversight.
### Standalone code to reproduce the issue
```shell
Exact steps to reproduce:
git clone https://github.com/tensorflow/tensorflow
mkdir tensorflow_build
cd tensorflow_build
cmake ..\tensorflow\tensorflow\lite
cmake --build .
```
### Relevant log output
```shell
Running the above, from the CMake output, it wasn't clear whether it built successfully or not, it seemed to just stop generating output:
root_profiler.cc
profiler.cc
telemetry_setting_internal.cc
sparsity_format_converter.cc
schema_utils.cc
Generating Code...
After then opening the Visual Studio 2022 project and building, it fails.
C1083: Cannot open include file 'sys/mman.h': No such file or directory.
LNK1104: Cannot open file '..\..\Debug\tensorflow-lite.lib'
C1083: Cannot open include file: 'Python.h': No such file or directory
C1083: Cannot open include file: 'pybind11/functional.h': No such file or directory
C1083: Cannot open include file: 'Python.h': No such file or directory
C1083: Cannot open include file: 'Python.h': No such file or directory
C1083: Cannot open include file: 'Python.h': No such file or directory
C1083: Cannot open include file: 'unistd.h': No such file or directory
C1083: Cannot open include file: 'getopt.h': No such file or directory
PLEASE help resolve these Visual Studio build problems. I have great need of tflite as well as tflite-micro to build in Visual Studio.
```
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PR_kwDOArmXAs5hnTYC
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Update flatbuffer_conversions.cc
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[
"Still contains a typo. It is _understand_ and not _unterstand_",
"fixed the typo. Pls re-evaluate.",
"Hi @Cassini-chris Please don't use \"add file\"/\"update file\"/\"fix file\"/etc. commit messages. These are hard to reason about when looking at the history of the file/repository. Instead, please write explanatory git commit messages.\r\n\r\nThe commit message is also the title of the PR if the PR has only one commit. It is thus twice important to have commit messages that are relevant, as PRs would be easier to understand and easier to analyze in search results.\r\n\r\nFor how to write good quality git commit messages, please consult https://cbea.ms/git-commit/\r\n\r\nThank you for your contribution!\r\n"
] | 2023-12-10T14:14:30 | 2023-12-16T05:40:06 | 2023-12-16T05:40:06 |
CONTRIBUTOR
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No registered '_MklLayerNorm' OpKernel for 'GPU' devices compatible with node {{node custom_model/layer_normalization/add}}
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[
"@YmdTb \r\nI was able to run the code successfully on colab, please find the gist [here](https://colab.research.google.com/gist/sushreebarsa/16e688d7193bb0c3596bbdd6622be702/untitled917.ipynb#scrollTo=MmcQoxDOfcLj).\r\nIf you are using an older version of MKL, update it to the latest version. This may resolve the compatibility issues.\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.",
"Encountered same issue, updating MKL to the latest version does not work.\r\nFound out that using the default epsilon=1e-3 in `LayerNormalization` will trigger this error, whereas setting it to a smaller float, say 1e-7 will not trigger the error. Try modifying epsilon to see if it works for you.",
"Had the same experience as @Yuanjimengmengda. Changing the epsilon value solved the problem.",
"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/62607\">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/62607\">No</a>\n",
"This issue is still present. I installed Ubuntu 23.10, installed nvidia drivers 545:\r\n\r\n+---------------------------------------------------------------------------------------+\r\n| NVIDIA-SMI 545.29.06 Driver Version: 545.29.06 CUDA Version: 12.3 |\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 NVIDIA GeForce RTX 4090 Off | 00000000:01:00.0 On | Off |\r\n| 0% 33C P8 28W / 450W | 465MiB / 24564MiB | 4% 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| 0 N/A N/A 5120 G /usr/lib/xorg/Xorg 233MiB |\r\n| 0 N/A N/A 5261 G /usr/bin/gnome-shell 55MiB |\r\n| 0 N/A N/A 9628 G ...51,262144 --variations-seed-version 160MiB |\r\n+---------------------------------------------------------------------------------------+\r\n\r\nCreated a new venv, installed tensorflow according to standard directions and got the following when running the given code above:\r\n\r\n2024-02-04 10:44:52.974186: W tensorflow/core/grappler/utils/graph_view.cc:849] No registered '_MklLayerNorm' OpKernel for GPU devices compatible with node {{node custom_model/layer_normalization/add}}\r\n\t. Registered: device='CPU'; T in [DT_BFLOAT16]\r\n device='CPU'; T in [DT_FLOAT]\r\n\r\nTraceback (most recent call last):\r\n File \"/home/nicholaus/git/glpwd.com/git/main/python/pepper/src/broken.py\", line 40, in <module>\r\n predictions = model.predict(X_test)\r\n ^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/nicholaus/venvs/pepper/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py\", line 70, in error_handler\r\n raise e.with_traceback(filtered_tb) from None\r\n File \"/home/nicholaus/venvs/pepper/lib/python3.11/site-packages/tensorflow/python/eager/execute.py\", line 53, in quick_execute\r\n tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\ntensorflow.python.framework.errors_impl.NotFoundError: Graph execution error:\r\n\r\nDetected at node custom_model/layer_normalization/add defined at (most recent call last):\r\n File \"/home/nicholaus/git/glpwd.com/git/main/python/pepper/src/broken.py\", line 40, in <module>\r\n\r\n File \"/home/nicholaus/venvs/pepper/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n\r\n File \"/home/nicholaus/venvs/pepper/lib/python3.11/site-packages/keras/src/engine/training.py\", line 2655, in predict\r\n\r\n File \"/home/nicholaus/venvs/pepper/lib/python3.11/site-packages/keras/src/engine/training.py\", line 2440, in predict_function\r\n\r\n File \"/home/nicholaus/venvs/pepper/lib/python3.11/site-packages/keras/src/engine/training.py\", line 2425, in step_function\r\n\r\n File \"/home/nicholaus/venvs/pepper/lib/python3.11/site-packages/keras/src/engine/training.py\", line 2413, in run_step\r\n\r\n File \"/home/nicholaus/venvs/pepper/lib/python3.11/site-packages/keras/src/engine/training.py\", line 2381, in predict_step\r\n\r\n File \"/home/nicholaus/venvs/pepper/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n\r\n File \"/home/nicholaus/venvs/pepper/lib/python3.11/site-packages/keras/src/engine/training.py\", line 590, in __call__\r\n\r\n File \"/home/nicholaus/venvs/pepper/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n\r\n File \"/home/nicholaus/venvs/pepper/lib/python3.11/site-packages/keras/src/engine/base_layer.py\", line 1149, in __call__\r\n\r\n File \"/home/nicholaus/venvs/pepper/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py\", line 96, in error_handler\r\n\r\n File \"/home/nicholaus/git/glpwd.com/git/main/python/pepper/src/broken.py\", line 23, in call\r\n\r\n File \"/home/nicholaus/venvs/pepper/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\r\n\r\n File \"/home/nicholaus/venvs/pepper/lib/python3.11/site-packages/keras/src/engine/base_layer.py\", line 1149, in __call__\r\n\r\n File \"/home/nicholaus/venvs/pepper/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py\", line 96, in error_handler\r\n\r\n File \"/home/nicholaus/venvs/pepper/lib/python3.11/site-packages/keras/src/layers/normalization/layer_normalization.py\", line 343, in call\r\n\r\nNo registered '_MklLayerNorm' OpKernel for 'GPU' devices compatible with node {{node custom_model/layer_normalization/add}}\r\n\t. Registered: device='CPU'; T in [DT_BFLOAT16]\r\n device='CPU'; T in [DT_FLOAT]\r\n\r\n\t [[custom_model/layer_normalization/add]] [Op:__inference_predict_function_1537]\r\n\r\nNote that I'm here after trying numerous other things on Ubuntu 22.04, including running 5.15 kernel, multiple nvidia driver versions, using conda instead of pip, etc.\r\n",
"I am having the same issue when I use the latest docker colab local runner. Using CPU works. Anyone has a solution for that?",
"I solved this error.\r\nAs the other people said, this error is occurred by the value of epsilon, which is one of the arguments of **LayerNormalization** layer in keras API.\r\nhttps://keras.io/api/layers/normalization_layers/layer_normalization/\r\nYou can see the specification of LayerNormalization layer in the above Web page.\r\n\r\n```\r\nkeras.layers.LayerNormalization(\r\n axis=-1,\r\n epsilon=0.001,\r\n center=True,\r\n scale=True,\r\n rms_scaling=False,\r\n beta_initializer=\"zeros\",\r\n gamma_initializer=\"ones\",\r\n beta_regularizer=None,\r\n gamma_regularizer=None,\r\n beta_constraint=None,\r\n gamma_constraint=None,\r\n \\*\\*kwargs\r\n)\r\n```\r\n\r\nThe default value of epsilon is 0.001(1e-3). I fixed it into 1e-7 and now I'm free from this horrible error.\r\n\r\nMy graphic card is RTX3060 and your graphic card is RTX4070 Ti, these two graphic cards do not support float16 data type operation in keras(I don't know why). And you can see **[DT_BFLOAT16]** in your error log. In the LayerNormalization layer, there's such an operation which adds **epsilon** to some value. Its default value is 1e-3(float16 type) so the error is occurred. If you fix the value of epsilon into 1e-7, its data type will be changed into float32 and the error will not be occurred.\r\n\r\nLike this\r\n```\r\n...\r\n self.layernorm_1 = layers.LayerNormalization(epsilon=1e-7)\r\n self.layernorm_2 = layers.LayerNormalization(epsilon=1e-7)\r\n...\r\n```",
"> I am having the same issue when I use the latest docker colab local runner. Using CPU works. Anyone has a solution for that?\r\n\r\nI replied below. Hope it works."
] | 2023-12-10T09:24:03 | 2024-02-16T13:41:04 | 2024-01-04T01:48:37 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15.0
### Custom code
Yes
### OS platform and distribution
Docker image: tensorflow/tensorflow:2.15.0-gpu
### Mobile device
Ubuntu 22.04.3 LTS
### Python version
3.11.0rc1
### Bazel version
_No response_
### GCC/compiler version
11.4.0
### CUDA/cuDNN version
12.3
### GPU model and memory
_No response_
### Current behavior?
I used Add and LayerNormalization layers at the same time. The fit works well. But when it comes to predict or anything else, it will occur errors. Detail is below.
Hint: If I use with tf.device('/cpu:0'): before predict, it is good. Idon't know why the prediction can't work in gpu.
What I want is to run all the train and predict on the gpu.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
from tensorflow.keras.layers import Dense, Add, LayerNormalization
from tensorflow.keras.models import Model
class CustomModel(tf.keras.Model):
def __init__(self):
super(CustomModel, self).__init__()
self.dense1 = Dense(64, activation='relu')
self.dense2 = Dense(32, activation='relu')
self.dense3 = Dense(10, activation='relu')
self.output_layer = Dense(10)
self.add_layer = Add()
self.norm_layer = LayerNormalization()
def call(self, inputs):
x = self.dense1(inputs)
x = self.dense2(x)
x = self.dense3(x)
x = self.add_layer([x, inputs])
x = self.norm_layer(x)
return self.output_layer(x)
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
model = CustomModel()
model.compile(optimizer='adam', loss='mse')
import numpy as np
X_train = np.random.rand(100, 10)
y_train = np.random.rand(100, 10)
model.fit(X_train, y_train, epochs=5)
X_test = np.random.rand(10, 10)
predictions = model.predict(X_test)
print(predictions)
```
### Relevant log output
```shell
2023-12-10 09:21:40.012550: 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`.
2023-12-10 09:21:40.038118: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2023-12-10 09:21:40.038186: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2023-12-10 09:21:40.039635: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2023-12-10 09:21:40.048947: 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.
2023-12-10 09:21:41.218693: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] 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.
2023-12-10 09:21:41.221912: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] 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.
2023-12-10 09:21:41.221964: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] 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.
Num GPUs Available: 1
2023-12-10 09:21:41.226791: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] 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.
2023-12-10 09:21:41.226836: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] 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.
2023-12-10 09:21:41.226858: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] 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.
2023-12-10 09:21:41.328324: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] 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.
2023-12-10 09:21:41.328371: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] 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.
2023-12-10 09:21:41.328385: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2022] Could not identify NUMA node of platform GPU id 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2023-12-10 09:21:41.328410: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] 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.
2023-12-10 09:21:41.328431: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9516 MB memory: -> device: 0, name: NVIDIA GeForce RTX 4070 Ti, pci bus id: 0000:01:00.0, compute capability: 8.9
Epoch 1/5
2023-12-10 09:21:44.298982: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8906
2023-12-10 09:21:44.471868: I external/local_xla/xla/service/service.cc:168] XLA service 0x7fdcd0a348f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2023-12-10 09:21:44.471895: I external/local_xla/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 4070 Ti, Compute Capability 8.9
2023-12-10 09:21:44.475045: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1702200104.523638 25028 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
4/4 [==============================] - 3s 4ms/step - loss: 1.4712
Epoch 2/5
4/4 [==============================] - 0s 3ms/step - loss: 1.3851
Epoch 3/5
4/4 [==============================] - 0s 3ms/step - loss: 1.3103
Epoch 4/5
4/4 [==============================] - 0s 3ms/step - loss: 1.2321
Epoch 5/5
4/4 [==============================] - 0s 3ms/step - loss: 1.1533
2023-12-10 09:21:45.056658: W tensorflow/core/grappler/utils/graph_view.cc:849] No registered '_MklLayerNorm' OpKernel for GPU devices compatible with node {{node custom_model/layer_normalization/add}}
. Registered: device='CPU'; T in [DT_BFLOAT16]
device='CPU'; T in [DT_FLOAT]
Traceback (most recent call last):
File "/pycharm_project/tttt.py", line 48, in <module>
predictions = model.predict(X_test)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/eager/execute.py", line 53, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
tensorflow.python.framework.errors_impl.NotFoundError: Graph execution error:
Detected at node custom_model/layer_normalization/add defined at (most recent call last):
File "/pycharm_project/tttt.py", line 48, in <module>
File "/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
File "/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py", line 2655, in predict
File "/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py", line 2440, in predict_function
File "/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py", line 2425, in step_function
File "/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py", line 2413, in run_step
File "/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py", line 2381, in predict_step
File "/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
File "/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py", line 590, in __call__
File "/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
File "/usr/local/lib/python3.11/dist-packages/keras/src/engine/base_layer.py", line 1149, in __call__
File "/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py", line 96, in error_handler
File "/pycharm_project/tttt.py", line 25, in call
File "/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
File "/usr/local/lib/python3.11/dist-packages/keras/src/engine/base_layer.py", line 1149, in __call__
File "/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py", line 96, in error_handler
File "/usr/local/lib/python3.11/dist-packages/keras/src/layers/normalization/layer_normalization.py", line 343, in call
No registered '_MklLayerNorm' OpKernel for 'GPU' devices compatible with node {{node custom_model/layer_normalization/add}}
. Registered: device='CPU'; T in [DT_BFLOAT16]
device='CPU'; T in [DT_FLOAT]
[[custom_model/layer_normalization/add]] [Op:__inference_predict_function_1537]
```
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| 2,033,950,384 |
PR_kwDOArmXAs5hl5Vt
| 62,605 |
Quantization scale verification to prevent spurious tf-tfl conversion
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"I am tagging @pkgoogle as he was so kind to advice me on #62196. \r\n\r\nI worked on the bug and created a draft PR. The main culprit for the spurious transformation are the pathological quantization scales. See below for an example of the MLIR debug output:\r\n```\r\n %22 = \"tfl.quantize\"(%21) {qtype = tensor<1x64x112x112x!quant.uniform<i8:f32, 1.3344405750530544E+36:127>>, volatile} : (tensor<1x64x112x112xf32>) -> ...\r\n```\r\nI have added a verification check aborting the quantization if the scale parameter exceeds `1e+10`. TF [already considers](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/mlir/lite/quantization/quantization_utils.h#L307) a quantization scale above `1e+1` problematic, and the [SmoothQuant paper](https://arxiv.org/abs/2211.10438) considers values of `7e+1` (for foundation models) to pose serious challenges. \r\n\r\nThe original [gist](https://colab.research.google.com/gist/pjpratik/f71dec9bd1f702c1a62fb8505b7d4a50/62196.ipynb) produces the following output when run with the code from the PR:\r\n```\r\nRuntimeError: Failed to quantize: <unknown>:0: error: loc(\"PadV2/constant_values\"): 'tfl.quantize' op Quant scale (1.334441e+36) too large\r\n<unknown>:0: note: loc(\"PadV2/constant_values\"): see current operation: %4 = \"tfl.quantize\"(%3) {qtype = tensor<!quant.uniform<i8:f32, 1.3344405750530544E+36:127>>, volatile} : (tensor<f32>) -> tensor<!quant.uniform<i8:f32, 1.3344405750530544E+36:127>>\r\n```\r\n\r\nThe draft PR also contains a test. In addition to the submitted code, I would still like to add the following:\r\n- Tests for `tfl.quantize` with `UniformQuantizedPerAxisType` quantization\r\n- Verifier for DequantizeOp and associated tests\r\n- End2End tests?\r\n\r\nBefore adding the functionality above, I would like to ask you two questions for clarification: \r\n- Do you agree with the broad contours of this PR, or have suggestions? \r\n- The tests I added (`@satisfy{violate}Threshold`)do not produce any output. (Verify this by running `./bazel-bin/tensorflow/compiler/mlir/tf-opt -verify-diagnostics -tfl-runtime-verify tensorflow/compiler/mlir/lite/tests/ops.mlir > ops_output.mlir` and see that the tests are missing). I'm surprised by this but realize that `@testQuantizedResizeNearestNeighbor` does not produce output, while `@testQuantize` does. Can you explain the different behavior of the two tests, what I need to do for @satisfyThreshold to be printed, and (just for my understanding) what utility the test of @testQuantizedResizeNearestNeighbor has if it does not produce any output?\r\n\r\nI am also tagging @jpienaar here. He kindly answered an [MLIR question](https://discourse.llvm.org/t/what-impact-does-a-failing-op-verifier-have-on-the-optimization-process/75450) I had, and I thought it might be worthwhile to add him too. \r\n\r\n\r\n",
"Hi @FabianSchuetze, Thank you for your hard work and dedication. I'm trying to understand the root cause a little bit better, so in the original model pre-quantization all the float32's are essentially in the 10^-36 range so our calculated quantization scale to invert that is essentially in the 10^36 range, which when paired with reduced precision in the quantized realm, causes issues. Is my understanding more or less correct there?",
"Hi @pkgoogle : Thanks a lot for your reply! I didn't get to work on this issue in the last days, but will do so soon and answer your questions. ",
"Thanks for your comment & question, @pkgoogle . Thanks to it, I have identified the source of the bug, and we can now reproduce it in a stylized program. \r\n\r\nPlease see the first cell of this [gist](https://colab.research.google.com/drive/1Jr6StaZEFJexUtBk98riYEUK617a-eDb?usp=sharing). The three aspects explaining the bug are:\r\n\r\nNetwork Architecture:\r\n- The program has a pad operation with a constant of ~ -3.4e+38 (lowest float32) directly before a maxpool operation. \r\n- The padding constant does not get fused with the maxpool operation by the compiler. In the model described here, the maxpool filter size exceeds the padding size. Thus, the padded constants get lost in the maxpool. \r\n\r\nDuring Quantization:\r\n- The padding constant gets quantized: - 3.4e+38 ~ 1.ee+36 * (q - 127) with quantized value of q=-128. \r\n- The quantization parameters get propagated to subsequent layers.\r\n\r\nUpon Loading:\r\n- The quantized add operation [gets parameterized](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/kernels/add.cc#L88): The scales of the inputs get loaded and violate their constraint that the \"real_output_multiplier\" is [below one](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/kernels/add.cc#L197) upon which the program aborts. \r\n\r\nI think these three aspects can explain the bug. \r\n\r\nNevertheless, I am surprised that the maxpool output has such a large scaling parameter. Consider the second cell of the gist. It prints the extrema of the input and output tensors of the maxpool operator:\r\n```\r\nExtrema Maxpool Inp 3.402823E+38, Output: 9.999899E-01\r\nExtrema Maxpool Inp 3.402823E+38, Output: 9.999982E-01\r\nExtrema Maxpool Inp 3.402823E+38, Output: 9.999997E-01\r\n```\r\nThe padding value explains the large (absolute values) of the input. The output is small because the maxpool absorbs the negative padding constants. Might this be worth to investigate? ",
"Hi @abattery, may you add any insight here?",
"Hi @abattery Any update on this PR? Please. Thank you!",
"Would also be interested in a feedback and also willing to continue work on this once I’ve heard some feedback . Thanks ",
"Hi @abattery Any update on this PR? Please. Thank you!",
"Hi @abattery Any update on this PR? Please. Thank you!",
"This is similar to the other issue which produced this change: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/kernels/sub.cc#L156. In that case the quantization scale was considered \"valid\" so we let the check through i.e. allowed it to be > 1. I haven't completely reviewed the case here, but if it is \"valid\" then we can allow it through as well."
] | 2023-12-09T17:15:57 | 2024-06-07T17:42:25 | null |
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Addresses https://github.com/tensorflow/tensorflow/issues/62196
In some situations, the tf-tfl converter can produce spurious quantization scales. The conversion nevertheless passes, but the tensorflow lite interpreter aborts when allocating tensors for the model. The PR adds a verification check for the mlir QuantOps (and DequantizeOps) ops.
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QuantOps verifier asserts that quant scale is below 1e+10
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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/62604/checks?check_run_id=19477354951) 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.",
"As @pkgoogle was already so kind to help me with #62196 , I decided to tag him here too. \r\n\r\nI worked on the bug and created a draft PR. The main culprit for the spurious transformation are the pathological quantization scales. See below for an example of the MLIR debug output:\r\n```\r\n %22 = \"tfl.quantize\"(%21) {qtype = tensor<1x64x112x112x!quant.uniform<i8:f32, 1.3344405750530544E+36:127>>, volatile} : (tensor<1x64x112x112xf32>) -> ...\r\n```\r\nI have added a verification check aborting the quantization if the scale parameter exceeds `1e+10`. TF [already considers](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/mlir/lite/quantization/quantization_utils.h#L307) a quantization scale above `1e+1` problematic, and the [SmoothQuant paper](https://arxiv.org/abs/2211.10438) considers values of `7e+1` (for foundation models) to pose serious challenges. \r\n\r\nThe original [gist.py](https://colab.research.google.com/gist/pjpratik/f71dec9bd1f702c1a62fb8505b7d4a50/62196.ipynb) produces the following output when run with the code from the PR:\r\n```\r\nRuntimeError: Failed to quantize: <unknown>:0: error: loc(\"PadV2/constant_values\"): 'tfl.quantize' op Quant scale (1.334441e+36) too large\r\n<unknown>:0: note: loc(\"PadV2/constant_values\"): see current operation: %4 = \"tfl.quantize\"(%3) {qtype = tensor<!quant.uniform<i8:f32, 1.3344405750530544E+36:127>>, volatile} : (tensor<f32>) -> tensor<!quant.uniform<i8:f32, 1.3344405750530544E+36:127>>\r\n```\r\n\r\nThe draft PR also contains a test. In addition to the submitted code, I would still like to add the following:\r\n- Tests for `tfl.quantize` with `UniformQuantizedPerAxisType` quantization\r\n- Verifier for DequantizeOp and associated tests\r\n- End2End tests?\r\n\r\nBefore adding the functionality above, I would like to ask you two questions for clarification: \r\n- Do you agree with the broad contours of this PR, or have suggestions? \r\n- The tests I added (`@satisfy{violate}Threshold`)do not produce any output. (Verify this by running `./bazel-bin/tensorflow/compiler/mlir/tf-opt -verify-diagnostics -tfl-runtime-verify tensorflow/compiler/mlir/lite/tests/ops.mlir > ops_output.mlir` and see that the tests are missing). I'm surprised by this but realize that `@testQuantizedResizeNearestNeighbor` does not produce output, while `@testQuantize` does. Can you explain the different behavior of the two tests, what I need to do for @satisfyThreshold to be printed, and (just for my understanding) what utility the test of @testQuantizedResizeNearestNeighbor has if it does not produce any output?\r\n\r\nI am also tagging @jpienaar here. He kindly answered an [MLIR question](https://discourse.llvm.org/t/what-impact-does-a-failing-op-verifier-have-on-the-optimization-process/75450) I had, and I thought it might be worthwhile to add him too. \r\n\r\n\r\n\r\n\r\n"
] | 2023-12-09T16:14:42 | 2023-12-09T17:12:52 | 2023-12-09T17:12:40 |
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Addresses #62196
In some situations, the tf-tfl converter can produce spurious quantization scales. The conversion nevertheless passes, but the tensorflow lite interpreter aborts when allocating tensors for the model. The PR adds a verification check for the mlir QuantOps (and DequantizeOps) ops.
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Process is aborted (core dumped) when axis is a large negative integer when calling tf.gather
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"Hi **@drewshark**,\r\nSorry for the delay, Here you have set the axis parameter to a constant value with a very large negative number (-9223372036854775808). The axis parameter should be a valid integer corresponding to the axis along which you want to perform the gather operation. If you want to gather elements along a specific axis, you should provide the correct axis number. I provided a [gist](https://colab.research.google.com/gist/Venkat6871/1afd6525e878c5d84b8b1821a652861e/62603_2-14_nightly-v.ipynb) for reference.\r\n\r\nThank you!",
"Hi @Venkat6871 \r\n\r\nIf tf.gather is not expected to accept axis to be a large negative number. It would be much helpful if it can throw a proper error message indicating the incorrect usage of this function. However, currently, this function call will directly lead to a program abort, which will directly kill my process. The current behavior seems dangerous.\r\n"
] | 2023-12-09T14:54:43 | 2024-05-22T07:03:59 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16.0
### 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?
I am actually using 2.10.0 but I find this issue insists in 2.16.0-dev20231209 (tf-nightly). Here is the code to reproduce:
```
import tensorflow as tf
params = tf.constant([[0.69]])
indices = tf.constant([16])
axis = tf.constant(-9223372036854775808, dtype='int64')
tf.gather(params,indices,axis=axis)
```
The process will directly be killed by the system when running above code.
Here is the related output:
```
2023-12-09 22:48:16.035701: F ./tensorflow/core/framework/tensor.h:832] Check failed: new_num_elements == NumElements() (0 vs. 1)
Aborted (core dumped)
```
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
params = tf.constant([[0.69]])
indices = tf.constant([16])
axis = tf.constant(-9223372036854775808, dtype='int64')
tf.gather(params,indices,axis=axis)
```
### Relevant log output
_No response_
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I_kwDOArmXAs55N5kC
| 62,602 |
tf.keras.layers.MaxPooling3D can not still work for bfloat 16 in tensorflow 2.12.0: No OpKernel was registered to support Op ‘MaxPool3D’
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[
"@mitsu0703,\r\nCould you please provide the complete code or the colab gist to reproduce the issue which helps us debug the issue in an effective way. 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.",
"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/62602\">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/62602\">No</a>\n"
] | 2023-12-09T07:18:54 | 2023-12-27T01:47:58 | 2023-12-27T01:47:54 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.12.0
### Custom code
Yes
### OS platform and distribution
mac ventura 13.6.1
### 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 GPU machine
### Current behavior?
I’m a researcher in the filed of neuroscience using MRI techniques.
I have the same question raised by Dr. Roger ( [Tensorflow question (MaxPool3d, MaxPool3D, MaxPooling3D)!) 1](https://discuss.tensorflow.org/t/tensorflow-question-maxpool3d-maxpool3d-maxpooling3d/11291)). I have the same trouble " No OpKernel was registered to support Op ‘MaxPool3D’".
In my understanding ([ModelCheckpoint callback fails when mixed precision is enabled in TF 2.11.0 · Issue #349 · keras-team/tf-keras · GitHub](https://github.com/keras-team/tf-keras/issues/349)), bfloat 16 did not work for bfloat 16 in the tf.keras.layers.MaxPooling3D one year ago. But the keras team fixed this issue in the verion of TF 2.12.0 or further newer verions. Is that correct? Please tell me if my understanding is correct or not. If this issue is fixed, any other problems??
### Standalone code to reproduce the issue
```shell
tf.keras.layers.MaxPooling3D in the version of tensorflow 2.12.0 did not compute bfloat 16 data.
```
### Relevant log output
```shell
The code history as follows:
input the folliwing:
python3 /Users/username/Downloads/SHIVA_PVS/predict_one_file.py -i /Users/username/mri/young_healthy/MRI_resliced.nii -m /Users/username/Downloads/SHIVA_PVS/PVS/v1/T1.PVS/20211030-162753_Unet3Dv2-10.7.2-1.8-T1.VRS_fold_1x6_pi_fold_0_model.h5 -b /Users/username/mri/young_healthy/brain_reslice.nii -o /Users/username/mri/young_healthy/pv_MRI_resliced --verbose --gpu -1
terminal returns:
2023-11-16 07:55:41.891278: 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.
Trying to run inference on GPU 0
WARNING:tensorflow:Mixed precision compatibility check (mixed_float16): WARNING
The dtype policy mixed_float16 may run slowly because this machine does not have a GPU. Only Nvidia GPUs with compute capability of at least 7.0 run quickly with mixed_float16.
If you will use compatible GPU(s) not attached to this host, e.g. by running a multi-worker model, you can ignore this warning. This message will only be logged once
INFO : Predicting fold : 20211030-162753_Unet3Dv2-10.7.2-1.8-T1.VRS_fold_1x6_pi_fold_0_model
Traceback (most recent call last):
File “/Users/username/Downloads/SHIVA_PVS/predict_one_file.py”, line 125, in
prediction = model.predict(
File “/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py”, line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File “/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/tensorflow/python/eager/execute.py”, line 53, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InvalidArgumentError: No OpKernel was registered to support Op ‘MaxPool3D’ used by {{node model/Enc_Max_D7/MaxPool3D}} with these attrs: [T=DT_HALF, data_format=“NDHWC”, ksize=[1, 2, 2, 2, 1], strides=[1, 2, 2, 2, 1], padding=“VALID”]
Registered devices: [CPU]
Registered kernels:
device=‘XLA_CPU_JIT’; T in [DT_FLOAT, DT_BFLOAT16, DT_HALF]
device=‘CPU’; T in [DT_FLOAT]
device=‘CPU’; T in [DT_BFLOAT16]
```
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Errors/Warnings in documentation webpage "https://www.tensorflow.org/tfx/transform/get_started#define_a_preprocessing_function"
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[
"@1ofseven Could you please share the different output you are observing other than the documentation?\r\nThe warnings seem to be intended, so please have a look at this [gist](https://colab.research.google.com/gist/sushreebarsa/52a06ba8579daed47f189599b6399bc4/62601.ipynb) and confirm the same?\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/62601\">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/62601\">No</a>\n"
] | 2023-12-08T23:50:42 | 2023-12-30T01:47:58 | 2023-12-30T01:47:51 |
NONE
| null | null | null |
Warning/error output on the documentation page instead of demonstration output from cell code.
webpage https://www.tensorflow.org/tfx/transform/get_started#define_a_preprocessing_function
## Example:
WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features.
WARNING:tensorflow:You are passing instance dicts and DatasetMetadata to TFT which will not provide optimal performance. Consider following the TFT guide to upgrade to the TFXIO format (Apache Arrow RecordBatch).
WARNING:tensorflow:You are passing instance dicts and DatasetMetadata to TFT which will not provide optimal performance. Consider following the TFT guide to upgrade to the TFXIO format (Apache Arrow RecordBatch).
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_transform/tf_utils.py:324: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
2023-04-13 09:15:56.867283: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:267] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_transform/tf_utils.py:324: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
WARNING:tensorflow:You are passing instance dicts and DatasetMetadata to TFT which will not provide optimal performance. Consider following the TFT guide to upgrade to the TFXIO format (Apache Arrow RecordBatch).
WARNING:tensorflow:You are passing instance dicts and DatasetMetadata to TFT which will not provide optimal performance. Consider following the TFT guide to upgrade to the TFXIO format (Apache Arrow RecordBatch).
WARNING:apache_beam.options.pipeline_options:Discarding unparseable args: ['/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/ipykernel_launcher.py', '-f', '/tmpfs/tmp/tmpzu0d2pwa.json', '--HistoryManager.hist_file=:memory:']
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpdhm3m_yu/tftransform_tmp/88750e1500194862a87b2f23e04367bc/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpdhm3m_yu/tftransform_tmp/88750e1500194862a87b2f23e04367bc/assets
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpdhm3m_yu/tftransform_tmp/8fad0af5a26242cc9733a752a7652277/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpdhm3m_yu/tftransform_tmp/8fad0af5a26242cc9733a752a7652277/assets
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:struct2tensor is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_decision_forests is not available.
INFO:tensorflow:tensorflow_text is not available.
INFO:tensorflow:tensorflow_text is not available.
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TFLite Hexagon Delegate accuracy with Depthwise conv 7x7 filter
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[
"@tensorbuffer Could you use Post-training quantization techniques instead of quantization-aware training. Also using Integer Quantization (INT8) instead of Float16 (FP16) quantization could help. Please let us know the outcome. Thank you!",
"Yes that's how this model is generated (post training quantization, and int8):\r\n converter = tf.lite.TFLiteConverter.from_keras_model(model)\r\n converter.optimizations = [tf.lite.Optimize.DEFAULT]\r\n converter.representative_dataset = representative_dataset_gen_rand\r\n converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]\r\n tflite_quant_model = converter.convert()\r\n\r\nYou can just take the model and run on CPU and DSP to compare the result, feed it with a random input. You can see the model before depthwiseconv node has similar outputs, and the model with the depthwiseconv node has quite different results.",
"Hi @pkgoogle,\r\n\r\nPlease look into the issue.\r\n\r\nThank You\r\n\r\n",
"Hi @sirakiin, can you please take a look? Thanks."
] | 2023-12-08T22:10:08 | 2023-12-26T22:50:35 | null |
NONE
| null | null | null |
### Describe the problem
We have a model that has accuracy issue with depthwiseconv, I attached two models, one is right before the depthwise conv node, the result of DSP and CPU has small difference (~0.2), another model has a depthwise conv (7x7 filter) and the result difference is huge (~18.).
I see that there was an issue before with 5x5 filter that got a workaround last year. I am guessing it has the same issue with 7x7 filter: https://github.com/tensorflow/tensorflow/issues/54481
We have qualcomm NDA and we have the source code of nnlib (they don't release update now), wondering if you know where the bug is in nnlib? If so it's best to modify in nnlib side, otherwise maybe you can modify your workaround toward the 7x7 filter?
### Source code / logs
[dwconv_debug.zip](https://github.com/tensorflow/tensorflow/files/13621244/dwconv_debug.zip)
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I_kwDOArmXAs55Mh0i
| 62,599 |
Upgrade protobuf
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[
"Hi @mering,\r\n\r\nI can see at master branch setup.py required protobuf versions are:\r\nhttps://github.com/tensorflow/tensorflow/blob/9c5b7edd94a3c156b741777f35e77614c434c37a/tensorflow/tools/pip_package/setup.py#L101\r\n\r\nNot sure of constraints of why this is not updated at workspace2.bzl . IMO we can update it to something like 4.25.1.\r\n\r\nCC: @vam-google , @learning-to-play ",
"Protobuf 3.21.9 is almost two years old and predates bzlmod support: https://github.com/protocolbuffers/protobuf/releases/tag/v21.9 (Oct 2022).\r\n\r\nThere have been breaking API changes as well in the protobuf C++ API which is causing a divergence between internal and external versions of other projects that are stuck on the old protobuf due to Tensorflow."
] | 2023-12-08T21:22:43 | 2024-05-08T20:43:51 | null |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
Latest
### Custom code
No
### OS platform and distribution
Linux
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
6
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Currently, [Protobuf 21 is used](https://github.com/tensorflow/tensorflow/blob/4178a583080190e931856545b28a7b708040208c/tensorflow/workspace2.bzl#L385). Please upgrade to a more recent version. As the whole Bazel workspace needs to use the same version of Protobuf, all projects using tensorflow as dependency are blocked upgrading the Protobuf version.
### Standalone code to reproduce the issue
Change https://github.com/tensorflow/tensorflow/blob/4178a583080190e931856545b28a7b708040208c/tensorflow/workspace2.bzl#L385 to a newer version.
### Relevant log output
_No response_
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[
"@mering,\r\nThank you for the request. Could you please elaborate about the Feature. Also, please specify the Use Cases for this feature. Thank you!",
"@tilakrayal The current Bazel `WORKSPACE` approach has several flaws which are fixed by the migration to Bazel modules (e.g. not requiring dependants to specify *all* transitive dependencies). I can't describe it better with my own words than the [Bzlmod user guide](https://docs.bazel.build/versions/5.4.1/bzlmod.html).",
"The team is working on adding bzlmod support, but since this is a big project this will take significant time",
"Do you have any info on the status of this feature ? Would love to have Bzlmod support to import TensorFlow seamlessly.",
"Not really. Left TF in the middle of 2022, but kept following the process from the sidelines.",
"Does anyone have a workaround to import and build TensorFlow from a Bzlmod project ? I created a StackOverflow for this matter but haven't go any response yet : https://stackoverflow.com/questions/78413217/import-tensorflow-as-bazel-dependency-for-c-project"
] | 2023-12-08T21:18:07 | 2024-05-09T13:44:46 | null |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
Latest
### Custom code
No
### OS platform and distribution
Linux
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
6
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Please support bzlmod by adding a `MODULE.bazel` file . See the [migration guide](https://bazel.build/external/migration).
### Standalone code to reproduce the issue
```shell
This file currently doesn't exist.
```
### Relevant log output
_No response_
|
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I_kwDOArmXAs55Lp0L
| 62,597 |
Failure to compile TF 2.15 from source on rocky 8.8
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[
"Any updates on this? Thanks",
"> Any updates on this? Thanks\r\n\r\nNot sure if this will help, but the issue is on the nvidia side. https://stackoverflow.com/questions/70301375/noinline-macro-conflict-between-glib-and-cuda",
"Thank you for the pointer. \r\nLooked at the links. Cuda headers do have mentioned wrappers for __noinline__ which seem to be the correct setting. \r\nClang community points to cuda sdk and its not clear who is supposed to be doing a patch or when one is valid. \r\n\r\nIs there any configuration of later versions of tensorflow that was done on RPM-based (CentOS or derivative) system?\r\nThis guide https://www.tensorflow.org/install/source#gpu seem to be for ubuntu and any configuration i tried for tensorflow\r\nfrom 2.13 and up is not working"
] | 2023-12-08T18:05:32 | 2024-05-01T17:46:14 | null |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15.0
### Custom code
No
### OS platform and distribution
rocky linux 8.8
### Mobile device
no
### Python version
3.10.2
### Bazel version
6.1.0
### GCC/compiler version
gcc 11.2.0 and clang 16.0.1
### CUDA/cuDNN version
12.2.0
### GPU model and memory
tesla v100
### Current behavior?
Compiling fails with an error. Log is shown below.
We are using all software that compiled in house as needed for the HPC cluster.
For the compilation the following modules are loaded
```
bazel/6.1.0 cuda/12.2.0 python/3.10.2 tensorRT/8.6.1.6 java/11 gcc/11.2.0 llvm/16.0.1 clang/16.0.1
```
CASE 1: Running .configure results in this .tf.configure.bazelrc file:
```
build --action_env PYTHON_BIN_PATH="/opt/apps/python/3.10.2/bin/python3"
build --action_env PYTHON_LIB_PATH="/opt/apps/python/3.10.2/lib/python3.10/site-packages"
build --python_path="/opt/apps/python/3.10.2/bin/python3"
build --config=tensorrt
build --action_env TF_CUDA_VERSION="12"
build --action_env TF_CUDNN_VERSION="8"
build --action_env TF_TENSORRT_VERSION="8"
build --action_env TF_NCCL_VERSION=""
build --action_env TF_CUDA_PATHS="/opt/apps/cuda/12.2.0,/opt/apps/tensorRT/8.6.1.6,/usr"
build --action_env CUDA_TOOLKIT_PATH="/opt/apps/cuda/12.2.0"
build --action_env TF_CUDA_COMPUTE_CAPABILITIES="7.0,8.0"
build --action_env LD_LIBRARY_PATH="/opt/apps/clang/16.0.1/lib:/opt/apps/llvm/16.0.1/lib:/opt/apps/gcc/11.2.0/lib64:/opt/apps/gcc/11.2.0/lib:/opt/apps/gcc/11.2.0/lib/gcc/x86_64-pc-linux-gnu/11.2.0:/opt/apps/tensorRT/8.6.1.6/lib:/opt/apps/python/3.10.2
/lib:/opt/apps/cuda/12.2.0/lib64:/opt/apps/cuda/12.2.0/nvvm/lib64:/opt/apps/cuda/12.2.0/cublas/lib64:/opt/apps/cuda/12.2.0/extras/CUPTI/lib64:/opt/apps/cuda/12.2.0/extras/Debugger/lib64"
build --config=cuda_clang
build --action_env CLANG_CUDA_COMPILER_PATH="/opt/apps/clang/16.0.1/bin/clang-16"
build --copt=-Wno-gnu-offsetof-extensions
build --config=cuda_clang
build:opt --copt=-mavx2
build:opt --host_copt=-mavx2
test --test_size_filters=small,medium
test --test_env=LD_LIBRARY_PATH
test:v1 --test_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-gpu,-oss_serial
test:v1 --build_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-gpu
test:v2 --test_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-gpu,-oss_serial,-v1only
test:v2 --build_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-gpu,-v1only
```
CASE 2: If i rerun configure and choose not to use clang as cuda compiler it results in a slightly
different .tf.configure.bazelrc file here are the diffs:
```
dont use clang
< build --action_env GCC_HOST_COMPILER_PATH="/opt/apps/gcc/11.2.0/bin/gcc"
< build --config=cuda
---
use clang
> build --config=cuda_clang
> build --action_env CLANG_CUDA_COMPILER_PATH="/opt/apps/clang/16.0.1/bin/clang-16"
> build --copt=-Wno-gnu-offsetof-extensions
> build --config=cuda_clang
```
Either way, with or without clang in configuration bazel build fails.
For the CASE 2 compilation went further, but bazel subcommand failed with errors apparently linking to
/lib64/libstdc++.so.6 ( see log part below). This library should not used as my LD_LIBRARY_PATH is set
to use clang 16 and gcc 11.2.0 path first and gcc compilation includes libstdc++.so.6 that has all needed CXXABI and GLIBCXX entries. Bazel binary and clang libraries are linked to use that correct libstdc++.so.6 supplied by gcc 11..2.0
### Standalone code to reproduce the issue
```shell
bazel build --config=opt --jobs=8 --verbose_failures --verbose_explanations \
--explain=/tmp/explain //tensorflow/tools/pip_package:build_pip_package
```
### Relevant log output
```shell
### CASE 1 log:
Extracting Bazel installation...
Starting local Bazel server and connecting to it...
WARNING: The following configs were expanded more than once: [tensorrt, cuda_clang, cuda]. For repeatable flags, repeats are counted twice and may lead to unexpected behavior.
INFO: Reading 'startup' options from /export/repositories/tensorflow admix/yamlspecs/tensorflow-2.15.0/.bazelrc: --windows_enable_symlinks
INFO: Options provided by the client:
Inherited 'common' options: --isatty=0 --terminal_columns=80
INFO: Reading rc options for 'build' from /export/repositories/tensorflow-admix/yamlspecs/tensorflow-2.15.0/.bazelrc:
Inherited 'common' options: --experimental_repo_remote_exec
INFO: Reading rc options for 'build' from /export/repositories/tensorflow-admix/yamlspecs/tensorflow-2.15.0/.bazelrc:
'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ar
es=true --noincompatible_remove_legacy_whole_archive --features=-force_no_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_sup
port=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility
INFO: Reading rc options for 'build' from /export/repositories/tensorflow-admix/yamlspecs/tensorflow-2.15.0/.tf_configure.bazelrc:
'build' options: --action_env PYTHON_BIN_PATH=/opt/apps/python/3.10.2/bin/python3 --action_env PYTHON_LIB_PATH=/opt/apps/python/3.10.2/lib/python3.10/site-packages --python_path=/opt/apps/python/3.10.2/bin/python3 --config=tensorrt
--action_env TF_CUDA_VERSION=12 --action_env TF_CUDNN_VERSION=8 --action_env TF_TENSORRT_VERSION=8 --action_env TF_NCCL_VERSION= --action_env TF_CUDA_PATHS=/opt/apps/cuda/12.2.0,/opt/apps/tensorRT/8.6.1.6,/usr --action_env CUDA_TOOLKI
T_PATH=/opt/apps/cuda/12.2.0 --action_env TF_CUDA_COMPUTE_CAPABILITIES=7.0,8.0 --action_env LD_LIBRARY_PATH=/opt/apps/clang/16.0.1/lib:/opt/apps/llvm/16.0.1/lib:/opt/apps/gcc/11.2.0/lib64:/opt/apps/gcc/11.2.0/lib:/opt/apps/gcc/11.2.0/
lib/gcc/x86_64-pc-linux-gnu/11.2.0:/opt/apps/tensorRT/8.6.1.6/lib:/opt/apps/python/3.10.2/lib:/opt/apps/cuda/12.2.0/lib64:/opt/apps/cuda/12.2.0/nvvm/lib64:/opt/apps/cuda/12.2.0/cublas/lib64:/opt/apps/cuda/12.2.0/extras/CUPTI/lib64:/op
t/apps/cuda/12.2.0/extras/Debugger/lib64 --config=cuda_clang --action_env CLANG_CUDA_COMPILER_PATH=/opt/apps/clang/16.0.1/bin/clang-16 --copt=-Wno-gnu-offsetof-extensions --config=cuda_clang
INFO: Found applicable config definition build:short_logs in file /export/repositories/tensorflow-admix/yamlspecs/tensorflow-2.15.0/.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file /export/repositories/tensorflow-admix/yamlspecs/tensorflow-2.15.0/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:tensorrt in file /export/repositories/tensorflow-admix/yamlspecs/tensorflow-2.15.0/.bazelrc: --repo_env TF_NEED_TENSORRT=1
INFO: Found applicable config definition build:cuda_clang in file /export/repositories/tensorflow-admix/yamlspecs/tensorflow-2.15.0/.bazelrc: --config=cuda --config=tensorrt --action_env=TF_CUDA_CLANG=1 --@local_config_cuda//:cuda_com
piler=clang --repo_env=TF_CUDA_COMPUTE_CAPABILITIES=sm_50,sm_60,sm_70,sm_75,compute_80
INFO: Found applicable config definition build:cuda in file /export/repositories/tensorflow-admix/yamlspecs/tensorflow-2.15.0/.bazelrc: --repo_env TF_NEED_CUDA=1 --crosstool_top=@local_config_cuda//crosstool:toolchain --@local_config_
cuda//:enable_cuda
INFO: Found applicable config definition build:tensorrt in file /export/repositories/tensorflow-admix/yamlspecs/tensorflow-2.15.0/.bazelrc: --repo_env TF_NEED_TENSORRT=1
INFO: Found applicable config definition build:cuda_clang in file /export/repositories/tensorflow-admix/yamlspecs/tensorflow-2.15.0/.bazelrc: --config=cuda --config=tensorrt --action_env=TF_CUDA_CLANG=1 --@local_config_cuda//:cuda_com
piler=clang --repo_env=TF_CUDA_COMPUTE_CAPABILITIES=sm_50,sm_60,sm_70,sm_75,compute_80
INFO: Found applicable config definition build:cuda in file /export/repositories/tensorflow-admix/yamlspecs/tensorflow-2.15.0/.bazelrc: --repo_env TF_NEED_CUDA=1 --crosstool_top=@local_config_cuda//crosstool:toolchain --@local_config_
cuda//:enable_cuda
INFO: Found applicable config definition build:tensorrt in file /export/repositories/tensorflow-admix/yamlspecs/tensorflow-2.15.0/.bazelrc: --repo_env TF_NEED_TENSORRT=1
INFO: Found applicable config definition build:opt in file /export/repositories/tensorflow-admix/yamlspecs/tensorflow-2.15.0/.tf_configure.bazelrc: --copt=-mavx2 --host_copt=-mavx2
INFO: Found applicable config definition build:linux in file /export/repositories/tensorflow-admix/yamlspecs/tensorflow-2.15.0/.bazelrc: --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wno-deprecated-de
clarations --copt=-Wno-ignored-attributes --copt=-Wno-array-bounds --copt=-Wunused-result --copt=-Werror=unused-result --copt=-Wswitch --copt=-Werror=switch --copt=-Wno-error=unused-but-set-variable --define=PREFIX=/usr --define=LIBDI
R=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --experimental_guard_against_concurrent_changes
INFO: Found applicable config definition build:dynamic_kernels in file /export/repositories/tensorflow-admix/yamlspecs/tensorflow-2.15.0/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS
Loading:
Loading:
DEBUG: /export/repositories/tensorflow-admix/yamlspecs/tensorflow-2.15.0/tensorflow/tools/toolchains/python/python_repo.bzl:21:14:
TF_PYTHON_VERSION variable was not set correctly, using default version. 3.10 Python
will be used.
### successful compile lines are removed for brevity ###
[15,675 / 19,610] Compiling tensorflow/compiler/mlir/tosa/transforms/legalize_tfl.cc; 33s local ... (8 actions running)
[15,676 / 19,610] Compiling tensorflow/compiler/mlir/tosa/transforms/legalize_tfl.cc; 38s local ... (8 actions, 7 running)
[15,679 / 19,612] Compiling tensorflow/compiler/mlir/tosa/transforms/legalize_tfl.cc; 39s local ... (8 actions running)
ERROR: /export/repositories/tensorflow-admix/yamlspecs/tensorflow-2.15.0/tensorflow/core/kernels/BUILD:5338:18: Compiling tensorflow/core/kernels/scatter_op_gpu.cu.cc failed: (Exit 1): clang-16 failed: error executing command (from ta
rget //tensorflow/core/kernels:scatter_op_gpu)
(cd /root/.cache/bazel/_bazel_root/e76370378c3e9e8e238b869c10fc760e/execroot/org_tensorflow && \
exec env - \
CLANG_CUDA_COMPILER_PATH=/opt/apps/clang/16.0.1/bin/clang-16 \
CUDA_TOOLKIT_PATH=/opt/apps/cuda/12.2.0 \
LD_LIBRARY_PATH=/opt/apps/clang/16.0.1/lib:/opt/apps/llvm/16.0.1/lib:/opt/apps/gcc/11.2.0/lib64:/opt/apps/gcc/11.2.0/lib:/opt/apps/gcc/11.2.0/lib/gcc/x86_64-pc-linux-gnu/11.2.0:/opt/apps/tensorRT/8.6.1.6/lib:/opt/apps/python/3.10.
2/lib:/opt/apps/cuda/12.2.0/lib64:/opt/apps/cuda/12.2.0/nvvm/lib64:/opt/apps/cuda/12.2.0/cublas/lib64:/opt/apps/cuda/12.2.0/extras/CUPTI/lib64:/opt/apps/cuda/12.2.0/extras/Debugger/lib64 \
PATH=/opt/apps/clang/16.0.1/bin:/opt/apps/llvm/16.0.1/bin:/opt/apps/gcc/11.2.0/bin:/usr/lib/jvm/java-11/bin:/usr/lib/jvm/jre-11/bin:/opt/apps/tensorRT/8.6.1.6/bin:/opt/apps/python/3.10.2/bin:/opt/apps/cuda/12.2.0/bin:/opt/apps/baz
el/6.1.0/bin:/opt/rcic/bin:/usr/share/Modules/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/root/bin \
PWD=/proc/self/cwd \
PYTHON_BIN_PATH=/opt/apps/python/3.10.2/bin/python3 \
PYTHON_LIB_PATH=/opt/apps/python/3.10.2/lib/python3.10/site-packages \
TF2_BEHAVIOR=1 \
TF_CUDA_CLANG=1 \
TF_CUDA_COMPUTE_CAPABILITIES=7.0,8.0 \
TF_CUDA_PATHS=/opt/apps/cuda/12.2.0,/opt/apps/tensorRT/8.6.1.6,/usr \
TF_CUDA_VERSION=12 \
TF_CUDNN_VERSION=8 \
TF_NCCL_VERSION='' \
TF_TENSORRT_VERSION=8 \
/opt/apps/clang/16.0.1/bin/clang-16 -MD -MF bazel-out/k8-opt/bin/tensorflow/core/kernels/_objs/scatter_op_gpu/scatter_op_gpu.cu.pic.d '-frandom-seed=bazel-out/k8-opt/bin/tensorflow/core/kernels/_objs/scatter_op_gpu/scatter_op_gpu.cu
.pic.o' -DEIGEN_MPL2_ONLY '-DEIGEN_MAX_ALIGN_BYTES=64' -DHAVE_SYS_UIO_H -DTF_USE_SNAPPY '-DBAZEL_CURRENT_REPOSITORY=""' -iquote . -iquote bazel-out/k8-opt/bin -iquote external/com_google_absl -iquote bazel-out/k8-opt/bin/external/com_
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# Configuration: c9cdf7815656ac88d2cb4421ef529bda74485e1ed473208091218e401f0f31b0
# Execution platform: @local_execution_config_platform//:platform
clang-16: warning: CUDA version is newer than the latest partially supported version 11.8 [-Wunknown-cuda-version]
In file included from tensorflow/core/kernels/scatter_op_gpu.cu.cc:20:
In file included from ./tensorflow/core/kernels/scatter_functor_gpu.cu.h:23:
In file included from ./tensorflow/core/framework/tensor_types.h:20:
In file included from ./tensorflow/core/platform/logging.h:19:
In file included from ./tensorflow/core/platform/types.h:21:
In file included from ./tensorflow/core/platform/tstring.h:19:
In file included from ./tensorflow/core/platform/cord.h:19:
In file included from external/local_tsl/tsl/platform/cord.h:21:
In file included from external/com_google_absl/absl/strings/cord.h:74:
In file included from external/com_google_absl/absl/base/internal/endian.h:22:
In file included from external/com_google_absl/absl/base/casts.h:28:
In file included from /opt/rh/gcc-toolset-12/root/usr/lib/gcc/x86_64-redhat-linux/12/../../../../include/c++/12/memory:77:
In file included from /opt/rh/gcc-toolset-12/root/usr/lib/gcc/x86_64-redhat-linux/12/../../../../include/c++/12/bits/shared_ptr.h:53:
/opt/rh/gcc-toolset-12/root/usr/lib/gcc/x86_64-redhat-linux/12/../../../../include/c++/12/bits/shared_ptr_base.h:196:22: error: use of undeclared identifier 'noinline'; did you mean 'inline'?
__attribute__((__noinline__))
^
bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/crt/host_defines.h:83:24: note: expanded from macro '__noinline__'
__attribute__((noinline))
^
In file included from tensorflow/core/kernels/scatter_op_gpu.cu.cc:20:
In file included from ./tensorflow/core/kernels/scatter_functor_gpu.cu.h:23:
In file included from ./tensorflow/core/framework/tensor_types.h:20:
In file included from ./tensorflow/core/platform/logging.h:19:
In file included from ./tensorflow/core/platform/types.h:21:
In file included from ./tensorflow/core/platform/tstring.h:19:
In file included from ./tensorflow/core/platform/cord.h:19:
In file included from external/local_tsl/tsl/platform/cord.h:21:
In file included from external/com_google_absl/absl/strings/cord.h:74:
In file included from external/com_google_absl/absl/base/internal/endian.h:22:
In file included from external/com_google_absl/absl/base/casts.h:28:
In file included from /opt/rh/gcc-toolset-12/root/usr/lib/gcc/x86_64-redhat-linux/12/../../../../include/c++/12/memory:77:
In file included from /opt/rh/gcc-toolset-12/root/usr/lib/gcc/x86_64-redhat-linux/12/../../../../include/c++/12/bits/shared_ptr.h:53:
/opt/rh/gcc-toolset-12/root/usr/lib/gcc/x86_64-redhat-linux/12/../../../../include/c++/12/bits/shared_ptr_base.h:196:22: error: type name does not allow function specifier to be specified
bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/crt/host_defines.h:83:24: note: expanded from macro '__noinline__'
__attribute__((noinline))
^
In file included from tensorflow/core/kernels/scatter_op_gpu.cu.cc:20:
In file included from ./tensorflow/core/kernels/scatter_functor_gpu.cu.h:23:
In file included from ./tensorflow/core/framework/tensor_types.h:20:
In file included from ./tensorflow/core/platform/logging.h:19:
In file included from ./tensorflow/core/platform/types.h:21:
In file included from ./tensorflow/core/platform/tstring.h:19:
In file included from ./tensorflow/core/platform/cord.h:19:
In file included from external/local_tsl/tsl/platform/cord.h:21:
In file included from external/com_google_absl/absl/strings/cord.h:74:
In file included from external/com_google_absl/absl/base/internal/endian.h:22:
In file included from external/com_google_absl/absl/base/casts.h:28:
In file included from /opt/rh/gcc-toolset-12/root/usr/lib/gcc/x86_64-redhat-linux/12/../../../../include/c++/12/memory:77:
In file included from /opt/rh/gcc-toolset-12/root/usr/lib/gcc/x86_64-redhat-linux/12/../../../../include/c++/12/bits/shared_ptr.h:53:
/opt/rh/gcc-toolset-12/root/usr/lib/gcc/x86_64-redhat-linux/12/../../../../include/c++/12/bits/shared_ptr_base.h:196:22: error: expected expression
bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include/crt/host_defines.h:83:33: note: expanded from macro '__noinline__'
__attribute__((noinline))
external/local_tsl/tsl/platform/default/logging.h:308:9: note: previous definition is here
#define CHECK(condition) \
^
In file included from tensorflow/core/kernels/scatter_op_gpu.cu.cc:20:
In file included from ./tensorflow/core/kernels/scatter_functor_gpu.cu.h:26:
In file included from ./tensorflow/core/util/gpu_kernel_helper.h:28:
In file included from ./tensorflow/core/util/gpu_launch_config.h:27:
In file included from ./tensorflow/core/platform/stream_executor.h:19:
In file included from external/local_xla/xla/stream_executor/cuda/cuda_platform_id.h:19:
In file included from external/local_xla/xla/stream_executor/platform.h:27:
In file included from external/local_xla/xla/stream_executor/device_options.h:27:
external/com_google_absl/absl/log/check.h:65:9: warning: 'QCHECK' macro redefined [-Wmacro-redefined]
#define QCHECK(condition) ABSL_QCHECK_IMPL((condition), #condition)
^
external/local_tsl/tsl/platform/default/logging.h:542:9: note: previous definition is here
#define QCHECK(condition) CHECK(condition)
^
In file included from tensorflow/core/kernels/scatter_op_gpu.cu.cc:20:
In file included from ./tensorflow/core/kernels/scatter_functor_gpu.cu.h:26:
In file included from ./tensorflow/core/util/gpu_kernel_helper.h:28:
In file included from ./tensorflow/core/util/gpu_launch_config.h:27:
In file included from ./tensorflow/core/platform/stream_executor.h:19:
In file included from external/local_xla/xla/stream_executor/cuda/cuda_platform_id.h:19:
In file included from external/local_xla/xla/stream_executor/platform.h:27:
In file included from external/local_xla/xla/stream_executor/device_options.h:27:
external/com_google_absl/absl/log/check.h:88:9: warning: 'DCHECK' macro redefined [-Wmacro-redefined]
#define DCHECK(condition) ABSL_DCHECK_IMPL((condition), #condition)
^
external/local_tsl/tsl/platform/default/logging.h:521:9: note: previous definition is here
#define DCHECK(condition) \
^
### a few more similar warnings ###
21 warnings and 3 errors generated when compiling for sm_50.
Target //tensorflow/tools/pip_package:build_pip_package failed to build
[15,687 / 19,612] checking cached actions
INFO: Elapsed time: 3750.761s, Critical Path: 251.21s
INFO: 15687 processes: 6668 internal, 9019 local.
FAILED: Build did NOT complete successfully
### CASE 2 log:
### showing only the errors at the end ###
[24,431 / 26,425] Compiling tensorflow/compiler/mlir/tensorflow/ir/tf_ops.cc [for tool]; 94s local ... (8 actions, 7 running)
[24,435 / 26,425] Compiling tensorflow/compiler/mlir/tensorflow/ir/tf_ops.cc [for tool]; 95s local ... (8 actions, 7 running)
[24,440 / 26,425] Compiling tensorflow/compiler/mlir/tensorflow/ir/tf_ops.cc [for tool]; 96s local ... (8 actions, 7 running)
[24,446 / 26,425] Compiling tensorflow/compiler/mlir/tensorflow/ir/tf_ops.cc [for tool]; 97s local ... (8 actions, 7 running)
bazel-out/k8-opt-exec-50AE0418/bin/tensorflow/python/framework/offset_counter: /lib64/libstdc++.so.6: version 'CXXABI_1.3.13' not found (required by /root/.cache/bazel/_bazel_root/e76370378c3e9e8e238b869c10fc760e/execroot/org_tensorflow/bazel-out/k8-opt-exec-50AE0418/bin/tensorflow/python/framework/../../../_solib_local/_U_S_Stensorflow_Spython_Sframework_Coffset_Ucounter___Utensorflow/libtensorflow_framework.so.2)
bazel-out/k8-opt-exec-50AE0418/bin/tensorflow/python/framework/offset_counter: /lib64/libstdc++.so.6: version 'GLIBCXX_3.4.29' not found (required by /root/.cache/bazel/_bazel_root/e76370378c3e9e8e238b869c10fc760e/execroot/org_tensorflow/bazel-out/k8-opt-exec-50AE0418/bin/tensorflow/python/framework/../../../_solib_local/_U_S_Stensorflow_Spython_Sframework_Coffset_Ucounter___Utensorflow/libtensorflow_framework.so.2)
bazel-out/k8-opt-exec-50AE0418/bin/tensorflow/python/framework/offset_counter: /lib64/libstdc++.so.6: version 'GLIBCXX_3.4.26' not found (required by /root/.cache/bazel/_bazel_root/e76370378c3e9e8e238b869c10fc760e/execroot/org_tensorflow/bazel-out/k8-opt-exec-50AE0418/bin/tensorflow/python/framework/../../../_solib_local/_U_S_Stensorflow_Spython_Sframework_Coffset_Ucounter___Utensorflow/libtensorflow_framework.so.2)
```
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"@janakiashwin There are common reasons for such issues like missing dependencies, version mismatch as per requirement or you might be using different environment. Please ensure to follow this [guide](https://www.tensorflow.org/install) for the installation. Could you try uninstalling and reinstalling TensorFlow and Keras from your environment with the latest compatible versions and let us know?\r\n\r\nThank you!\r\n",
"Thanks . Your suggestion worked.\r\n\r\nI updated conda.\r\nconda update -n base conda\r\n\r\nI updated pip.\r\npip install --upgrade pip\r\n\r\nThen I installed tensorflow.\r\npip install tensorflow\r\n\r\nIt worked.\r\n\r\nThanks and Regards,\r\nJanaki\r\n\r\nOn Fri, Dec 8, 2023 at 6:18 PM sushreebarsa ***@***.***>\r\nwrote:\r\n\r\n> @janakiashwin <https://github.com/janakiashwin> There are common reasons\r\n> for such issues like missing dependencies, version mismatch as per\r\n> requirement or you might be using different environment. Please ensure to\r\n> follow this guide <https://www.tensorflow.org/install> for the\r\n> installation. Could you try uninstalling and reinstalling TensorFlow and\r\n> Keras from your environment.\r\n> and let us know?\r\n>\r\n> Thank you!\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/tensorflow/tensorflow/issues/62596#issuecomment-1847110446>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/BDZKYSW4X6JIUSLBLXNNSYTYIMEBJAVCNFSM6AAAAABAMSIUR2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTQNBXGEYTANBUGY>\r\n> .\r\n> You are receiving this because you were mentioned.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"@janakiashwin Glad it worked fine for you!\r\nCould you please move this issue to closed status if it is resolved?\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/62596\">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/62596\">No</a>\n"
] | 2023-12-08T12:17:11 | 2023-12-24T01:49:07 | 2023-12-24T01:49:04 |
NONE
| null | null | null |
I am using anaconda. I created a new environment, pip installed keras and tensorflow
pip install keras
It installed keras 2.10.0
pip install tensorflow.
tensorflow 2.10.0
The following packages will be DOWNGRADED:
openssl 3.0.12-h2bbff1b_0 --> 1.1.1w-h2bbff1b_0
pip 23.3.1-py312haa95532_0 --> 23.3.1-py310haa95532_0
python 3.12.0-h1d929f7_0 --> 3.10.13-h966fe2a_0
setuptools 68.0.0-py312haa95532_0 --> 68.0.0-py310haa95532_0
wheel 0.41.2-py312haa95532_0 --> 0.41.2-py310haa95532_0
When I run the python file with script:
from keras.models import load_model
I get the error:
C:\Users\acer\drowse_project\script>python maindrow.py
Traceback (most recent call last):
File "C:\Users\acer\anaconda3\envs\env_drow\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 62, in <module>
from tensorflow.python._pywrap_tensorflow_internal import *
ImportError: DLL load failed while importing _pywrap_tensorflow_internal: A dynamic link library (DLL) initialization routine failed.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\acer\drowse_project\script\maindrow.py", line 18, in <module>
from keras.models import load_model
File "C:\Users\acer\anaconda3\envs\env_drow\lib\site-packages\keras\__init__.py", line 20, in <module>
from keras import distribute
File "C:\Users\acer\anaconda3\envs\env_drow\lib\site-packages\keras\distribute\__init__.py", line 18, in <module>
from keras.distribute import sidecar_evaluator
File "C:\Users\acer\anaconda3\envs\env_drow\lib\site-packages\keras\distribute\sidecar_evaluator.py", line 17, in <module>
import tensorflow.compat.v2 as tf
File "C:\Users\acer\anaconda3\envs\env_drow\lib\site-packages\tensorflow\__init__.py", line 37, in <module>
from tensorflow.python.tools import module_util as _module_util
File "C:\Users\acer\anaconda3\envs\env_drow\lib\site-packages\tensorflow\python\__init__.py", line 36, in <module>
from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
File "C:\Users\acer\anaconda3\envs\env_drow\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 77, in <module>
raise ImportError(
ImportError: Traceback (most recent call last):
File "C:\Users\acer\anaconda3\envs\env_drow\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 62, in <module>
from tensorflow.python._pywrap_tensorflow_internal import *
ImportError: DLL load failed while importing _pywrap_tensorflow_internal: A dynamic link library (DLL) initialization routine failed.
Failed to load the native TensorFlow runtime.
See https://www.tensorflow.org/install/errors for some common causes and solutions.
If you need help, create an issue at https://github.com/tensorflow/tensorflow/issues and include the entire stack trace above this error message.
Please advise. Thanks a lot!
|
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I_kwDOArmXAs55HqSi
| 62,595 |
Missing gen_array_ops.py file in tensorflow/python/ops folder
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[
"@sandeep1404 The \"gen_array_ops.py\" file is indeed not directly present in the TensorFlow source code you might be looking at. It's actually auto-generated during the TensorFlow build process.\r\nYou won't find a physical gen_array_ops.py file in the source, it's implicitly created through the build process and made available for import. You can see the generated code in the tensorflow/python/ops directory after building TensorFlow.\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/62595\">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/62595\">No</a>\n"
] | 2023-12-08T07:06:37 | 2023-12-30T01:48:01 | 2023-12-30T01:47:53 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.14
### Custom code
Yes
### OS platform and distribution
Linux ubuntu 22.04
### Mobile device
_No response_
### Python version
3.8
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I just need where is the gen_array_ops.py file since its is imported in array_ops.py from tensorflow.python.ops import gen_array_ops
### Standalone code to reproduce the issue
```shell
Hi , the question might be silly but where is the file gen_array_ops.py since in the file we are doing import gen_array_ops and we are importing it from tensorflow.python.ops import gen_array_ops, but i cannot find the file gen_array_ops.py instead i can see the array_ops.py file are they both same? Am i missing something.
```
### Relevant log output
_No response_
|
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I_kwDOArmXAs55HCtb
| 62,593 |
Can you comment out "find Threads REQUIRED" when compiling tflite library files?
|
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[
"Hi @panhu,\r\n\r\nPlease let me know if you followed this[ document] (https://www.tensorflow.org/lite/guide/build_cmake#build_tensorflow_lite_c_library) for build from source. The error might be due to pthread libraries. Verify once ```pthread``` library is included or not. Please let us know if the issue still persists.\r\n\r\nYes, TFlite provides C APIs to interact with the C environment. TFLite can run in a pure C environment. \r\n\r\nThank you\r\n",
"I performed the operation based on that document and then used the following command for cross compilation:\r\n\r\nARMCC_FLAGS=\"-march=rv32imafdcpzpsfoperand_xtheade -mabi=ilp32d -mcmodel=medlow -g2 -Os -DSHL_BUILD_REF -DSHL_BUILD_GREF -DSHL_BUILD_E907 -DSHL_BUILD_RTOS\"\r\n\r\nARMCC_PREFIX=/home/bin/riscv64-unknown-elf-\r\n\r\ncmake -DCMAKE_C_COMPILER=${ARMCC_PREFIX}gcc -DCMAKE_C_FLAGS=\"${ARMCC_FLAGS}\" -DCMAKE_VERBOSE_MAKEFILE:BOOL=ON -DCMAKE_SYSTEM_NAME=Generic -DCMAKE_SYSTEM_PROCESSOR=riscv64 ../lite/c\r\n\r\n\r\n\r\nHowever, my toolchain does not support the use of pthreads, so can I not use pthreads during compilation.",
"Hi @panhu,\r\n Make sure to enable flags related to RISCv64 . Please go through the [documentation](https://github.com/riscv-collab/riscv-gnu-toolchain/blob/master/README.md) and other supporting references [1](https://github.com/riscv-software-src/riscv-tools/issues/184), [2](https://github.com/congvm-cs/librealsense_for_arm/issues/2). Maybe you have to use this tool chain ```riscv-gnu-toolchain```. \r\nPlease let us know for further assistance.\r\n\r\nThank You",
"Thanks,May I ask, the default quantization method used by tflite is max_ Min or channel quantization?",
"Hi @panhu,\r\n\r\nThe default quantization method used by tflite is channel quantization. This means that, depending on its own minimum and maximum values, each weight channel is individually mapped to a smaller range of fixed-point values (usually 8 bits). For most models, this method achieves a balance between accuracy and efficiency.\r\n\r\nThank You\r\n\r\n\r\n\r\n\r\n\r\n ",
"Thank you for your reply. I would like to ask why my model only has four convolutional layers, each with a relu layer behind it. However, why does using tflite to quantify the model still output negative values during inference.",
"Hi @panhu,\r\n\r\n1. The ReLU introduces non-linearity to the network by adding Relu with each Conv layers, which makes it capable of modeling more complex functions than linear models. Coming to the no.of convolutional layers depends on the model, usecase,etc. Without complete information about your model, the analysis is not possible. \r\n2. The output of your TFLite model might be negative due to \"zero point\" or ReLu or loss of precision. Carefully selecting Zero point to weights and activations might solve your issue. \r\n\r\nAs the primary issue was addressed, Could you please feel free to close the current issue and create new issue for the other queries so that it will be easily tracked.\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/62593\">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/62593\">No</a>\n"
] | 2023-12-08T04:02:04 | 2023-12-20T06:24:40 | 2023-12-20T06:24:36 |
NONE
| null | null | null |
**System information**
- Linux
- TensorFlow Lite in Play Services SDK version
**Standalone code to reproduce the issue**
I want to use the C toolchain to compile the C interface of tflite, but the toolchain does not support using the Threads library, so I commented find_ Package (Threads Required), result in an error running abseil-cpp again, xxx links to target "Threads:: Threads" but the target was not found.
Can tflite run in a pure c environment?
|
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I_kwDOArmXAs55G9hv
| 62,592 |
keras outputs differently in different versions on tensorflow backend
|
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[
"@cheyennee Keras and TensorFlow undergo continuous development, and internal changes in their implementation can lead to subtle differences in the way models are executed and how outputs are generated. So it might give a different outcome which we expect to be better in every new release. We would recommend you to use the latest version as the older ones are not actively supported.\r\nThank you!",
"Same problem can be found in this [model](https://drive.google.com/file/d/1gJXl0CHUwDMPN3oWsn0ztgCRC-YKALsv/view?usp=sharing). In version 2.14, model works well. while in version 2.3.1, it throws error: \r\n```\r\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Only ranks up to 5 supported: [10,2,4,5,3,3]\r\n\t [[node 15_dense/BiasAdd (defined at \\software\\anaconda\\anaconda\\envs\\muffin3.6-1\\lib\\site-packages\\tensorflow_core\\python\\framework\\ops.py:1751) ]] [Op:__inference_keras_scratch_graph_10145]\r\n\r\nFunction call stack:\r\nkeras_scratch_graph\r\n```",
"@cheyennee Thank you for your response here!\r\nThis issue is not replicating the latest and we also recommend to use the newer version. Version 2.3.1 is not actively supported so could you please move this issue to closed status?\r\nThank you!\r\n",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62592\">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/62592\">No</a>\n"
] | 2023-12-08T03:29:49 | 2023-12-29T01:46:06 | 2023-12-29T01:46:03 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.14.0
### Custom code
Yes
### OS platform and distribution
windows colab
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I have a keras [model](https://drive.google.com/file/d/1RoNy5rs4vlOV37NmrTuRrf1GR_4FvY9V/view?usp=drive_link) and an [input](https://drive.google.com/file/d/126zU8p3Jm6HTqRoI4p1Lni1VMkM1QeGi/view?usp=drive_link). I use model to predict this input. I found that in keras v2.3.1, it predicts nan. However, in keras v2.14.0, it outputs normally.
### Standalone code to reproduce the issue
```shell
import keras
import numpy as np
print(keras.__version__)
from keras.datasets import mnist
(x_train,y_train),(x_test,y_test) = mnist.load_data()
model = keras.models.load_model('/content/322-tensorflow.h5')
input = np.load('/content/input.npy')
model.compile(loss='mean_squared_logarithmic_error', optimizer='adadelta')
history = model.fit(x_train, y_train)
model.predict(input)
```
### Relevant log output
```shell
keras v2.14.0
WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.
1875/1875 [==============================] - 27s 11ms/step - loss: 6202843795574403104768.0000
1/1 [==============================] - 0s 408ms/step
array([[-1902.7032 , 141.09955, -277.39343, 1343.9507 , 1219.6061 ,
893.0104 , 997.8686 , 2026.3091 , -905.83954, -349.66815]],
dtype=float32)
```
keras v2.3.1
```
Epoch 1/1
10/10 [==============================] - 1s 104ms/step - loss: 97095230641965394441535488.0000
[[nan nan nan nan nan nan nan nan nan nan]]
```
```
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PR_kwDOArmXAs5hemgJ
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Add float16 inference option for TFLite benchmark app
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[
"Hi @terryheo Can you please review this PR ? Thank you!"
] | 2023-12-08T01:45:53 | 2024-01-04T05:53:28 | 2024-01-03T09:14:08 |
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## Background
Native float16 inference is introduced in [Tensorflow Blog](https://blog.tensorflow.org/2023/11/half-precision-inference-doubles-on-device-inference-performance.html).
To try this on benchmark app(benchmark_model), we need set flag of XNNPackDelegateOptions, it's not accessible.
This PR enable to access the flag for float16 inference from command line args.
## Change
- Add `--xnnpack_force_fp16` option in benchmark_model
```bash
benchmark_model --graph=... \
--use_xnnpack=true \
--xnnpack_force_fp16=true # enable float16 inference.
```
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Advisory GHSA-9jjw-hf72-3mxw contains invalid semver
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[
"Hi,\r\n\r\nThanks for reporting the issue.\r\n\r\n`2.4.0-rc0` is the naming convention we used during the release which you can refer the same here https://github.com/tensorflow/tensorflow/releases/tag/v2.4.0-rc0.\r\n\r\nDid you mean it should be `2.4.0-rc0` not` 2.4.0-rc.0`",
"It should be `2.4.0-rc0`, with a dash in front of `rc`. While `pip install` supports the case where the dash is missing, this is not standards compliant and tooling that is more strict will be broken.",
"@mihaimaruseac , The package table seems to be generated though other file, could you please help me with the source file to make the necessary changes. Thanks",
"It's not on files on the repository. It's on the vulnerabilities (example https://github.com/advisories/GHSA-9jjw-hf72-3mxw).",
"I updated https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9jjw-hf72-3mxw so maybe this will result in a fix",
"There is no change on the https://github.com/advisories/GHSA-9jjw-hf72-3mxw link, probably this should be handled via GitHub support instead.\r\n\r\nAlternatively, may I recommend switching to data provided by [osv.dev](https://osv.dev/)?"
] | 2023-12-07T21:26:44 | 2024-01-02T17:43:12 | null |
NONE
| null | null | null |
### Issue type
Documentation Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
N/A
### Custom code
No
### OS platform and distribution
N/A
### Mobile device
N/A
### Python version
N/A
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
https://github.com/advisories/GHSA-9jjw-hf72-3mxw
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9jjw-hf72-3mxw
The semver string is `2.4.0rc0` but it should be `2.4.0-rc.0`. This causes problems for tools and scripts that parse the advisory database.
### Standalone code to reproduce the issue
```shell
N/A
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5hcuTS
| 62,589 |
remove unnecessary tests
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[
"cc: @nitins17 ",
"Thanks for the PR. Are these blocking the current MacStadium builds? If not, lets keep it as is because our internal macOS Arm64 builds will be using the scripts in https://github.com/tensorflow/tensorflow/tree/master/ci/official. Those do not install any build dependencies from requirements.txt AFAIK so we should be good there as well. ",
"@nitins17 These are blocking at the moment, this PR just brings the files up to parity with the workaround that is currently in place ",
"Okay, thanks for clarifying, LGTM! "
] | 2023-12-07T17:44:14 | 2023-12-14T18:57:00 | 2023-12-14T18:57:00 |
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Removing the tests that caused issues due to duplicated dependencies. Also these tests have been deemed redundant.
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I_kwDOArmXAs55EHLL
| 62,588 |
building tf data for siamese modelling
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[
"@pure-rgb,\r\nWhile I was trying to access the official doc it is providing an error. Could you please provide the colab gist which you are trying and help us to debug the issue in an effective way. 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.",
"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/62588\">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/62588\">No</a>\n"
] | 2023-12-07T16:34:33 | 2023-12-27T01:48:00 | 2023-12-27T01:47:56 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15
### 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?
In this [example](https://keras.io/examples/vision/siamese_contrastive/), raw numpy method is used to build a pipeline for siamese model. I tried to build in tf data api but couldn't do it. Let's say, I've following folders
```
folder_1/
folder_2/
folder_3/
folder_4/
```
I like to build dataset with multi-input, such that positive-positive and positive-negative pair. I couldn't find any tf ops to make it possible.
### Standalone code to reproduce the issue
```shell
https://keras.io/examples/vision/siamese_contrastive/
```
### Relevant log output
_No response_
|
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model_save method crashing python
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[
"Can you also post the model here? A link to colab would be nice.\r\n\r\nIf you cannot share the model (private architecture, private data, etc.), can you try to provide a minimal reproducible example? This is needed so the team can debug",
"Hi @rejahn ,\r\n\r\nPlease submit minimal repro code snippet. We need details like callback code and MODEL_FOLDER you are using to check with dummy model.",
"Making a small change in the `tensorflow/python/saved_model/nested_structure_coder.py` [here](https://github.com/tensorflow/tensorflow/blob/4b2ee4534df8208ae3a6e87a6386427288ae6765/tensorflow/python/saved_model/nested_structure_coder.py#L195C1-L195C69) will solve the problem.\r\n\r\nChange:\r\n```encoded_dict.dict_value.fields[key].CopyFrom(encode_fn(value))``` \r\nto \r\n```encoded_dict.dict_value.fields[str(key)].CopyFrom(encode_fn(value))``` \r\n\r\nDo you guys have maybe an idea what the problem could be?",
"What if you change `str` with `repr` in the suggested fix?",
"Actually, that shouldn't matter, it's just a key in a dict.",
"Changing `str` with `repr` works as well - I am able to save our subclassed model.",
"What if you try `hash(key)` just before trying to insert into the dict?",
"using `hash(key)`:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/user/repos/drivan/model/train_model.py\", line 141, in <module>\r\n tf.keras.models.save_model(model, MODEL_FOLDER)\r\n File \"/home/user/repos/drivan/.venv/lib/python3.8/site-packages/keras/saving/saving_api.py\", line 145, in save_model\r\n return legacy_sm_saving_lib.save_model(\r\n File \"/home/user/repos/drivan/.venv/lib/python3.8/site-packages/keras/utils/traceback_utils.py\", line 70, in error_handler\r\n raise e.with_traceback(filtered_tb) from None\r\n File \"/home/user/repos/drivan/.venv/lib/python3.8/site-packages/tensorflow/python/saved_model/nested_structure_coder.py\", line 199, in do_encode\r\n encoded_dict.dict_value.fields[hash(key)].CopyFrom(encode_fn(value))\r\nTypeError: bad argument type for built-in operation\r\n```\r\n",
"Oh, no, I meant just printing it before. Something like\r\n\r\n```python\r\nprint(hash(key))\r\nencoded_dict.dict_value.fields[str(key)].CopyFrom(encode_fn(value))\r\n```\r\n\r\n(Using `str(key)` in the dict as we know that that doesn't crash)",
"Sorry about the confusion. \r\n\r\nI am getting following:\r\n\r\n```\r\n-6062824253216937473\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-5821025861464018857\r\n-6062824253216937473\r\n-696857728539794345\r\n1\r\n2\r\n4\r\n8\r\n16\r\n-696857728539794345\r\n1\r\n2\r\n4\r\n8\r\n16\r\n-696857728539794345\r\n1\r\n2\r\n4\r\n8\r\n16\r\n-696857728539794345\r\n1\r\n2\r\n4\r\n8\r\n16\r\n-696857728539794345\r\n1\r\n2\r\n4\r\n8\r\n16\r\n-696857728539794345\r\n1\r\n2\r\n4\r\n8\r\n16\r\n-696857728539794345\r\n1\r\n2\r\n4\r\n8\r\n16\r\n-696857728539794345\r\n1\r\n2\r\n4\r\n8\r\n16\r\n1\r\n2\r\n4\r\n8\r\n16\r\n-696857728539794345\r\n1\r\n2\r\n4\r\n8\r\n16\r\n-696857728539794345\r\n1\r\n2\r\n4\r\n8\r\n16\r\n-696857728539794345\r\n1\r\n2\r\n4\r\n8\r\n16\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n-696857728539794345\r\n```",
"So no crash if I understand correctly?\r\n\r\nIn this case, I think it would be nice if you can provide us with a minimal model architecture (I don't think we need training data, but some randomized input could work) that could reproduce the issue. I'm out of guesses for what could go wrong :(",
"Hi @rejahn ,\r\n\r\nIt seems we may not able to help without model architecture. I just tried a dummy model with your provided details and its working fine as per attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/31ccc5609560d0ccf7e59f9aebca439f/62587.ipynb). \r\n\r\nThanks!\r\n\r\n",
"I will try to work on a minimal reproducible example. For now, I will close the issue and open again as soon as I have an example.",
"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/62587\">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/62587\">No</a>\n"
] | 2023-12-07T14:53:28 | 2023-12-20T15:58:47 | 2023-12-20T15:58:43 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.12.1
### 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
11.8/8.6
### GPU model and memory
NVIDIA Quadro P3200 6GB
### Current behavior?
I am currently having problems to save a model. After the training is completed, python crashes when calling [save_model](https://www.tensorflow.org/api_docs/python/tf/keras/saving/save_model) or creating a [ModelCheckpoint](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/ModelCheckpoint)
### Standalone code to reproduce the issue
```shell
### Training: ###
history = model.fit(train_data, validation_data=val_data,
epochs=EPOCHS, callbacks=callbacks)
print("finished fitting model")
# Saving model:
if not os.path.exists(MODEL_FOLDER):
print("path doesn't exist")
os.makedirs(MODEL_FOLDER)
tf.keras.models.save_model(model, MODEL_FOLDER, save_format='tf')
print("Model has been saved at" + MODEL_FOLDER)
```
### Relevant log output
```shell
Epoch 1/2
[ Warnings...]
210/210 [==============================] - ETA: 0s - loss: 0.4397
Epoch 1: val_loss did not improve from 0.10000
210/210 [==============================] - 53s 131ms/step - loss: 0.4397 - val_loss: 0.2221
Epoch 2/2
210/210 [==============================] - ETA: 0s - loss: 0.1960
Epoch 2: val_loss did not improve from 0.10000
210/210 [==============================] - 27s 125ms/step - loss: 0.1960 - val_loss: 0.1421
finished fitting model
WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 68). These functions will not be directly callable after loading.
Traceback (most recent call last):
File "/home/user/repos/drivan/model/train_model.py", line 147, in <module>
tf.keras.models.save_model(model, MODEL_FOLDER, save_format='tf')
File "/home/user/repos/drivan/.venv/lib/python3.8/site-packages/keras/saving/saving_api.py", line 145, in save_model
return legacy_sm_saving_lib.save_model(
File "/home/user/repos/drivan/.venv/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/user/repos/drivan/.venv/lib/python3.8/site-packages/tensorflow/python/saved_model/nested_structure_coder.py", line 196, in do_encode
encoded_dict.dict_value.fields[key].CopyFrom(encode_fn(value))
TypeError: bad argument type for built-in operation
```
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How to create a Tensorflow lite C Static library
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[
"Install Bazel:\r\nTensorFlow uses Bazel as its build tool. Install Bazel by following the instructions on the official Bazel website.\r\n\r\nClone TensorFlow Repository:\r\nClone the TensorFlow repository from GitHub:\r\n\r\n\r\ngit clone https://github.com/tensorflow/tensorflow.git\r\nNavigate to TensorFlow Lite directory:\r\n\r\n\r\ncd tensorflow/tensorflow/lite\r\nBuild TensorFlow Lite Static Library:\r\nConfigure the build:\r\nConfigure the build with Bazel, specifying the target for a static library:\r\n\r\n\r\nbazel build -c opt --config=monolithic tensorflow/lite:libtensorflowlite.a\r\nThis assumes that the TensorFlow Lite source code is located in the tensorflow directory.\r\n\r\nFind the built library:\r\nThe built static library will be located in the bazel-bin/tensorflow/lite directory:\r\n\r\n\r\nls bazel-bin/tensorflow/lite\r\nLook for a file named libtensorflowlite.a.",
"@Ravitejachatti Thank you for your response here.\r\n@Tamilarasan-C Please do follow the above comment here and refer to this guide as well\r\nhttps://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/cmake\r\nThank you!",
"Hi @Ravitejachatti and @sushreebarsa,\r\n\r\nThanks for the response.\r\n\r\n@Ravitejachatti, I tried as you mentioned and I got the below error,\r\nERROR: Skipping 'tensorflow/lite:libtensorflowlite.a': no such package 'tensorflow/lite/tensorflow/lite': BUILD file not found in any of the following directories. Add a BUILD file to a directory to mark it as a package.\r\n - /home/user/new_static_build/tensorflow/tensorflow/lite/tensorflow/lite\r\n \r\nThen I tried with this command \"bazel build -c opt --config=monolithic //tensorflow/lite:libtensorflowlite.a\" and got below error,\r\nERROR: Skipping '//tensorflow/lite:libtensorflowlite.a': no such target '//tensorflow/lite:libtensorflowlite.a': target 'libtensorflowlite.a' not declared in package 'tensorflow/lite' defined by /home/user/new_static_build/tensorflow/tensorflow/lite/BUILD (did you mean 'libtensorflowlite.so'? Tip: use `query \"//tensorflow/lite:*\"` to see all the targets in that package)\r\n\r\nSeems like there should be details about the static library to be built in the BUILD file, please provide the required details.\r\n\r\nThanks.",
"Hi @Tamilarasan-C ,\r\n\r\nPlease do check the following steps\r\n1. Check the build file at the location '/home/user/new_static_build/tensorflow/tensorflow/lite/tensorflow/lite/BUILD'.\r\n2. Verify that the target named **libtensorflowlite.a** is declared or not.\r\n3. Also use bazel query **\"//tensorflow/lite:*\"** as per error log to locate the target named **libtensorflowlite.a** .\r\n\r\nLet us know the response\r\n\r\nThank You\r\n\r\n",
"HI @LakshmiKalaKadali ,\r\n\r\nThere is no such target (libtensorflowlite.a) specified in the BUILD file.\r\n\r\nI tried to build in a new workspace and getting a error given below:\r\n`ERROR: An error occurred during the fetch of repository 'llvm-raw':\r\n Traceback (most recent call last):\r\n File \"/home/user/bazel_static_build/tensorflow_src/third_party/repo.bzl\", line 83, column 30, in _tf_http_archive_impl\r\n ctx.patch(patch_file, strip = 1)\r\nError in patch: Error applying patch /home/user/bazel_static_build/bazel_static_build/tensorflow_src/third_party/llvm/toolchains.patch: Incorrect Chunk: the chunk content doesn't match the target\r\n**Original Position**: 89\r\n\r\n**Original Content**:\r\n# TODO: We should split out host vs. target here.\r\nllvm_config_defines = os_defines + select({\r\n \"@bazel_tools//src/conditions:windows\": native_arch_defines(\"X86\", \"x86_64-pc-win32\"),\r\n \"@bazel_tools//src/conditions:darwin_arm64\": native_arch_defines(\"AArch64\", \"arm64-apple-darwin\"),\r\n \"@bazel_tools//src/conditions:darwin_x86_64\": native_arch_defines(\"X86\", \"x86_64-unknown-darwin\"),\r\n \"@bazel_tools//src/conditions:linux_aarch64\": native_arch_defines(\"AArch64\", \"aarch64-unknown-linux-gnu\"),\r\n \"@bazel_tools//src/conditions:linux_ppc64le\": native_arch_defines(\"PowerPC\", \"powerpc64le-unknown-linux-gnu\"),\r\n \"@bazel_tools//src/conditions:linux_s390x\": native_arch_defines(\"SystemZ\", \"systemz-unknown-linux_gnu\"),\r\n\r\n**Revised Content**:\r\n# TODO: We should split out host vs. target here.\r\nllvm_config_defines = os_defines + select({\r\n \"@bazel_tools//src/conditions:windows\": native_arch_defines(\"X86\", \"x86_64-pc-win32\"),\r\n \"//llvm:macos_arm64\": native_arch_defines(\"AArch64\", \"arm64-apple-darwin\"),\r\n \"//llvm:macos_x86_64\": native_arch_defines(\"X86\", \"x86_64-unknown-darwin\"),\r\n \"//llvm:macos_x86_64_default\": native_arch_defines(\"X86\", \"x86_64-unknown-darwin\"),\r\n \"@bazel_tools//src/conditions:linux_aarch64\": native_arch_defines(\"AArch64\", \"aarch64-unknown-linux-gnu\"),\r\n \"@bazel_tools//src/conditions:linux_ppc64le\": native_arch_defines(\"PowerPC\", \"powerpc64le-unknown-linux-gnu\"),\r\n \"@bazel_tools//src/conditions:linux_s390x\": native_arch_defines(\"SystemZ\", \"systemz-unknown-linux_gnu\"),`\r\n \r\nIs there a way to modify the CMakeLists.txt file in the /tensorflow/lite directory and create a static build with cmake ?\r\n\r\nThank you.",
"Hi @pkgoogle,\r\n\r\nPlease look into the issue.\r\n\r\n Thank You",
"Hi @Tamilarasan-C, for this case I recommend following this https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/guide/build_cmake.md for steps 1-3\r\n\r\nthen do \r\n```\r\ncmake ../tensorflow_src/tensorflow/lite/c -DTFLITE_C_BUILD_SHARED_LIBS:BOOL=OFF\r\ncmake --build . -j\r\n```\r\n\r\nthis should build libtensorflowlite_c.a in your tflite_build directory:\r\n```sh\r\ntflite_build$ ls\r\nabseil-cpp compile_commands.json fft2d FXdiv ml_dtypes pthreadpool-download tmp\r\nbuildtests.sh cpuinfo flatbuffers FXdiv-download neon2sse pthreadpool-source xnnpack\r\ncheck.sh debug.sh flatbuffers-flatc FXdiv-source psimd release.sh\r\nCMakeCache.txt _deps FP16 gemmlowp psimd-download ruy\r\nCMakeFiles eigen FP16-download libtensorflowlite_c.a psimd-source src\r\ncmake_install.cmake farmhash FP16-source Makefile pthreadpool tensorflow-lite\r\n```\r\n\r\nmay I ask why you need a static library instead of a dynamic library?",
"Hi @pkgoogle,\r\n\r\nThanks for the response, the reason I need a static library is because there is no file system in the deployment platform to use a shared library.\r\n\r\nI did as you mentioned and got a static library, but while linking I still get a lot of undefined reference errors like the one below,\r\n\r\n/usr/bin/ld: /home/user/static_build/build/libtensorflowlite_c.a(c_api.cc.o): in function `TfLiteModelCreate':\r\nc_api.cc:(.text+0x10a): undefined reference to `tflite::DefaultErrorReporter()'\r\n\r\nI am compiling and linking in this format,\r\ng++ main.c -o main [library path] [library]\r\n\r\nwhen I grepped for the missing reference in the library created there was a match found but I get the error too.\r\n\r\nThanks.",
"Hi @Tamilarasan-C, would just creating the executable (for the correct platform) be sufficient for you? i.e. is that your true goal? (I would assume that's why you're linking). You can try using gcc instead as sometimes there's compatibility issues when linking c programs with g++ instead of gcc.\r\n\r\nGenerally it is recommended you use cmake or bazel to automatically build to an executable for you though they obviously take a little bit of start-up time to learn and use. We can try helping you achieve that if that will help you achieve your goals. Additionally, clang is now and in the future better supported.\r\n\r\nAlso if you can provide your full command (the actual library path and actual library) please do so. include any -L prefix or essentially the literal command you use, feel free to exclude any PII (like your username). Also if you are comfortable sharing your main.c file, that will help us deduce what the issue may be.",
"Hi @pkgoogle,\r\n\r\nInitially I am trying to do inference in linux environment (for POC) and finally it will be deployed in Xtensa (true goal).\r\n\r\nI used gcc instead of g++ thid time but it didn't work.\r\nThis is the complete command used for building: gcc -Wall -Wextra -I/home/user/static_build_shared_lib_off/tensorflow_src -I/home/user/lib/stb -I/home/user/lib/stb/deprecated main.c -o predictor -lm -L/home/user/static_build_shared_lib_off/build -ltensorflowlite_c\r\n\r\nAlso, attaching the main.c and Makefile used to build it.\r\n[predict.zip](https://github.com/tensorflow/tensorflow/files/13723277/predict.zip)\r\n\r\nAttaching stb (used in the main.c file) related dependencies as well.\r\n[stb.zip](https://github.com/tensorflow/tensorflow/files/13735438/stb.zip)\r\n\r\nLinking this same program (main.c) with shared library works fine.\r\n\r\nThank you.",
"Hi @Tamilarasan-C, can you try something like this:\r\n\r\n```\r\n# assuming libtensorflowlite_c.a is in the same directory\r\ngcc -static -Wall -Wextra -I/home/user/static_build_shared_lib_off/tensorflow_src -I/home/user/lib/stb -I/home/user/lib/stb/deprecated main.c -o predictor -lm -L/home/user/static_build_shared_lib_off/build libtensorflowlite_c.a\r\n```\r\n\r\nLet me know how that goes or if it fails let me know the error message.",
"Hi @pkgoogle,\r\n\r\nTried the command you provided and got this error,\r\n`xxx@yyy:~/static_build_shared_lib_off/predict_c$ gcc -static -Wall -Wextra -I/home/user/static_build_shared_lib_off/tensorflow_src -I/home/user/lib/stb -I/home/user/lib/stb/deprecated main.c -o predictor -lm -L/home/user/static_build_shared_lib_off/build libtensorflowlite_c.a\r\ngcc: error: libtensorflowlite_c.a: No such file or directory`\r\n\r\nIn the same command I used \"-ltensorflowlite_c\" instead of \"libtensorflowlite_c.a\" and got the usual undefined reference errors.\r\n\r\nThanks.",
"Hi @Tamilarasan-C, was libtensorflowlite_c.a in the current directory when you tried that command?",
"Hi @pkgoogle,\r\n\r\nI tried the command provided by you by copying the libtensorflowlite_c.a file to the location of main.c file (current directory), but still I am getting undefined reference errors.\r\n\r\nAlso, could you please give information regarding what all packages should be added for the APIs used in main.c file if I create a custom CMakeLists.txt to build a static library.\r\n\r\nThank you.",
"Hi @Tamilarasan-C, so the problem is there are actually a a lot of dependent transitive static libraries that need to be linked, https://www.tensorflow.org/lite/guide/build_cmake#step_5_build_tensorflow_lite, especially look at the note `This generates a static library libtensorflow-lite.a in the current directory but the library isn't self-contained since all the transitive dependencies are not included. To use the library properly, you need to create a CMake project. Please refer the \"Create a CMake project which uses TensorFlow Lite\" section.`\r\n\r\nI recommend getting the minimal project working and compiled on your build system: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/examples/minimal then adjusting the cmake project to your needs, the cmake project should statically link when built.\r\n\r\nexample output from the build process:\r\n```\r\n[ 0%] Building CXX object _deps/ruy-build/ruy/CMakeFiles/ruy_denormal.dir/denormal.cc.o\r\n[ 0%] Building CXX object _deps/abseil-cpp-build/absl/time/CMakeFiles/absl_time_zone.dir/internal/cctz/src/zone_info_source.cc.o\r\n[ 0%] Building CXX object _deps/flatbuffers-build/CMakeFiles/flatbuffers.dir/src/util.cpp.o\r\n[ 0%] Building CXX object _deps/ruy-build/ruy/CMakeFiles/ruy_apply_multiplier.dir/apply_multiplier.cc.o\r\n[ 0%] Building CXX object _deps/ruy-build/ruy/CMakeFiles/ruy_have_built_path_for_avx512.dir/have_built_path_for_avx512.cc.o\r\n[ 0%] Building CXX object _deps/ruy-build/ruy/CMakeFiles/ruy_have_built_path_for_avx2_fma.dir/have_built_path_for_avx2_fma.cc.o\r\n[ 0%] Building C object _deps/cpuinfo-build/CMakeFiles/cpuinfo.dir/src/x86/isa.c.o\r\n[ 0%] Building CXX object _deps/ruy-build/ruy/CMakeFiles/ruy_have_built_path_for_avx.dir/have_built_path_for_avx.cc.o\r\n[ 0%] Building C object _deps/cpuinfo-build/CMakeFiles/cpuinfo.dir/src/x86/cache/descriptor.c.o\r\n[ 0%] Building C object _deps/cpuinfo-build/CMakeFiles/cpuinfo.dir/src/x86/cache/deterministic.c.o\r\n[ 0%] Building C object _deps/cpuinfo-build/CMakeFiles/cpuinfo.dir/src/x86/cache/init.c.o\r\n[ 0%] Building C object _deps/cpuinfo-build/CMakeFiles/cpuinfo.dir/src/x86/linux/init.c.o\r\n[ 0%] Linking CXX static library libruy_profiler_instrumentation.a\r\n[ 0%] Linking CXX static library libruy_have_built_path_for_avx.a\r\n[ 0%] Linking CXX static library libruy_have_built_path_for_avx2_fma.a\r\n[ 0%] Linking CXX static library libabsl_flags_commandlineflag_internal.a\r\n[ 0%] Building C object _deps/cpuinfo-build/CMakeFiles/cpuinfo.dir/src/x86/linux/cpuinfo.c.o\r\n[ 0%] Building C object _deps/cpuinfo-build/CMakeFiles/cpuinfo.dir/src/linux/smallfile.c.o\r\n[ 0%] Linking CXX static library libruy_have_built_path_for_avx512.a\r\n[ 0%] Building C object _deps/cpuinfo-build/CMakeFiles/cpuinfo.dir/src/linux/multiline.c.o\r\n[ 0%] Building C object _deps/cpuinfo-build/CMakeFiles/cpuinfo.dir/src/linux/processors.c.o\r\n[ 0%] Building C object _deps/cpuinfo-build/CMakeFiles/cpuinfo.dir/src/linux/cpulist.c.o\r\n[ 0%] Built target microkernel-utils\r\n[ 0%] Linking CXX static library libruy_system_aligned_alloc.a\r\n[ 0%] Linking CXX static library libruy_denormal.a\r\n[ 0%] Linking CXX static library libabsl_spinlock_wait.a\r\n[ 0%] Built target ruy_have_built_path_for_avx2_fma\r\n[ 0%] Built target ruy_profiler_instrumentation\r\n[ 0%] Built target ruy_have_built_path_for_avx\r\n[ 0%] Built target ruy_have_built_path_for_avx512\r\n[ 0%] Built target absl_flags_commandlineflag_internal\r\n[ 0%] Linking CXX static library libruy_apply_multiplier.a\r\n[ 0%] Building CXX object _deps/ruy-build/ruy/CMakeFiles/ruy_block_map.dir/block_map.cc.o\r\n[ 0%] Built target ruy_system_aligned_alloc\r\n[ 0%] Built target ruy_denormal\r\n[ 0%] Linking C static library libcpuinfo.a\r\n[ 0%] Building CXX object _deps/ruy-build/ruy/CMakeFiles/ruy_prepacked_cache.dir/prepacked_cache.cc.o\r\n[ 0%] Building CXX object _deps/ruy-build/ruy/CMakeFiles/ruy_allocator.dir/allocator.cc.o\r\n[ 0%] Built target absl_spinlock_wait\r\n[ 0%] Linking CXX static library libruy_wait.a\r\n[ 0%] Linking CXX static library libabsl_exponential_biased.a\r\n[ 0%] Linking CXX static library libabsl_strerror.a\r\n[ 0%] Linking CXX static library libabsl_log_severity.a\r\n[ 0%] Built target ruy_apply_multiplier\r\n[ 0%] Built target cpuinfo\r\n[ 0%] Built target ruy_wait\r\n[ 0%] Built target absl_exponential_biased\r\n[ 0%] Building CXX object _deps/ruy-build/ruy/CMakeFiles/ruy_cpuinfo.dir/cpuinfo.cc.o\r\n[ 0%] Building CXX object _deps/ruy-build/ruy/CMakeFiles/ruy_blocking_counter.dir/blocking_counter.cc.o\r\n[ 0%] Built target absl_strerror\r\n[ 0%] Linking CXX static library libfarmhash.a\r\n[ 0%] Built target absl_log_severity\r\n[ 0%] Linking CXX static library libruy_block_map.a\r\n[ 0%] Building CXX object _deps/abseil-cpp-build/absl/base/CMakeFiles/absl_raw_logging_internal.dir/internal/raw_logging.cc.o\r\n[ 0%] Linking CXX static library libruy_prepacked_cache.a\r\n[ 0%] Linking CXX static library libruy_allocator.a\r\n[ 0%] Built target farmhash\r\n[ 0%] Linking CXX static library libabsl_civil_time.a\r\n[ 0%] Built target ruy_block_map\r\n[ 0%] Linking CXX static library libruy_cpuinfo.a\r\n[ 0%] Built target ruy_prepacked_cache\r\n[ 0%] Built target ruy_allocator\r\n[ 0%] Linking CXX static library libruy_blocking_counter.a\r\n[ 0%] Built target absl_civil_time\r\n[ 0%] Built target ruy_cpuinfo\r\n[ 0%] Linking CXX static library libabsl_int128.a\r\n[ 7%] Building CXX object _deps/ruy-build/ruy/CMakeFiles/ruy_tune.dir/tune.cc.o\r\n```\r\n\r\nAs you can see it statically links by default, I think this will accomplish your goals. Let me know if you have any questions.",
"Hi @pkgoogle ,\r\n\r\nI used the CMakeLists.txt of minimal example and added some more APIs I need to the minimal.cc file and it worked with the static library linked.\r\n\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/62586\">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/62586\">No</a>\n",
"@pkgoogle what if I want to generate libtensorflow.a file for using the tensorflow c api . Is this the way to do this as well ?\r\n",
"Hi @johntharian28, it should be -- try it out and let me know the results."
] | 2023-12-07T10:34:26 | 2024-02-07T18:44:06 | 2024-01-05T05:19:36 |
NONE
| null | null | null |
### 1. System information
- OS Platform and Distribution: Linux Ubuntu 20.04
- TensorFlow installation: Built from source
- TensorFlow library: Release 2.16.0
### 2. Query:
Hi,
I can find build instructions for Tensorflow lite C **Shared library**, I need instructions to build a **Static library** or know if there is one already built in some other location.
Thanks.
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