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PR_kwDOArmXAs5nSLLG
| 62,990 |
validate batch_size arg of tf.raw_ops.RecordInput
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Validate `batch_size` argument of `RecordInputOp` to prevent `abort` in case of -ve batch_size.
Might fix #62977 .
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Fails to convert to TFLite model in TF 2.16.0rc0
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[
"When a keras model is created with TF2.16.0 ([keras model available here)](https://www.dropbox.com/scl/fi/z0womeh2hj51i6tqj7wc0/TF2.16.0_model_classifier_CNN.keras?rlkey=c3dqmdqh3c516twhpdxjercg7&dl=0), the conversion still fails but with a different error:\r\n\r\n[log2.txt](https://github.com/tensorflow/tensorflow/files/14324786/log2.txt)\r\n",
"Hi @feranick, do you mind uploading the model on github via a zip file? There are licensing issues with dropbox so we cannot use it. Thanks for your help.",
"The models are quite large and beyind the github 25MB limit. Here they are from an open server:\r\n\r\nhttps://gridedgedm.mit.edu/tf_tmp/TF2.15.0_model_classifier_CNN.keras\r\nhttps://gridedgedm.mit.edu/tf_tmp/TF2.16.0_model_classifier_CNN.keras\r\n",
"Hi @feranick, I was able to replicate on 2.16 in this [gist](https://colab.sandbox.google.com/gist/pkgoogle/ed2e7fdf2acb7b0d9c8bbe5921cb64a3/62989.ipynb) (You have to upload your TF2.16.0... file to colab). Is there any particular ops you are using in this model or do you have any additional information on the creation of this model?\r\n\r\nHi @majiddadashi, can you please take a look? Thanks.",
"> Hi @feranick, I was able to replicate on 2.16 in this [gist](https://colab.sandbox.google.com/gist/pkgoogle/ed2e7fdf2acb7b0d9c8bbe5921cb64a3/62989.ipynb) (You have to upload your TF2.16.0... file to colab). Is there any particular ops you are using in this model or do you have any additional information on the creation of this model?\r\n> \r\nHi @pkgoogle, thank you for looking into this. This below is a snapshot of the code relevant to the creation of the model. I hope it's helpful. I have been using it for a long time with TF2.15.0 and below with no issues...\r\n[model.txt](https://github.com/tensorflow/tensorflow/files/14378663/model.txt)\r\n\r\n\r\n\r\n",
"Here's also the `tf.lite.experimental.Analyzer.analyze(model_content=tflite_model)` output for both the TF2.15.0 and TF2.16.0rc0 models (the latter failing).\r\n[analyzer_2.15_model.txt](https://github.com/tensorflow/tensorflow/files/14378749/analyzer_2.15_model.txt)\r\n[analyzer_2.16_model.txt](https://github.com/tensorflow/tensorflow/files/14378750/analyzer_2.16_model.txt)\r\n\r\n",
"If you casually compare the config.jason in the keras model files you can see that the variable `batch_input_shape` in TF2.15.0 was changed to `batch_shape` in TF2.16. This variable remain undefined in TF2.15. The config.json are attached.\r\n[TF2.16rc0_config.json](https://github.com/tensorflow/tensorflow/files/14378950/TF2.16rc0_config.json)\r\n[TF2.15_config.json](https://github.com/tensorflow/tensorflow/files/14378952/TF2.15_config.json)\r\n",
"Even replacing `batch_shape` with `batch_input_shape`, there are still errors when converting the keras model into a tflite:\r\n```\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/optimizers/optimizer.py](https://localhost:8080/#) in _process_kwargs(self, kwargs)\r\n 140 )\r\n 141 else:\r\n--> 142 raise TypeError(\r\n 143 f\"{k} is not a valid argument, kwargs should be empty \"\r\n 144 \" for `optimizer_experimental.Optimizer`.\"\r\n\r\nTypeError: loss_scale_factor is not a valid argument, kwargs should be empty for `optimizer_experimental.Optimizer`.\r\n```\r\n`loss_scale_factor` seems to be defined only in TF2.16.0, but there not in the TF2.15 model.\r\n",
"Tested the above models with the pre-release, TF 2.16.0-rc0, using keras 3. Now they both crash. Log attached.\r\n[TF2.15_convertLog.txt](https://github.com/tensorflow/tensorflow/files/14425168/TF2.15_convertLog.txt)\r\n[TF2.16_convertLog.txt](https://github.com/tensorflow/tensorflow/files/14425169/TF2.16_convertLog.txt)\r\n\r\nWhen used in \"legacy\" mode (using tf-keras~=2.16rc0), the crash is identical as my previous report above (i.e. old TF2.15 models are converted, new TF2.16 are not).",
"So in summary: any model built with `TF2.15` or earlier or with `TF2.16.0-rc0` using `keras 2` (and `tf_keras=2.16rc0`) can be successfully converted in tflite. Any model built with `TF2.16.0-rc0` using `keras 3` **cannot** be converted to tflite. Any model built with `TF2.15` or earlier or with `TF2.16.0-rc0` using `keras 2` **cannot** be converted using `TF2.16.0-rc0` in keras 3 mode.\r\n\r\n**In essence, conversion to tflite is completely broken with keras 3.**",
"The models created with `keras v2` and `v3` appear to be completely incompatible with each other. The way to have a successful conversion is to basically do the training, model loading and conversion within the same keras version.\r\nSo for `keras v2`, saving after training:\r\n```\r\nimport tf_keras as keras\r\nmodel.save(dP.model_name)\r\n```\r\nfor the TFlite conversion:\r\n```\r\ndef convertModelToTFLite(model_file):\r\n import tensorflow as tf\r\n import tf_keras as keras\r\n model = keras.models.load_model(model_file)\r\n converter = tf.lite.TFLiteConverter.from_keras_model(model) # TensorFlow 2.15 and earlier\r\n converter.optimizations = [tf.lite.Optimize.DEFAULT]\r\n tflite_model = converter.convert()\r\n tf.lite.experimental.Analyzer.analyze(model_content=tflite_model)\r\n open(convFile, \"wb\").write(tflite_model)\r\n```\r\nFor loading a `v2` model:\r\n```\r\nmodel = keras.models.load_model(model_name)\r\n```\r\nFor `keras v3`, saving after training:\r\n```\r\nimport keras\r\nmodel.export(dP.model_name)\r\n```\r\nFor loading a `v3` model:\r\n```\r\nmodel = keras.models.Sequential()\r\nmodel.add(keras.layers.TFSMLayer(model_name, call_endpoint='serve'))\r\n```\r\nfor the TFlite conversion:\r\n```\r\n def convertModelToTFLite(model_file):\r\n import tensorflow as tf\r\n import keras\r\n model = keras.layers.TFSMLayer(model_file, call_endpoint='serve') # TensorFlow >= 2.16.0\r\n converter = tf.lite.TFLiteConverter.from_keras_model(model)\r\n \r\n converter.optimizations = [tf.lite.Optimize.DEFAULT]\r\n tflite_model = converter.convert()\r\n tf.lite.experimental.Analyzer.analyze(model_content=tflite_model)\r\n open(convFile, \"wb\").write(tflite_model)\r\n```\r\n\r\nThis of course is not ideal in the long term, when \"legacy\" `v2` models will require maintenance of legacy code to deal with it. **A solution could be an ad-hoc conversion API for models from `v2` to `v3`.**"
] | 2024-02-18T22:39:42 | 2024-02-28T21:55:53 | null |
CONTRIBUTOR
| null | null | null |
### 1. System information
- Ubuntu 22.04
- Built from source, TF 2.16.0rc0
- TensorFlow library: TF 2.16.0 (branch r2.16), commit 03d0e0b
### 2. Code
Code attached.
[ConvertToTFLite.py.txt](https://github.com/tensorflow/tensorflow/files/14324713/ConvertToTFLite.py.txt)
### 3. Failure after conversion
Conversion of a model is successful when using TF2.15.0 or earlier. Conversion of a model created with TF2.15.0 or earlier fails when using TF 2.16.0 for conversion. [The initial keras model is available here.](https://www.dropbox.com/scl/fi/1ldtdr90qsgbns0snu2ba/TF2.15.0_model_classifier_CNN.keras?rlkey=lf1cm56oyt21v1xqer40t0b1w&dl=0) Log provided below.
### Log - attached.
[log.txt](https://github.com/tensorflow/tensorflow/files/14324717/log.txt)
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I_kwDOArmXAs5_nvWL
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TF Lite: error: use of deleted function 'std::atomic<bool> in tensorflow/tensorflow/lite/core/async/task_internal.h
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[
"This is resolved when setting c++17 as default.:\r\n```\r\n @@ -37,7 +37,7 @@ @org_tensorflow//tensorflow:workspace0.bzl\r\n \r\n load(\"@coral_crosstool//:configure.bzl\", \"cc_crosstool\")\r\n - cc_crosstool(name = \"crosstool\", cpp_version = \"c++14\") \r\n + cc_crosstool(name = \"crosstool\", cpp_version = \"c++17\")\r\n\r\n```",
"Hi @feranick,\r\n\r\n As you mentioned, the issue is resolved, could please feel free to close the issue.\r\n\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/62988\">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/62988\">No</a>\n"
] | 2024-02-18T18:18:14 | 2024-02-23T05:32:33 | 2024-02-23T05:32:29 |
CONTRIBUTOR
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15.0
### Custom code
No
### OS platform and distribution
Ubuntu Linux 22.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
6.1
### GCC/compiler version
Ubuntu clang version 14.0.0-1ubuntu1.1
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When compiling libcoral from [here](https://github.com/feranick/libcoral) - a fork of the [libcoral](https://github.com/google-coral/libedgetpu) library to allow support for modern versions of TFs, tensorflow is downloaded, and compiled. However, compilation stops because of the use of a deleted functions `deleted function 'std::atomic<bool>::atomic(const std::atomic<bool>&)'` and `'std::atomic<_Tp>::atomic(const std::atomic<_Tp>&) [with _Tp = TfLiteStatus]' in `org_tensorflow/tensorflow/lite/core/async/task_internal.h` (see log below). Normally this is not compiled when building the regular `tflite_runtime`, but it is called for compilation in this specific case when it is used to link to the `libcoral` library.
The two calls in
`std::atomic_bool scheduled_ = false;`
and
`std::atomic<TfLiteStatus> status_ = kTfLiteOk;`
should be changed to allow for correct initialization.
### Standalone code to reproduce the issue
```shell
git clone https://github.com/feranick/libcoral
cd libcoral
git submodule init && git submodule update
make DOCKER_IMAGE=ubuntu:22.04 DOCKER_CPUS="k8" DOCKER_TARGETS=tests docker-build
```
### Relevant log output
```shell
--sandbox_debug to see verbose messages from the sandbox and retain the sandbox build root for debugging
In file included from external/org_tensorflow/tensorflow/lite/core/async/task_internal.cc:15:
external/org_tensorflow/tensorflow/lite/core/async/task_internal.h:141:33: error: use of deleted function 'std::atomic<bool>::atomic(const std::atomic<bool>&)'
141 | std::atomic_bool scheduled_ = false;
| ^~~~~
In file included from external/org_tensorflow/tensorflow/lite/core/async/task_internal.h:18,
from external/org_tensorflow/tensorflow/lite/core/async/task_internal.cc:15:
/usr/include/c++/11/atomic:72:5: note: declared here
72 | atomic(const atomic&) = delete;
| ^~~~~~
/usr/include/c++/11/atomic:76:15: note: after user-defined conversion: 'constexpr std::atomic<bool>::atomic(bool)'
76 | constexpr atomic(bool __i) noexcept : _M_base(__i) { }
| ^~~~~~
In file included from external/org_tensorflow/tensorflow/lite/core/async/task_internal.cc:15:
external/org_tensorflow/tensorflow/lite/core/async/task_internal.h:144:39: error: use of deleted function 'std::atomic<_Tp>::atomic(const std::atomic<_Tp>&) [with _Tp = TfLiteStatus]'
144 | std::atomic<TfLiteStatus> status_ = kTfLiteOk;
| ^~~~~~~~~
In file included from external/org_tensorflow/tensorflow/lite/core/async/task_internal.h:18,
from external/org_tensorflow/tensorflow/lite/core/async/task_internal.cc:15:
/usr/include/c++/11/atomic:227:7: note: declared here
227 | atomic(const atomic&) = delete;
| ^~~~~~
/usr/include/c++/11/atomic:231:17: note: after user-defined conversion: 'constexpr std::atomic<_Tp>::atomic(_Tp) [with _Tp = TfLiteStatus]'
231 | constexpr atomic(_Tp __i) noexcept : _M_i(__i) { }
| ^~~~~~
external/org_tensorflow/tensorflow/lite/core/async/task_internal.cc: In member function 'bool tflite::async::ExecutionTask::GetTensorIdx(TfLiteIoType, const char*, int*) const':
external/org_tensorflow/tensorflow/lite/core/async/task_internal.cc:40:7: warning: init-statement in selection statements only available with '-std=c++17' or '-std=gnu++17'
40 | if (auto it_idx = map->find(name); it_idx != map->end()) {
| ^~~~
external/org_tensorflow/tensorflow/lite/core/async/task_internal.cc: In member function 'TfLiteBufferHandle tflite::async::ExecutionTask::GetBufferHandle(int) const':
external/org_tensorflow/tensorflow/lite/core/async/task_internal.cc:57:7: warning: init-statement in selection statements only available with '-std=c++17' or '-std=gnu++17'
57 | if (auto it = io_data_.find(tensor_index); it != io_data_.end()) {
| ^~~~
external/org_tensorflow/tensorflow/lite/core/async/task_internal.cc: In member function 'TfLiteSynchronization* tflite::async::ExecutionTask::GetSynchronization(int) const':
external/org_tensorflow/tensorflow/lite/core/async/task_internal.cc:90:7: warning: init-statement in selection statements only available with '-std=c++17' or '-std=gnu++17'
90 | if (auto it = io_data_.find(tensor_index); it != io_data_.end()) {
| ^~~~
INFO: Elapsed time: 29.134s, Critical Path: 17.36s
INFO: 442 processes: 259 internal, 183 linux-sandbox.
FAILED: Build did NOT complete successfully
```
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v2.15.0 docker image print error messages like "Unable to register cuDNN/cuFFT... factory "
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[
"Hi @fancyerii ,\r\n\r\nThanks for reporting.Its known issue.It seems this might be due to duplicate registry AFAIK. Though its logging an error it seems the GPU works fine.In your logs also the GPUs also detected and it will not affecting the execution. Could you confirm whether there is a problem with execution of your code?\r\n\r\nSame issue was discussed in #62002, #62075.May please refer for more responses there. Thanks!",
"CC: @learning-to-play",
"@fancyerii , Just to verify please cross check whether cudnn_version: 8 & cuda_version 12.2 are there or not using the code below.\r\n\r\n```\r\nfrom tensorflow.python.platform import build_info\r\n\r\nprint(\"cudnn_version\",build_info.build_info['cudnn_version'])\r\nprint(\"cuda_version\",build_info.build_info['cuda_version'])\r\n```",
"@SuryanarayanaY , I'm having the same problem as the OP on latest-gpu, just run your recommended cross check and am getting:\r\n\r\n```\r\n>>> print(\"cudnn_version\",build_info.build_info['cudnn_version'])\r\ncudnn_version 8\r\n>>> print(\"cuda_version\",build_info.build_info['cuda_version'])\r\ncuda_version 12.2\r\n```\r\n\r\nSo you're saying I just should ignore the \"unable to register...\" then? They are the only reason I at all went through the trouble of attempting to use Docker images... I'll revert back to my native install then. Thanks.\r\n\r\nNote: lines starting with \"E\" tend to be interpreted as \"errors\", I guess, i.e. possibly blocking problems. I guess this makes this issue quite a serious one.",
"In fact, there is one more difference for me between native and Docker. With native, I am also getting:\r\n\r\n```\r\nW tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n```\r\n\r\nAnd that seems to be fixed by neither a regular nor a local `pip install` of `tensorrt` and friends. So I guess the Docker image still has an added value for me. Unless there is an obvious fix for the local TensorRT problem (I am installing TensorRT according to NVIDIA's instructions as far as I know)?",
"same problem here :("
] | 2024-02-18T02:51:35 | 2024-03-03T23:15:32 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.15.0
### Custom code
No
### OS platform and distribution
ubuntu 18.04
### 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?
```
$ docker pull tensorflow/tensorflow:2.15.0-gpu
$ docker run -it --gpus all tensorflow/tensorflow:2.15.0-gpu
$import tensorflow as tf
2024-02-18 02:43:26.283147: 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
2024-02-18 02:43:26.283204: 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
2024-02-18 02:43:26.283926: 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
2024-02-18 02:43:26.289011: 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.
>>> print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
Num GPUs Available: 8
```
I tried some examples and it worked. But why this error messages show?
### Standalone code to reproduce the issue
```shell
Ubuntu 18.04 nvidia-driver 535.104.12
```
### Relevant log output
_No response_
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Add missing space in `Fingerprint`'s docstring
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[
"Hi @hoel-bagard, Please submit multiple typo fixes in a single PR as the CPU/GPU hours are wasted on CI. \r\nHence, we do not encourage one liner grammatical changes as it is an expensive process. Thank you for your contribution!"
] | 2024-02-18T01:51:11 | 2024-03-07T08:46:32 | 2024-03-07T08:46:29 |
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I_kwDOArmXAs5_lBRX
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C++ API `GatherV2` aborts with inappropriate input
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[
"@Sehun0819 The values in indices should be within the valid range of params along the gather_dim dimension. Accessing invalid indices results in errors. Thank you!",
"@sushreebarsa \r\nHi!\r\nPlease note that it crashed in the middle of shape inference step, not by being checked status.\r\nDoesn't it need appropriate input checker for preventing crash?\r\n",
"Hi @Sehun0819 ,\r\n\r\nThe suspected code pointed is right.The `InferenceContext::UnknownShapeOfRank` calls assertion below.\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/e193d8ea7776ef5c6f5d769b6fb9c070213e737a/tensorflow/core/framework/shape_inference.cc#L705\r\n\r\nThe behaviour of `CHECK_GE` is such that it asserts whether `rank` is >=0 and if condition fails it aborts and terminate the program. It will be nice to have functionality like `OP_REQUIRES` which will raise exception instead of abortion.Will look into it."
] | 2024-02-17T18:19:19 | 2024-02-28T05:54:25 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.17
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04 LTS
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
6.5.0
### GCC/compiler version
clang 16
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
C++ API `GatherV2` aborts with inappropriate input.
Suspected code location is [here](https://github.com/tensorflow/tensorflow/blob/e193d8ea7776ef5c6f5d769b6fb9c070213e737a/tensorflow/core/ops/array_ops.cc#L1240-L1242),
```C++
c->set_output(0, c->UnknownShapeOfRank(c->Rank(params_shape) +
c->Rank(indices_shape) - 1 -
batch_dims));
```
which calls `UnknownShapeOfRank` with negative value due to lack of validity check.
### Standalone code to reproduce the issue
```C++
#include "tensorflow/cc/framework/scope.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/public/session.h"
#include "tensorflow/cc/ops/array_ops.h"
#include "tensorflow/cc/ops/standard_ops.h"
using namespace tensorflow;
int main() {
SessionOptions options;
ConfigProto & config = options.config;
config.set_inter_op_parallelism_threads(1);
config.set_intra_op_parallelism_threads(1);
config.set_use_per_session_threads(false);
std::unique_ptr<tensorflow::Session>
session(tensorflow::NewSession(options));
Scope scope = Scope::NewRootScope();
auto params = ops::RandomUniformInt(scope, {1}, 0, 15);
auto attrs =
ops::GatherV2::Attrs()
.BatchDims(2);
auto target = ops::GatherV2(scope.WithOpName("target"), params, 1, 1, attrs);
GraphDef graph_def;
TF_CHECK_OK(scope.ToGraphDef(&graph_def));
Status status = session->Create(graph_def);
if (!status.ok()) {
LOG(WARNING) << "Could not create session: " << status.message();
}
std::vector<Tensor> outputs;
status = session->Run({}, {"target"}, {""}, &outputs);
if (!status.ok()) {
LOG(WARNING) << "Could not run session: " << status.message();
}
return 0;
}
```
### Relevant log output
```shell
2024-02-18 03:14:31.909510: F tensorflow/core/framework/shape_inference.cc:705] Check failed: rank >= 0 (0 vs. -2)rank must not be negative
Aborted (core dumped)
```
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I_kwDOArmXAs5_k8HG
| 62,984 |
C++ API `MatrixDiagV3` aborts with tensor type of `k`
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[
"@Sehun0819 If MatrixDiagV3 doesn't work due to type limitations, could you consider alternative approaches to achieve the desired functionality like tf.linalg.diag?\r\nThank you!",
"@sushreebarsa \r\nHi! Even there exist alternatives this should be patched to avoid crash right?\r\nPlease check python version of [reproduction](https://colab.research.google.com/drive/1R11FohcZZ-Ky1rpO-bJPOcztHdRIQE5R?usp=sharing).\r\n```Python\r\nimport tensorflow as tf\r\n\r\ntf.raw_ops.MatrixDiagV3(\r\n diagonal=tf.constant([0.0],dtype=tf.float32),\r\n k=tf.constant([1,2],shape=[2],dtype=tf.int32),\r\n num_rows=1,\r\n num_cols=1,\r\n padding_value=0.0,\r\n align='RIGHT_LEFT',\r\n name=None\r\n)\r\n```",
"Hi @Sehun0819 ,\r\n\r\nThanks for reporting. Here the checkfail is happening when the `diagonal` is of `rank=1` and `k[0] != k[1]` which needs to taken care. This is not problem with tensor type of `k` . \r\n\r\nYou can try below code which works for `k `as tensor also.\r\n\r\n```\r\nimport tensorflow as tf\r\ntf.raw_ops.MatrixDiagV3(\r\n diagonal=tf.constant([10],dtype=tf.float32),\r\n k=tf.constant([1,1],shape=[2],dtype=tf.int32), // k=(1,1) also works\r\n num_rows=-1,\r\n num_cols=-1,\r\n padding_value=0.0,\r\n align='RIGHT_LEFT',\r\n name=None\r\n)\r\n```\r\n\r\nProposing a probable fix for this.\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/62984\">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/62984\">No</a>\n"
] | 2024-02-17T18:02:41 | 2024-04-16T17:20:57 | 2024-04-16T17:20:54 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.17
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04 LTS
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
6.5.0
### GCC/compiler version
clang 16
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
C++ API `MatrixDiagV3` aborts with tensor type of `k`.
Usually `k` would be given as an array of integers. But it can also accept a tensor, and it can lead to crash.
Suspected code location is [here](https://github.com/tensorflow/tensorflow/blob/e193d8ea7776ef5c6f5d769b6fb9c070213e737a/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L241),
But I have no idea why it is not triggered when it runs with an integer array `k`.
### Standalone code to reproduce the issue
Note: You may have to run it several times to see crash.
```C++
#include "tensorflow/cc/framework/scope.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/public/session.h"
#include "tensorflow/cc/ops/array_ops.h"
#include "tensorflow/cc/ops/standard_ops.h"
using namespace tensorflow;
int main() {
SessionOptions options;
ConfigProto & config = options.config;
config.set_inter_op_parallelism_threads(1);
config.set_intra_op_parallelism_threads(1);
config.set_use_per_session_threads(false);
std::unique_ptr<tensorflow::Session>
session(tensorflow::NewSession(options));
Scope scope = Scope::NewRootScope();
Input k = ops::RandomUniformInt(scope, {2}, 0, (1 << 15) - 1);
auto target = ops::MatrixDiagV3(scope.WithOpName("target"), {0.0f}, k, 1, 1, 0.0f);
GraphDef graph_def;
TF_CHECK_OK(scope.ToGraphDef(&graph_def));
Status status = session->Create(graph_def);
if (!status.ok()) {
LOG(WARNING) << "Could not create session: " << status.message();
}
std::vector<Tensor> outputs;
status = session->Run({}, {"target"}, {"target"}, &outputs);
if (!status.ok()) {
LOG(WARNING) << "Could not run session: " << status.message();
}
return 0;
}
```
### Relevant log output
```shell
2024-02-18 02:54:37.171134: F tensorflow/core/framework/tensor_shape.cc:356] Check failed: d >= 0 (0 vs. -1)
Aborted (core dumped)
```
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I_kwDOArmXAs5_k3ti
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`tf.raw_ops.FakeQuantWithMinMaxArgs` aborts when `min>0` in Debug build
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[
"@Sehun0819,\r\nI tried to execute the mentioned code on tf-nightly(2.17.0-dev20240218) on both [GPU](https://colab.sandbox.google.com/gist/tilakrayal/34910c3146d13fe56c2ac1860a449205/untitled1733.ipynb) and [CPU](https://colab.sandbox.google.com/gist/tilakrayal/dfc7675fe1cf044fd034bb83b7404068/untitled1732.ipynb) and it was executed without any issue/error. Kindly find the gist attached. Thank you!",
"@tilakrayal \r\nHi!\r\nAs I mentioned, it leads to crash only when TensorFlow is built in Debug mode(i.e., built from source with `--config=dbg`).\r\nI guess TF in the gists installed by `pip` is not a Debug build, and it won't lead to crash.\r\nWould it be able to check it in TF debug build?"
] | 2024-02-17T17:48:24 | 2024-03-19T09:52:03 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.17
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04 LTS
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
6.5.0
### GCC/compiler version
clang 16
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`tf.raw_ops.FakeQuantWithMinMaxArgs` aborts when `min>0` in Debug build(compiled with --config=dbg).
It crashes [here](https://github.com/tensorflow/tensorflow/blob/e193d8ea7776ef5c6f5d769b6fb9c070213e737a/tensorflow/core/kernels/fake_quant_ops_functor.h#L86),
```C++
eigen_assert(min <= 0.0f && "min should be <= 0.0");
```
and it doesn't seem to be consistent with [document](https://www.tensorflow.org/api_docs/python/tf/raw_ops/FakeQuantWithMinMaxArgs).
Note that `tf.raw_ops.FakeQuantWithMinMaxArgsGradient` has the same situation.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
# it aborts only when compiled with `--config=dbg`
tf.raw_ops.FakeQuantWithMinMaxArgs(
inputs=tf.random.normal([1,1,1]),
min=1,
max=6,
num_bits=8,
narrow_range=False)
```
### Relevant log output
```shell
python: ./tensorflow/core/kernels/fake_quant_ops_functor.h:86: void tensorflow::FakeQuantWithMinMaxArgsFunctor<Eigen::ThreadPoolDevice>::operator()(const Device &, ConstFlat<float>, const float, const float, const int, const int, Flat<float>) [Device = Eigen::ThreadPoolDevice]: Assertion `min <= 0.0f && "min should be <= 0.0"' failed.
Aborted (core dumped)
```
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I_kwDOArmXAs5_k0yz
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`tf.raw_ops.LoopCond` aborts in Debug build
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[
"Hi @Sehun0819 ,\r\n\r\nThanks for reporting. Could you please confirm whether this works fine with TF2.16 or lower ? I don't see any changes in `.bazelrc` either.",
"@SuryanarayanaY\r\nI just found TF2.16 behaves in the same manner. Below is execution log in my environment.\r\nNote that it was also built with `--config=dbg`.\r\n```\r\nPython 3.11.7 (main, Dec 15 2023, 18:12:31) [GCC 11.2.0] on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import tensorflow as tf\r\n2024-02-19 21:38:38.963814: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n>>> tf.__version__\r\n'2.16.0-rc0'\r\n>>> tf.raw_ops.LoopCond(input=True)\r\n2024-02-19 21:38:54.052798: F tensorflow/core/graph/graph_partition.cc:644] Check failed: !frame_name.empty()\r\nAborted (core dumped)\r\n```",
"> In release build, it does not crash.\r\n\r\nHi @Sehun0819 , As per your note above, could you please confirm which build it was success ?",
"@SuryanarayanaY\r\nDoes 'success' mean successful bug reproducing?\r\nIt was TF2.17 built from source with `--config=dbg` flag.",
"Hi @Sehun0819 , I mean in any older TF version debug build `tf.raw_ops.LoopCond(input=True)` works without crash? This would be helpful whether this a regression problem or it exists since a while ?",
"@SuryanarayanaY \r\nI have no idea because I just checked TF2.16 and TF2.17, and I haven't build previous versions from source."
] | 2024-02-17T17:39:07 | 2024-03-19T09:47:46 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.17
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04 LTS
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
6.5.0
### GCC/compiler version
clang 16
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`tf.raw_ops.LoopCond` aborts in Debug build(compiled with `--config=dbg`).
In release build, it does not crash.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
# it aborts only when compiled with `--config=dbg`
tf.raw_ops.LoopCond(input=True)
```
### Relevant log output
```shell
2024-02-18 02:37:24.039192: F tensorflow/core/graph/graph_partition.cc:644] Check failed: !frame_name.empty()
Aborted (core dumped)
```
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`tf.raw_ops.DrawBoundingBoxesV2` aborts with inappropriate input
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[
"@Sehun0819 Could you please provide the access to the codebase or give the gist to replicate the issue reported?\r\nPlease ensure both tensors have the same data type, usually tf.float32. For input requirements please refer to this [doc](https://www.tensorflow.org/api_docs/python/tf/image/draw_bounding_boxes). I faced a different [error](https://colab.research.google.com/gist/sushreebarsa/1eb1d147b40ffc034f11c33a47405bb1/62981.ipynb) while replicating the issue.\r\nThank you!",
"@sushreebarsa\r\nSorry for confusing. `colors` should be `0.0`, not `0.0f`.\r\nPlease check [it](https://colab.research.google.com/drive/1wzwA3ALjPxIBaP-aajrYnKHiOWv2FgDg?usp=sharing).",
"@sachinprasadhs I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/950efa69aa2ec419266732519795e09f/untitled5.ipynb). Please have a look at this issue.\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/62981\">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/62981\">No</a>\n"
] | 2024-02-17T15:56:59 | 2024-03-04T18:24:31 | 2024-03-04T18:24:27 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.17
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04 LTS
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
6.5.0
### GCC/compiler version
clang 16
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`tf.raw_ops.DrawBoundingBoxesV2` aborts with inappropriate input([gist](https://colab.research.google.com/drive/1k5R4CumbxgK0Mn4SRAdYFmBvWQfnw7RV#scrollTo=7ng8DC7cxLH6)).
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
tf.raw_ops.DrawBoundingBoxesV2(
images=tf.random.normal([1,1,1]),
boxes=tf.random.normal([1]),
colors=0.0,
name=None
)
```
### Relevant log output
```shell
2024-02-18 00:55:43.130977: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (3 vs. 3)
Aborted (core dumped)
```
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PR_kwDOArmXAs5nJ3F9
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Spnaish read me
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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/62980/checks?check_run_id=21680016410) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.",
"Hi @BronzyPlum6390 Can you please sign CLA. Thank you!"
] | 2024-02-17T05:33:40 | 2024-02-19T16:11:57 | 2024-02-19T16:11:57 |
NONE
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I_kwDOArmXAs5_gPR0
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Cannot copy from a TensorFlowLite tensor (StatefulPartitionedCall:1) with shape [1, 10] to a Java object with shape [1, 10, 4].
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"@khizii I think there is a mismatch between the output shape of your TFLite model. The model produces 10 values, while you're expecting 40 (10 x 4).",
"@khizii,\r\nCould you please take a look at the output_details, specifically the shape of each output tensor, you will see that the order of outputs is actually [classes, boxes, num_detections, scores] or [scores, boxes, num_detections, classes] (because the shape of output at index 3 is same as shape of output 0, so one of them is scores and other is classes). \r\n\r\nIts likely that your Java code assumes [boxes, classes, scores, num_detections] - which is why the error says that you are trying to copy a wrong-shaped tensor into another one.\r\nhttps://github.com/tensorflow/tensorflow/issues/46692\r\nhttps://github.com/tensorflow/tensorflow/issues/55341\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/62979\">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/62979\">No</a>\n"
] | 2024-02-16T18:16:19 | 2024-03-05T01:46:51 | 2024-03-05T01:46:48 |
NONE
| null | null | null |
hey there i am a new in the field of android development i tried to run the project of custeom object detection on android app. the app works fines in pretrained model but when i tried to put my own custom model on it then it crashes and it crash report it shows this:
E :53: GetCmdlineFileContents: Failed to open /proc/650/cmdline (No such file or directory)
Cannot copy from a TensorFlowLite tensor (StatefulPartitionedCall:1) with shape [1, 10] to a Java object with shape [1, 10, 4].
if anyone solves this error please help me i will be thankful to you:
Below is the part of my code:
if you want to see the full code please refer to this github:
https://github.com/pramod722445/Custom_Object_Detection_App
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C++ API `SparseApplyAdadelta` segfaults due to lack of shape check
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[
"Hi, I'm Paridhi and I'm new to this community. I would like to contribute, I've looked at the issues with labels - \"good fist time\" and \"contribution welcome\" but those issues are in stale state. \r\nPlease let me know if I can begin with this issue or any other relatively easier issue present in the queue. Thank you.",
"@Sehun0819,\r\nThe related PR which was proposed C++ API SparseApplyAdadelta segfaults due to lack of input shape check has been merged and also the changes are available in the `training_ops.cc` file\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/ops/training_ops.cc#L63\r\n\r\n```python\r\n const auto rank = c->Rank(grad);\r\n if (!rank) {\r\n return absl::InvalidArgumentError(absl::StrCat(\r\n \"Argument grad must not be a scalar. \", \"Got grad with rank \", rank));\r\n```\r\n\r\nThank you!"
] | 2024-02-16T09:54:18 | 2024-06-12T12:00:39 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.17
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04 LTS
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
6.5.0
### GCC/compiler version
clang 16
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
C++ API `SparseApplyAdadelta` segfaults due to lack of input shape check.
[Error location](https://github.com/tensorflow/tensorflow/blob/f647f0cf7c36c7bb7ec531f11df1a028e343749a/tensorflow/core/ops/training_ops.cc#L63):
```C++
TF_RETURN_IF_ERROR(c->Merge(c->Dim(indices, 0), c->Dim(grad, 0), &unused));
```
At `c->Dim(grad, 0)`, it reads 0th dim without checking rank of `grad`. Therefore when a scalar(0-rank) is given for arg `grad` it crashes.
Note that same things happen for other `SparseApply*` APIs(`SparseApplyAdagrad`, `SparseApplyAdagradDA`, `SparseApplyFtrl`, `SparseApplyFtrlV2`, `SparseApplyMomentum`, `SparseApplyProximalAdagrad`, `SparseApplyProximalGradientDescent`).
### Standalone code to reproduce the issue
```C++
#include "tensorflow/cc/framework/scope.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/public/session.h"
#include "tensorflow/cc/ops/array_ops.h"
#include "tensorflow/cc/ops/standard_ops.h"
using namespace tensorflow;
int main() {
SessionOptions options;
std::unique_ptr<tensorflow::Session>
session(tensorflow::NewSession(options));
Scope scope = Scope::NewRootScope();
Input var = ops::Variable(scope, {1,1,1,1}, DT_FLOAT);
Input accum = ops::Variable(scope, {1,1,1,1}, DT_FLOAT);
Input accum_update = ops::Variable(scope, {1,1,1,1}, DT_FLOAT);
auto target = ops::SparseApplyAdadelta(scope.WithOpName("target"), var, accum, accum_update, 0.1f, 0.1f, 0.1f, 0.1f, {1});
GraphDef graph_def;
TF_CHECK_OK(scope.ToGraphDef(&graph_def));
Status status = session->Create(graph_def);
if (!status.ok()) {
LOG(FATAL) << "Could not create session: " << status.message();
}
std::vector<Tensor> outputs;
status = session->Run({}, {"target"}, {""}, &outputs);
if (!status.ok()) {
LOG(FATAL) << "Could not run session: " << status.message();
}
return 0;
}
```
### Relevant log output
```shell
Segmentation fault (core dumped)
```
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I_kwDOArmXAs5_cCvE
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`tf.raw_ops.RecordInput` aborts with negative `batch_size`
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[
"[gist](https://colab.research.google.com/drive/1ehgZdC8sSkTuZJ0BqLuFOHHoG24gm45L?usp=sharing)",
"Hi @Sehun0819 ;\r\n\r\nI have replicated the issue with tf-nightly(2.17.0-dev20240218) version.Attached snapshot below for reference.\r\n\r\n\r\n<img width=\"1488\" alt=\"Screenshot 2024-02-19 at 13 41 47\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/95e4b31a-d22a-4ed0-a5fe-733e8aa7e04f\">\r\n",
"The same behaviour exists in Tf2.15v also.",
"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/62977\">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/62977\">No</a>\n"
] | 2024-02-16T07:50:24 | 2024-03-05T06:20:00 | 2024-03-05T06:19:57 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.17
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04 LTS
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
6.5.0
### GCC/compiler version
clang 16
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`tf.raw_ops.RecordInput` aborts with negative `batch_size`
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
tf.raw_ops.RecordInput(
file_pattern="a",
file_random_seed=301,
file_shuffle_shift_ratio=0,
file_buffer_size=10000,
file_parallelism=16,
batch_size=-1,
compression_type='',
name=None)
```
### Relevant log output
```shell
2024-02-16 16:31:18.185444: F tensorflow/core/framework/tensor_shape.cc:201] Non-OK-status: InitDims(dim_sizes) status: INVALID_ARGUMENT: Expected shape dimensions to be non-negative, got -1
Aborted (core dumped)
```
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I_kwDOArmXAs5_azzn
| 62,976 |
Understanding `Could not find TensorRT`
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[
"@tymorrow TensorRT installation might require additional steps or dependencies, particularly on specific platforms like Ubuntu. TensorRT is an NVIDIA-specific library optimized for NVIDIA GPUs. It might not be available on non-NVIDIA hardware or specific Linux distributions like Ubuntu due to package management differences.\r\nThank you!",
"Hi @sushreebarsa, could you elaborate on why the warning does not appear on Windows or MacOS?\r\n\r\nI'm also wondering why the warning appears at all; is it specifically to tell me what you just said?\r\nIf so, could it instead say what you just said to be more informative?\r\n\r\nThank you for your time.",
"Any update? Been getting the same issue for about a month now.\r\n\r\nThis what i am getting:\r\n\r\n```\r\n2024-04-21 08:00:13.414353: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n2024-04-21 08:00:13.552935: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2024-04-21 08:00:14.655097: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2024-04-21 08:00:16.735268: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:17:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2024-04-21 08:00:16.828498: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\r\n```\r\n\r\nI have manually installed TensorRT and still have the same error\r\n\r\nHere is my Dockerfile for reference:\r\n\r\n```\r\nFROM nvidia/cuda:12.0.0-devel-ubuntu20.04\r\n\r\nENV DEBIAN_FRONTEND noninteractive\r\n\r\nRUN apt-get update && \\\r\n apt-get install -y git ffmpeg libssl-dev lsb-release wget curl\r\n\r\n\r\nENV PATH=\"/root/miniconda3/bin:${PATH}\"\r\nARG PATH=\"/root/miniconda3/bin:${PATH}\"\r\n\r\nRUN wget \\\r\n https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \\\r\n && mkdir /root/.conda \\\r\n && bash Miniconda3-latest-Linux-x86_64.sh -b \\\r\n && rm -f Miniconda3-latest-Linux-x86_64.sh s\r\n\r\nRUN conda --version\r\n# conda 24.1.2\r\n\r\n\r\n# CUDNN\r\nRUN apt clean\r\nRUN apt update \r\nRUN apt autoremove\r\n\r\nRUN wget https://developer.download.nvidia.com/compute/cudnn/9.1.0/local_installers/cudnn-local-repo-ubuntu2004-9.1.0_1.0-1_amd64.deb\r\nRUN dpkg -i cudnn-local-repo-ubuntu2004-9.1.0_1.0-1_amd64.deb\r\nRUN cp /var/cudnn-local-repo-ubuntu2004-9.1.0/cudnn-*-keyring.gpg /usr/share/keyrings/\r\nRUN apt-get update\r\nRUN apt-get -y install cudnn-cuda-12\r\n\r\n\r\n# CUBLASS\r\nRUN curl https://developer.download.nvidia.com/hpc-sdk/ubuntu/DEB-GPG-KEY-NVIDIA-HPC-SDK | gpg --dearmor -o /usr/share/keyrings/nvidia-hpcsdk-archive-keyring.gpg\r\nRUN echo 'deb [signed-by=/usr/share/keyrings/nvidia-hpcsdk-archive-keyring.gpg] https://developer.download.nvidia.com/hpc-sdk/ubuntu/amd64 /' | tee /etc/apt/sources.list.d/nvhpc.list\r\nRUN apt-get update -y\r\nRUN apt-get install -y nvhpc-24-3\r\nRUN apt install -f\r\n\r\n#TENSORRT\r\nRUN wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/secure/8.6.1/local_repos/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0_1.0-1_amd64.deb\r\nRUN dpkg -i nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0_1.0-1_amd64.deb\r\nRUN cp /var/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0/*-keyring.gpg /usr/share/keyrings/\r\nRUN apt-get update\r\nRUN apt-get install -y tensorrt\r\nRUN apt-get install -y libnvinfer-lean8\r\nRUN apt-get install -y libnvinfer-vc-plugin8\r\nRUN python3 -m pip install numpy\r\n\r\nRUN dpkg-query -W tensorrt\r\n#tensorrt\t10.0.0.6-1+cuda12.4\r\n\r\nRUN conda install python=3.9.18\r\n\r\nRUN pip install boto3 spleeter tensorrt cuda-python\r\nRUN pip install git+https://github.com/facebookresearch/audiocraft.git\r\nRUN pip install torch==2.1.0+cu121 --index-url https://download.pytorch.org/whl/cu121\r\n\r\nRUN conda install -c nvidia cuda-python cudatoolkit\r\n\r\nRUN export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-12.0/targets/x86_64-linux/lib\r\n\r\nRUN find / -name '*libcudart*'\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/REDIST/cuda/12.3/targets/x86_64-linux/lib/libcudart.so.12.3.101\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/REDIST/cuda/12.3/targets/x86_64-linux/lib/libcudart.so.12\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/REDIST/cuda/12.3/targets/x86_64-linux/lib/libcudart.so\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/cuda/12.3/targets/x86_64-linux/lib/libcudart.so.12\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/cuda/12.3/targets/x86_64-linux/lib/libcudart.so\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/cuda/12.3/targets/x86_64-linux/lib/libcudart.so.12.3.101\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/cuda/12.3/targets/x86_64-linux/lib/libcudart_static.a\r\n/root/miniconda3/lib/python3.9/site-packages/nvidia/cuda_runtime/lib/libcudart.so.12\r\n/root/miniconda3/lib/python3.9/site-packages/torch/lib/libcudart-9335f6a2.so.12\r\n/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudart.so.12\r\n/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudart.so.12.0.107\r\n/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudart.so\r\n/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudart_static.a\r\n\r\nRUN find / -name '*tensorrt*'\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/REDIST/cuda/12.3/targets/x86_64-linux/lib/libcudart.so.12.3.101\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/REDIST/cuda/12.3/targets/x86_64-linux/lib/libcudart.so.12\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/REDIST/cuda/12.3/targets/x86_64-linux/lib/libcudart.so\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/cuda/12.3/targets/x86_64-linux/lib/libcudart.so.12\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/cuda/12.3/targets/x86_64-linux/lib/libcudart.so\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/cuda/12.3/targets/x86_64-linux/lib/libcudart.so.12.3.101\r\n/opt/nvidia/hpc_sdk/Linux_x86_64/24.3/cuda/12.3/targets/x86_64-linux/lib/libcudart_static.a\r\n/root/miniconda3/lib/python3.9/site-packages/nvidia/cuda_runtime/lib/libcudart.so.12\r\n/root/miniconda3/lib/python3.9/site-packages/torch/lib/libcudart-9335f6a2.so.12\r\n/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudart.so.12\r\n/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudart.so.12.0.107\r\n/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudart.so\r\n/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudart_static.a\r\nExpand\r\n\r\n=> Step 35: RUN find / -name '*tensorrt*'\r\n/root/miniconda3/lib/python3.9/site-packages/tensorrt_bindings\r\n/root/miniconda3/lib/python3.9/site-packages/tensorrt_bindings/tensorrt.so\r\n/root/miniconda3/lib/python3.9/site-packages/tensorrt_bindings-8.6.1.dist-info\r\n/root/miniconda3/lib/python3.9/site-packages/tensorrt_libs\r\n/root/miniconda3/lib/python3.9/site-packages/tensorrt_libs-8.6.1.dist-info\r\n/root/miniconda3/lib/python3.9/site-packages/tensorrt\r\n/root/miniconda3/lib/python3.9/site-packages/tensorrt-8.6.1.post1.dist-info\r\n/root/miniconda3/lib/python3.9/site-packages/tensorflow/python/compiler/tensorrt\r\n/root/miniconda3/lib/python3.9/site-packages/tensorflow/_api/v2/compat/v2/experimental/tensorrt\r\n/root/miniconda3/lib/python3.9/site-packages/tensorflow/_api/v2/experimental/tensorrt\r\n/root/miniconda3/lib/python3.9/site-packages/tensorflow/compiler/tf2tensorrt\r\n/root/miniconda3/lib/python3.9/site-packages/tensorflow/include/tensorflow/compiler/tf2tensorrt\r\n/root/miniconda3/lib/python3.9/site-packages/torch/_dynamo/backends/tensorrt.py\r\n/root/miniconda3/lib/python3.9/site-packages/torch/_dynamo/backends/__pycache__/tensorrt.cpython-39.pyc\r\n/root/miniconda3/lib/python3.9/site-packages/torch/ao/quantization/backend_config/tensorrt.py\r\n/root/miniconda3/lib/python3.9/site-packages/torch/ao/quantization/backend_config/__pycache__/tensorrt.cpython-39.pyc\r\n/usr/share/doc/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0\r\n/usr/share/doc/tensorrt-10.0.0.6\r\n/usr/share/doc/tensorrt\r\n/usr/share/keyrings/nv-tensorrt-local-9A1EDFBA-keyring.gpg\r\n/usr/lib/python3.8/dist-packages/tensorrt\r\n/usr/lib/python3.8/dist-packages/tensorrt/tensorrt.so\r\n/usr/lib/python3.8/dist-packages/tensorrt-10.0.0b6.dist-info\r\n/usr/lib/python3.8/dist-packages/tensorrt_lean\r\n/usr/lib/python3.8/dist-packages/tensorrt_lean/tensorrt_lean.so\r\n/usr/lib/python3.8/dist-packages/tensorrt_lean-10.0.0b6.dist-info\r\n/usr/lib/python3.8/dist-packages/tensorrt_dispatch\r\n/usr/lib/python3.8/dist-packages/tensorrt_dispatch/tensorrt_dispatch.so\r\n/usr/lib/python3.8/dist-packages/tensorrt_dispatch-10.0.0b6.dist-info\r\n/usr/src/tensorrt\r\n/usr/src/tensorrt/samples/python/yolov3_onnx/onnx_to_tensorrt.py\r\n/etc/apt/sources.list.d/nv-tensorrt-local-ubuntu2004-8.6.1-cuda-12.0.list\r\n/var/lib/apt/lists/_var_nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0_InRelease\r\n/var/lib/apt/lists/_var_nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0_Packages.lz4\r\n/var/lib/dpkg/info/tensorrt.md5sums\r\n/var/lib/dpkg/info/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0.list\r\n/var/lib/dpkg/info/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0.conffiles\r\n/var/lib/dpkg/info/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0.md5sums\r\n/var/lib/dpkg/info/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0.postinst\r\n/var/lib/dpkg/info/tensorrt.list\r\n/var/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0\r\n/var/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0/nv-tensorrt-local-9A1EDFBA-keyring.gpg\r\n/var/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0/tensorrt-dev_8.6.1.6-1+cuda12.0_amd64.deb\r\n/var/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0/tensorrt-libs_8.6.1.6-1+cuda12.0_amd64.deb\r\n/var/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0/tensorrt_8.6.1.6-1+cuda12.0_amd64.deb\r\n/nv-tensorrt-local-repo-ubuntu2004-8.6.1-cuda-12.0_1.0-1_amd64.deb\r\n\r\n\r\nRUN python3 -c \"import tensorrt; print(tensorrt.__path__)\"\r\nRUN python3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\r\nRUN python -c \"import tensorflow.compiler as tf_cc; print(tf_cc.tf2tensorrt._pywrap_py_utils.get_linked_tensorrt_version())\"\r\nRUN pip list\r\n\r\nRUN nvcc --version\r\n#nvcc: NVIDIA (R) Cuda compiler driver\r\n#Copyright (c) 2005-2023 NVIDIA Corporation\r\n#Built on Fri_Jan__6_16:45:21_PST_2023\r\n#Cuda compilation tools, release 12.0, V12.0.140\r\n#Build cuda_12.0.r12.0/compiler.32267302_0\r\n\r\nRUN lsb_release -a\r\n# No LSB modules are available.\r\n# Distributor ID: Ubuntu\r\n# Description: Ubuntu 20.04.5 LTS\r\n# Release: 20.04\r\n# Codename: focal\r\n```"
] | 2024-02-16T01:50:04 | 2024-04-22T00:37:28 | null |
NONE
| null | null | null |
### Issue type
Others
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.12, 2.15, nightly
### Custom code
No
### OS platform and distribution
Linux Ubuntu 22.04, MacOS 12, Windows 2022
### Mobile device
_No response_
### Python version
3.8 - 3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
A warning "Could not find TensorRT" is produced when importing TensorFlow on Ubuntu 22.04.
However, this warning does **not** appear on Windows 2022 and MacOS 12.
| |MacOS12|Windows 2022|Ubuntu 22.04|
|---|---|---|---|
|TF 2.12 w/ Python 3.8|no TensorRT warning|no TensorRT warning|**TensorRT warning**|
|TF 2.12 w/ Python 3.9|no TensorRT warning|no TensorRT warning|**TensorRT warning**|
|TF 2.12 w/ Python 3.10|no TensorRT warning|no TensorRT warning|**TensorRT warning**|
|TF 2.15 w/ Python 3.10|_not checked_|_not checked_|**TensorRT warning**|
|TF nightly w/ Python 3.10|_not checked_|_not checked_|**TensorRT warning**|
I would expect the behavior to be consistent across platforms unless their is an Ubuntu-specific reason.
This issue is intended to help me understand:
1. Why is there this inconsistency?
2. Can it either be made consistent or can the warning be updated to explain the platform-specific benefits behind why TensorRT should be installed?
Additional remarks:
* I am not trying to utilize TensorRT, so installing an additional package to satisfy this message is not a desired fix, but it may be for others.
* I am also not trying to suppress logging warnings, just trying to understand the purpose of it only appearing on Ubuntu.
Thank you for your time!
### Standalone code to reproduce the issue
```shell
import tensorflow
```
### Relevant log output
```shell
W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
```
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PR_kwDOArmXAs5nCO_i
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r2.16 cherry-pick: a3f935dcbc2 "Pin Docker images to specific tags for the release."
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[] | 2024-02-15T23:36:06 | 2024-02-15T23:40:50 | 2024-02-15T23:40:49 |
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Refer to the original commit: https://github.com/tensorflow/tensorflow/commit/a3f935dcbc28ca3482ae5be106adf0184a7762bd
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I_kwDOArmXAs5_aSMh
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build tensorflow v2.15.0 failed
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] | null |
[
"@fancyerii,\r\nCould you please let us know that there is any specific reason to use cuda-**11.8** with tensorflow **2.15**. The latest tensorflow v2.15 is compatible with the CUDA 12.2 which might be the reason. Could you please try to follow the tested build configurations from the official document for the smooth installation.\r\nhttps://www.tensorflow.org/install/source#gpu\r\n\r\n`cp -r clang+llvm-16.0.0-x86_64-linux-gnu-ubuntu-18.04/* /usr`\r\n\r\nThank you!\r\n\r\n",
"I just don't want to install many versions of cuda. I tried to v2.14.0, and it worked. Maybe I should use docker to avoid install cuda.",
"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/62974\">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/62974\">No</a>\n"
] | 2024-02-15T23:10:18 | 2024-02-16T13:24:22 | 2024-02-16T13:24:19 |
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
No
### OS platform and distribution
ubuntu 18.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
6.1.0
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
can't build
### Standalone code to reproduce the issue
```shell
I want to build tensorflow v2.15.0 with cuda-11.8, cudnn 8.9 and tensorrt 8.6 in ubuntu 16.04.
1. configure
$ ./configure
You have bazel 6.1.0 installed.
Please specify the location of python. [Default is /nas/lili/codes/pt/tf/buildvenv/bin/python3]:
Found possible Python library paths:
/nas/lili/codes/pt/tf/buildvenv/lib/python3.9/site-packages
Please input the desired Python library path to use. Default is [/nas/lili/codes/pt/tf/buildvenv/lib/python3.9/site-packages]
Do you wish to build TensorFlow with ROCm support? [y/N]: n
No ROCm support will be enabled for TensorFlow.
Do you wish to build TensorFlow with CUDA support? [y/N]: y
CUDA support will be enabled for TensorFlow.
Do you wish to build TensorFlow with TensorRT support? [y/N]: y
TensorRT support will be enabled for TensorFlow.
Found CUDA 11.8 in:
/usr/local/cuda-11.8/targets/x86_64-linux/lib
/usr/local/cuda-11.8/targets/x86_64-linux/include
Found cuDNN 8 in:
/usr/local/cuda-11.8/targets/x86_64-linux/lib
/usr/local/cuda-11.8/targets/x86_64-linux/include
Found TensorRT 8.6.1 in:
/home/ubuntu/TensorRT-8.6.1.6/targets/x86_64-linux-gnu/lib
/home/ubuntu/TensorRT-8.6.1.6/include
Please specify a list of comma-separated CUDA compute capabilities you want to build with.
You 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.
Please 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]:
Do you want to use clang as CUDA compiler? [Y/n]: y
Clang will be used as CUDA compiler.
Please specify clang path that to be used as host compiler. [Default is /usr/local/bin/clang]:
You have Clang 16.0.0 installed.
Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -Wno-sign-compare]:
Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: n
Not configuring the WORKSPACE for Android builds.
Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See .bazelrc for more details.
--config=mkl # Build with MKL support.
--config=mkl_aarch64 # Build with oneDNN and Compute Library for the Arm Architecture (ACL).
--config=monolithic # Config for mostly static monolithic build.
--config=numa # Build with NUMA support.
--config=dynamic_kernels # (Experimental) Build kernels into separate shared objects.
--config=v1 # Build with TensorFlow 1 API instead of TF 2 API.
Preconfigured Bazel build configs to DISABLE default on features:
--config=nogcp # Disable GCP support.
--config=nonccl # Disable NVIDIA NCCL support.
Configuration finished
```
2. build
```
$ bazel build //tensorflow/tools/pip_package:build_pip_package
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 /nas/lili/codes/pt/tf/tensorflow/.bazelrc: --windows_enable_symlinks
INFO: Options provided by the client:
Inherited 'common' options: --isatty=1 --terminal_columns=141
INFO: Reading rc options for 'build' from /nas/lili/codes/pt/tf/tensorflow/.bazelrc:
Inherited 'common' options: --experimental_repo_remote_exec
INFO: Reading rc options for 'build' from /nas/lili/codes/pt/tf/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 /nas/lili/codes/pt/tf/tensorflow/.tf_configure.bazelrc:
'build' options: --action_env PYTHON_BIN_PATH=/nas/lili/codes/pt/tf/buildvenv/bin/python3 --action_env PYTHON_LIB_PATH=/nas/lili/codes/pt/tf/buildvenv/lib/python3.9/site-packages --python_path=/nas/lili/codes/pt/tf/buildvenv/bin/python3 --config=tensorrt --action_env TF_CUDA_PATHS=/usr/local/cuda-11.8 --action_env CUDA_TOOLKIT_PATH=/usr/local/cuda-11.8 --action_env TENSORRT_INSTALL_PATH=/home/ubuntu/TensorRT-8.6.1.6 --action_env TF_CUDA_COMPUTE_CAPABILITIES=3.5,7.0 --action_env LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64 --config=cuda_clang --action_env CLANG_CUDA_COMPILER_PATH=/usr/local/bin/clang-16 --copt=-Wno-gnu-offsetof-extensions --config=cuda_clang
INFO: Found applicable config definition build:short_logs in file /nas/lili/codes/pt/tf/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file /nas/lili/codes/pt/tf/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:tensorrt in file /nas/lili/codes/pt/tf/tensorflow/.bazelrc: --repo_env TF_NEED_TENSORRT=1
INFO: Found applicable config definition build:cuda_clang in file /nas/lili/codes/pt/tf/tensorflow/.bazelrc: --config=cuda --config=tensorrt --action_env=TF_CUDA_CLANG=1 --@local_config_cuda//:cuda_compiler=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 /nas/lili/codes/pt/tf/tensorflow/.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 /nas/lili/codes/pt/tf/tensorflow/.bazelrc: --repo_env TF_NEED_TENSORRT=1
INFO: Found applicable config definition build:cuda_clang in file /nas/lili/codes/pt/tf/tensorflow/.bazelrc: --config=cuda --config=tensorrt --action_env=TF_CUDA_CLANG=1 --@local_config_cuda//:cuda_compiler=clang --repo_env=TF_CUDA_COMPUTE_CAPABILITIES=sm_50,sm_60,sm_70,sm_75,compu
te_80
INFO: Found applicable config definition build:cuda in file /nas/lili/codes/pt/tf/tensorflow/.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 /nas/lili/codes/pt/tf/tensorflow/.bazelrc: --repo_env TF_NEED_TENSORRT=1
INFO: Found applicable config definition build:linux in file /nas/lili/codes/pt/tf/tensorflow/.bazelrc: --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wno-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-array-bounds --copt=-Wunused-result --copt=-Werror=unused-result --copt=-Wswitch --copt=-Werror=switch --copt=-Wno-error=unused-but-set-variable --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --experimental_guard_against_concurrent_changes
INFO: Found applicable config definition build:dynamic_kernels in file /nas/lili/codes/pt/tf/tensorflow/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS
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: Analyzed target //tensorflow/tools/pip_package:build_pip_package (701 packages loaded, 49740 targets configured).
INFO: Found 1 target...
[32,640 / 32,707] 2 actions running
ERROR: /nas/lili/codes/pt/tf/tensorflow/tensorflow/BUILD:1301:21: Linking tensorflow/libtensorflow_cc.so.2.15.0 failed: (Exit 1): clang-16 failed: error executing command (from target //tensorflow:libtensorflow_cc.so.2.15.0) /usr/local/bin/clang-16 @bazel-out/k8-opt/bin/tensorflow/libtensorflow_cc.so.2.15.0-2.params
bazel-out/k8-opt/bin/tensorflow/core/kernels/sparse/libkernels.pic.lo(sparse_mat_mul_op.pic.o): In function `tensorflow::CSRSparseMatMulGPUOp<Eigen::GpuDevice, float>::Compute(tensorflow::OpKernelContext*)':
sparse_mat_mul_op.cc:(.text._ZN10tensorflow20CSRSparseMatMulGPUOpIN5Eigen9GpuDeviceEfE7ComputeEPNS_15OpKernelContextE[_ZN10tensorflow20CSRSparseMatMulGPUOpIN5Eigen9GpuDeviceEfE7ComputeEPNS_15OpKernelContextE]+0x11d0): undefined reference to `cusparseDestroyCsrgemm2Info'
bazel-out/k8-opt/bin/tensorflow/core/kernels/sparse/libkernels.pic.lo(sparse_mat_mul_op.pic.o): In function `tensorflow::functor::CSRSparseSparseMatrixMatMul<Eigen::GpuDevice, float>::Initialize()':
sparse_mat_mul_op.cc:(.text._ZN10tensorflow7functor27CSRSparseSparseMatrixMatMulIN5Eigen9GpuDeviceEfE10InitializeEv[_ZN10tensorflow7functor27CSRSparseSparseMatrixMatMulIN5Eigen9GpuDeviceEfE10InitializeEv]+0x104): undefined reference to `cusparseCreateCsrgemm2Info'
bazel-out/k8-opt/bin/tensorflow/core/kernels/sparse/libkernels.pic.lo(sparse_mat_mul_op.pic.o): In function `tensorflow::functor::CSRSparseSparseMatrixMatMul<Eigen::GpuDevice, float>::~CSRSparseSparseMatrixMatMul()':
sparse_mat_mul_op.cc:(.text._ZN10tensorflow7functor27CSRSparseSparseMatrixMatMulIN5Eigen9GpuDeviceEfED2Ev[_ZN10tensorflow7functor27CSRSparseSparseMatrixMatMulIN5Eigen9GpuDeviceEfED2Ev]+0x22): undefined reference to `cusparseDestroyCsrgemm2Info'
bazel-out/k8-opt/bin/tensorflow/core/kernels/sparse/libkernels.pic.lo(sparse_mat_mul_op.pic.o): In function `tensorflow::functor::CSRSparseSparseMatrixMatMul<Eigen::GpuDevice, float>::~CSRSparseSparseMatrixMatMul()':
sparse_mat_mul_op.cc:(.text._ZN10tensorflow7functor27CSRSparseSparseMatrixMatMulIN5Eigen9GpuDeviceEfED0Ev[_ZN10tensorflow7functor27CSRSparseSparseMatrixMatMulIN5Eigen9GpuDeviceEfED0Ev]+0x22): undefined reference to `cusparseDestroyCsrgemm2Info'
bazel-out/k8-opt/bin/tensorflow/core/kernels/sparse/libkernels.pic.lo(sparse_mat_mul_op.pic.o): In function `tensorflow::CSRSparseMatMulGPUOp<Eigen::GpuDevice, double>::Compute(tensorflow::OpKernelContext*)':
sparse_mat_mul_op.cc:(.text._ZN10tensorflow20CSRSparseMatMulGPUOpIN5Eigen9GpuDeviceEdE7ComputeEPNS_15OpKernelContextE[_ZN10tensorflow20CSRSparseMatMulGPUOpIN5Eigen9GpuDeviceEdE7ComputeEPNS_15OpKernelContextE]+0x11d0): undefined reference to `cusparseDestroyCsrgemm2Info'
bazel-out/k8-opt/bin/tensorflow/core/kernels/sparse/libkernels.pic.lo(sparse_mat_mul_op.pic.o): In function `tensorflow::functor::CSRSparseSparseMatrixMatMul<Eigen::GpuDevice, double>::Initialize()':
sparse_mat_mul_op.cc:(.text._ZN10tensorflow7functor27CSRSparseSparseMatrixMatMulIN5Eigen9GpuDeviceEdE10InitializeEv[_ZN10tensorflow7functor27CSRSparseSparseMatrixMatMulIN5Eigen9GpuDeviceEdE10InitializeEv]+0x104): undefined reference to `cusparseCreateCsrgemm2Info'
bazel-out/k8-opt/bin/tensorflow/core/kernels/sparse/libkernels.pic.lo(sparse_mat_mul_op.pic.o): In function `tensorflow::functor::CSRSparseSparseMatrixMatMul<Eigen::GpuDevice, double>::~CSRSparseSparseMatrixMatMul()':
sparse_mat_mul_op.cc:(.text._ZN10tensorflow7functor27CSRSparseSparseMatrixMatMulIN5Eigen9GpuDeviceEdED2Ev[_ZN10tensorflow7functor27CSRSparseSparseMatrixMatMulIN5Eigen9GpuDeviceEdED2Ev]+0x22): undefined reference to `cusparseDestroyCsrgemm2Info'
bazel-out/k8-opt/bin/tensorflow/core/kernels/sparse/libkernels.pic.lo(sparse_mat_mul_op.pic.o): In function `tensorflow::functor::CSRSparseSparseMatrixMatMul<Eigen::GpuDevice, double>::~CSRSparseSparseMatrixMatMul()':
sparse_mat_mul_op.cc:(.text._ZN10tensorflow7functor27CSRSparseSparseMatrixMatMulIN5Eigen9GpuDeviceEdED0Ev[_ZN10tensorflow7functor27CSRSparseSparseMatrixMatMulIN5Eigen9GpuDeviceEdED0Ev]+0x22): undefined reference to `cusparseDestroyCsrgemm2Info'
bazel-out/k8-opt/bin/tensorflow/core/kernels/sparse/libkernels.pic.lo(sparse_mat_mul_op.pic.o): In function `tensorflow::CSRSparseMatMulGPUOp<Eigen::GpuDevice, std::complex<float> >::Compute(tensorflow::OpKernelContext*)':
sparse_mat_mul_op.cc:(.text._ZN10tensorflow20CSRSparseMatMulGPUOpIN5Eigen9GpuDeviceESt7complexIfEE7ComputeEPNS_15OpKernelContextE[_ZN10tensorflow20CSRSparseMatMulGPUOpIN5Eigen9GpuDeviceESt7complexIfEE7ComputeEPNS_15OpKernelContextE]+0x11d0): undefined reference to `cusparseDestroyCsrgemm2Info'
bazel-out/k8-opt/bin/tensorflow/core/kernels/sparse/libkernels.pic.lo(sparse_mat_mul_op.pic.o): In function `tensorflow::functor::CSRSparseSparseMatrixMatMul<Eigen::GpuDevice, std::complex<float> >::Initialize()':
sparse_mat_mul_op.cc:(.text._ZN10tensorflow7functor27CSRSparseSparseMatrixMatMulIN5Eigen9GpuDeviceESt7complexIfEE10InitializeEv[_ZN10tensorflow7functor27CSRSparseSparseMatrixMatMulIN5Eigen9GpuDeviceESt7complexIfEE10InitializeEv]+0x104): undefined reference to `cusparseCreateCsrgemm2Info'
bazel-out/k8-opt/bin/tensorflow/core/kernels/sparse/libkernels.pic.lo(sparse_mat_mul_op.pic.o): In function `tensorflow::functor::CSRSparseSparseMatrixMatMul<Eigen::GpuDevice, std::complex<float> >::~CSRSparseSparseMatrixMatMul()':
sparse_mat_mul_op.cc:(.text._ZN10tensorflow7functor27CSRSparseSparseMatrixMatMulIN5Eigen9GpuDeviceESt7complexIfEED2Ev[_ZN10tensorflow7functor27CSRSparseSparseMatrixMatMulIN5Eigen9GpuDeviceESt7complexIfEED2Ev]+0x22): undefined reference to `cusparseDestroyCsrgemm2Info'
bazel-out/k8-opt/bin/tensorflow/core/kernels/sparse/libkernels.pic.lo(sparse_mat_mul_op.pic.o): In function `tensorflow::functor::CSRSparseSparseMatrixMatMul<Eigen::GpuDevice, std::complex<float> >::~CSRSparseSparseMatrixMatMul()':
sparse_mat_mul_op.cc:(.text._ZN10tensorflow7functor27CSRSparseSparseMatrixMatMulIN5Eigen9GpuDeviceESt7complexIfEED0Ev[_ZN10tensorflow7functor27CSRSparseSparseMatrixMatMulIN5Eigen9GpuDeviceESt7complexIfEED0Ev]+0x22): undefined reference to `cusparseDestroyCsrgemm2Info'
bazel-out/k8-opt/bin/tensorflow/core/kernels/sparse/libkernels.pic.lo(sparse_mat_mul_op.pic.o): In function `tensorflow::CSRSparseMatMulGPUOp<Eigen::GpuDevice, std::complex<double> >::Compute(tensorflow::OpKernelContext*)':
sparse_mat_mul_op.cc:(.text._ZN10tensorflow20CSRSparseMatMulGPUOpIN5Eigen9GpuDeviceESt7complexIdEE7ComputeEPNS_15OpKernelContextE[_ZN10tensorflow20CSRSparseMatMulGPUOpIN5Eigen9GpuDeviceESt7complexIdEE7ComputeEPNS_15OpKernelContextE]+0x11d7): undefined reference to `cusparseDestroyCsrgemm2Info'
bazel-out/k8-opt/bin/tensorflow/core/kernels/sparse/libkernels.pic.lo(sparse_mat_mul_op.pic.o): In function `tensorflow::functor::CSRSparseSparseMatrixMatMul<Eigen::GpuDevice, std::complex<double> >::Initialize()':
sparse_mat_mul_op.cc:(.text._ZN10tensorflow7functor27CSRSparseSparseMatrixMatMulIN5Eigen9GpuDeviceESt7complexIdEE10InitializeEv[_ZN10tensorflow7functor27CSRSparseSparseMatrixMatMulIN5Eigen9GpuDeviceESt7complexIdEE10InitializeEv]+0x104): undefined reference to `cusparseCreateCsrgemm2Info'
bazel-out/k8-opt/bin/tensorflow/core/kernels/sparse/libkernels.pic.lo(sparse_mat_mul_op.pic.o): In function `tensorflow::functor::CSRSparseSparseMatrixMatMul<Eigen::GpuDevice, std::complex<double> >::~CSRSparseSparseMatrixMatMul()':
sparse_mat_mul_op.cc:(.text._ZN10tensorflow7functor27CSRSparseSparseMatrixMatMulIN5Eigen9GpuDeviceESt7complexIdEED2Ev[_ZN10tensorflow7functor27CSRSparseSparseMatrixMatMulIN5Eigen9GpuDeviceESt7complexIdEED2Ev]+0x22): undefined reference to `cusparseDestroyCsrgemm2Info'
bazel-out/k8-opt/bin/tensorflow/core/kernels/sparse/libkernels.pic.lo(sparse_mat_mul_op.pic.o): In function `tensorflow::functor::CSRSparseSparseMatrixMatMul<Eigen::GpuDevice, std::complex<double> >::~CSRSparseSparseMatrixMatMul()':
sparse_mat_mul_op.cc:(.text._ZN10tensorflow7functor27CSRSparseSparseMatrixMatMulIN5Eigen9GpuDeviceESt7complexIdEED0Ev[_ZN10tensorflow7functor27CSRSparseSparseMatrixMatMulIN5Eigen9GpuDeviceESt7complexIdEED0Ev]+0x22): undefined reference to `cusparseDestroyCsrgemm2Info'
bazel-out/k8-opt/bin/tensorflow/core/util/libcuda_sparse.pic.lo(cuda_sparse.pic.o): In function `absl::lts_20230125::Status tensorflow::GpuSparse::Csrmv<float>(cusparseOperation_t, int, int, int, float const*, float const*, int const*, int const*, float const*, float const*, float*) const':
cuda_sparse.cc:(.text._ZNK10tensorflow9GpuSparse5CsrmvIfEEN4absl12lts_202301256StatusE19cusparseOperation_tiiiPKT_S8_PKiSA_S8_S8_PS6_+0x20f): undefined reference to `cusparseCsrmvEx_bufferSize'
cuda_sparse.cc:(.text._ZNK10tensorflow9GpuSparse5CsrmvIfEEN4absl12lts_202301256StatusE19cusparseOperation_tiiiPKT_S8_PKiSA_S8_S8_PS6_+0x389): undefined reference to `cusparseCsrmvEx'
bazel-out/k8-opt/bin/tensorflow/core/util/libcuda_sparse.pic.lo(cuda_sparse.pic.o): In function `absl::lts_20230125::Status tensorflow::GpuSparse::Csrmv<double>(cusparseOperation_t, int, int, int, double const*, double const*, int const*, int const*, double const*, double const*, double*) const':
cuda_sparse.cc:(.text._ZNK10tensorflow9GpuSparse5CsrmvIdEEN4absl12lts_202301256StatusE19cusparseOperation_tiiiPKT_S8_PKiSA_S8_S8_PS6_+0x215): undefined reference to `cusparseCsrmvEx_bufferSize'
cuda_sparse.cc:(.text._ZNK10tensorflow9GpuSparse5CsrmvIdEEN4absl12lts_202301256StatusE19cusparseOperation_tiiiPKT_S8_PKiSA_S8_S8_PS6_+0x38f): undefined reference to `cusparseCsrmvEx'
bazel-out/k8-opt/bin/tensorflow/core/util/libcuda_sparse.pic.lo(cuda_sparse.pic.o): In function `absl::lts_20230125::Status tensorflow::GpuSparse::Csrmv<std::complex<float> >(cusparseOperation_t, int, int, int, std::complex<float> const*, std::complex<float> const*, int const*, int const*, std::complex<float> const*, std::complex<float> const*, std::complex<float>*) const':
cuda_sparse.cc:(.text._ZNK10tensorflow9GpuSparse5CsrmvISt7complexIfEEEN4absl12lts_202301256StatusE19cusparseOperation_tiiiPKT_SA_PKiSC_SA_SA_PS8_+0x215): undefined reference to `cusparseCsrmvEx_bufferSize'
cuda_sparse.cc:(.text._ZNK10tensorflow9GpuSparse5CsrmvISt7complexIfEEEN4absl12lts_202301256StatusE19cusparseOperation_tiiiPKT_SA_PKiSC_SA_SA_PS8_+0x38f): undefined reference to `cusparseCsrmvEx'
bazel-out/k8-opt/bin/tensorflow/core/util/libcuda_sparse.pic.lo(cuda_sparse.pic.o): In function `absl::lts_20230125::Status tensorflow::GpuSparse::Csrmv<std::complex<double> >(cusparseOperation_t, int, int, int, std::complex<double> const*, std::complex<double> const*, int const*, int const*, std::complex<double> const*, std::complex<double> const*, std::complex<double>*) const':
cuda_sparse.cc:(.text._ZNK10tensorflow9GpuSparse5CsrmvISt7complexIdEEEN4absl12lts_202301256StatusE19cusparseOperation_tiiiPKT_SA_PKiSC_SA_SA_PS8_+0x215): undefined reference to `cusparseCsrmvEx_bufferSize'
cuda_sparse.cc:(.text._ZNK10tensorflow9GpuSparse5CsrmvISt7complexIdEEEN4absl12lts_202301256StatusE19cusparseOperation_tiiiPKT_SA_PKiSC_SA_SA_PS8_+0x38f): undefined reference to `cusparseCsrmvEx'
bazel-out/k8-opt/bin/tensorflow/core/util/libcuda_sparse.pic.lo(cuda_sparse.pic.o): In function `absl::lts_20230125::Status tensorflow::GpuSparse::CsrgemmBufferSize<float>(int, int, int, cusparseMatDescr*, int, int const*, int const*, cusparseMatDescr*, int, int const*, int const*, csrgemm2Info*, unsigned long*)':
cuda_sparse.cc:(.text._ZN10tensorflow9GpuSparse17CsrgemmBufferSizeIfEEN4absl12lts_202301256StatusEiiiP16cusparseMatDescriPKiS8_S6_iS8_S8_P12csrgemm2InfoPm+0x89): undefined reference to `cusparseScsrgemm2_bufferSizeExt'
bazel-out/k8-opt/bin/tensorflow/core/util/libcuda_sparse.pic.lo(cuda_sparse.pic.o): In function `absl::lts_20230125::Status tensorflow::GpuSparse::CsrgemmBufferSize<double>(int, int, int, cusparseMatDescr*, int, int const*, int const*, cusparseMatDescr*, int, int const*, int const*, csrgemm2Info*, unsigned long*)':
cuda_sparse.cc:(.text._ZN10tensorflow9GpuSparse17CsrgemmBufferSizeIdEEN4absl12lts_202301256StatusEiiiP16cusparseMatDescriPKiS8_S6_iS8_S8_P12csrgemm2InfoPm+0x89): undefined reference to `cusparseDcsrgemm2_bufferSizeExt'
bazel-out/k8-opt/bin/tensorflow/core/util/libcuda_sparse.pic.lo(cuda_sparse.pic.o): In function `absl::lts_20230125::Status tensorflow::GpuSparse::CsrgemmBufferSize<std::complex<float> >(int, int, int, cusparseMatDescr*, int, int const*, int const*, cusparseMatDescr*, int, int const*, int const*, csrgemm2Info*, unsigned long*)':
cuda_sparse.cc:(.text._ZN10tensorflow9GpuSparse17CsrgemmBufferSizeISt7complexIfEEEN4absl12lts_202301256StatusEiiiP16cusparseMatDescriPKiSA_S8_iSA_SA_P12csrgemm2InfoPm+0x89): undefined reference to `cusparseCcsrgemm2_bufferSizeExt'
bazel-out/k8-opt/bin/tensorflow/core/util/libcuda_sparse.pic.lo(cuda_sparse.pic.o): In function `absl::lts_20230125::Status tensorflow::GpuSparse::CsrgemmBufferSize<std::complex<double> >(int, int, int, cusparseMatDescr*, int, int const*, int const*, cusparseMatDescr*, int, int const*, int const*, csrgemm2Info*, unsigned long*)':
cuda_sparse.cc:(.text._ZN10tensorflow9GpuSparse17CsrgemmBufferSizeISt7complexIdEEEN4absl12lts_202301256StatusEiiiP16cusparseMatDescriPKiSA_S8_iSA_SA_P12csrgemm2InfoPm+0x89): undefined reference to `cusparseZcsrgemm2_bufferSizeExt'
bazel-out/k8-opt/bin/tensorflow/core/util/libcuda_sparse.pic.lo(cuda_sparse.pic.o): In function `tensorflow::GpuSparse::CsrgemmNnz(int, int, int, cusparseMatDescr*, int, int const*, int const*, cusparseMatDescr*, int, int const*, int const*, cusparseMatDescr*, int*, int*, csrgemm2Info*, void*)':
cuda_sparse.cc:(.text._ZN10tensorflow9GpuSparse10CsrgemmNnzEiiiP16cusparseMatDescriPKiS4_S2_iS4_S4_S2_PiS5_P12csrgemm2InfoPv+0x8a): undefined reference to `cusparseXcsrgemm2Nnz'
bazel-out/k8-opt/bin/tensorflow/core/util/libcuda_sparse.pic.lo(cuda_sparse.pic.o): In function `absl::lts_20230125::Status tensorflow::GpuSparse::Csrgemm<float>(int, int, int, cusparseMatDescr*, int, float const*, int const*, int const*, cusparseMatDescr*, int, float const*, int const*, int const*, cusparseMatDescr*, float*, int*, int*, csrgemm2Info*, void*)':
cuda_sparse.cc:(.text._ZN10tensorflow9GpuSparse7CsrgemmIfEEN4absl12lts_202301256StatusEiiiP16cusparseMatDescriPKT_PKiSB_S6_iS9_SB_SB_S6_PS7_PiSD_P12csrgemm2InfoPv+0xb3): undefined reference to `cusparseScsrgemm2'
bazel-out/k8-opt/bin/tensorflow/core/util/libcuda_sparse.pic.lo(cuda_sparse.pic.o): In function `absl::lts_20230125::Status tensorflow::GpuSparse::Csrgemm<double>(int, int, int, cusparseMatDescr*, int, double const*, int const*, int const*, cusparseMatDescr*, int, double const*, int const*, int const*, cusparseMatDescr*, double*, int*, int*, csrgemm2Info*, void*)':
cuda_sparse.cc:(.text._ZN10tensorflow9GpuSparse7CsrgemmIdEEN4absl12lts_202301256StatusEiiiP16cusparseMatDescriPKT_PKiSB_S6_iS9_SB_SB_S6_PS7_PiSD_P12csrgemm2InfoPv+0xb3): undefined reference to `cusparseDcsrgemm2'
bazel-out/k8-opt/bin/tensorflow/core/util/libcuda_sparse.pic.lo(cuda_sparse.pic.o): In function `absl::lts_20230125::Status tensorflow::GpuSparse::Csrgemm<std::complex<float> >(int, int, int, cusparseMatDescr*, int, std::complex<float> const*, int const*, int const*, cusparseMatDescr*, int, std::complex<float> const*, int const*, int const*, cusparseMatDescr*, std::complex<float>*, int*, int*, csrgemm2Info*, void*)':
cuda_sparse.cc:(.text._ZN10tensorflow9GpuSparse7CsrgemmISt7complexIfEEEN4absl12lts_202301256StatusEiiiP16cusparseMatDescriPKT_PKiSD_S8_iSB_SD_SD_S8_PS9_PiSF_P12csrgemm2InfoPv+0xb3): undefined reference to `cusparseCcsrgemm2'
bazel-out/k8-opt/bin/tensorflow/core/util/libcuda_sparse.pic.lo(cuda_sparse.pic.o): In function `absl::lts_20230125::Status tensorflow::GpuSparse::Csrgemm<std::complex<double> >(int, int, int, cusparseMatDescr*, int, std::complex<double> const*, int const*, int const*, cusparseMatDescr*, int, std::complex<double> const*, int const*, int const*, cusparseMatDescr*, std::complex<double>*, int*, int*, csrgemm2Info*, void*)':
cuda_sparse.cc:(.text._ZN10tensorflow9GpuSparse7CsrgemmISt7complexIdEEEN4absl12lts_202301256StatusEiiiP16cusparseMatDescriPKT_PKiSD_S8_iSB_SD_SD_S8_PS9_PiSF_P12csrgemm2InfoPv+0xb3): undefined reference to `cusparseZcsrgemm2'
clang-16: error: linker command failed with exit code 1 (use -v to see invocation)
Target //tensorflow/tools/pip_package:build_pip_package failed to build
Use --verbose_failures to see the command lines of failed build steps.
INFO: Elapsed time: 1154.437s, Critical Path: 625.33s
INFO: 32644 processes: 6751 internal, 25893 local.
FAILED: Build did NOT complete successfully
```
```
### Relevant log output
_No response_
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PR_kwDOArmXAs5nBU6I
| 62,973 |
Typo correction on the doc in parse_op.py
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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/62973/checks?check_run_id=21627410424) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request."
] | 2024-02-15T20:05:12 | 2024-02-15T20:06:55 | 2024-02-15T20:06:52 |
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Typo correction on the doc in parse_op.py
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I_kwDOArmXAs5_ZNHy
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Can't link TFLite Ops in xcode anymore
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[
"Hi @andrew-lyons,\r\n\r\nTry to specify both `TensorFlowLiteSwift` and `TensorFlowLiteSelectTfOps` are the same nightly versions in `pod` file target.\r\n```\r\n # Specify in your pod file target\r\n pod 'TensorFlowLiteSwift', '~> 0.0.1-nightly'\r\n pod 'TensorFlowLiteSelectTfOps', '~> 0.0.1-nightly'\r\n\r\n```\r\nThank You\r\n",
"@LakshmiKalaKadali I changed them both to nightly builds, and still receive the same issue:\r\n\r\n```\r\nINFO: Initialized TensorFlow Lite runtime.\r\nTensorFlow Lite Error: 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\r\nTensorFlow Lite Error: Node number 1 (FlexMutableHashTableV2) failed to prepare.\r\n```\r\n\r\nI am unable to apply the linker flag in the tflite documentation [here](https://www.tensorflow.org/lite/guide/ops_select) because of the linker error I pasted in my original post body. Perhaps xcode 15 does not allow the usage of `-force-load`?",
"Hi @andrew-lyons, I believe you are correct that 15.0.1 does not allow 'force_load', though we are not xcode experts so you might want to check with them just in case. Another alternative is to see if you can reduce your version of xcode so that you may continue your work.\r\n\r\n@yishuangP can you please take a look? Thanks.\r\n\r\n",
"@pkgoogle @yishuangP I'm attempting to downgrade xcode, however being on macOS Sonoma means that xcode 14 is not officially supported for use. Is there any other way to link the select ops other than using the `Other Flags`?",
"@yishuangP @pkgoogle is there anything on this end that can be done for this? It seems tflite is unusable currently with these newest versions.",
"Hey @andrew-lyons,\r\n\r\nI just ran into this exact issue myself. Turns out you need to add\r\n\r\n`-force-load`\r\n\r\nand \r\n\r\n`/(your local path)/ios/App/Pods/TensorFlowLiteSelectTfOps/Frameworks/TensorFlowLiteSelectTfOps.xcframework/ios-arm64/TensorFlowLiteSelectTfOps.framework/TensorFlowLiteSelectTfOps`\r\n\r\neach in a separate line. Otherwise it get's double quoted which messes things up.",
"Hi Guys\r\n\r\nit has to be \r\n\r\n-force_load \r\n\r\ninstead of \r\n\r\n-force-load\r\n\r\nSo yes this works in terms of that the linker is found but i still get the error:\r\n\r\nTensorFlow Lite Error: 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\r\nTensorFlow Lite Error: Node number 41 (FlexTranspose) failed to prepare.",
"@robin-torwell Thanks for that, just getting back around to this. I was able to move forward using your comment 😄 \r\n\r\nUnfortunately, getting another issue:\r\n```\r\nTensorFlow Lite Error: Op type not registered 'RegexSplitWithOffsets' in binary running on localhost. Make sure the Op and Kernel are registered in the binary running in this process. Note that if you are loading a saved graph which used ops from tf.contrib (e.g. `tf.contrib.resampler`), accessing should be done before importing the graph, as contrib ops are lazily registered when the module is first accessed.\r\n```\r\n\r\nSo looking into this now, but I suppose for this issue we could call this taken care of.",
"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/62972\">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/62972\">No</a>\n"
] | 2024-02-15T19:46:34 | 2024-04-11T17:32:25 | 2024-04-11T17:32:22 |
NONE
| null | null | null |
**System information**
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- Macbook M1 Pro
- XCode version 15.0.1
- TensorFlow installed from (source or binary):
```
pod 'TensorFlowLiteSwift', '~> 2.14.0', :subspecs => ['Metal']
pod 'TensorFlowLiteSelectTfOps', '~> 0.0.1-nightly'
```
**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 Colab/Jupyter/any notebook.
Also, please include a link to a GraphDef or the model if possible.
**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.
---
I'm not sure if a reproducible example is necessary, perhaps I'm wrong. The documentation says to include the TFLite Select Ops via adding a linker flag to 'Other Linker Flags' in the app's Build Settings. This results in the following:
```
Unknown argument: '-force_load /(my local path)/ios/App/Pods/TensorFlowLiteSelectTfOps/Frameworks/TensorFlowLiteSelectTfOps.xcframework/ios-arm64/TensorFlowLiteSelectTfOps.framework/TensorFlowLiteSelectTfOps'
```
Is there a different method for linking the select ops? As listed above this is on XCode version 15.0.1.
|
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I_kwDOArmXAs5_TtHX
| 62,971 |
Not able to add metadata to tfltie model with 2 output heads
|
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[
"@akuma308 The most straightforward solution is to create individual metadata entries for each of the two output tensors. Please refer to the ImageSegmenterWriter [documentation](https://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/task/vision/segmenter/ImageSegmenter) for examples on how to construct metadata objects for multiple outputs. Kindly use the latest TF version 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."
] | 2024-02-15T08:01:24 | 2024-03-05T01:46:51 | 2024-03-05T01:46:51 |
NONE
| null | null | null |
### 1. System information
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 20.0.5 LTS
- TensorFlow installation (pip package or built from source): pip
- TensorFlow library (version, if pip package or github SHA, if built from source): tensorflow==2.10.0
### 2. Code
Below is the screenshot of tflite model properties.
After this when we try adding the model metadata via below code snippet, it throws us error . (Taking inspiration from [here](https://www.tensorflow.org/lite/models/convert/metadata_writer_tutorial#image_segmenters) )
`ImageSegmenterWriter = image_segmenter.MetadataWriter
_MODEL_PATH = "custom_model.tflite"
# Task Library expects label files that are in the same format as the one below.
_LABEL_FILE = "custom_model_labels.txt"
_SAVE_TO_PATH = "custom_model_metadata.tflite"
# Normalization parameters is required when reprocessing the image. It is
# optional if the image pixel values are in range of [0, 255] and the input
# tensor is quantized to uint8. See the introduction for normalization and
# quantization parameters below for more details.
# https://www.tensorflow.org/lite/models/convert/metadata#normalization_and_quantization_parameters)
_INPUT_NORM_MEAN = 127.5
_INPUT_NORM_STD = 127.5
# Create the metadata writer.
writer = ImageSegmenterWriter.create_for_inference(
writer_utils.load_file(_MODEL_PATH), [_INPUT_NORM_MEAN], [_INPUT_NORM_STD],
[_LABEL_FILE])
# Verify the metadata generated by metadata writer.
print(writer.get_metadata_json())
# Populate the metadata into the model.
writer_utils.save_file(writer.populate(), _SAVE_TO_PATH)
`
The error we get is follows
`ValueError: The number of output tensors (2) should match the number of output tensor metadata (1)`
Is there a different way to add metadata for image segmenter,did my research, also understood that the deeplab_v3 has 1 output head and that is why the above addition of metadata make sense.
Please suggest.
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I_kwDOArmXAs5_TrM4
| 62,970 |
Tensorflow build fails with ModuleNotFoundError: No module named 'six.moves'
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[
"@Mathanraj-Sharma You are using an older version of TF which is not actively supported. Could you please check this [doc](https://www.tensorflow.org/install/source#ubuntu) for reference?\r\nThank you!",
"@sushreebarsa I need that specific version of tf needed to be built, I would appreciate any input/suggestion to overcome this error ",
"@Mathanraj-Sharma You may use the latest TF version 2.15 and let us know?\r\n\r\n\r\n\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/62970\">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/62970\">No</a>\n"
] | 2024-02-15T07:55:35 | 2024-03-07T01:41:20 | 2024-03-07T01:41:17 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.4.4
### Custom code
No
### OS platform and distribution
Linux x86_64 CentOS7
### Mobile device
_No response_
### Python version
3.12
### Bazel version
3.1.0
### GCC/compiler version
7.3.1
### CUDA/cuDNN version
-
### GPU model and memory
-
### Current behavior?
I am trying to custom-build tensorflow `2.4.4` for Python 3.12 for a specific use case. I can build it up to Python 3.11 without any issues, but it fails with `ModuleNotFoundError: No module named 'six.moves' ` for 3.12.
I ensured `six` is installed in my python environment, appreciate any help with this
```bash
[root@795834bfc245 tensorflow-2.4.4]# pip list
Package Version
------------------- -------
Keras-Preprocessing 1.1.2
numpy 1.26.0
pip 24.0
PyYAML 5.1b5
setuptools 69.1.0
six 1.16.0
wheel 0.42.0
```
### Standalone code to reproduce the issue
```shell
source /opt/rh/devtoolset-7/enable
wget https://github.com/tensorflow/tensorflow/archive/v2.4.4.tar.gz
tar xf v${TENSORFLOW_VERSION}.tar.gz
cd tensorflow-2.4.4
wget -qO- https://raw.githubusercontent.com/easybuilders/easybuild-easyconfigs/develop/easybuild/easyconfigs/t/TensorFlow/TensorFlow-2.4.0_fix-eigen-on-power.patch | git apply
./configure
BAZEL_LINKLIBS=-l%:libstdc++.a bazel test --host_javabase="@local_jdk//:jdk" --config opt //tensorflow/tools/lib_package:libtensorflow_test
```
### Relevant log output
```shell
[root@795834bfc245 tensorflow-2.4.4]# BAZEL_LINKLIBS=-l%:libstdc++.a bazel test --host_javabase="@local_jdk//:jdk" --config opt //tensorflow/tools/lib_package:libtensorflow_test --verbose_failures --sandbox_debug
Starting local Bazel server and connecting to it...
WARNING: The following configs were expanded more than once: [v2]. For repeatable flags, repeats are counted twice and may lead to unexpected behavior.
INFO: Options provided by the client:
Inherited 'common' options: --isatty=1 --terminal_columns=249
INFO: Reading rc options for 'test' from /tensorflow-2.4.4/.bazelrc:
Inherited 'common' options: --experimental_repo_remote_exec
INFO: Reading rc options for 'test' from /tensorflow-2.4.4/.bazelrc:
Inherited 'build' options: --apple_platform_type=macos --define framework_shared_object=true --define open_source_build=true --java_toolchain=//third_party/toolchains/java:tf_java_toolchain --host_java_toolchain=//third_party/toolchains/java:tf_java_toolchain --define=tensorflow_enable_mlir_generated_gpu_kernels=0 --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 --noincompatible_prohibit_aapt1 --enable_platform_specific_config --config=short_logs --config=v2
INFO: Reading rc options for 'test' from /tensorflow-2.4.4/.tf_configure.bazelrc:
Inherited 'build' options: --action_env PYTHON_BIN_PATH=/root/.pyenv/versions/3.12.1/bin/python3 --action_env PYTHON_LIB_PATH=/root/.pyenv/versions/3.12.1/lib/python3.12/site-packages --python_path=/root/.pyenv/versions/3.12.1/bin/python3 --action_env TF_CONFIGURE_IOS=0
INFO: Reading rc options for 'test' from /tensorflow-2.4.4/.bazelrc:
'test' options: --define open_source_build=true --config=v2
INFO: Reading rc options for 'test' from /tensorflow-2.4.4/.tf_configure.bazelrc:
'test' options: --flaky_test_attempts=3 --test_size_filters=small,medium
INFO: Found applicable config definition build:short_logs in file /tensorflow-2.4.4/.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file /tensorflow-2.4.4/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition test:v2 in file /tensorflow-2.4.4/.tf_configure.bazelrc: --test_tag_filters=-benchmark-test,-no_oss,-gpu,-oss_serial,-v1only --build_tag_filters=-benchmark-test,-no_oss,-gpu,-v1only
INFO: Found applicable config definition build:v2 in file /tensorflow-2.4.4/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition test:v2 in file /tensorflow-2.4.4/.tf_configure.bazelrc: --test_tag_filters=-benchmark-test,-no_oss,-gpu,-oss_serial,-v1only --build_tag_filters=-benchmark-test,-no_oss,-gpu,-v1only
INFO: Found applicable config definition build:opt in file /tensorflow-2.4.4/.tf_configure.bazelrc: --copt=-mtune=generic --host_copt=-mtune=generic --copt=-march=x86-64 --host_copt=-march=x86-64 --copt=-msse --host_copt=-msse --copt=-msse2 --host_copt=-msse2 --copt=-msse3 --host_copt=-msse3 --copt=-msse4.1 --host_copt=-msse4.1 --copt=-msse4.2 --host_copt=-msse4.2 --copt=-mavx --host_copt=-mavx --define with_default_optimizations=true
INFO: Found applicable config definition build:linux in file /tensorflow-2.4.4/.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
INFO: Found applicable config definition build:dynamic_kernels in file /tensorflow-2.4.4/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS
DEBUG: Rule 'io_bazel_rules_go' indicated that a canonical reproducible form can be obtained by modifying arguments shallow_since = "1557349968 -0400"
DEBUG: Repository io_bazel_rules_go instantiated at:
no stack (--record_rule_instantiation_callstack not enabled)
Repository rule git_repository defined at:
/root/.cache/bazel/_bazel_root/bb36d39c3ffc7f5337f278aed1ceaa52/external/bazel_tools/tools/build_defs/repo/git.bzl:195:18: in <toplevel>
DEBUG: Rule 'io_bazel_rules_docker' indicated that a canonical reproducible form can be obtained by modifying arguments shallow_since = "1556410077 -0400"
DEBUG: Repository io_bazel_rules_docker instantiated at:
no stack (--record_rule_instantiation_callstack not enabled)
Repository rule git_repository defined at:
/root/.cache/bazel/_bazel_root/bb36d39c3ffc7f5337f278aed1ceaa52/external/bazel_tools/tools/build_defs/repo/git.bzl:195:18: in <toplevel>
INFO: Analyzed target //tensorflow/tools/lib_package:libtensorflow_test (218 packages loaded, 18761 targets configured).
INFO: Found 1 test target...
ERROR: /tensorflow-2.4.4/tensorflow/tools/lib_package/BUILD:71:1: PackageTar tensorflow/tools/lib_package/cheaders.tar failed (Exit 1): build_tar failed: error executing command
(cd /root/.cache/bazel/_bazel_root/bb36d39c3ffc7f5337f278aed1ceaa52/execroot/org_tensorflow && \
exec env - \
LD_LIBRARY_PATH=/opt/rh/devtoolset-7/root/usr/lib64:/opt/rh/devtoolset-7/root/usr/lib:/opt/rh/devtoolset-7/root/usr/lib64/dyninst:/opt/rh/devtoolset-7/root/usr/lib/dyninst:/opt/rh/devtoolset-7/root/usr/lib64:/opt/rh/devtoolset-7/root/usr/lib \
PATH=/opt/rh/devtoolset-7/root/usr/bin:/usr/lib64/ccache:/root/.pyenv/shims:/root/.pyenv/bin:/usr/local/llvm/bin/:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin \
PYTHON_BIN_PATH=/root/.pyenv/versions/3.12.1/bin/python3 \
PYTHON_LIB_PATH=/root/.pyenv/versions/3.12.1/lib/python3.12/site-packages \
TF2_BEHAVIOR=1 \
TF_CONFIGURE_IOS=0 \
bazel-out/host/bin/external/bazel_tools/tools/build_defs/pkg/build_tar --flagfile bazel-out/k8-opt/bin/tensorflow/tools/lib_package/cheaders.args)
Execution platform: @local_execution_config_platform//:platform
Traceback (most recent call last):
File "/root/.cache/bazel/_bazel_root/bb36d39c3ffc7f5337f278aed1ceaa52/execroot/org_tensorflow/bazel-out/host/bin/external/bazel_tools/tools/build_defs/pkg/build_tar.runfiles/bazel_tools/tools/build_defs/pkg/build_tar.py", line 24, in <module>
from absl import app
File "/root/.cache/bazel/_bazel_root/bb36d39c3ffc7f5337f278aed1ceaa52/execroot/org_tensorflow/bazel-out/host/bin/external/bazel_tools/tools/build_defs/pkg/build_tar.runfiles/bazel_tools/third_party/py/abseil/absl/app.py", line 38, in <module>
from absl import flags
File "/root/.cache/bazel/_bazel_root/bb36d39c3ffc7f5337f278aed1ceaa52/execroot/org_tensorflow/bazel-out/host/bin/external/bazel_tools/tools/build_defs/pkg/build_tar.runfiles/bazel_tools/third_party/py/abseil/absl/flags/__init__.py", line 40, in <module>
from absl.flags import _argument_parser
File "/root/.cache/bazel/_bazel_root/bb36d39c3ffc7f5337f278aed1ceaa52/execroot/org_tensorflow/bazel-out/host/bin/external/bazel_tools/tools/build_defs/pkg/build_tar.runfiles/bazel_tools/third_party/py/abseil/absl/flags/_argument_parser.py", line 29, in <module>
from absl.flags import _helpers
File "/root/.cache/bazel/_bazel_root/bb36d39c3ffc7f5337f278aed1ceaa52/execroot/org_tensorflow/bazel-out/host/bin/external/bazel_tools/tools/build_defs/pkg/build_tar.runfiles/bazel_tools/third_party/py/abseil/absl/flags/_helpers.py", line 38, in <module>
from six.moves import range # pylint: disable=redefined-builtin
^^^^^^^^^^^^^^^^^^^^^^^^^^^
ModuleNotFoundError: No module named 'six.moves'
----------------
Note: The failure of target @bazel_tools//tools/build_defs/pkg:build_tar (with exit code 1) may have been caused by the fact that it is a Python 2 program that was built in the host configuration, which uses Python 3. You can change the host configuration (for the entire build) to instead use Python 2 by setting --host_force_python=PY2.
If this error started occurring in Bazel 0.27 and later, it may be because the Python toolchain now enforces that targets analyzed as PY2 and PY3 run under a Python 2 and Python 3 interpreter, respectively. See https://github.com/bazelbuild/bazel/issues/7899 for more information.
----------------
Target //tensorflow/tools/lib_package:libtensorflow_test failed to build
INFO: Elapsed time: 164.994s, Critical Path: 51.27s
INFO: 2782 processes: 2782 local.
FAILED: Build did NOT complete successfully
//tensorflow/tools/lib_package:libtensorflow_test FAILED TO BUILD
FAILED: Build did NOT complete successfully
```
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I_kwDOArmXAs5_ThIf
| 62,969 |
Could not find device for node GenerateBoundingBoxProposals
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[
"@sachinprasadhs,\r\nI was able to reproduce the issue on tensorflow v2.15 and tf-nightly. Kindly find the gist of it [here](https://colab.sandbox.google.com/gist/tilakrayal/5ee8137476e04b096fbfbb5f6b3c21c6/untitled1730.ipynb).",
"@drewshark TensorFlow Nightly can potentially resolve this issue as the bug fix might be included there."
] | 2024-02-15T07:24:02 | 2024-03-22T18:13:11 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### 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
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When using tf.image.generate_bounding_box_proposals in CPU, it raises the following error:
```
NotFoundError: Could not find device for node: {{node GenerateBoundingBoxProposals}} = GenerateBoundingBoxProposals[post_nms_topn=300]
All kernels registered for op GenerateBoundingBoxProposals:
device='GPU'
[Op:GenerateBoundingBoxProposals] name:
```
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
scores = tf.constant([[0.9, 0.8, 0.7], [0.6, 0.5, 0.4]])
bbox_deltas = tf.constant([[1, 1, 1, 1], [2, 2, 2, 2]])
image_info = tf.constant([100, 100, 1])
anchors = tf.constant([[10, 10, 20, 20], [30, 30, 40, 40], [50, 50, 60, 60]])
result = tf.image.generate_bounding_box_proposals(scores, bbox_deltas, image_info, anchors)
print(result)
```
### Relevant log output
_No response_
|
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| 62,968 |
Generate lock files with TF aligned bazel version 6.5.0
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fix: fix more tests
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Executed 3006 out of 3056 tests: 3040 tests pass, 6 fail to build and 10 fail locally.
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I_kwDOArmXAs5_SXaA
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I can not install transflow in my laptop
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[
"I believe the issue is with your `python version`.\r\n\r\nYou've specified your python version to be 3.12. Checkout the official [TensorFlow website](https://www.tensorflow.org/install). Currently it is supported for `python 3.8-3.11`.\r\n\r\n<img width=\"927\" alt=\"Screenshot 2024-02-15 at 11 18 00 AM\" src=\"https://github.com/tensorflow/tensorflow/assets/76887609/c31fd2d0-517a-4dd0-bc15-ab6758c36f9d\">\r\n",
"> I believe the issue is with your `python version`.\r\n> \r\n> You've specified your python version to be 3.12. Checkout the official [TensorFlow website](https://www.tensorflow.org/install). Currently it is supported for `python 3.8-3.11`.\r\n> \r\n> <img alt=\"Screenshot 2024-02-15 at 11 18 00 AM\" width=\"927\" src=\"https://private-user-images.githubusercontent.com/76887609/304963676-c31fd2d0-517a-4dd0-bc15-ab6758c36f9d.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MDc5ODE2MDAsIm5iZiI6MTcwNzk4MTMwMCwicGF0aCI6Ii83Njg4NzYwOS8zMDQ5NjM2NzYtYzMxZmQyZDAtNTE3YS00ZGQwLWJjMTUtYWI2NzU4YzM2ZjlkLnBuZz9YLUFtei1BbGdvcml0aG09QVdTNC1ITUFDLVNIQTI1NiZYLUFtei1DcmVkZW50aWFsPUFLSUFWQ09EWUxTQTUzUFFLNFpBJTJGMjAyNDAyMTUlMkZ1cy1lYXN0LTElMkZzMyUyRmF3czRfcmVxdWVzdCZYLUFtei1EYXRlPTIwMjQwMjE1VDA3MTUwMFomWC1BbXotRXhwaXJlcz0zMDAmWC1BbXotU2lnbmF0dXJlPTNkMzAyYTg3ZWI4YWI0ZTQ3MzdiY2I5ODM0NTBjYTNkZWI2YTkyYzg5MjA2MTVhYzJiNTdlYTgzMjFlM2MyMjQmWC1BbXotU2lnbmVkSGVhZGVycz1ob3N0JmFjdG9yX2lkPTAma2V5X2lkPTAmcmVwb19pZD0wIn0.iEqK1V6aNfLFZjsEbaRLIXeZWN5cKXMLqXZxGKgY8Wo\">\r\n\r\nthank you so much, i just reinstalled my python and it work ",
"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/62966\">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/62966\">No</a>\n"
] | 2024-02-15T02:29:28 | 2024-02-15T07:50:14 | 2024-02-15T07:16:52 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15.0
### Custom code
Yes
### OS platform and distribution
windows 10 x64
### Mobile device
_No response_
### Python version
3.12.2
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
when i installing transflow for project it show this : ERROR: Could not find a version that satisfies the requirement tensorflow==2.15.0 (from versions: none)
ERROR: No matching distribution found for tensorflow==2.15.0
### Standalone code to reproduce the issue
```shell
import cv2
import cvlib as cv
from cvlib.object_detection import draw_bbox
from vidgear.gears import CamGear
stream =CamGear(soruce='https://www.youtube.com/watch?v=cH7VBI4QQzA&ab_channel=JAPAN4K', stream_mode=True, logging=True)
count=0
while True:
frame = stream.read()
#count += 1
#if count % 6 != 0:
#continue
frame=cv2.resize(frame,(1020,600))
cv2.imshow("FRAME",frame)
if cv2.waitKey(1)&0xFF==27:
break
stream.release()
cv2.destroyAllWindows()
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5m6-0B
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fixing many tests getting count to 23 failing 6 failing to build 3027 passing
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PR_kwDOArmXAs5m6xNL
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r2.16 cherry-pick: 74473a8b225 "Add .bazelversion to requirements_updater"
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Refer to the original commit: https://github.com/tensorflow/tensorflow/commit/74473a8b2257b32ace8b250eda2247adcb81b728
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Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence warning when iterating over a dataset
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"@p-s-p-s I tried to replicate the issue on colab and didn't face the error reported. Could you check this [gist](https://colab.research.google.com/gist/sushreebarsa/243351ba215c7da47efe5f712de96d81/62963.ipynb) and let us know?\r\nThank you!",
"@sushreebarsa tf 2.15 is not affected, but 2.16 and 2.17 are.",
"@sushreebarsa The reason you couldn't reproduce the error in colab is because the warnings are suppressed by default. Could you please check this colab https://colab.research.google.com/drive/1JuQriKXe-aJBAbValQK-8BFGtktzw4IW?usp=sharing ?",
"@p-s-p-s TF v2.15 is the latest stable version so error is not appearing there.\r\nWe recommend you to use the stable TF version. \r\nThank you!",
"@sushreebarsa I reported this issue in order to make it fixed before 2.16 release. Moreover, tf 2.15 with https://github.com/tensorflow/tensorflow/commit/04fb826f98b92dd172ad665d8a5522a2f8201867 applied is also internally affected by this issue. \r\n```\r\n>>> import tensorflow as tf\r\n2024-02-21 10:47:33.109381: 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\r\n2024-02-21 10:47:33.109409: 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\r\n2024-02-21 10:47:33.110044: 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\r\n2024-02-21 10:47:33.113595: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n>>> range_ds = tf.data.Dataset.range(10)\r\n2024-02-21 10:47:54.344759: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22462 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:01:00.0, compute capability: 8.6\r\n>>> \r\n>>> for d in range_ds:\r\n... print(d)\r\n... \r\ntf.Tensor(0, shape=(), dtype=int64)\r\ntf.Tensor(1, shape=(), dtype=int64)\r\ntf.Tensor(2, shape=(), dtype=int64)\r\ntf.Tensor(3, shape=(), dtype=int64)\r\ntf.Tensor(4, shape=(), dtype=int64)\r\ntf.Tensor(5, shape=(), dtype=int64)\r\ntf.Tensor(6, shape=(), dtype=int64)\r\ntf.Tensor(7, shape=(), dtype=int64)\r\ntf.Tensor(8, shape=(), dtype=int64)\r\ntf.Tensor(9, shape=(), dtype=int64)\r\n2024-02-21 10:47:56.048435: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\r\n>>> tf.__version__\r\n'2.15.0'\r\n```\r\nI am not sure what causes the problem, but as a symptomatic solution it is possible to disable some warnings like this:\r\n```\r\n if (!absl::StrContains(status.message(), \"End of sequence\")) {\r\n LOG(WARNING) << \"Local rendezvous is aborting with status: \" << status;\r\n }\r\n```\r\n\r\n",
"@sachinprasadhs I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/b2b9055007dfbb8eced10c86fab08815/copy-of-62963.ipynb), please have a look. Thank you!",
"Can confirm this issue with tf-nightly `'2.17.0-dev20240210'`\r\n\r\n```\r\n2024-02-26 04:03:26.379054: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence \r\n\t [[{{node IteratorGetNext}}]] \r\n\t [[IteratorGetNext/_4]] \r\n2024-02-26 04:03:26.379063: I tensorflow/core/framework/local_rendezvous.cc:422] Local rendezvous recv item cancelled. Key hash: 381694510697024129\r\n2024-02-26 04:03:26.379073: I tensorflow/core/framework/local_rendezvous.cc:422] Local rendezvous recv item cancelled. Key hash: 6451170228096927380\r\n```",
"Hello, I'd like to look into this issue and try to fix it, if that is possible.",
"Issue running default tensorflow training job after docker rebuild only on RTX-A4500\r\n\r\n```\r\nfrom tensorflow/tensorflow:latest-gpu\r\n\r\n\r\n[+] Building 137.8s (9/9) FINISHED docker:default\r\n => [internal] load build definition from Dockerfile 0.0s\r\n => => transferring dockerfile: 285B 0.0s\r\n => [internal] load .dockerignore 0.0s\r\n => => transferring context: 2B 0.0s\r\n => [internal] load metadata for docker.io/tensorflow/tensorflow:latest-gpu 1.2s\r\n => [auth] tensorflow/tensorflow:pull token for registry-1.docker.io 0.0s\r\n => [1/3] FROM docker.io/tensorflow/tensorflow:latest-gpu@sha256:4ab9ffddd6ffacc9251ac6439f431eb38d66200d3f52397b5d 135.7s\r\n\r\n\r\n [[RemoteCall]]\r\n25/25 ━━━━━━━━━━━━━━━━━━━━ 8s 316ms/step - accuracy: 0.1601 - loss: 8.1802\r\nEpoch 3/100\r\n24/25 ━━━━━━━━━━━━━━━━━━━━ 0s 310ms/step - accuracy: 0.2835 - loss: 7.41432024-03-10 04:24:44.885256: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\r\n [[{{node MultiDeviceIteratorGetNextFromShard}}]]\r\n2024-03-10 04:24:44.885302: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\r\n [[{{node MultiDeviceIteratorGetNextFromShard}}]]\r\n [[RemoteCall]]\r\n2024-03-10 04:24:44.896965: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\r\n [[{{node MultiDeviceIteratorGetNextFromShard}}]]\r\n2024-03-10 04:24:44.897036: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\r\n [[{{node MultiDeviceIteratorGetNextFromShard}}]]\r\n [[RemoteCall]]\r\n25/25 ━━━━━━━━━━━━━━━━━━━━ 8s 307ms/step - accuracy: 0.2795 - loss: 7.2638\r\n\r\n```\r\nhttps://github.com/ObrienlabsDev/machine-learning/issues/16\r\n\r\n",
"My understanding is the error will not affect the execution but the iterator was not usable after the error? https://stackoverflow.com/questions/53930242/how-to-fix-a-outofrangeerror-end-of-sequence-error-when-training-a-cnn-with-t",
"@salaki \r\nI didn't observe any negative impact, except it is quite annoying to receive this warning every time you iterate over a dataset. As a temporary fix for 2.16.1 I just commented out this line in [tensorflow/core/framework/local_rendezvous.cc](https://github.com/tensorflow/tensorflow/commit/04fb826f98b92dd172ad665d8a5522a2f8201867#diff-03234cff994e694d13b64ea69e4920cb17f1e07fb9a3aab1f1783ef0f9635741)\r\n`// LOG(WARNING) << \"Local rendezvous is aborting with status: \" << status;`\r\nand recompiled TF from source. ",
"Another example to reproduce this issue with Python 3.12.2 and TensorFlow 2.16.1 is the fourth installment of the introductory videos, \"TensorFlow ML Zero to Hero\". The fourth part uses this notebook. When training every second epoch falls over with the issue reported here.\r\n\r\nWhen I switch to TensorFlow 2.15.1, I also have to downgrade to Python version 3.11.8 which is something I'd like to avoid. Ideally TensorFlow 2.15.1 should be made available to the most recent stable release of Python at least until a newer stable version of TensorFlow becomes available. Combo Python 3.11.8 and TensorFlow 2.15.1 works for the given notebook.\r\n\r\nHere is the link to that notebook that I mentioned. It runs fine online but not locally if using Python 3.12.2 and TensorFlow 2.16.1.\r\n\r\nhttps://colab.research.google.com/github/lmoroney/dlaicourse/blob/master/Course%202%20-%20Part%208%20-%20Lesson%202%20-%20Notebook%20(RockPaperScissors).ipynb\r\n\r\nThis link is also accessible form the description in the video at https://www.youtube.com/watch?v=u2TjZzNuly8\r\n\r\nI hope having another example to reproduce the problem helps with resolving this issue. Keep up the good work!",
"@google-admin @goolge Just please fire all these \"issue triagers\". They are a waste of our time, and a waste of your money. All they do is copy paste the code in collab with blindfolds, fuck it up with a 90% chance, and tell you you're wrong. They are a disgrace to our intellect.",
"Same issue on a larger training project :\r\n\r\n\r\nI don't know if it helps or if it is related think one of the recent additions to the code was to use strategies and scopes : \r\nstrategy = tf.distribute.OneDeviceStrategy(device='/gpu:0')\r\nwith strategy.scope():",
"Hi all, this didn't make it our (tf.data team's) way until just now, when an internal user flagged it. This should be fixed with https://github.com/tensorflow/tensorflow/commit/4924ec6c0b68ba3fb8f73a6383881cd4194ed802.",
"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/62963\">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/62963\">No</a>\n",
"The error is even on the official tf website, so hopefully it will soon be fixed. \r\n[https://www.tensorflow.org/tutorials/quickstart/advanced](https://www.tensorflow.org/tutorials/quickstart/advanced )",
"> Same issue on a larger training project : \r\n> \r\n> I don't know if it helps or if it is related think one of the recent additions to the code was to use strategies and scopes : strategy = tf.distribute.OneDeviceStrategy(device='/gpu:0') with strategy.scope():\r\n\r\nIn my circumstance, the distribution strategy is not related to this problem after my double check.",
"Similar error. Fixed it by removing the steps_per_epoch argument from model.fit() and model.evaluate()\r\n\r\nimport sys\r\nfrom matplotlib import pyplot\r\nfrom keras.utils import to_categorical\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Conv2D\r\nfrom keras.layers import MaxPooling2D\r\nfrom keras.layers import Dense\r\nfrom keras.layers import Flatten\r\nfrom keras.optimizers import SGD\r\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\r\nimport tensorflow as tf\r\nimport numpy as np\r\n\r\nphysical_devices = tf.config.list_physical_devices('GPU')\r\ntry:\r\n tf.config.experimental.set_memory_growth(physical_devices[0], True)\r\nexcept:\r\n # Invalid device or cannot modify virtual devices once initialized.\r\n pass\r\n\r\n # define cnn model\r\ndef define_model():\r\n model = Sequential()\r\n model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(200, 200, 3)))\r\n model.add(MaxPooling2D((2, 2)))\r\n model.add(Flatten())\r\n model.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\r\n model.add(Dense(1, activation='sigmoid'))\r\n # compile model\r\n opt = SGD(learning_rate=0.001, momentum=0.9)\r\n model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\r\n return model\r\n\r\n# create data generator\r\ndatagen = ImageDataGenerator(rescale=1.0/255.0)\r\nmodel = define_model()\r\n\r\n# prepare iterators\r\ntrain_it = datagen.flow_from_directory('/workspace/workspace/cats_and_dogs_data/dogs-vs-cats/train/',\r\n class_mode='binary', batch_size=64, target_size=(200, 200))\r\ntest_it = datagen.flow_from_directory('/workspace/workspace/cats_and_dogs_data/dogs-vs-cats/test1/',\r\n class_mode='binary', batch_size=64, target_size=(200, 200))\r\n\r\n# fit model\r\nhistory = model.fit(train_it, validation_data=test_it, epochs=20, verbose=1)\r\n\r\n\r\n# evaluate model\r\n_, acc = model.evaluate(test_it, verbose=1)\r\nprint('> %.3f' % (acc * 100.0))\r\n\r\n\r\n",
"I can reproduce the warning on Python 3.12 and TF 2.16. In addition, when my (custom) dataset has this 'issue' then I also get messages when calling `model.evaluate(ds)`. That just doesn't look like it's safe to ignore it. Example:\r\n\r\n```\r\n 919/Unknown 1s 2ms/step - loss: 1.05462024-05-23 10:15:02.410200: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\r\n\t [[{{node IteratorGetNext}}]]\r\n/usr/lib/python3.12/contextlib.py:158: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\r\n self.gen.throw(value)\r\n927/927 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 1.0546\r\n```\r\n\r\n**PS:** I DO have enough data.",
"**I made this disappear by simply using `.repeat()` and **not using** `.cache()`** on my training and validation data batches.\r\n\r\nMy script has a very generic input pipeline based on the [ tensorflow semantic segmentation tutorial](https://www.google.com/search?client=firefox-b-d&q=tensorflow+semenatic+segmentation) with tf 2.16 and python 3.10"
] | 2024-02-14T23:38:34 | 2024-06-10T23:29:04 | 2024-04-12T17:32:49 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
There is a warning which appears after the last iteration over a dataset:
W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence
This warning report was introduced by this commit: https://github.com/tensorflow/tensorflow/commit/04fb826f98b92dd172ad665d8a5522a2f8201867
I believe that simple iteration over a dataset shouldn't cause such behavior.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
range_ds = tf.data.Dataset.range(10)
for d in range_ds:
print(d)
```
### Relevant log output
```shell
tf.Tensor(0, shape=(), dtype=int64)
tf.Tensor(1, shape=(), dtype=int64)
tf.Tensor(2, shape=(), dtype=int64)
tf.Tensor(3, shape=(), dtype=int64)
tf.Tensor(4, shape=(), dtype=int64)
tf.Tensor(5, shape=(), dtype=int64)
tf.Tensor(6, shape=(), dtype=int64)
tf.Tensor(7, shape=(), dtype=int64)
tf.Tensor(8, shape=(), dtype=int64)
tf.Tensor(9, shape=(), dtype=int64)
2024-02-15 08:27:36.782604: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence
```
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I_kwDOArmXAs5_RPyK
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Setting nvidia-driver to work with tensorflow 2.15 [GPU]
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[
"@andvsilva These errors suggest conflicts between different implementations of cuDNN, cuFFT, and cuBLAS libraries. \r\nPlease verify that CUDA environment variables like CUDA_HOME and CUDA_VISIBLE_DEVICES are set correctly.\r\nAlso please do ensure you have the latest NVIDIA drivers installed for your GPU. \r\nI tried to replicate this on colab, please find the [gist](https://colab.research.google.com/gist/sushreebarsa/34a237fbfd90c3102aa6987a58f6e4b6/untitled933.ipynb#scrollTo=qzOkxZu-lWyX) here.\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/62962\">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/62962\">No</a>\n"
] | 2024-02-14T21:23:20 | 2024-03-05T01:46:55 | 2024-03-05T01:46:52 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15
### Custom code
Yes
### OS platform and distribution
"Ubuntu 23.04
### Mobile device
"Ubuntu 23.04
### Python version
Python 3.11.4
### Bazel version
bazel 6.1.0
### GCC/compiler version
gcc (Ubuntu 12.3.0-1ubuntu1~23.04) 12.3.0
### CUDA/cuDNN version
CUDA Version: 12.3
### GPU model and memory
NVIDIA GeForce GTX 750 Ti
### Current behavior?
I trying to use GPU with tensorflow in jupyter notebook.
pre-req: https://www.tensorflow.org/install/source#gpu
from terminal:
```Wed Feb 14 18:16:03 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 545.29.06 Driver Version: 545.29.06 CUDA Version: 12.3 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce GTX 750 Ti Off | 00000000:01:00.0 On | N/A |
| 33% 37C P8 1W / 38W | 436MiB / 4096MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| 0 N/A N/A 2553 G /usr/lib/xorg/Xorg 187MiB |
| 0 N/A N/A 2695 G /usr/bin/gnome-shell 26MiB |
| 0 N/A N/A 3356 G ...sion,SpareRendererForSitePerProcess 53MiB |
| 0 N/A N/A 3743 G ...onEnabled --variations-seed-version 129MiB |
| 0 N/A N/A 42678 C /usr/bin/python3 27MiB |
+---------------------------------------------------------------------------------------+
```
```
$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Sun_Jul_28_19:07:16_PDT_2019
Cuda compilation tools, release 10.1, V10.1.243
$ python --version
Python 3.11.4
~ ⌚ 18:14:55
$ bazel --version
bazel 6.1.0
~ ⌚ 18:15:05
$ gcc --version
gcc (Ubuntu 12.3.0-1ubuntu1~23.04) 12.3.0
Copyright (C) 2022 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
$ cat /etc/os-release
PRETTY_NAME="Ubuntu 23.04"
NAME="Ubuntu"
VERSION_ID="23.04"
VERSION="23.04 (Lunar Lobster)"
VERSION_CODENAME=lunar
ID=ubuntu
ID_LIKE=debian
HOME_URL="https://www.ubuntu.com/"
SUPPORT_URL="https://help.ubuntu.com/"
BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"
PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"
UBUNTU_CODENAME=lunar
LOGO=ubuntu-logo
```
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
## OUTPUT:
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
```
### Relevant log output
```shell
2024-02-14 17:58:40.696088: 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
2024-02-14 17:58:40.696166: 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
2024-02-14 17:58:40.829634: 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
2024-02-14 17:58:40.984397: 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.
2024-02-14 17:58:43.234200: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2.15.0
2024-02-14 17:58:44.809184: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-02-14 17:58:45.101985: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2256] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
```
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Cannot find Tensorflow API document (html)
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[
"@ShuyinOuyang,\r\nCould you please confirm whether you are mentioning these official documents.\r\nhttps://www.tensorflow.org/community/contribute/docs\r\nhttps://www.tensorflow.org/community/contribute/docs_ref\r\n\r\nIf you are referring to the same documents, those are not changeable/modify by the community. Only the developers can modify according to the updates.\r\n\r\nIf you want any changes that need to happen, please feel free to mention here. Internally, can discuss with the developer and try to make changes if those are valid. Community can raise the PR from here for the other changes. https://github.com/tensorflow/tensorflow/pulls\r\n\r\nThank you",
"Thanks for getting in touch! \r\nWhat I want is the HTML files of Tensorflow's online document (v2.15, latest release version), especially for API references. Just like https://numpy.org/doc/, where I can download the HTML zip file. Or if you have any scripts, like in https://github.com/pytorch/pytorch/tree/main/docs, where I could use the command 'make html' to generate the API document, I would appreciate it if you could tell me where to find it.\r\n"
] | 2024-02-14T15:22:00 | 2024-02-21T08:10:19 | null |
NONE
| null | null | null |
### Issue type
Documentation Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 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?
I try Tensorflow/Tensorflow and Tensorflow/docs, but I cannot find the document about API usage. It says Tensorflow's API document (HTML files) are generated based on source code docstring. If so, where can I find the script of it?
### Standalone code to reproduce the issue
```shell
N/A
```
### Relevant log output
_No response_
|
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| 2,134,319,194 |
PR_kwDOArmXAs5m27vL
| 62,960 |
Fix calculation in resize_bicubic_op_test
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[
"Hi @jakeharmon8 Can you please review this PR ? Thank you!",
"Hi @MichaelHudgins Can you please review this PR ? Thank you!"
] | 2024-02-14T13:10:04 | 2024-06-07T16:48:34 | null |
CONTRIBUTOR
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Ensure that the baseline calculation matches the optimised case even when on a compiler and architecture that can use higher precision intermediate values which result in different calculated values. This is done by splitting one calculation into two parts so that the intermediate value is truncated so that it can be stored in a float. This ensures that the baseline and the optimised calculations do not diverge.
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I_kwDOArmXAs5_NV9f
| 62,959 |
Checkpointing support for GPU datasets
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[] | 2024-02-14T12:02:33 | 2024-02-15T20:34:42 | null |
NONE
| null | null | null |
### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.3
### GPU model and memory
_No response_
### Current behavior?
Hello! Saving a checkpoint for a dataset placed on the GPU fails with:
```
Cannot assign a device for operation SerializeIterator (...) because no supported kernel for GPU devices is available.
```
(full error message below)
I expected the checkpoint for dataset to be saved as it would be for CPU-placed dataset.
Is there a way to overcome this limitation? Is it possible for you to add checkpointing support for GPU datasets?
Thank you for your help!
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import tensorflow_datasets as tfds
ds = tfds.load('mnist', split='train', shuffle_files=True)
ds = ds.apply(tf.data.experimental.prefetch_to_device("/gpu:0"))
it = iter(ds)
mgr = tf.train.Checkpoint(it)
cpt = mgr.save("/tmp/")
```
### Relevant log output
```shell
Traceback (most recent call last):
File "/home/skarpinski/tfgpu.py", line 9, in <module>
cpt = mgr.save("/tmp/")
^^^^^^^^^^^^^^^^^
File "/home/skarpinski/.local/lib/python3.11/site-packages/tensorflow/python/checkpoint/checkpoint.py", line 2533, in save
return self._write(
^^^^^^^^^^^^
File "/home/skarpinski/.local/lib/python3.11/site-packages/tensorflow/python/checkpoint/checkpoint.py", line 2369, in _write
output = self._saver.save(file_prefix=file_prefix, options=options)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/skarpinski/.local/lib/python3.11/site-packages/tensorflow/python/checkpoint/checkpoint.py", line 1356, in save
save_path, new_feed_additions = self._save_cached_when_graph_building(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/skarpinski/.local/lib/python3.11/site-packages/tensorflow/python/checkpoint/checkpoint.py", line 1284, in _save_cached_when_graph_building
self._gather_serialized_tensors(object_graph_tensor))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/skarpinski/.local/lib/python3.11/site-packages/tensorflow/python/checkpoint/checkpoint.py", line 1245, in _gather_serialized_tensors
save_util.serialize_graph_view(self._graph_view,
File "/home/skarpinski/.local/lib/python3.11/site-packages/tensorflow/python/checkpoint/save_util.py", line 309, in serialize_graph_view
serialized_tensors = _get_and_write_tensors_to_serialize(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/skarpinski/.local/lib/python3.11/site-packages/tensorflow/python/checkpoint/save_util.py", line 157, in _get_and_write_tensors_to_serialize
trackable, tensor_dict = _get_tensors_from_legacy_saveable(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/skarpinski/.local/lib/python3.11/site-packages/tensorflow/python/checkpoint/save_util.py", line 190, in _get_tensors_from_legacy_saveable
save_util_v1.generate_saveable_objects(
File "/home/skarpinski/.local/lib/python3.11/site-packages/tensorflow/python/checkpoint/save_util_v1.py", line 180, in generate_saveable_objects
maybe_saveable = saveable_object_util.create_saveable_object(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/skarpinski/.local/lib/python3.11/site-packages/tensorflow/python/training/saving/saveable_object_util.py", line 473, in create_saveable_object
return factory(name=key)
^^^^^^^^^^^^^^^^^
File "/home/skarpinski/.local/lib/python3.11/site-packages/tensorflow/python/training/saving/saveable_object_util.py", line 536, in create_saveable
tensor_dict = save_fn()
^^^^^^^^^
File "/home/skarpinski/.local/lib/python3.11/site-packages/tensorflow/python/data/ops/iterator_ops.py", line 900, in _serialize_to_tensors
serialized_iterator = gen_dataset_ops.serialize_iterator(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/skarpinski/.local/lib/python3.11/site-packages/tensorflow/python/ops/gen_dataset_ops.py", line 6523, in serialize_iterator
_ops.raise_from_not_ok_status(e, name)
File "/home/skarpinski/.local/lib/python3.11/site-packages/tensorflow/python/framework/ops.py", line 5983, in raise_from_not_ok_status
raise core._status_to_exception(e) from None # pylint: disable=protected-access
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot assign a device for operation SerializeIterator: Could not satisfy explicit device specification '/job:localhost/replica:0/task:0/device:GPU:0' because no supported kernel for GPU devices is available.
Colocation Debug Info:
Colocation group had the following types and supported devices:
Root Member(assigned_device_name_index_=1 requested_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' assigned_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' resource_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' supported_device_types_=[CPU] possible_devices_=[]
SerializeIterator: CPU
_Arg: GPU CPU
Colocation members, user-requested devices, and framework assigned devices, if any:
resource__handle (_Arg) framework assigned device=/job:localhost/replica:0/task:0/device:GPU:0
SerializeIterator (SerializeIterator) /job:localhost/replica:0/task:0/device:GPU:0
Op: SerializeIterator
Node attrs: external_state_policy=2
Registered kernels:
device='CPU'
[[{{node SerializeIterator}}]] [Op:SerializeIterator] name:
```
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Convert tf.variable into tf. constant
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"@schm0 Directly converting a tf.Variable to a tf.constant in TensorFlow is not possible as tf.Variable is designed to be mutable and track its value through iterations. There are a few ways to achieve this such as below;\r\n1. Access the variable's current value\r\n```\r\ncurrent_value = someTFvariable.read_value()\r\n```\r\n2. Create a constant tensor from the value\r\n```\r\nnew_var = tf.constant(current_value)\r\n```\r\n\r\nThank you!",
"Thanks for your answer but this results in:\r\n`TypeError: List of Tensors when single Tensor expected.`\r\nDoes it matter that the variable gets assigned with state_ops.assign_add?\r\nI can't find much info about state_ops.",
"@schm0 Could you please let us know the exact TF version you are using? \r\nIn order to expedite the trouble-shooting process, please provide a code snippet to reproduce the issue reported here. Thank you!\r\n",
"> @schm0 Could you please let us know the exact TF version you are using? In order to expedite the trouble-shooting process, please provide a code snippet to reproduce the issue reported here. Thank you!\r\n\r\nI use TensorFlow-DirectML 1.15.8.\r\nIt's optimizer class.\r\nThe variable gets declared as follows:\r\n```\r\nwith tf.device('/CPU:0') :\r\n with tf.variable_scope(self.name):\r\n self.iterations = tf.Variable(0, dtype=tf.int64, name='iterations')\r\n```\r\nand in the actual \"main\" optimizer function:\r\n```\r\n....\r\nupdates = []\r\nupdates += [ state_ops.assign_add( self.iterations, 1) ]\r\n....\r\nreturn control_flow_ops.group(*updates, name=self.name+'_updates')\r\n```\r\nSo this is a global iteration counter.\r\nI need to calculate an offset from this value which I then can subtract from it.\r\nBut because the iterations are counted upwards by 1 the offset changes too.\r\nSo I need to create a static/constant value.",
"@schm0 TF v1.15 is an older version which is not actively supported. We request you to kindly upgrade to the latest TF version. Thank you!",
"Well...\r\nIsn't this the lastest version that's supports directml?\r\nSo it is not possible to create a constant value from this state_op variable?",
"@schm0 it is not possible to directly create a constant value from a state_op variable like self.iterations in TensorFlow-DirectML 1.15.8. This is because state_ops are designed to modify existing state (tf.Variable) rather than create new constants. You could potentially manipulate the self-iterations tensor using TensorFlow operations, such as slicing or masking, to extract a constant portion as an offset.\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/62958\">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/62958\">No</a>\n"
] | 2024-02-14T08:55:10 | 2024-03-08T01:46:44 | 2024-03-08T01:46:41 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
v1.15.8-1-g3800a8e1cd 1.15.8
### Custom code
Yes
### OS platform and distribution
Windows 10
### Mobile device
_No response_
### Python version
3.7.17
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Hello!
I'm trying to create "snapshot" (constant) of an iterations counter (tf.variable) like this:
`new_var = tf.constant(someTFvariable)`
But it results:
`TypeError: Failed to convert object of type <class 'tensorflow.python.ops.variables.RefVariable'> to Tensor. Contents: <tf.Variable 'src_dst_opt/iters:0' shape=() dtype=int64_ref>. Consider casting elements to a supported type.
`
The variable got declared like this:
```
with tf.device('/CPU:0') :
with tf.variable_scope(self.name):
self.iterations = tf.Variable(0, dtype=tf.int64, name='iters')
```
`new_var = tf.constant(tf.cast(someTFvariable, dtype=tf.int64))`
Results in:
`Error: List of Tensors when single Tensor expected`
I guess there is an easy solution to this but I can't figure out.
Thanks.
### Standalone code to reproduce the issue
```shell
N/A
```
### Relevant log output
_No response_
|
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Internal config change
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[
"Hi @kanglant This PR is in draft, any update on this? Please. Thank you!"
] | 2024-02-13T22:10:59 | 2024-04-11T21:07:38 | 2024-04-11T21:07:38 |
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[
"Hi @kanglant This PR is in draft, any update on this? Please. Thank you!"
] | 2024-02-13T22:03:34 | 2024-04-11T21:07:13 | 2024-04-11T21:07:13 |
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Second test
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PR_kwDOArmXAs5mypxN
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feat: migrate all pybind_extension, ingroduce single pywrap_library
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TFLite CMake Task Library build
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[
"@Moddingear Please ensure you are using a TensorFlow Lite version compatible with your Coral TPU model. Your OpenCV version should support Coral TPU acceleration as well. Thank you!",
"Hello, thanks for the response.\r\nIn OpenCV (4.9.0), I didn't see any backends that would correspond with the Coral TPU. Calling dnn::getAvailableBackends() only lists DNN_BACKEND_OPENCV with DNN_TARGET_CPU (I have libusb-dev installed)\r\nAs for The TFLite version, I don't know what I should be downloading...",
"Hi @Moddingear, can you let me know what setup/installation you have performed as well as the commands which are running into issues? i.e. Have you tried compiling? Are you only using C++? Which task library are you trying to use? If you can share with us your steps & files, we can probably help you better. Thanks for your help.\r\n\r\nDoes this minimal example work for you? https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/examples/minimal\r\n\r\nPlease also review this https://coral.ai/docs/edgetpu/tflite-cpp/#run-an-inference-with-the-libcoral-api and see if that works for you or not.",
"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/62954\">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/62954\">No</a>\n"
] | 2024-02-13T13:16:16 | 2024-03-13T01:47:54 | 2024-03-13T01:47:39 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
master
### Custom code
No
### OS platform and distribution
Debian 12
### 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?
https://www.tensorflow.org/lite/inference_with_metadata/task_library/object_detector#run_inference_in_c
No includes are given.
Class does not exist in Tensorflow Lite.
Same for other classes in the task library.
I'm just trying to use the Coral TPU to accelerate DNN execution wiht OpenCV, the fact that it's this hard to even get working in infuriating.
### Standalone code to reproduce the issue
```shell
I have followed https://www.tensorflow.org/lite/guide/build_cmake#create_a_cmake_project_which_uses_tensorflow_lite
in a cmake project.
```
### Relevant log output
_No response_
|
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I_kwDOArmXAs5_E7YF
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What was the reason of using the Tensorflow 2.8.0 in the custom object detction
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[
"@rohitkumar9989,\r\nThere is an issue with the `tf-lite-modelmaker`. This issue is unlikely to be resolved soon. Below are the couple of options to use **tflite-model-maker**:\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\n\r\n2. Attempt to fix your current setup, can you try pip install scann?\r\n\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\n\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\nhttps://github.com/tensorflow/tensorflow/issues/62942#issuecomment-1938193409\r\nhttps://github.com/tensorflow/tensorflow/issues/60431\r\n\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/62953\">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/62953\">No</a>\n"
] | 2024-02-13T10:35:36 | 2024-03-01T01:48:22 | 2024-03-01T01:48:19 |
NONE
| null | null | null |
In the tensorflow custom object detection using the tensorflow lite, we have implemented the usage of the tensorflow 2.8.0 version.
`
sudo apt -y install libportaudio2
pip install -q --use-deprecated=legacy-resolver tflite-model-maker
pip install -q pycocotools
pip install -q opencv-python-headless==4.1.2.30
pip uninstall -y tensorflow && pip install -q tensorflow==2.8.0
`
The reason for the implementation was not defined in the docs.
Many people say that the issue was due to the compatibility version of the Colab.
If its due to the compatibility version of the colab, and If I tried to run the model on my local PC which has rtx 4060, will the model work normally without any issues (Im running the tensorflow==2.15.0 version)
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I_kwDOArmXAs5_EwNX
| 62,952 |
How to compare two image
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[
"@swapnilyadavmpsedc,\r\nThere are multiple options for the similar taks. One of them is extracting features from images using pre-trained models like **VGG16** or **Inceptionv3** and then comparing those features with metrics like cosine similarity or Euclidean distance.\r\n\r\nhttps://www.tensorflow.org/api_docs/python/tf/keras/applications/vgg16/VGG16\r\nhttps://www.tensorflow.org/api_docs/python/tf/keras/applications/inception_v3/InceptionV3\r\n\r\nAlso you can try training a **Siamese network** with image pairs and labels (similar/dissimilar). This approach learns complex similarity relationships. A Siamese Network is a type of network architecture that contains two or more identical subnetworks used to generate feature vectors for each input and compare them.\r\n\r\nhttps://keras.io/examples/vision/siamese_network/\r\n\r\nReference: https://www.tensorflow.org/hub/tutorials/tf_hub_delf_module\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/62952\">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/62952\">No</a>\n"
] | 2024-02-13T10:12:16 | 2024-02-28T01:47:03 | 2024-02-28T01:47:00 |
NONE
| null | null | null |
How to compare two image using tensorflow
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I_kwDOArmXAs5_EWSH
| 62,951 |
`tf.raw_ops.Dilation2DBackpropInput` aborts due to lack of `out_backprop` rank check
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[
"@sushreebarsa \r\nPlease check [it](https://colab.research.google.com/drive/1-MsYT05migfMHIRovgLAU78orUdnSyC8?usp=sharing).",
"@sachinprasadhs I was able to replicate [this](https://colab.research.google.com/gist/sushreebarsa/7821a0b8eea35ca280e87a7fd6a0ab9e/62951.ipynb) issue on colab, please have a look at this. \r\nThank you! ",
"Hi @Sehun0819 ,\r\n\r\nThanks for reporting along with debug.Added a fix in the attached PR."
] | 2024-02-13T09:16:31 | 2024-03-05T01:03:07 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.17
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04 LTS
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
6.5.0
### GCC/compiler version
clang 16
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`tf.raw_ops.Dilation2DBackpropInput` aborts due to lack of `out_backprop` rank check. `out_backprop.tensor<T, 4>()` aborts if input `out_backprop` is not a 4-dim tensor. Adding rank check of `out_backprop` in [`ParseSizes`](https://github.com/tensorflow/tensorflow/blob/8340d650e1c9d396b49900c8e374da87a3d24cea/tensorflow/core/kernels/dilation_ops.cc#L65) would works. If the rank is less than 4, it aborts [here](https://github.com/tensorflow/tensorflow/blob/8340d650e1c9d396b49900c8e374da87a3d24cea/tensorflow/core/kernels/dilation_ops.cc#L249). Note that same things happen on `tf.raw_ops.Dilation2DBackpropFilter`.
[Error Location](https://github.com/tensorflow/tensorflow/blob/8340d650e1c9d396b49900c8e374da87a3d24cea/tensorflow/core/kernels/dilation_ops.cc#L263-L267):
```C++
functor::DilationBackpropInput<Device, T>()(
context->eigen_device<Device>(), input.tensor<T, 4>(),
filter.tensor<T, 3>(), out_backprop.tensor<T, 4>(), stride_rows,
stride_cols, rate_rows, rate_cols, pad_top, pad_left,
in_backprop->tensor<T, 4>());
```
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
tf.raw_ops.Dilation2DBackpropInput(
input=tf.random.normal([1,1,1,1]),
filter=tf.random.normal([1,1,1]),
out_backprop=tf.random.normal([1,1,1,1,1]),
strides=[1,1,1,1],
rates=[1,1,1,1],
padding="VALID")
```
### Relevant log output
```shell
2024-02-13 18:06:49.413421: F tensorflow/core/framework/tensor_shape.cc:45] Check failed: NDIMS == dims() (4 vs. 5)Asking for tensor of 4 dimensions from a tensor of 5 dimensions
Aborted (core dumped)
```
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I_kwDOArmXAs5_EPld
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`tf.raw_ops.Conv2DBackpropInput` aborts due to lack of input check
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[
"@Sehun0819 Could you please let us know which TF version you are using ? I tried to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/53bc365aabf476ce3a6bdea5ec62e32d/62950.ipynb) but I faced `InvalidArgumentError` . Kindly check the gist and let us know?\r\nThank you!",
"@sushreebarsa\r\nHi!\r\nFirst, I'm using tf 2.17 as I mentioned.\r\n\r\nI think this issue is related to [intel code](https://github.com/tensorflow/tensorflow/blob/8340d650e1c9d396b49900c8e374da87a3d24cea/tensorflow/core/kernels/mkl/mkl_conv_ops.h#L643-L645) which lacks check of strides size. In my opinion the invocation in gist seems to execute [here](https://github.com/tensorflow/tensorflow/blob/8340d650e1c9d396b49900c8e374da87a3d24cea/tensorflow/core/kernels/conv_grad_input_ops.h#L294-L296) instead of MKL kernel, because of the environment of gist is not intel.\r\nI was able to get the same output when I ran the script with `TF_ENABLE_ONEDNN_OPTS=0`.\r\nSo, would you check the reproducibility in intel environment?",
"Hi @Sehun0819 ,\r\n\r\nThe issue is replicable by enabling TF_ENABLE_ONEDNN_OPTS = 1. Attached screenshot for reference.\r\n\r\n<img width=\"1502\" alt=\"Screenshot 2024-03-02\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/04f71d21-4cc3-4e99-b6e5-701214bb03b4\">\r\n",
"@Sehun0819,\r\nI tried to execute the mentioned code on both GPU and CPU with tf-nightly by enabling **TF_ENABLE_ONEDNN_OPTS = 1** and observed that it is not aborting as previous versions. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/7e20f38ae1a9506ac7c56c2eee4a3ba3/untitled1953.ipynb). Thank you!"
] | 2024-02-13T08:59:51 | 2024-06-12T10:46:46 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.17
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04 LTS
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
6.5.0
### GCC/compiler version
clang 16
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Calling `tf.raw_ops.Conv2DBackpropInput` aborts with wrong size of `strides` due to lack of check. Additional `OP_REQUIRES` which makes sure `stridees_.size() >= 4` would be required.
[Bug Location](https://github.com/tensorflow/tensorflow/blob/8340d650e1c9d396b49900c8e374da87a3d24cea/tensorflow/core/kernels/mkl/mkl_conv_ops.h#L643-L645):
```C++
OP_REQUIRES_OK(context, context->GetAttr("strides", &strides_));
int stride_n = GetTensorDim(strides_, data_format_, 'N');
int stride_c = GetTensorDim(strides_, data_format_, 'C');
```
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
tf.raw_ops.Conv2DBackpropInput(
input_sizes=[1,1,1,1],
filter=tf.random.normal([1,1,1,1]),
out_backprop=tf.random.normal([1,1,1,1]),
strides=[1,1,1],
padding="VALID",
dilations=[1,1,1,1])
```
### Relevant log output
```shell
2024-02-13 17:53:01.673661: F ./tensorflow/core/util/tensor_format.h:428] Check failed: index >= 0 && index < num_total_dims Invalid index from the dimension: 3, 0, C
Aborted (core dumped)
```
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PR_kwDOArmXAs5msyKJ
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Update setup.py, requirements.in and generate lock files
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"Please do not merge this yet. ",
"> Shouldn't you be using tensorboard 2.16.1?\r\n\r\nMy bad, I was reviewing the old commit. It is actually using 2.16.1"
] | 2024-02-13T01:58:15 | 2024-02-14T19:46:51 | 2024-02-14T19:46:51 |
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Update Keras and Tensorboard with latest version for 2.16.0-rc0
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Update Keras and Tensorboard with latest version for 2.16.0-rc0 in setup.py and requirements.in
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Update version numbers for TensorFlow 2.16.0-rc0
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Before merging this PR, please double check that it has correctly updated
`core/public/version.h`, `tools/pip_package/setup.py`, and
`tensorflow/tensorflow.bzl`. Also review the execution notes below:
```
Major: 2 -> 2
Minor: 16 -> 16
Patch: 0 -> 0
WARNING: Below are potentially instances of lingering old version string
"2.16.0" in source directory "tensorflow/" that are not updated by this script.
Please check them manually!
tensorflow/tools/pip_package/v2/setup.py:51:2.16.0
tensorflow/tools/pip_package/v2/setup.py:117:2.16.0
tensorflow/tools/pip_package/setup.py:51:2.16.0
tensorflow/tools/pip_package/setup.py:124:2.16.0
tensorflow/tensorflow.bzl:85:2.16.0
tensorflow/lite/tools/versioning/runtime_version.cc:146:2.16.0
tensorflow/lite/tools/versioning/runtime_version.cc:438:2.16.0
tensorflow/lite/tools/versioning/runtime_version.cc:439:2.16.0
tensorflow/lite/tools/versioning/runtime_version.cc:440:2.16.0
tensorflow/lite/tools/versioning/runtime_version.cc:441:2.16.0
tensorflow/lite/tools/versioning/runtime_version.cc:442:2.16.0
tensorflow/lite/tools/versioning/runtime_version.cc:443:2.16.0
tensorflow/lite/tools/versioning/runtime_version.cc:444:2.16.0
WARNING: Below are potentially instances of lingering old version string
"2.16.0" in source directory "tensorflow/" that are not updated by this script.
Please check them manually!
tensorflow/tools/pip_package/v2/setup.py:51:2.16.0
tensorflow/tools/pip_package/v2/setup.py:117:2.16.0
tensorflow/tools/pip_package/setup.py:51:2.16.0
tensorflow/tools/pip_package/setup.py:124:2.16.0
tensorflow/tensorflow.bzl:85:2.16.0
tensorflow/lite/tools/versioning/runtime_version.cc:146:2.16.0
tensorflow/lite/tools/versioning/runtime_version.cc:438:2.16.0
tensorflow/lite/tools/versioning/runtime_version.cc:439:2.16.0
tensorflow/lite/tools/versioning/runtime_version.cc:440:2.16.0
tensorflow/lite/tools/versioning/runtime_version.cc:441:2.16.0
tensorflow/lite/tools/versioning/runtime_version.cc:442:2.16.0
tensorflow/lite/tools/versioning/runtime_version.cc:443:2.16.0
tensorflow/lite/tools/versioning/runtime_version.cc:444:2.16.0
```
|
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PR_kwDOArmXAs5mqIi7
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Update release notes for TensorFlow 2.16.0
|
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|
This PR is intentionally incomplete. One of the Release Owners for 2.16.0
needs to fill in the internal release notes for this version before the PR gets
submitted. Click on the :pencil2: icon in the header for `RELEASE.md` under
"Files Changed" above.
|
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PR_kwDOArmXAs5mqBof
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Fuse Conv3D with binary ops and activation functions during tflite conversion
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[
"Hi @qukhan Can you please review this PR ? Thank you!",
"Hi @qukhan Can you please review this PR ? Thank you!",
"Hi @qukhan Can you please review this PR ? Thank you!"
] | 2024-02-12T16:41:14 | 2024-06-07T16:47:50 | null |
NONE
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The pull request adds optimization patterns to the tflite converter for fusing Conv3D operations with binary ops (Add, Sub) and activation functions.
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I_kwDOArmXAs5-8RHq
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Checksum Error during TensorFlow Lite Build for kissfft v130
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[
"@jyochichili Please double-check that the downloaded KissFFT-v130-RELEASE-X86_64.zip file is complete and hasn't been corrupted during download. Some file-hosting services might introduce errors. Kindly ensure your internet connection is stable and that no interruptions occurred during the download. We recommend you to kindly upgrade to the latest TF version as you are using an older version which is not actively supported.\r\nThank you!",
"Hi @sushreebarsa, \r\nThank you for the response.\r\nyou are right that the kissfft lib is not properly downloaded or corrupted, but I doubt if network issue is the culprit here, as the other libs (tf_lite_micro_person_data_grayscale_2020_05_27, ruy, gemmlowp) has no issue in downloading. I have also noticed that the in the case of failure, the incorrect checksum is always same, which I assume won't be the case when the file is corrupted. Also switching to the latest version won't help in my case as my models are only compatible to v2.6.2.\r\nCan you please help through this issue?\r\n\r\nThanks.\r\n\r\nPlease note the disk size of downloaded Kissftt lib(tensorflow-2.6.2/tensorflow/lite/micro/tools/make/downloads/kissfft) in the failure scenario is only 4K (original size 248K), so I can confirm that the file is not properly downloaded.",
"@jyochichili Sometimes, downloads can fail due to temporary network hiccups. Please retry downloading the kissfft library a few times. If possible, kindly change the download directory to a location with sufficient space. \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.",
"I have found that within tflite(v2.6.2), the URL for downloading the KISSFFT library doesn’t involve secure download(https), which seems to be causing an incomplete download of KISSFFT library. The issue doesn’t persist in the later versions, as the URL has been updated to use a secure https connection.\r\n\r\nThanks.",
"@jyochichili Thank you for the response!\r\nCould you please move this issue to closed status if it has been resolved?\r\nThank you!",
"This issue got fixed by upgrading to the later versions of tflite. Closing this issue.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62944\">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/62944\">No</a>\n"
] | 2024-02-12T09:31:29 | 2024-02-21T06:03:14 | 2024-02-21T06:03:11 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.6.2
### Custom code
No
### 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
_No response_
### GPU model and memory
_No response_
### Current behavior?
While building the application with Tensorflow v2.6.2 downloaded from source https://github.com/tensorflow/tensorflow/archive/v2.6.2.zip and with Tensorflow PYPI package(2.6.2) installed, I am getting checksum error while downloading kissfft library.
This issue is seen from past 8 days.
### Standalone code to reproduce the issue
```shell
Try downloading and verifying the kissfft installation,
downloading http://mirror.tensorflow.org/github.com/mborgerding/kissfft/archive/v130.zip
```
### Relevant log output
```shell
downloading http://mirror.tensorflow.org/github.com/mborgerding/kissfft/archive/v130.zip
+ [[ 0 -eq 0 ]]
+ break
++ openssl dgst -md5 /tmp/tmp.H0LiyFKNBY/temp_file
++ sed 's/.* //g'
Checksum error for 'http://mirror.tensorflow.org/github.com/mborgerding/kissfft/archive/v130.zip'. Expected 438ba1fef5783cc5f5f201395cc477ca but found d41d8cd98f00b204e9800998ecf8427e
+ DOWNLOADED_MD5=d41d8cd98f00b204e9800998ecf8427e
+ '[' 438ba1fef5783cc5f5f201395cc477ca '!=' d41d8cd98f00b204e9800998ecf8427e ']'
+ echo 'Checksum error for '\''http://mirror.tensorflow.org/github.com/mborgerding/kissfft/archive/v130.zip'\''. Expected 438ba1fef5783cc5f5f201395cc477ca but found d41d8cd98f00b204e9800998ecf8427e'
+ exit 1
```
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PR_kwDOArmXAs5mmLNT
| 62,943 |
🐛 fix undefined symbols (TensorFlowLiteCMetal)
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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/62943/checks?check_run_id=21462858286) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request."
] | 2024-02-12T04:34:21 | 2024-02-19T11:12:00 | 2024-02-19T11:12:00 |
CONTRIBUTOR
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I build `TensorFlowLiteCMetal`, and use it in my project.
The C function `TFLGpuDelegateOptionsDefault` which is exported in header file, but strip from framework.
https://github.com/tensorflow/tensorflow/blob/5297f6d8a67750f2bde7f5483526998f00ec15fe/tensorflow/lite/delegates/gpu/metal_delegate.h#L55C1-L59C81
<img width="835" alt="Screen-20240212@2x" src="https://github.com/tensorflow/tensorflow/assets/33711476/b3279f38-7108-4c65-aef2-b2938e5bb8e1">
After update `tensorflow/lite/iOS/allowlist_TensorFlowLiteCMetal.txt`. This symbol can be resolved correctly
|
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I_kwDOArmXAs5-7Bmq
| 62,942 |
TFlite Model Maker installation issue with Python 3.10 in Colab.
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[
"@Venkat6871 Can you please tell me if Googl's team has resolved this issue since it has been from a long time in colab environment. ",
"Hi @dsbyprateekg ,\r\n\r\nThe Google team is working on the tflite-model-maker issue and it will take time to resolve, in the mean time please try using mediapipe-model-maker instead: here is an example [gist](https://colab.sandbox.google.com/gist/pkgoogle/93fb7581fab1ea14728c61adf584ca13/media_pipe_example.ipynb). Let us know if for some reason you can't use mediapipe model maker to accomplish your goals.\r\n\r\nThank You",
"Thanks @LakshmiKalaKadali , for now, I can proceed with mediapipe.",
"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/62942\">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/62942\">No</a>\n",
"Hi,\r\nWhat alternatives are available to complete this example with BERT and tflite_model_maker?, since it seems to only work with Python version 3.9\r\nhttps://www.tensorflow.org/lite/models/modify/model_maker/question_answer"
] | 2024-02-12T04:30:09 | 2024-04-24T20:45:30 | 2024-02-12T11:26:56 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15.0
### Custom code
No
### OS platform and distribution
Ubuntu 22.04.03 LTS
### Mobile device
_No response_
### Python version
3.10.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
T4
### Current behavior?
Installing tflite model maker using following commands-
!pip install -q tflite-model-maker
!pip install -q pycocotools
!pip install -q ipykernel
!pip install -q numpy<1.23.4,>=1.17.3
Error-
`ERROR: Cannot install tflite-model-maker==0.1.2, tflite-model-maker==0.2.0, tflite-model-maker==0.2.1, tflite-model-maker==0.2.2, tflite-model-maker==0.2.3, tflite-model-maker==0.2.4, tflite-model-maker==0.2.5, tflite-model-maker==0.3.3, tflite-model-maker==0.3.4, tflite-model-maker==0.4.0, tflite-model-maker==0.4.1, tflite-model-maker==0.4.2 and tflite-model-maker==0.4.3 because these package versions have conflicting dependencies.
ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.6/1.6 MB 12.1 MB/s eta 0:00:00
/bin/bash: line 1: 1.23.4,: No such file or directory`
### Standalone code to reproduce the issue
```shell
!pip install -q tflite-model-maker
!pip install -q pycocotools
!pip install -q ipykernel
!pip install -q numpy<1.23.4,>=1.17.3
```
### Relevant log output
```shell
logs-
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Preparing metadata (setup.py) ... done
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Preparing metadata (setup.py) ... done
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Preparing metadata (setup.py) ... done
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ERROR: Cannot install tflite-model-maker==0.1.2, tflite-model-maker==0.2.0, tflite-model-maker==0.2.1, tflite-model-maker==0.2.2, tflite-model-maker==0.2.3, tflite-model-maker==0.2.4, tflite-model-maker==0.2.5, tflite-model-maker==0.3.3, tflite-model-maker==0.3.4, tflite-model-maker==0.4.0, tflite-model-maker==0.4.1, tflite-model-maker==0.4.2 and tflite-model-maker==0.4.3 because these package versions have conflicting dependencies.
ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts
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/bin/bash: line 1: 1.23.4,: No such file or directory
```
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[Bug] compiling the tf lite benchmark tool fails on macos (CMake)
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[
"Building just the tf lite binary works\r\n``` bash\r\ncmake --build . -j\r\n```",
"``` bash\r\ncmake ../tensorflow/lite/c\r\ncmake --build . -j\r\n```\r\n\r\nalso works",
"Hi @CaptainDario,\r\n\r\nI was able to replicate with your exact steps and cmake. I also tried with bazel and that seemed to work fine:\r\n```\r\nbazel build tensorflow/lite/tools/benchmark:benchmark_model\r\n```\r\n\r\nHi @terryheo, can you please take a look for the cmake flow? Thanks.",
"@terryheo yes, the bazel build works."
] | 2024-02-11T21:24:15 | 2024-02-14T09:56:51 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15
### Custom code
No
### OS platform and distribution
MacOS 13.5.2
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
Apple clang version 15.0.0 (clang-1500.1.0.2.5) Target: arm64-apple-darwin22.6.0 Thread model: posix InstalledDir: /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I am trying to compile the tf lite benchmark tool on MacOS.
For this I ran the following commands on tf 2.15 and master
```bash
git clone https://github.com/tensorflow/tensorflow.git tensorflow
mkdir tflite_build
cd tflite_build
cmake ../tensorflow/tensorflow/lite
cmake --build . -j -t benchmark_model
```
This crashes with this error
```bash
[ 95%] Linking CXX executable benchmark_model
ld: Undefined symbols:
_TfLiteCoreMlDelegateCreate, referenced from:
tflite::evaluation::CreateCoreMlDelegate() in utils.cc.o
_TfLiteCoreMlDelegateDelete, referenced from:
tflite::evaluation::CreateCoreMlDelegate() in utils.cc.o
clang: error: linker command failed with exit code 1 (use -v to see invocation)
make[3]: *** [tools/benchmark/benchmark_model] Error 1
make[2]: *** [tools/benchmark/CMakeFiles/benchmark_model.dir/all] Error 2
make[1]: *** [tools/benchmark/CMakeFiles/benchmark_model.dir/rule] Error 2
make: *** [benchmark_model] Error 2
```
I would expect the benchmark to be compiled successfully.
### Standalone code to reproduce the issue
```shell
Look above
```
### Relevant log output
```shell
cmake --build . -j -t benchmark_model
[ 0%] Built target fft2d_fftsg
[ 0%] Built target microkernel-utils
[ 0%] Built target pthreadpool
[ 0%] Built target ruy_system_aligned_alloc
[ 0%] Built target ruy_have_built_path_for_avx512
[ 0%] Built target ruy_have_built_path_for_avx2_fma
[ 0%] Built target absl_flags_commandlineflag_internal
[ 4%] Built target ruy_have_built_path_for_avx
[ 4%] Built target absl_spinlock_wait
[ 4%] Built target ruy_profiler_instrumentation
[ 4%] Built target cpuinfo
[ 4%] Built target ruy_denormal
[ 4%] Built target absl_exponential_biased
[ 4%] Built target eight_bit_int_gemm
[ 4%] Built target ruy_wait
[ 4%] Built target farmhash
[ 4%] Built target absl_civil_time
[ 4%] Built target absl_log_severity
[ 4%] Built target absl_int128
[ 4%] Built target ruy_apply_multiplier
[ 9%] Built target indirection
[ 9%] Built target fft2d_fftsg2d
[ 9%] Built target normalization
[ 9%] Built target absl_strerror
[ 14%] Built target logging
[ 14%] Built target packing
[ 14%] Built target allocator
[ 14%] Built target microparams-init
[ 14%] Built target flatbuffers
[ 42%] Built target microkernels-prod
[ 42%] Built target ruy_cpuinfo
[ 42%] Built target ruy_allocator
[ 42%] Built target absl_raw_logging_internal
[ 42%] Built target ruy_block_map
[ 42%] Built target absl_time_zone
[ 42%] Built target ruy_prepacked_cache
[ 42%] Built target memory
[ 42%] Built target ruy_blocking_counter
[ 42%] Built target hardware-config
[ 42%] Built target mutex
[ 42%] Built target operator-run
[ 42%] Built target operator-utils
[ 42%] Built target post-operation
[ 42%] Built target cache
[ 42%] Built target ruy_tune
[ 42%] Built target absl_bad_variant_access
[ 42%] Built target absl_debugging_internal
[ 42%] Built target absl_bad_optional_access
[ 42%] Built target absl_throw_delegate
[ 42%] Built target absl_cordz_functions
[ 42%] Built target ruy_thread_pool
[ 42%] Built target absl_base
[ 42%] Built target ruy_pack_arm
[ 42%] Built target absl_stacktrace
[ 42%] Built target ruy_kernel_avx512
[ 42%] Built target ruy_pack_avx2_fma
[ 42%] Built target ruy_kernel_avx
[ 42%] Built target ruy_pack_avx512
[ 42%] Built target ruy_kernel_arm
[ 47%] Built target operators
[ 47%] Built target ruy_pack_avx
[ 47%] Built target ruy_ctx
[ 47%] Built target absl_crc_cpu_detect
[ 47%] Built target absl_city
[ 52%] Built target absl_low_level_hash
[ 52%] Built target ruy_kernel_avx2_fma
[ 52%] Built target absl_demangle_internal
[ 52%] Built target absl_strings_internal
[ 52%] Built target absl_malloc_internal
[ 52%] Built target ruy_context
[ 52%] Built target ruy_trmul
[ 52%] Built target ruy_prepare_packed_matrices
[ 52%] Built target absl_graphcycles_internal
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[ 57%] Built target subgraph
[ 57%] Built target ruy_context_get_ctx
[ 57%] Built target ruy_frontend
[ 61%] Built target absl_strings
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[ 61%] Built target absl_flags_commandlineflag
[ 61%] Built target absl_hash
[ 61%] Built target absl_crc32c
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[ 61%] Built target XNNPACK
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[ 61%] Built target absl_flags_private_handle_accessor
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[ 66%] Built target absl_flags_marshalling
[ 66%] Built target absl_synchronization
[ 66%] Built target absl_cord_internal
[ 66%] Built target absl_flags_program_name
[ 66%] Built target absl_cordz_handle
[ 66%] Built target absl_hashtablez_sampler
[ 66%] Built target absl_cordz_info
[ 66%] Built target absl_raw_hash_set
[ 66%] Built target absl_flags_config
[ 66%] Built target absl_flags_internal
[ 71%] Built target absl_cord
[ 71%] Built target absl_flags_reflection
[ 71%] Built target absl_status
[ 71%] Built target absl_flags
[ 95%] Built target tensorflow-lite
[ 95%] Linking CXX executable benchmark_model
ld: Undefined symbols:
_TfLiteCoreMlDelegateCreate, referenced from:
tflite::evaluation::CreateCoreMlDelegate() in utils.cc.o
_TfLiteCoreMlDelegateDelete, referenced from:
tflite::evaluation::CreateCoreMlDelegate() in utils.cc.o
clang: error: linker command failed with exit code 1 (use -v to see invocation)
make[3]: *** [tools/benchmark/benchmark_model] Error 1
make[2]: *** [tools/benchmark/CMakeFiles/benchmark_model.dir/all] Error 2
make[1]: *** [tools/benchmark/CMakeFiles/benchmark_model.dir/rule] Error 2
make: *** [benchmark_model] Error 2
```
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PR_kwDOArmXAs5mk7Zs
| 62,940 |
fixed typo
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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/62940/checks?check_run_id=21451947074) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.",
"Hi @mahdiaslanimk It looks like your PR relates to the Keras component. Please submit it to the github.com/keras-team/keras repository instead. Thank you!\r\n@mihaimaruseac, @fchollet, @qlzh727 ",
"Right, I miss that part, sorry about it. Will remove approval",
"Hi @mahdiaslanimk As mentioned above comments, please submit the PR to the github.com/keras-team/keras repository instead. Thank you!\r\nCc @fchollet, @qlzh727\r\n\r\nThank you @mihaimaruseac. "
] | 2024-02-11T13:55:40 | 2024-02-20T05:22:00 | 2024-02-20T05:21:56 |
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PR_kwDOArmXAs5mkiyK
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testing forking and cloning
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"thanks"
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I_kwDOArmXAs5-4lFz
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Question: Why no GPU support on Windows Native?
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[
"@aaronsuydam The introduction of Windows Subsystem for Linux 2 (WSL 2) provided a more efficient and consistent platform for GPU acceleration on Windows, leveraging established Linux builds. This delivers full access to GPU-enabled builds and superior performance.\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 Hey! Sorry for the late response here. I am trying to develop a Windows app that leverages some functionality that has been implemented using tensor flow. Will the lack of GPU support impact that? As in, can a windows app that has some backend functionality that uses TF gpu stuff work on a system without wsl installed? I don't want users to have to install wsl to get that benefit, maybe i can find a workaround if that's the case? This is also just me being curious, I'm a college student, so this is really just a hobby for me. No stress either way.",
"@aaronsuydam If you don't want to use WSL2 then there are a few alternatives. If your app's functionality can be achieved with TensorFlow 2.10 or older, you can utilize the native GPU support offered by those versions. However, this might limit access to newer features and optimizations. \r\nMicrosoft released the DirectML Plugin for TensorFlow, enabling limited GPU acceleration via Microsoft's DirectML hardware acceleration library. \r\nThank you!",
"@sushreebarsa thanks for the quick reply. I looked into the DirectML plugin, and it looks like they paused development in favor of stuff with the ONNX Runtime.\r\n\r\nFrom their repo: \"⚠️ Development of TensorFlow-DirectML-Plugin has been paused until further notice. To take advantage of the latest DirectML features and performance improvements for inference scenarios, we recommend taking a look at [ONNX Runtime](https://github.com/microsoft/onnxruntime). ⚠️\"\r\n\r\nAre there plans to support feature integration with this new direction they are taking?",
"@aaronsuydam You are right! Microsoft paused development of the TensorFlow-DirectML-Plugin in October 2023 in favor of focusing on ONNX Runtime for inference scenarios with DirectML. Currently, there's no concrete information about potential feature integration between these two directions. Please stay informed about future developments. \r\nThank you!",
"Will do! Thank you for the information!",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62938\">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/62938\">No</a>\n"
] | 2024-02-11T00:03:56 | 2024-02-22T15:51:11 | 2024-02-22T15:51:08 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Windows 11
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
GTX 1660Ti
### Current behavior?
Hey there! This is just me trying to ask a question, I did some googling to try and figure it out, but I was just wondering why support for CUDA acceleration was dropped for TF > 2.10 on windows native installations? I'd be curious just learning what would be needed to manually work around that (I'm a CpE major, would be a fun activity i think)? I may be way out of my league, but I am curious!
### Standalone code to reproduce the issue
```shell
--
```
### Relevant log output
```shell
--
```
|
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New Features for TFLite Delegates accuracy and correctness tools
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The new features are:
- **Option to use reference and test model for the evaluation tools.** This supports the cases, where the delegate does not perform inline model compilation for target compute IP but requires model to be preprocessed in advance. For instance the workflow with Ethos-U NPU - the model is converted by Arm Vela tool, which replaces part of the compute graph with custom nodes recognized by the particular delegate. To evaluate the accuracy in this case one needs to supply both the reference and converted (test) model. The reference to be used with reference inference and test model with delegate.
- **Add support for int32 input/output**
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Fix native Flatbuffers build
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Reflect the rename of FindFlatbuffers.cmake file introduced in d8f98dd to native_tools/flatbuffers/CMakeLists.txt.
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Enable the simultaneous use of Flex delegate and other delegate
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"Can you add tests in subgraph_test to show what this fixes?",
"Hi @robert-kalmar This PR is in draft, any update on this? Please. Thank you!\r\n",
"Hi @gbaned , \r\nI put the PR into the draft due to @qukhan request for the test case. We have a test case with one of our external delegate, however still investigating how to test it in isolation in TFLite. \r\n\r\n",
"Hi @robert-kalmar This PR is in draft, any update on this? Please. Thank you!",
"Hi @robert-kalmar Any update on this PR? Please. Thank you!"
] | 2024-02-10T20:29:08 | 2024-06-07T16:47:27 | null |
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Fixes problem when Flex Delegate and other (e.g. external) delegate is used.
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Enable external delegate for the label_image example
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Add option to build shared library with cmake
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"Hi @robert-kalmar Can you please rebase your branch and resolve the conflicts? Thank you!"
] | 2024-02-10T20:06:45 | 2024-06-06T07:49:39 | null |
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The shared library build correspond to the build rules in bazel for shared library.
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Add evaluation tools to cmake build
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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/62932/checks?check_run_id=21440532536) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.",
"Hi @robert-kalmar Can you please sign CLA. Thank you!",
"Hi @gbaned, can you help to identify what is wrong with the CLA? Based on the report Irina's CLA is ok, and my is missing. \r\n\r\nWhat is strange, as I have the same corporate CLA, and it got verified fine e.g. on PR here [https://github.com/tensorflow/tensorflow/pull/62933](https://github.com/tensorflow/tensorflow/pull/62933) ",
"Hi @robert-kalmar Sorry for the delay in response. Can you please make sure to use same GitHub username and email-id associated with it. Thank you!\r\n",
"Hi @robert-kalmar I'm going to go ahead and close this PR, because it seems to have stalled. If you're still interested in pursing this (and responding to my comments), please feel free to reopen! Thank you for your contribution!"
] | 2024-02-10T19:53:19 | 2024-04-26T10:31:03 | 2024-04-26T10:31:03 |
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The pull request extends the CMake with option to build the tools for accuracy and correctness evaluation of the delegates (https://www.tensorflow.org/lite/performance/delegates#accuracy_correctness). Supports both native and cross-compilation scenario.
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Update eigen_backward_spatial_convolutions.h
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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/62931/checks?check_run_id=21413642480) 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.",
"This code doesn't actually compile with the latest version of tensorflow - there's some kind of type mismatch that causes the convolution kernels to fail to build.",
"If it doesn't even build, not really point in the pull request being active. It was more of an idea on how to speed up the \"meat\" of the implementation."
] | 2024-02-09T16:08:57 | 2024-02-16T16:26:13 | 2024-02-16T16:22:56 |
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Replaced backward spatial convolution with faster and a bit more streamlined implementation based on TF 1.13.1, with some minor changes. Really shines at larger input/output Tensor sizes (128x128 and larger, batch agnostic) with at least 8 input and 8 output channels. It is upto 30% faster than the current method with 64 input/output channels, or 25% faster with 32 in_ch/out_ch .
It is sadly 2x slower on small number of channels (4 in_ch/out_ch and smaller) on my machine. Tested on Clang 16.
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Source build: error loading package '@local_config_nccl//'
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[
"@adamjstewart Please ensure NCCL is correctly installed and accessible on your system. This error suggests the @local_config_nccl// package isn't loaded, which often happens if NCCL libraries are missing or not configured properly.\r\nThank you!",
"It should be. We're setting:\r\n```bash\r\nexport NCCL_HDR_PATH=<prefix>/include\r\nexport NCCL_INSTALL_PATH=<prefix>\r\nexport TF_NCCL_VERSION=2\r\n```\r\nNot sure how to debug whether or not NCCL is configured properly, but the PyTorch build succeeds with the same NCCL installation. Any other env vars we should set to help Bazel find NCCL?",
"@adamjstewart You might need to set environment variables like NCCL_HOME or NCCL_LIBRARY_DIR to point to the NCCL installation directory. Thank you!",
"Unfortunately this didn't help, the outcome is the same:\r\n\r\n* [build log](https://github.com/tensorflow/tensorflow/files/14261254/spack-build-out.txt)\r\n* [build env](https://github.com/tensorflow/tensorflow/files/14261256/spack-build-env-mods.txt)\r\n\r\nBased on the build log, it looks like NCCL is correctly detected (both before and after adding these new env vars).",
"This seems to have been broken in commit a089c4b2777dcd2f88f55229b920102f10264427, reverting it seems to fix at least this problem.",
"I don't work on TensorFlow, please don't assign TensorFlow issues to me.",
"I'll also note that commit is from October (it's far too late to revert it) and essential for JAX, so someone from the TF team will need to figure out what's going on. We can't revert it without breaking JAX releases.",
"> @adamjstewart Please ensure NCCL is correctly installed and accessible on your system. This error suggests the @local_config_nccl// package isn't loaded, which often happens if NCCL libraries are missing or not configured properly. Thank you!\r\n\r\nI ran into same error yesterday. I'm using NCCL 2.20.3-1 installed using local deb repo for Ubuntu 22.04 according to the very simple instructions posted on NVIDIA website. \r\n\r\n```\r\ndpkg -i nccl-local-repo-ubuntu2204-2.20.3-cuda12.3_1.0-1_amd64.deb\r\napt install libnccl2=2.20.3-1+cuda12.3 libnccl-dev=2.20.3-1+cuda12.3\r\n```\r\n\r\n",
"Building a newer version, or with newer CUDA, seems to do the trick!",
"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/62930\">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/62930\">No</a>\n"
] | 2024-02-09T13:41:07 | 2024-03-11T12:59:45 | 2024-03-11T12:55:12 |
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
Linux Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.11.6
### Bazel version
6.1.0
### GCC/compiler version
GCC 11.4.0
### CUDA/cuDNN version
CUDA 11.8.0, cuDNN 8.9.7.29-11
### GPU model and memory
CUDA arch 80
### Current behavior?
When I try to build TF 2.15 with CUDA support, Bazel crashes with the following error message:
```
ERROR: /tmp/root/spack-stage/spack-stage-py-tensorflow-2.15.0-quhwfp323rasnmbciyohvdpehay7zlxo/spack-src/tensorflow/tools/pip_package/BUILD:203:10: error loading package '@local_config_nccl//': Label '//tensorflow/platform/default:cuda_build_defs.bzl' is invalid because 'tensorflow/platform/default' is not a package; perhaps you meant to put the colon here: '//tensorflow:platform/default/cuda_build_defs.bzl'? and referenced by '//tensorflow/tools/pip_package:licenses'
ERROR: Analysis of target '//tensorflow/tools/pip_package:build_pip_package' failed; build aborted:
```
This issue does not occur when I build for CPU or ROCm.
### Standalone code to reproduce the issue
```shell
git clone https://github.com/adamjstewart/spack.git
git switch packages/tf
. spack/share/spack/setup-env.sh
spack install py-tensorflow+cuda cuda_arch=80
```
### Relevant log output
* [build log](https://github.com/tensorflow/tensorflow/files/14222381/spack-build-out.txt)
* [build env](https://github.com/tensorflow/tensorflow/files/14222380/spack-build-env-mods.txt)
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An error in the official pruning guide
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"import tensorflow as tf\r\nimport tempfile\r\nfrom tensorflow import keras\r\n\r\n# Load MNIST dataset\r\nmnist = keras.datasets.mnist\r\n(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\r\n\r\n# Normalize the input image so that each pixel value is between 0 and 1\r\ntrain_images = train_images / 255.0\r\ntest_images = test_images / 255.0\r\n\r\n# Define the model architecture\r\nmodel = keras.Sequential([\r\n keras.layers.InputLayer(input_shape=(28, 28)),\r\n keras.layers.Reshape(target_shape=(28, 28, 1)),\r\n keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation='relu'),\r\n keras.layers.MaxPooling2D(pool_size=(2, 2)),\r\n keras.layers.Flatten(),\r\n keras.layers.Dense(10)\r\n])\r\n\r\n# Compile the model\r\nmodel.compile(optimizer='adam',\r\n loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\r\n metrics=['accuracy'])\r\n\r\n# Train the model\r\nmodel.fit(train_images, train_labels, epochs=4, validation_split=0.1)\r\n\r\n# Evaluate baseline test accuracy and save the model for later usage\r\n_, baseline_model_accuracy = model.evaluate(test_images, test_labels, verbose=0)\r\nprint('Baseline test accuracy:', baseline_model_accuracy)\r\n\r\n# Define the pruning parameters\r\npruning_params = {\r\n 'pruning_schedule': tfmot.sparsity.keras.PolynomialDecay(initial_sparsity=0.50,\r\n final_sparsity=0.80,\r\n begin_step=0,\r\n end_step=100)\r\n}\r\n\r\n# Apply pruning to the model\r\nmodel_for_pruning = tfmot.sparsity.keras.prune_low_magnitude(model, **pruning_params)\r\n\r\n# Compile the pruned model\r\nmodel_for_pruning.compile(optimizer='adam',\r\n loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\r\n metrics=['accuracy'])\r\n\r\n# Fine-tune the pruned model\r\nmodel_for_pruning.fit(train_images, train_labels, epochs=2, validation_split=0.1)\r\n\r\n# Evaluate pruned model accuracy\r\n_, pruned_model_accuracy = model_for_pruning.evaluate(test_images, test_labels, verbose=0)\r\nprint('Pruned model test accuracy:', pruned_model_accuracy)\r\n\r\n# Save the pruned model\r\n_, keras_file = tempfile.mkstemp('.h5')\r\ntf.keras.models.save_model(model_for_pruning, keras_file, include_optimizer=False)\r\nprint(f\"Model saved at {keras_file}\")\r\n",
"`import tensorflow as tf\r\nimport tempfile\r\nfrom tensorflow import keras\r\n\r\n# Load MNIST dataset\r\nmnist = keras.datasets.mnist\r\n(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\r\n\r\n# Normalize the input image so that each pixel value is between 0 and 1\r\ntrain_images = train_images / 255.0\r\ntest_images = test_images / 255.0\r\n\r\n# Define the model architecture\r\nmodel = keras.Sequential([\r\n keras.layers.InputLayer(input_shape=(28, 28)),\r\n keras.layers.Reshape(target_shape=(28, 28, 1)),\r\n keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation='relu'),\r\n keras.layers.MaxPooling2D(pool_size=(2, 2)),\r\n keras.layers.Flatten(),\r\n keras.layers.Dense(10)\r\n])\r\n\r\n# Compile the model\r\nmodel.compile(optimizer='adam',\r\n loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\r\n metrics=['accuracy'])\r\n\r\n# Train the model\r\nmodel.fit(train_images, train_labels, epochs=4, validation_split=0.1)\r\n\r\n# Evaluate baseline test accuracy and save the model for later usage\r\n_, baseline_model_accuracy = model.evaluate(test_images, test_labels, verbose=0)\r\nprint('Baseline test accuracy:', baseline_model_accuracy)\r\n\r\n# Define the pruning parameters\r\npruning_params = {\r\n 'pruning_schedule': tfmot.sparsity.keras.PolynomialDecay(initial_sparsity=0.50,\r\n final_sparsity=0.80,\r\n begin_step=0,\r\n end_step=100)\r\n}\r\n\r\n# Apply pruning to the model\r\nmodel_for_pruning = tfmot.sparsity.keras.prune_low_magnitude(model, **pruning_params)\r\n\r\n# Compile the pruned model\r\nmodel_for_pruning.compile(optimizer='adam',\r\n loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\r\n metrics=['accuracy'])\r\n\r\n# Fine-tune the pruned model\r\nmodel_for_pruning.fit(train_images, train_labels, epochs=2, validation_split=0.1)\r\n\r\n# Evaluate pruned model accuracy\r\n_, pruned_model_accuracy = model_for_pruning.evaluate(test_images, test_labels, verbose=0)\r\nprint('Pruned model test accuracy:', pruned_model_accuracy)\r\n\r\n# Save the pruned model\r\n_, keras_file = tempfile.mkstemp('.h5')\r\ntf.keras.models.save_model(model_for_pruning, keras_file, include_optimizer=False)\r\nprint(f\"Model saved at {keras_file}\")\r\n`",
"const tf = ***@***.***/tfjs-node');\r\n\r\n// Load MNIST dataset\r\nconst mnist = tf.data.mnist;\r\nconst { trainImages, trainLabels, testImages, testLabels } = mnist.getData();\r\n\r\n// Normalize the input image so that each pixel value is between 0 and 1\r\nconst normalizedTrainImages = trainImages.div(255);\r\nconst normalizedTestImages = testImages.div(255);\r\n\r\n// Define the model architecture\r\nconst model = tf.sequential();\r\nmodel.add(tf.layers.inputLayer({ inputShape: [28, 28] }));\r\nmodel.add(tf.layers.reshape({ targetShape: [28, 28, 1] }));\r\nmodel.add(tf.layers.conv2d({ filters: 12, kernelSize: [3, 3], activation: 'relu' }));\r\nmodel.add(tf.layers.maxPooling2d({ poolSize: [2, 2] }));\r\nmodel.add(tf.layers.flatten());\r\nmodel.add(tf.layers.dense({ units: 10 }));\r\n\r\n// Compile the model\r\nmodel.compile({\r\n optimizer: 'adam',\r\n loss: 'sparseCategoricalCrossentropy',\r\n metrics: ['accuracy'],\r\n});\r\n\r\n// Train the model\r\nawait model.fit(normalizedTrainImages, trainLabels, { epochs: 4, validationSplit: 0.1 });\r\n\r\n// Evaluate baseline test accuracy\r\nconst baselineModelAccuracy = await model.evaluate(normalizedTestImages, testLabels);\r\nconsole.log('Baseline test accuracy:', baselineModelAccuracy[1]);\r\n\r\n// Define the pruning parameters\r\nconst pruningParams = {\r\n pruningSchedule: tfmot.sparsity.polynomialDecay({\r\n initialSparsity: 0.50,\r\n finalSparsity: 0.80,\r\n beginStep: 0,\r\n endStep: 100,\r\n }),\r\n};\r\n\r\n// Apply pruning to the model\r\nconst prunedModel = tfmot.sparsity.pruneLowMagnitude(model, pruningParams);\r\n\r\n// Compile the pruned model\r\nprunedModel.compile({\r\n optimizer: 'adam',\r\n loss: 'sparseCategoricalCrossentropy',\r\n metrics: ['accuracy'],\r\n});\r\n\r\n// Fine-tune the pruned model\r\nawait prunedModel.fit(normalizedTrainImages, trainLabels, { epochs: 2, validationSplit: 0.1 });\r\n\r\n// Evaluate pruned model accuracy\r\nconst prunedModelAccuracy = await prunedModel.evaluate(normalizedTestImages, testLabels);\r\nconsole.log('Pruned model test accuracy:', prunedModelAccuracy[1]);\r\n\r\n// Save the pruned model\r\nawait prunedModel.save('file://path/to/your/model');\r\nconsole.log('Pruned model saved.');\r\n\r\nREGARDS,\r\nKRISHNA MISHRA\r\nJANAKPUR-2,NEPAL\r\nE-MAIL:- ***@***.***\r\n________________________________\r\nFrom: Santabot123 ***@***.***>\r\nSent: Friday, February 9, 2024 6:59 PM\r\nTo: tensorflow/tensorflow ***@***.***>\r\nCc: Subscribed ***@***.***>\r\nSubject: [tensorflow/tensorflow] An error in the official pruning guide (Issue #62929)\r\n\r\n\r\nIssue type\r\n\r\nBug\r\n\r\nHave you reproduced the bug with TensorFlow Nightly?\r\n\r\nNo\r\n\r\nSource\r\n\r\nsource\r\n\r\nTensorFlow version\r\n\r\n2.15\r\n\r\nCustom code\r\n\r\nNo\r\n\r\nOS platform and distribution\r\n\r\nGoogle Colab\r\n\r\nMobile device\r\n\r\nNo response\r\n\r\nPython version\r\n\r\nNo response\r\n\r\nBazel version\r\n\r\nNo response\r\n\r\nGCC/compiler version\r\n\r\nNo response\r\n\r\nCUDA/cuDNN version\r\n\r\nNo response\r\n\r\nGPU model and memory\r\n\r\nNo response\r\n\r\nCurrent behavior?\r\n\r\nI followed this official pruning guide: https://www.tensorflow.org/model_optimization/guide/pruning/pruning_with_keras\r\n\r\n\r\nI opened code in google colab (https://colab.research.google.com/github/tensorflow/model-optimization/blob/master/tensorflow_model_optimization/g3doc/guide/pruning/pruning_with_keras.ipynb) and ran all cells..\r\n\r\n\r\nAnd there is an ValueError: `prune_low_magnitude` can only prune an object of the following types: keras.models.Sequential, keras functional model, keras.layers.Layer, list of keras.layers.Layer. You passed an object of type: Sequential. in line model_for_pruning = prune_low_magnitude(model, **pruning_params)\r\n\r\n\r\nI tried this code on my local machine and it gives the same error.\r\nThis looks like an bug.\r\n\r\nStandalone code to reproduce the issue\r\n\r\nCode from offical guide: https://colab.research.google.com/github/tensorflow/model-optimization/blob/master/tensorflow_model_optimization/g3doc/guide/pruning/pruning_with_keras.ipynb\r\n\r\nRelevant log output\r\n\r\nNo response\r\n\r\n—\r\nReply to this email directly, view it on GitHub<https://github.com/tensorflow/tensorflow/issues/62929>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AZCZ53ZDUVNIQNPWNCCHJYLYSYOMRAVCNFSM6AAAAABDBODQVKVHI2DSMVQWIX3LMV43ASLTON2WKOZSGEZDOMJRHA3TANI>.\r\nYou are receiving this because you are subscribed to this thread.Message ID: ***@***.***>\r\n",
"I solved this problem with: <br>\r\n```! pip install tf-keras ```<br>\r\nand replace ```from tensorflow import keras ``` with ```import tf_keras as keras```",
"@Santabot123,\r\nI tried to execute the mentioned official document with the **tensorflow v2.15, keras v3.0 and the tensorflow-model-optimization - 0.8.0** and it was executed without any issue/error. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/0eaea793a5186842e8aa4d3b2bd6bfa3/pruning_with_keras.ipynb). 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/62929\">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/62929\">No</a>\n"
] | 2024-02-09T13:06:32 | 2024-02-28T01:47:05 | 2024-02-28T01:47:02 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15
### Custom code
No
### OS platform and distribution
Google Colab
### 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 followed this official pruning guide: https://www.tensorflow.org/model_optimization/guide/pruning/pruning_with_keras
<br>
I opened code in google colab (https://colab.research.google.com/github/tensorflow/model-optimization/blob/master/tensorflow_model_optimization/g3doc/guide/pruning/pruning_with_keras.ipynb) and ran all cells..
<br>
And there is an ```ValueError: `prune_low_magnitude` can only prune an object of the following types: keras.models.Sequential, keras functional model, keras.layers.Layer, list of keras.layers.Layer. You passed an object of type: Sequential.``` in line ```model_for_pruning = prune_low_magnitude(model, **pruning_params)```
<br>
I tried this code on my local machine and it gives the same error.
This looks like an bug.
### Standalone code to reproduce the issue
```shell
Code from offical guide: https://colab.research.google.com/github/tensorflow/model-optimization/blob/master/tensorflow_model_optimization/g3doc/guide/pruning/pruning_with_keras.ipynb
```
### Relevant log output
_No response_
|
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I_kwDOArmXAs5-xk_u
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tf-nightly: wrong packaging metadata prevents installation using poetry on Linux
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[
"Same issue on MacOS 14.2 and python 3.11.7. ",
"This also happens for `tensorflow-macos` 2.16.1, though that package is supposed to be not used as of 2.16 in favor of the base`tensorflow` package."
] | 2024-02-09T11:14:10 | 2024-03-26T17:30:07 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf-nightly 2.16.0.dev20240209
### Custom code
No
### OS platform and distribution
Linux Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I cannot install `tf-nightly` using `poetry` on Linux.
I'm getting the following error:
```
$> poetry add tf-nightly
Using version ^2.16.0.dev20240209 for tf-nightly
Updating dependencies
Resolving dependencies... (0.0s)
Package 'tf-nightly' is listed as a dependency of itself.
```
I'm using the following versions:
- Poetry: 1.7.1
- Pip: 24.0
- Python: 3.11.7
It looks to me like the package metadata is wrong:
Quoting from https://pypi.org/pypi/tf-nightly/2.16.0.dev20240209/json ,
the `requires_dist` field contains `tf-nightly ==2.16.0-dev20240209 ; platform_system == "Darwin" and platform_machine == "arm64"'`.
If I understand `poetry`s output correctly, this is the self-dependency that is not allowed.
To reproduce this, just create an empty directory, paste the following as your initial `pyproject.toml`
```toml
[tool.poetry]
name = "tf-package-bug"
version = "0.1.0"
description = ""
authors = ["Your Name <[email protected]>"]
readme = "README.md"
[tool.poetry.dependencies]
python = "^3.11"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
```
And then run
- `pyenv local 3.11.7` (this is how I'm activating a Python 3.11 installation)
- `python -m pip install poetry` (make sure Poetry is installed for your Python 3.11)
- `poetry install --no-root` (Creates the virtual env using Python 3.11)
- `poetry add tf-nightly` (Throws the error above)
### Standalone code to reproduce the issue
```shell
The steps to reproduce are noted above.
```
### Relevant log output
_No response_
|
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I_kwDOArmXAs5-v-0H
| 62,927 |
Error with Custom Keras Model
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[
"@basnetr Could you try to modify your CustomModel class to incorporate the inputs and output attributes into the configuration returned by get_config(). It's recommended to use model.save('my_model.keras') for the native Keras format.\r\nThank you!",
"@sushreebarsa Incorporating inputs and outputs doesn't work (these are tensors that give errors: `TypeError: Cannot serialize object KerasTensor`), and I don't think it's the main issue, attributes such as `layers`, `input_layers` and `output_layers` are still missing in the inherited class that are present in the parent class. Also no difference using `.keras` vs. `.h5` in this case.\r\n\r\nI think the issue might be related to keras and tensorflow incompatibility - at least for tensorflow 2.12, 2.13 and 2.15. \r\nA quick fix for the issue was to force install keras 2.11 (downgrading from 2.13.1) for tensorflow 2.13.1. This creates dependency conflicts but works in my case. \r\n\r\nAnother fix for just the custom model was to inherit CustomModel from `tensorflow.python.keras.Model` instead of `tensorflow.keras.Model` but this will further cause other issues moving forward, example: `tensorflow.keras.optimizers` are not supported for training `tensorflow.python.keras.Model`.",
"@basnetr I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/e8fc1806183c88fe31aa0e162f068f01/62927.ipynb). This issue might be occurring due to Keras and TensorFlow incompatibility in versions between 2.12 and 2.15. While downgrading Keras can be a temporary fix, it's generally not recommended due to potential dependency conflicts and lack of bug fixes/security updates. 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.",
"@sushreebarsa Awaiting a permanent fix."
] | 2024-02-09T05:34:22 | 2024-02-27T10:19:37 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
v2.13.0-17-gf841394b1b7 2.13.1
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 20.04.6 LTS
### Mobile device
_No response_
### Python version
3.9.18
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Expected output (ran using v2.8.3-90-g1b8f5c396f0 2.8.4):
```
{'name': 'custom_model', 'layers': [{'class_name': 'InputLayer', 'config': {'batch_input_shape': (None, 96, 96, 3), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'input_1'}, 'name': 'input_1', 'inbound_nodes': []}, {'class_name': 'Conv2D', 'config': {'name': 'conv2d', 'trainable': True, 'dtype': 'float32', 'filters': 8, 'kernel_size': (3, 3), 'strides': (1, 1), 'padding': 'valid', 'data_format': 'channels_last', 'dilation_rate': (1, 1), 'groups': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'name': 'conv2d', 'inbound_nodes': [[['input_1', 0, 0, {}]]]}], 'input_layers': [['input_1', 0, 0]], 'output_layers': [['conv2d', 0, 0]]}
{'name': 'model', 'layers': [{'class_name': 'InputLayer', 'config': {'batch_input_shape': (None, 96, 96, 3), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'input_1'}, 'name': 'input_1', 'inbound_nodes': []}, {'class_name': 'Conv2D', 'config': {'name': 'conv2d', 'trainable': True, 'dtype': 'float32', 'filters': 8, 'kernel_size': (3, 3), 'strides': (1, 1), 'padding': 'valid', 'data_format': 'channels_last', 'dilation_rate': (1, 1), 'groups': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'name': 'conv2d', 'inbound_nodes': [[['input_1', 0, 0, {}]]]}], 'input_layers': [['input_1', 0, 0]], 'output_layers': [['conv2d', 0, 0]]}
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.
WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.
Model: "custom_model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 96, 96, 3)] 0
conv2d (Conv2D) (None, 94, 94, 8) 224
=================================================================
Total params: 224
Trainable params: 224
Non-trainable params: 0
_________________________________________________________________
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 96, 96, 3)] 0
conv2d (Conv2D) (None, 94, 94, 8) 224
=================================================================
Total params: 224
Trainable params: 224
Non-trainable params: 0
_________________________________________________________________
```
### Standalone code to reproduce the issue
```shell
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf # noqa: E402
class CustomModel(tf.keras.Model):
def __init__(self, **kwargs):
super(CustomModel, self).__init__(**kwargs)
input_shape = (96, 96, 3)
inputs = tf.keras.layers.Input(shape=input_shape)
outputs = tf.keras.layers.Conv2D(
filters=8, kernel_size=3, strides=1)(inputs)
cust_model = CustomModel(inputs=inputs, outputs=outputs)
print(cust_model.get_config())
model = tf.keras.Model(inputs=inputs, outputs=outputs)
print(model.get_config())
cust_model_path = '/home/ubuntu/automltraining/my_custom_model.h5'
cust_model.save(cust_model_path)
model_path = '/home/ubuntu/automltraining/my_model.h5'
model.save(model_path)
cust_model2 = tf.keras.models.load_model(
cust_model_path, custom_objects={"CustomModel": CustomModel})
model2 = tf.keras.models.load_model(model_path)
cust_model2.summary()
model2.summary()
```
### Relevant log output
```shell
{'name': 'custom_model', 'trainable': True}
{'name': 'model', 'trainable': True, 'layers': [{'module': 'keras.layers', 'class_name': 'InputLayer', 'config': {'batch_input_shape': (None, 96, 96, 3), 'dtype': 'float32', 'sparse': False, 'ragged': False, 'name': 'input_1'}, 'registered_name': None, 'name': 'input_1', 'inbound_nodes': []}, {'module': 'keras.layers', 'class_name': 'Conv2D', 'config': {'name': 'conv2d', 'trainable': True, 'dtype': 'float32', 'filters': 8, 'kernel_size': (3, 3), 'strides': (1, 1), 'padding': 'valid', 'data_format': 'channels_last', 'dilation_rate': (1, 1), 'groups': 1, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}, 'bias_initializer': {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}, 'registered_name': None, 'build_config': {'input_shape': (None, 96, 96, 3)}, 'name': 'conv2d', 'inbound_nodes': [[['input_1', 0, 0, {}]]]}], 'input_layers': [['input_1', 0, 0]], 'output_layers': [['conv2d', 0, 0]]}
/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.
saving_api.save_model(
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
Traceback (most recent call last):
File "/home/ubuntu/automltraining/get_config_debug.py", line 28, in <module>
cust_model2 = tf.keras.models.load_model(
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/keras/src/saving/saving_api.py", line 238, in load_model
return legacy_sm_saving_lib.load_model(
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/ubuntu/miniconda3/envs/automl/lib/python3.9/site-packages/keras/src/engine/training.py", line 3246, in from_config
raise TypeError(
TypeError: Unable to revive model from config. When overriding the `get_config()` method, make sure that the returned config contains all items used as arguments in the constructor to <class '__main__.CustomModel'>, which is the default behavior. You can override this default behavior by defining a `from_config(cls, config)` class method to specify how to create an instance of CustomModel from its config.
Received config={'name': 'custom_model', 'trainable': True}
Error encountered during deserialization: __init__() missing 2 required positional arguments: 'inputs' and 'outputs'
```
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[
"@AshebirGetuBelete Could you please provide more context on the issue reported here? \r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further."
] | 2024-02-08T20:15:03 | 2024-02-28T01:47:04 | 2024-02-28T01:47:03 |
NONE
| null | null | null |
### 1. System information
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- TensorFlow installation (pip package or built from source):
- TensorFlow library (version, if pip package or github SHA, if built from source):
### 2. Code
Provide code to help us reproduce your issues using one of the following options:
#### Option A: Reference colab notebooks
1) Reference [TensorFlow Model Colab](https://colab.research.google.com/gist/ymodak/e96a4270b953201d5362c61c1e8b78aa/tensorflow-datasets.ipynb?authuser=1): Demonstrate how to build your TF model.
2) Reference [TensorFlow Lite Model Colab](https://colab.research.google.com/gist/ymodak/0dfeb28255e189c5c48d9093f296e9a8/tensorflow-lite-debugger-colab.ipynb): Demonstrate how to convert your TF model to a TF Lite model (with quantization, if used) and run TFLite Inference (if possible).
```
(You can paste links or attach files by dragging & dropping them below)
- Provide links to your updated versions of the above two colab notebooks.
- Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model.
```
#### Option B: Paste your code here or provide a link to a custom end-to-end colab
```
(You can paste links or attach files by dragging & dropping them below)
- Include code to invoke the TFLite Converter Python API and the errors.
- Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model.
```
### 3. Failure after conversion
If the conversion is successful, but the generated model is wrong, then state what is wrong:
- Model produces wrong results and/or has lesser accuracy.
- Model produces correct results, but it is slower than expected.
### 4. (optional) RNN conversion support
If converting TF RNN to TFLite fused RNN ops, please prefix [RNN] in the title.
### 5. (optional) Any other info/logs
Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.
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[
"@zavataafnan,\r\nThe image which you provided is related to the **I(information)** which might not be affecting the execution of the code. Also I suspect this issue might be a mismatch of the Bazel version you are trying with the tensorflow v2.15. \r\nhttps://www.tensorflow.org/install/source#cpu_2\r\n\r\nCould you please try with the compatible Bazel 6.1.0 with the tensorflow v2.15. Thank you!",
"@tilakrayal,\r\nthank you for your response. My Bazel version is 7.0.2. Unfortunately, downgrading Bazel to 6.1.0 is not straightforward in Mac. it has many dependencies like OpenJDK. \r\nwhen do you think it will be released with the upper Bazel version?\r\nCan I also use a snapshot of tensorflow that maybe is okay with higher Bazel?\r\n\r\nBest Regards,\r\nMostafa",
"@zavataafnan,\r\nWe can't mention right now that for which tensorflow releases the upper(higher) Bazel version would be compatible. It will be mentioned in the same official document which was provided. Also for the smooth tensorflow usage, we strongly recommend the community to use the compatible versions rather than higher or lower versions.\r\n\r\nAlso please take a look at this official [document](https://github.com/bazelbuild/bazelisk?tab=readme-ov-file#how-does-bazelisk-know-which-bazel-version-to-run) for the reference which provides the information about the Bazelisk for which Bazel version to run. 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/62925\">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/62925\">No</a>\n"
] | 2024-02-08T19:57:13 | 2024-02-28T01:47:09 | 2024-02-28T01:47:05 |
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
macOS 14.2.1 (23C71)
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I want to use TensorFlow to train a neural network with macOS 14 with a m2 CPU.
With the latest TensorFlow, I can run the code and train without problem in Windows. But I got this error when I ran in macOS:
### Standalone code to reproduce the issue
```shell
Please see this github repository.
I run test3.py
https://github.com/zavataafnan/ann_tensor
there is also a data file.
```
### Relevant log output
_No response_
|
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I_kwDOArmXAs5-tasi
| 62,924 |
Fix typo in https://www.tensorflow.org/datasets
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[
"@gpokat,\r\nThank you for the issue. The document states that **All datasets are exposed as tf.data.Datasets** in this case, it seems to refer to multiple datasets simultaneously. Also when trying to access information about `tf.data.Datasets` leads to the documentation for a correct `tf.data.dataset` API, which might not be the issue.\r\n\r\nMeanwhile I will try to check with developer team and provide more information. Thank you!",
"Thanks for fast response. I understand that the link leads to the correct Class and to correct page, but in the text it's highlighted like class instance with incorrect name (bold blue) which is confusing. The tf.data.Dataset is literally the class name without s at the end.",
"@gpokat,\r\nThe file which you mentioned is the auto-generated file which will be changed for the every tensorflow version release.\r\nI suspect the mentioned typo error also might get changed in the upcoming releases. Thank you!",
"Thanks for your consideration and fix! ",
"@gpokat,\r\nCould you please feel free to move this issue to closed status. Thank you!",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62924\">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/62924\">No</a>\n"
] | 2024-02-08T18:37:22 | 2024-03-13T13:59:29 | 2024-03-13T13:59:25 |
NONE
| null | null | null |
At the web page https://www.tensorflow.org/datasets need to fix typo in 'datasets' in
There is no tf.data.Dataset**s** it's tf.data.Dataset with no s at the end.
thanks.
|
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I_kwDOArmXAs5-ssBU
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TFLite Converter, add possibility to ignore some OPs from quantization
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[
"@adamp87 One workaround could be to quantize the entire model, then fine-tune specific layers with FP32 weights and activation. This approach can be less efficient and might not perfectly address accuracy concerns. Thank you!",
"@sushreebarsa Not sure how would that be possible, is there a documentation how to perform such a task? Currently my workaround is to split the Model `call()` code. I think a solution could be something similar like the `ignored_scope` at openvino. [reference](https://docs.openvino.ai/2022.3/basic_qauntization_flow.html#tune-quantization-parameters)",
"Hi @abattery, can you please take a look? Thanks.",
"Hi @abattery,\r\n\r\nin the meantime I got a suggestion to experiment with [QuantizationDebugger](https://www.tensorflow.org/api_docs/python/tf/lite/experimental/QuantizationDebugger). Please see ticket: https://github.com/PINTO0309/onnx2tf/issues/578\r\n\r\nWhat is your opinion about it? Is this the right way? Im still having some issues that could be discussed.\r\n\r\nThank you!"
] | 2024-02-08T16:53:20 | 2024-03-08T17:36:33 | null |
NONE
| null | null | null |
### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
v2.13.0-17-gf841394b1b7
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
3.10.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Quantizing models to integer works as expected, but because some of the final operations work in INT8, a large accuracy drop can be observed in some models.
This ticket is a feature request to be able to exclude specific operations from quantization and execute in FP32. OpenVINO supports this feature as `ignored_scope` param during quantization. [Link to OpenVINO quantizer documentation.](https://docs.openvino.ai/2022.3/basic_qauntization_flow.html#tune-quantization-parameters) Considering how Edge TPU works, the solution should be to set where to stop quantization and execute the rest of the OPs in FP32 on the CPU.
Lets take yolov8n as an example and convert the pytorch model to TF using onnx2tf. Lets compare the main branch in [FULL INT8](https://github.com/adamp87/ultralytics/tree/main) quantization, with a dirty hack by detaching the last operations and executing as [INT8 + FP32](https://github.com/adamp87/ultralytics/tree/tflite_detach_dirty). As a note, Edge TPU compiled models larger than 192pixel input execute the head on the CPU as some Transpose operations are too large for the TPU.
| Model yolo8n | mAP50 | mAP50-95 | Note | Speed on Intel CPU |
| ------------- | ------------- | ------------- | ------------- | ------------- |
| Baseline FP32 | 52.6 | 37.4 | Main branch | N/A |
| TFLite Full INT8 | 48.8 | 32.9 | per-tensor | 162.2 ms |
| TFLite INT8 + FP32 | 50.3 | 35.2 | per-tensor | 166.0ms |
| TFLite Full INT8 | 49.8 | 33.9 | per-channel | N/A |
| TFLite INT8 + FP32 | 51.4 | 36.3 | per-channel | N/A |
### Standalone code to reproduce the issue
```shell
https://github.com/adamp87/ultralytics/blob/tflite_detach_dirty/yolo8_full_int8_nohead_test.ipynb
```
### Relevant log output
_No response_
|
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I_kwDOArmXAs5-shja
| 62,922 |
Github link to tf.nest.map_structure is broken in the docs
|
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[
"@chmendoza You're right, there have been reports already of some TensorFlow API GitHub source code links not working correctly. It's a known issue. Tensorflow has been released at 2.15.0.post1 ([see pypi](https://pypi.org/project/tensorflow/)), so the docs reflect like that. Seems like the repo is missing the appropriate tag for which an internal fix was raised. Thank you!",
"Apologies if creating noise, but here is another one with a broken link: https://www.tensorflow.org/api_docs/python/tf/nn/softmax",
"@chmendoza Yes, there are a few other apis are also there which are having the same issue as you mentioned. We are expecting it to be fixed in the next release. \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/62922\">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/62922\">No</a>\n"
] | 2024-02-08T16:29:59 | 2024-02-29T01:46:30 | 2024-02-29T01:46:27 |
NONE
| null | null | null |
### Issue type
Documentation Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.8
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
The "View source on Github" button in https://www.tensorflow.org/api_docs/python/tf/nest/map_structure takes me to a file that doesn't exist. Also, the doc says "Refer to [tf.nest](https://www.tensorflow.org/api_docs/python/tf/nest) for the definition of a structure.", but if I go to that link, there is no such definition.
### Standalone code to reproduce the issue
```shell
n/a
```
### Relevant log output
```shell
n/a
```
|
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PR_kwDOArmXAs5mW-cJ
| 62,921 |
Do not rely on string conversion of anonymous namespace
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[
"@qukhan Thanks for the review. I have updated as requested."
] | 2024-02-08T10:45:17 | 2024-02-14T15:29:22 | 2024-02-12T03:39:43 |
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The conversion to a string of the name of an anonymous namespace can differ between compilers so do not rely on it. Instead give the namespace a name that can then be used in strings to check the desired functionality in the test.
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Method: "Eigen::SpatialConvolutionBackwardKernel" in "tensorflow/core/kernels/eigen_backward_spatial_convolutions.h" is painfully slow !
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[
"So, I've tried experimenting. Adding force eval past the patch extraction / reshape on the input tensor:\r\n```\r\ninput\r\n.extract_image_patches(\r\nkernelRows, kernelCols, row_stride, col_stride,\r\nrow_in_stride, col_in_stride, 1, 1, padding_top,\r\npadding_bottom, padding_left, padding_right, OutScalar(0))\r\n.reshape(pre_contract_dims).eval()\r\n```\r\nmakes this method 3x faster, from 9,155 seconds to 3.026 seconds on the previously aformentioned (64,800,800) input and backward output with (64,64,5,5) kernel, which is more in-line with expected performance. \r\nPlease, look into this further.",
"Might have found even better solution...\r\nSwitching the order of contraction from output.contract(input) to input.contract(output) without any force eval and with post contraction shuffling, resulted in even faster speeds without possibly higher memory consumption: 2.2 seconds instead of the original 9 seconds or the 3 second with force eval code.\r\nExample of the return statement for col major layout (of course template arguments have to be modified accordingly): \r\n``` \r\ninput\r\n.extract_image_patches(\r\nkernelRows, kernelCols, row_stride, col_stride,\r\nrow_in_stride, col_in_stride, 1, 1, padding_top,\r\npadding_bottom, padding_left, padding_right, OutScalar(0))\r\n.reshape(pre_contract_dims)\r\n.contract(\r\noutput_backward.reshape(output_dims),\r\ncontract_dims).shuffle(DSizes<TensorIndex, 2>{1,0})\r\n.reshape(kernel_dims)\r\n```\r\nThis does work with 3D input/output tensors correctly, ~~didn't test it with higher dimensional outputs, but I have feeling there has to be some additional shuffling to be done.~~\r\nConfirmed that the method behaves correctly with 4D Tensors, it is even up to 5x faster than the original with batch size of 16.",
"> Might have found even better solution... Switching the order of contraction from output.contract(input) to input.contract(output) without any force eval and with post contraction shuffling, resulted in even faster speeds without possibly higher memory consumption: 2.2 seconds instead of the original 9 seconds or the 3 second with force eval code. Example of the return statement for col major layout (of course template arguments have to be modified accordingly):\r\n> \r\n> ```\r\n> input\r\n> .extract_image_patches(\r\n> kernelRows, kernelCols, row_stride, col_stride,\r\n> row_in_stride, col_in_stride, 1, 1, padding_top,\r\n> padding_bottom, padding_left, padding_right, OutScalar(0))\r\n> .reshape(pre_contract_dims)\r\n> .contract(\r\n> output_backward.reshape(output_dims),\r\n> contract_dims).shuffle(DSizes<TensorIndex, 2>{1,0})\r\n> .reshape(kernel_dims)\r\n> ```\r\n> \r\n> This does work with 3D input/output tensors correctly, ~didn't test it with higher dimensional outputs, but I have feeling there has to be some additional shuffling to be done.~ Confirmed that the method behaves correctly with 4D Tensors, it is even up to 5x faster than the original with batch size of 16.\r\n\r\nFeel free to submit a pull request with your changes.",
"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/62920\">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/62920\">No</a>\n",
"I'm just really stupid, I was looking at totally different TF version. Sorry to waste your time. Still old v1.13.1 backward convolution code with my modification is 20% faster than the current version in 2.15.\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/62920\">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/62920\">No</a>\n"
] | 2024-02-07T22:39:28 | 2024-02-09T10:33:48 | 2024-02-09T10:09:10 |
NONE
| null | null | null |
### Issue type
Performance
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
1.13.1
### Custom code
Yes
### OS platform and distribution
Windows
### Mobile device
---
### Python version
---
### Bazel version
---
### GCC/compiler version
Clang 16
### CUDA/cuDNN version
---
### GPU model and memory
---
### Current behavior?
I've "borrowed" this method in order to use it in own C++ NN project. It is consistently 3x slower than a simple naive nested for loop implementation on moderately sized inputs: eg. convolution with filter(64,64,5,5), stride 1 and no inflation. This implementation takes 10 seconds on 64x800x800 input and 64x800x800 backward output, meanwhile naive loop takes only 3 seconds, so there is a huge difference !
In reference, all the other spatial convolutions (Eigen::SpatialConvolution and Eigen::SpatialConvolutionBackwardInput) easily beat naive implementations by more than 30%.
### Standalone code to reproduce the issue
Here is a very broad implementation of algorithm that beats your implementation (dimensions, stride and inflation are handled externally), but that is besides the point.
I also know that your implementation has to work with a large range of inputs, but surely there has to be a way to make it faster, at least for most commonly used kernels in image processing. The current contraction method does seem very inefficient.
```shell
using Tensor = Eigen::Tensor<float, 3, Eigen::ColMajor, int>;
using Tenarr = Eigen::Tensor<float, 4, Eigen::ColMajor, int>;
void Conv2D_WGrad(Tenarr& ker, const Tensor& in, const Tensor& out, int pad_w, int pad_h) {
alignas(64) float tmp[4 * 4096] = {};
int ker_mem = ker.dimension(0) * sizeof(float);
for(int i = 0; i < ker.dimension(2); i++) {
for(int j = 0; j < ker.dimension(1); j++) {
memset(tmp, 0, ker_mem);
int in_i = i - pad_h;
int in_j = j - pad_w;
int clip_k = std::max(0, in_i - in.dimension(2) + out.dimension(2));
int clip_l = std::max(0, in_j - in.dimension(1) + out.dimension(1));
for(int k = std::max(-in_i, 0); k < out.dimension(2) - clip_k; k++) {
for(int l = std::max(-in_j, 0); l < out.dimension(1) - clip_l; l++) {
for(int ich = 0; ich < in.dimension(0); ich++) {
for(int och = 0; och < out.dimension(0); och++) {
tmp[och + ich * out.dimension(0)] += in(ich, in_j + l, in_i + k) * out(och, l, k);
}
}
}
}
memcpy(&ker(0, 0, j, i), tmp, ker_mem);
}
}
}
```
Thank you for your consideration.
```
### Relevant log output
_No response_
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PR_kwDOArmXAs5mSj4m
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[Linaro:ARM_CI] Update checkout action
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[] | 2024-02-07T17:43:41 | 2024-02-09T23:18:11 | 2024-02-09T23:18:11 |
CONTRIBUTOR
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The checkout action previously in use depends on node.js 16 which is now deprecated so update the action to one that depends on node.js 20
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I_kwDOArmXAs5-jy2A
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Every TF version is broken, you all here please change your field of activity
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[
"@AndreyStille , Sorry to hear that you are facing issues with the TensorFlow. \r\n\r\nFostering collaboration from both the community and the `TensorFlow` team is essential for the continuous improvement of the framework.\r\n\r\nWe encourage community users to report bug or to request for any new feature or to make changes to the framework as per contributing guidelines.\r\nAlso, please keep a close tab on the latest bug fixes and improvements on `TensorFlow` here https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md\r\n\r\nOur team would be happy to help you with the issue, could you please let us know more about the issue you are facing?\r\n\r\n\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/62918\">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/62918\">No</a>\n"
] | 2024-02-07T15:38:18 | 2024-02-23T01:46:43 | 2024-02-23T01:46:40 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
all
### 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?
---
### Standalone code to reproduce the issue
```shell
---
```
### Relevant log output
_No response_
|
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MLP subsequence is inconsistent with full sequence
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[
"@ErikGartner Though, I was able to replicate the issue [here](https://colab.research.google.com/gist/sushreebarsa/a4124f20da08e2f1f618a5dc491f6037/tensorflow-subsequence-inconsistency.ipynb#scrollTo=FpCjBG40Br5U). One workaround could be as follows;\r\nInstead of processing individual subsequences, we could process the entire data in batches at once and then extract the relevant subsequences after the model prediction. This avoids the issues associated with individual data points.\r\nThank you!",
"Thanks for your quick reply @sushreebarsa!\r\n\r\nThe test case above is just to demonstrate the issues. In our project we process streams of data and we get the subsequences over time and wish to use them to predict the full sequence. Hence we cannot use the full data as a workaround.\r\n\r\nIs this being treated internally as a bug or is this a side effect from how Tensorflow handles the computations internally?\r\n\r\nAll the best,\r\nErik"
] | 2024-02-07T15:08:07 | 2024-02-08T21:23:37 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
v1.12.1-105526-gf9a553f735c 2.16.0-dev20240206
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
**Issue:** Given a two layer MLP with a reshape, I notice that `model(data)[:, :end_idx])` does not equal `model(data[:, :end_idx])`.
That is, comparing the output of the model on a subsequence of the data is _not_ the same as the corresponding subsequence of the output, even when the MLP should not have any interactions across the time-dimension.
Can you confirm this is expected behaviour? I notice that GPUs (T4 on colab) has more inconsistencies than CPU, so I assume this is some sort of computional optimizations.
Is there anyway to guarantee that the outputs match? I have tried `tf.config.experimental.enable_op_determinism()`.
Thanks!
Erik
### Standalone code to reproduce the issue
```shell
Colab: https://colab.research.google.com/drive/1ffw4XhJKocu71ORANA_70eG7Tl20-cLh?usp=sharing
Code from colab:
import tensorflow as tf
tf.keras.utils.set_random_seed(1)
tf.config.experimental.enable_op_determinism()
class BrokenModel(tf.keras.Model):
def __init__(self,):
super().__init__()
self.dense1 = tf.keras.layers.Dense(
32, activation=None,
)
self.dense2 = tf.keras.layers.Dense(
256 , activation=None,
)
def call(self, x: tf.Tensor):
dims = tf.shape(x)
# First dense
x = self.dense1(x)
# Flatten last two dimensions
x = tf.reshape(
x, (dims[0], dims[1], -1)
)
# Second dense
x = self.dense2(x)
return x
def evaluate_subsequence_consistency(
model,
data,
):
orig_output = model(data)
for end_idx in range(0, data.shape[1]):
# Slice subsequence
output = model(data[:, :end_idx])
# Check if output is the same
res_str = ""
for j in range(0, end_idx):
# Assumes output has shape [B, T, ...]
# Check if each time step is identical between subsequence and full sequence
orig_data = orig_output[:, j].numpy()
new_data = output[:, j].numpy()
if tf.experimental.numpy.allclose(orig_data, new_data):
res_str += "."
else:
res_str += "X"
print(f"model(data)[:, :{end_idx}] vs model(data[:, :{end_idx}]) => {res_str}")
model = BrokenModel()
data = tf.ones((1, 313, 33, 3))
evaluate_subsequence_consistency(model, data)
```
### Relevant log output
```shell
X = output does not match, . = output is matching
model(data)[:, :1] vs model(data[:, :1]) => X
model(data)[:, :2] vs model(data[:, :2]) => XX
model(data)[:, :3] vs model(data[:, :3]) => XXX
model(data)[:, :4] vs model(data[:, :4]) => XXXX
model(data)[:, :5] vs model(data[:, :5]) => XXXXX
model(data)[:, :6] vs model(data[:, :6]) => XXXXXX
model(data)[:, :7] vs model(data[:, :7]) => XXXXXXX
model(data)[:, :8] vs model(data[:, :8]) => XXXXXXXX
model(data)[:, :9] vs model(data[:, :9]) => XXXXXXXXX
model(data)[:, :10] vs model(data[:, :10]) => XXXXXXXXXX
model(data)[:, :11] vs model(data[:, :11]) => XXXXXXXXXXX
model(data)[:, :12] vs model(data[:, :12]) => XXXXXXXXXXXX
model(data)[:, :13] vs model(data[:, :13]) => XXXXXXXXXXXXX
model(data)[:, :14] vs model(data[:, :14]) => XXXXXXXXXXXXXX
model(data)[:, :15] vs model(data[:, :15]) => XXXXXXXXXXXXXXX
model(data)[:, :16] vs model(data[:, :16]) => XXXXXXXXXXXXXXXX
model(data)[:, :17] vs model(data[:, :17]) => XXXXXXXXXXXXXXXXX
model(data)[:, :18] vs model(data[:, :18]) => XXXXXXXXXXXXXXXXXX
model(data)[:, :19] vs model(data[:, :19]) => XXXXXXXXXXXXXXXXXXX
model(data)[:, :20] vs model(data[:, :20]) => XXXXXXXXXXXXXXXXXXXX
```
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I_kwDOArmXAs5-jVL5
| 62,916 |
TFL StridedSlice not lowered to TFL Slice if masks are set and strides are 1
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[
"@michaelpoluektov Could you please avoid using masks with StridedSlice when strides are all 1. Instead, explicitly use the Slice operator for better performance. \r\n\r\nThank you!",
"@sushreebarsa Thanks for your reply! I'm working on a TFLite backend, and unfortunately I don't get to pick which operators our clients use.\r\n\r\nI hacked together a pass that does this, but I don't see a reason some version of this shouldn't be included upstream. I don't think this is best practice though: I believe the MLIR way of doing this is to define a canonicalization rule for strided slice?\r\n\r\nI am willing to submit a PR but I'm not particularly experienced with the TF codebase: where do you have canonicalizations defined? Are you running an MLIR `CanonicalizerPass` anywhere?\r\n\r\n```cpp\r\n#include \"mlir/Pass/Pass.h\"\r\n#include \"mlir/Transforms/GreedyPatternRewriteDriver.h\"\r\n#include \"tensorflow/compiler/mlir/lite/ir/tfl_ops.h\"\r\n\r\nnamespace mlir {\r\n\r\nnamespace {\r\n// Replace TFL StridedSlice with TFL Slice wherever possible.\r\nstruct ReplaceStridedSlice\r\n : public PassWrapper<ReplaceStridedSlice, OperationPass<func::FuncOp>> {\r\n MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(ReplaceStridedSlice)\r\n\r\n void getDependentDialects(DialectRegistry ®istry) const final {\r\n registry.insert<TFL::TensorFlowLiteDialect>();\r\n }\r\n StringRef getArgument() const final { return \"xcore-replace-stridedslice\"; }\r\n StringRef getDescription() const final {\r\n return \"Replace TFL StridedSlice with TFL Slice\";\r\n }\r\n void runOnOperation() override;\r\n};\r\n\r\n// Utility to check if a StridedSliceOp can be replaced with a SliceOp\r\nbool canReplaceWithSlice(TFL::StridedSliceOp stridedSliceOp) {\r\n // Check input shape is static\r\n auto inputType = stridedSliceOp.getInput().getType().dyn_cast<ShapedType>();\r\n if (!inputType || !inputType.hasStaticShape()) {\r\n return false;\r\n }\r\n\r\n // Check all strides are 1\r\n DenseIntElementsAttr stridesAttr;\r\n matchPattern(stridedSliceOp.getStrides(), m_Constant(&stridesAttr));\r\n if (!stridesAttr)\r\n return false;\r\n for (auto stride : stridesAttr)\r\n if (!stride.isOne())\r\n return false;\r\n\r\n if (stridedSliceOp.getEllipsisMask() != 0 ||\r\n stridedSliceOp.getNewAxisMask() != 0) {\r\n return false;\r\n }\r\n return true;\r\n}\r\n\r\nstruct ReplaceStridedSlicePattern\r\n : public OpRewritePattern<TFL::StridedSliceOp> {\r\n using OpRewritePattern<TFL::StridedSliceOp>::OpRewritePattern;\r\n\r\n LogicalResult matchAndRewrite(TFL::StridedSliceOp stridedSliceOp,\r\n PatternRewriter &rewriter) const override {\r\n\r\n if (!canReplaceWithSlice(stridedSliceOp))\r\n return failure();\r\n\r\n auto inputType = stridedSliceOp.getInput().getType().dyn_cast<ShapedType>();\r\n auto rank =\r\n stridedSliceOp.getInput().getType().cast<ShapedType>().getRank();\r\n\r\n // Get begin/end attributes\r\n DenseIntElementsAttr beginAttr;\r\n matchPattern(stridedSliceOp.getBegin(), m_Constant(&beginAttr));\r\n if (!beginAttr)\r\n return failure();\r\n auto begin = beginAttr.getValues<int32_t>();\r\n\r\n DenseIntElementsAttr endAttr;\r\n matchPattern(stridedSliceOp.getEnd(), m_Constant(&endAttr));\r\n if (!beginAttr)\r\n return failure();\r\n auto end = endAttr.getValues<int32_t>();\r\n\r\n std::vector<int32_t> newBegin(rank), newSize(rank);\r\n\r\n // If mask is set, set begin and end to 0 and input shape\r\n // respectively\r\n // If mask is not set, set begin and end to the actual values\r\n // If the value is negative, it means size - value\r\n // StridedSliceOp has an end attribute, SliceOp has size\r\n // Size is end - begin.\r\n for (int i = 0; i < rank; i++) {\r\n if (stridedSliceOp.getBeginMask() & (1 << i))\r\n newBegin[i] = 0;\r\n else\r\n newBegin[i] =\r\n begin[i] < 0 ? inputType.getShape()[i] + begin[i] : begin[i];\r\n if (stridedSliceOp.getEndMask() & (1 << i))\r\n newSize[i] = inputType.getShape()[i] - newBegin[i];\r\n else {\r\n auto currentEnd =\r\n end[i] < 0 ? inputType.getShape()[i] + end[i] : end[i];\r\n newSize[i] = currentEnd - newBegin[i];\r\n }\r\n }\r\n int64_t shrinkMask = stridedSliceOp.getShrinkAxisMask();\r\n std::vector<int32_t> newOutputShape;\r\n for (int i = 0; i < rank; ++i) {\r\n if (!(shrinkMask & (1 << i))) { // Check if we should NOT shrink\r\n newOutputShape.push_back(newSize[i]); // Retain size\r\n }\r\n }\r\n\r\n auto shapeAttrType =\r\n RankedTensorType::get({rank}, rewriter.getIntegerType(32));\r\n\r\n // create constant ops for begin and size\r\n auto beginConstantOp = rewriter.create<arith::ConstantOp>(\r\n stridedSliceOp.getLoc(), shapeAttrType,\r\n DenseIntElementsAttr::get(shapeAttrType, newBegin));\r\n auto sizeConstantOp = rewriter.create<arith::ConstantOp>(\r\n stridedSliceOp.getLoc(), shapeAttrType,\r\n DenseIntElementsAttr::get(shapeAttrType, newSize));\r\n\r\n // RankedTensorType needs int64_t\r\n std::vector<int64_t> newSize64(newSize.begin(), newSize.end());\r\n\r\n // create sliceOp\r\n auto sliceOp = rewriter.create<TFL::SliceOp>(\r\n stridedSliceOp.getLoc(),\r\n RankedTensorType::get(ArrayRef<int64_t>(newSize64),\r\n stridedSliceOp.getType().getElementType()),\r\n stridedSliceOp.getInput(), beginConstantOp, sizeConstantOp);\r\n\r\n // add reshape if shrinkMask is not 0\r\n if (shrinkMask != 0) {\r\n auto newShapeAttrType =\r\n RankedTensorType::get({static_cast<int64_t>(newOutputShape.size())},\r\n rewriter.getIntegerType(32));\r\n auto shapeConstantOp = rewriter.create<arith::ConstantOp>(\r\n stridedSliceOp.getLoc(), newShapeAttrType,\r\n DenseIntElementsAttr::get(newShapeAttrType, newOutputShape));\r\n std::vector<int64_t> newOutputShape64(newOutputShape.begin(),\r\n newOutputShape.end());\r\n auto newOutputType = RankedTensorType::get(\r\n newOutputShape64, sliceOp.getType().getElementType());\r\n auto reshape = rewriter.create<TFL::ReshapeOp>(\r\n stridedSliceOp.getLoc(), newOutputType, sliceOp, shapeConstantOp);\r\n rewriter.replaceOp(stridedSliceOp, reshape.getOutput());\r\n } else {\r\n rewriter.replaceOp(stridedSliceOp, sliceOp.getOutput());\r\n }\r\n return success();\r\n }\r\n};\r\n\r\nvoid ReplaceStridedSlice::runOnOperation() {\r\n auto *ctx = &getContext();\r\n func::FuncOp func = getOperation();\r\n RewritePatternSet patterns(ctx);\r\n patterns.insert<ReplaceStridedSlicePattern>(ctx);\r\n (void)applyPatternsAndFoldGreedily(func, std::move(patterns));\r\n}\r\n} // namespace\r\n\r\n// Creates an instance of the ReplaceStridedSlice pass.\r\nstd::unique_ptr<OperationPass<func::FuncOp>> createReplaceStridedSlicePass() {\r\n return std::make_unique<ReplaceStridedSlice>();\r\n}\r\n\r\nstatic PassRegistration<ReplaceStridedSlice> pass;\r\n\r\n} // namespace mlir\r\n\r\n```",
"Hi @michaelpoluektov, https://github.com/tensorflow/tensorflow/tree/master/tensorflow/compiler/mlir/lite this readme is a good high level overview of the passes TF --> TFL goes through, I would say at this point it is outdated but it's a good start to understand the original intention.\r\n\r\nAlso as noted in the README: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/mlir/lite/tf_tfl_passes.cc has the actual passes, there are multiple Canonicalization passes, but it's best to review the code to determine where your logic may fit. If you are able to create a PR we will greatly appreciate the help. Thanks!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further."
] | 2024-02-07T14:41:54 | 2024-03-07T01:41:21 | 2024-03-07T01:41:20 |
NONE
| null | null | null |
The following code:
```python
import numpy as np
import tensorflow as tf
input_shape = (8, 10, 10)
input_data = tf.keras.Input(shape=input_shape, dtype=tf.int8, batch_size=1)
sliced_output = input_data[:, 1:4, 2:]
model = tf.keras.Model(inputs=input_data, outputs=sliced_output)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
def representative_dataset_gen():
for _ in range(100):
yield [np.random.uniform(low=-127, high=127, size=input_shape).astype(np.int8)]
converter.representative_dataset = representative_dataset_gen
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8
tflite_model = converter.convert()
model_name = f'test_slice.tflite'
with open(model_name, 'wb') as f:
f.write(tflite_model)
```
Will produce a TFLite model with the following MLIR:
```
func.func @main(%arg0: tensor<1x8x10x10xi8> {tf_saved_model.index_path = ["input_1"]}) -> (tensor<1x3x8x10xi8> {tf_saved_model.index_path = ["tf.__operators__.getitem"]}) attributes {tf.entry_function = {inputs = "serving_default_input_1:0", outputs = "PartitionedCall:0"}, tf_saved_model.exported_names = ["serving_default"]} {
%0 = "tfl.pseudo_const"() {value = dense<[0, 1, 2, 0]> : tensor<4xi32>} : () -> tensor<4xi32>
%1 = "tfl.pseudo_const"() {value = dense<[0, 4, 0, 10]> : tensor<4xi32>} : () -> tensor<4xi32>
%2 = "tfl.pseudo_const"() {value = dense<1> : tensor<4xi32>} : () -> tensor<4xi32>
%3 = "tfl.strided_slice"(%arg0, %0, %1, %2) {begin_mask = 9 : i32, ellipsis_mask = 0 : i32, end_mask = 13 : i32, new_axis_mask = 0 : i32, offset = false, shrink_axis_mask = 0 : i32} : (tensor<1x8x10x10xi8>, tensor<4xi32>, tensor<4xi32>, tensor<4xi32>) -> tensor<1x3x8x10xi8>
return %3 : tensor<1x3x8x10xi8>
}
```
Despite begin and end being constants, and the strides being all set to 1, `tfl.strided_slice` is not lowered to `tfl.slice`, because the TensorFlow code generates a strided slice with masks. A pass was added in 6db21275a7579e0772f134c029b737a8c6407e01 to lower strided slices with masks set to zero, but this case is not covered.
I would like to add a pass that lowers such cases:
- if begin_mask is set, and begin is constant, set begin_mask to zero and set the corresponding element in begin to zero
- same for end_mask, except a check has to be added to make sure the input shape is static
It seems like `ellipsis_mask` is being lowered already, and `new_axis_mask` inserts a `tfl.reshape` after the strided slice (in the TF to TFLite conversion), however `shrink_axis_mask` is not lowered: I would like to lower it to include a reshape as well.
After all of those transformations, the existing lowering pass should be able to convert any `strided_slice` with strides set to 1 to a regular `tfl.slice`.
|
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I_kwDOArmXAs5-iRJT
| 62,915 |
tf.distribute.MirroredStrategy giving NaN in loss and accuracy with 4090 Multi-GPU (x2-4)
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[
"@maziarzamani,\r\nI tried to execute the mentioned code on both single-gpu & multi-gpu and observed that the Loss and accuracy is not Nan values. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/9fcda1c4363966e1151afc003220194d/untitled1725.ipynb) and screen shot of the multi-gpu for the reference.\r\n\r\n\r\n\r\n\r\n\r\nAlso please have a look at this similar issue for the reference.\r\nhttps://github.com/tensorflow/tensorflow/issues/41657\r\nhttps://github.com/tensorflow/tensorflow/issues/36224\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/62915\">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/62915\">No</a>\n"
] | 2024-02-07T12:21:17 | 2024-02-24T01:45:46 | 2024-02-24T01:45:44 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
v2.15.0-2-g0b15fdfcb3f 2.15.0
### Custom code
Yes
### OS platform and distribution
Ubuntu 22.04.3 LTS
### Mobile device
_No response_
### Python version
Python 3.10.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.3
### GPU model and memory
4090 / 24GB
### Current behavior?
Hardware configuration:
- Intel W3475
- 4* NVIDIA RTX 4090
- 128GB DDR5 ECC RAM
- Asus W790E-SAGE SE
When running Multi-GPU training with MirroredStrategy I get NaN in loss after a short while of running the first epoch.
Running a Single-GPU works fine.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import keras
def get_compiled_model():
# Make a simple 2-layer densely-connected neural network.
inputs = keras.Input(shape=(784,))
x = keras.layers.Dense(256, activation="relu")(inputs)
x = keras.layers.Dense(256, activation="relu")(x)
outputs = keras.layers.Dense(10)(x)
model = keras.Model(inputs, outputs)
model.compile(
optimizer=keras.optimizers.Adam(),
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[keras.metrics.SparseCategoricalAccuracy()],
)
return model
def get_dataset():
batch_size = 32
num_val_samples = 10000
# Return the MNIST dataset in the form of a `tf.data.Dataset`.
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
# Preprocess the data (these are Numpy arrays)
x_train = x_train.reshape(-1, 784).astype("float32") / 255
x_test = x_test.reshape(-1, 784).astype("float32") / 255
y_train = y_train.astype("float32")
y_test = y_test.astype("float32")
# Reserve num_val_samples samples for validation
x_val = x_train[-num_val_samples:]
y_val = y_train[-num_val_samples:]
x_train = x_train[:-num_val_samples]
y_train = y_train[:-num_val_samples]
return (
tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(batch_size),
tf.data.Dataset.from_tensor_slices((x_val, y_val)).batch(batch_size),
tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(batch_size),
)
# Create a MirroredStrategy.
strategy = tf.distribute.MirroredStrategy()
print("Number of devices: {}".format(strategy.num_replicas_in_sync))
# Open a strategy scope.
with strategy.scope():
# Everything that creates variables should be under the strategy scope.
# In general this is only model construction & `compile()`.
model = get_compiled_model()
# Train the model on all available devices.
train_dataset, val_dataset, test_dataset = get_dataset()
model.fit(train_dataset, epochs=2, validation_data=val_dataset)
# Test the model on all available devices.
model.evaluate(test_dataset)
```
### Relevant log output
```shell
python3 test.py
2024-02-07 12:18:53.214106: 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`.
2024-02-07 12:18:53.239167: 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
2024-02-07 12:18:53.239189: 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
2024-02-07 12:18:53.239879: 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
2024-02-07 12:18:53.243863: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI AVX512_BF16 AVX_VNNI AMX_TILE AMX_INT8 AMX_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-02-07 12:18:53.654794: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-02-07 12:18:54.616202: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22284 MB memory: -> device: 0, name: NVIDIA GeForce RTX 4090, pci bus id: 0000:16:00.0, compute capability: 8.9
2024-02-07 12:18:54.616782: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 22287 MB memory: -> device: 1, name: NVIDIA GeForce RTX 4090, pci bus id: 0000:34:00.0, compute capability: 8.9
2024-02-07 12:18:54.617432: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 22287 MB memory: -> device: 2, name: NVIDIA GeForce RTX 4090, pci bus id: 0000:52:00.0, compute capability: 8.9
2024-02-07 12:18:54.617945: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 22287 MB memory: -> device: 3, name: NVIDIA GeForce RTX 4090, pci bus id: 0000:70:00.0, compute capability: 8.9
Number of devices: 4
Epoch 1/2
2024-02-07 12:18:57.622003: I external/local_tsl/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory
2024-02-07 12:18:58.617348: I external/local_xla/xla/service/service.cc:168] XLA service 0x7fdad6459df0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2024-02-07 12:18:58.617381: I external/local_xla/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 4090, Compute Capability 8.9
2024-02-07 12:18:58.617384: I external/local_xla/xla/service/service.cc:176] StreamExecutor device (1): NVIDIA GeForce RTX 4090, Compute Capability 8.9
2024-02-07 12:18:58.617387: I external/local_xla/xla/service/service.cc:176] StreamExecutor device (2): NVIDIA GeForce RTX 4090, Compute Capability 8.9
2024-02-07 12:18:58.617390: I external/local_xla/xla/service/service.cc:176] StreamExecutor device (3): NVIDIA GeForce RTX 4090, Compute Capability 8.9
2024-02-07 12:18:58.622639: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2024-02-07 12:18:58.650080: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8904
2024-02-07 12:18:58.652158: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8904
2024-02-07 12:18:58.653847: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8904
2024-02-07 12:18:58.655977: 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 00:00:1707308338.717054 15378 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
1563/1563 [==============================] - 10s 5ms/step - loss: nan - sparse_categorical_accuracy: 0.2065 - val_loss: nan - val_sparse_categorical_accuracy: 2.6972
Epoch 2/2
1563/1563 [==============================] - 7s 4ms/step - loss: nan - sparse_categorical_accuracy: nan - val_loss: nan - val_sparse_categorical_accuracy: nan
313/313 [==============================] - 1s 2ms/step - loss: nan - sparse_categorical_accuracy: nan
```
```
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TFLite selective builds using TF ops (flex delegate) for embedded linux on aarch64
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[
"Hi @JoshPPrieto, I wouldn't call it expected, we always appreciate it when the users share any research they have already done as it usually does help us. Feel free to make a PR that helps resolve the issue... if it affects other builds we'll have to analyze whether the effect is a regression or not but it'll be easier for us to examine when the PR is created.\r\n\r\n@terryheo, can you please take a look? Thanks.",
"Hi again, sorry for the delay.\r\n\r\nAs the changes contain some issues with latest master (remaining double registrations), rather than raising a PR (I don't think it complies with contribution guidelines), I'm sharing the commit (https://github.com/nxp-imx/tensorflow/commit/bd29abc2798e1bb28ac571bbb403d980eac52df0) to continue the discussion.\r\n\r\nPlease, @pkgoogle @terryheo, let me know if the PR is still preferred or something else is needed for reproduction/analysis.\r\n\r\n\r\n"
] | 2024-02-07T09:53:13 | 2024-03-13T08:50:57 | null |
NONE
| null | null | null |
### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.10 ... 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 22.04
### Mobile device
Yocto based Linux running kernel 6.1.x
### 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?
Hi,
From what it is described in [Select TensorFlow operators](https://www.tensorflow.org/lite/guide/ops_select) and [Reduce TensorFlow Lite binary size](https://www.tensorflow.org/lite/guide/reduce_binary_size), it is possible to generate reduced size binaries (minimal TFLite runtime + specific Flex ops) for Android, and it is also described how to build custom C/C++ shared libraries containing the Flex ops that are part of the given models during the build process.
When building the shared libs with models containing flex ops and `elinux_aarch64` as config, the expected artifacts are built with the full size, including all the TF ops rather than selecting the ones from the model. The behavior should be reproduced when trying to build benchmark-model with flex (or even the standalone shared lib):
tmp/BUILD (change `init_tensorflow` visibility if needed, `custom-model.tflite` should be a model containing at least 1 flex op)
```
load("@org_tensorflow//tensorflow:tensorflow.bzl", "tf_cc_binary", "clean_dep")
load("@org_tensorflow//tensorflow/lite:build_def.bzl", "tflite_copts", "tflite_copts_warnings", "tflite_linkopts")
load("@org_tensorflow//tensorflow/lite/delegates/flex:build_def.bzl", "tflite_flex_shared_library")
tflite_flex_shared_library(
name = "tensorflowlite_flex_dynamic",
models=[
":custom-model.tflite",
],
)
cc_import(
name = "libtensorflowlite_flex_dynamic",
shared_library = ":tensorflowlite_flex_dynamic",
)
tf_cc_binary(
name = "benchmark_model_plus_flex_dynamic",
srcs = [
"//tensorflow/lite/tools/benchmark:benchmark_plus_flex_main.cc",
],
copts = tflite_copts() + tflite_copts_warnings(),
linkopts = tflite_linkopts(),
deps = [
":libtensorflowlite_flex_dynamic",
"//tensorflow/lite/tools/benchmark:benchmark_tflite_model_lib",
"//tensorflow/lite/testing:init_tensorflow",
"//tensorflow/lite/tools:logging",
],
)
```
Build command
```
bazel build -c opt \
--cxxopt=`--std=c++17` \
--config=monolithic \
--config=elinux_aarch64 \
--host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
--verbose_failures \
//tmp:tensorflowlite_flex_dynamic
```
I'd like to know if building it with the full size is the expected behavior. If yes, I'd like to know if enabling it is part of the roadmap as it _**seems**_ like a low fruit hanging (it already works for same arch in Android).
---
I experimented a little bit, partially succeeding (could build reduced size binaries by breaking plenty of bazel targets). I'm adding it to the issue with the hope that someone might find it helpful (disclaimer: take it with a pinch of salt as I've got skill issues with TF internals and the bazel build system).
+ Naively, find all targets that selectively depend on `android` or `mobile` configs and ensure that a branch includes `elinux_aarch64` (usually the targets are `portable_tensorflow_lib` and `portable_tensorflow_lib_lite`). This can be done adding `elinux_aarch64` as part of `mobile` configuration and/or `if_*` functions wrapping `select()`. Long story short, replicate dependencies used for `android` config (when makes sense). Ensure that `IS_MOBILE_PLATFORM` is defined for `portable_tensorflow_lib_lite`.
+ Caveats: the python tool to generate the `ops_to_register.h` header (`print_selective_registration_header`) is built natively, and there are some conflicts. One of them is a double registration issue (maybe related to some diamond dependency problem?). The other is related to some missing shared libs used by the tool. The only way I found to fix this is changing dependencies of targets from `tensorflow/python/*/BUILD`. Of course, this affects other dependent targets. I also tried to duplicate targets with modified names to avoid breaking the original ones, but I was unsuccessful.
If relevant, I can try to share the full patch. Just remember the changes will affect building other targets.
Some size results for `libtensorflowlite_flex.so`
(opt, all) -> 99 Mb
(opt, selected) -> 7 Mb
(dbg, all) -> 5.2 Gb
(dbg, selected) -> 2.1 Gb
### Standalone code to reproduce the issue
```shell
Check *current behavior* section. Generate the tmp/BUILD file, ensure a model with flex ops is present, and name of the `models` argument in `tflite_flex_shared_library` is correct.
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5mOWHQ
| 62,913 |
Fix compile error in case CUDA_VERSION < 11060
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[] | 2024-02-07T06:55:07 | 2024-02-26T02:25:04 | 2024-02-26T02:25:04 |
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I met and tried to fix the following compile error with cuda version 11.02:
```
external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:2177:34: error: ‘TF_CUPTI_HAS_CHANNEL_ID’ was not declared in this scope
2177 | AddKernelActivityEvent<TF_CUPTI_HAS_CHANNEL_ID>(
| ^~~~~~~~~~~~~~~~~~~~~~~
external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:2178:76: error: no matching function for call to ‘AddKernelActivityEvent<<expression error> >(xla::profiler::CuptiTraceCollector*&, xla::profiler::{anonymous}::CuptiActivityKernelTy*)’
2178 | collector_, reinterpret_cast<CuptiActivityKernelTy *>(record));
```
building command:
```shell
wget https://github.com/tensorflow/tensorflow/archive/v2.15.0.zip && unzip v2.15.0.zip && cd tensorflow-2.15.0
TF_ENABLE_XLA=1 TF_NEED_CUDA=1 TF_CUDA_CLANG=0 TF_CUDA_COMPUTE_CAPABILITIES=8.0,7.5 TF_NEED_IGNITE=0 TF_NEED_OPENCL_SYCL=0 TF_NEED_ROCM=0 TF_DOWNLOAD_CLANG=0 TF_NEED_MPI=0 TF_SET_ANDROID_WORKSPACE=0 bash -c 'echo | ./configure'
bazelisk build --config=mkl --config=monolithic --config=noaws --config=nogcp --config=nonccl --config=nohdfs --@local_config_cuda//:cuda_compiler=clang --action_env=CLANG_CUDA_COMPILER_PATH=/usr/lib/llvm-16/bin/clang --action_env=TF_CUDNN_VERSION=8 --action_env=TF_CUDA_VERSION=11.2 tensorflow/tools/lib_package:libtensorflow
```
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PR_kwDOArmXAs5mOUHb
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fix compile error in case CUDA_VERSION < 11060
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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/62912/checks?check_run_id=21305303548) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request."
] | 2024-02-07T06:47:19 | 2024-02-07T06:49:31 | 2024-02-07T06:49:31 |
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I met and tried to fix the following compile error with cuda version 11.02:
```
external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:2177:34: error: ‘TF_CUPTI_HAS_CHANNEL_ID’ was not declared in this scope
2177 | AddKernelActivityEvent<TF_CUPTI_HAS_CHANNEL_ID>(
| ^~~~~~~~~~~~~~~~~~~~~~~
external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:2178:76: error: no matching function for call to ‘AddKernelActivityEvent<<expression error> >(xla::profiler::CuptiTraceCollector*&, xla::profiler::{anonymous}::CuptiActivityKernelTy*)’
2178 | collector_, reinterpret_cast<CuptiActivityKernelTy *>(record));
```
building command:
```shell
wget https://github.com/tensorflow/tensorflow/archive/v2.15.0.zip && unzip v2.15.0.zip && cd tensorflow-2.15.0
TF_ENABLE_XLA=1 TF_NEED_CUDA=1 TF_CUDA_CLANG=0 TF_CUDA_COMPUTE_CAPABILITIES=8.0,7.5 TF_NEED_IGNITE=0 TF_NEED_OPENCL_SYCL=0 TF_NEED_ROCM=0 TF_DOWNLOAD_CLANG=0 TF_NEED_MPI=0 TF_SET_ANDROID_WORKSPACE=0 bash -c 'echo | ./configure'
bazelisk build --config=mkl --config=monolithic --config=noaws --config=nogcp --config=nonccl --config=nohdfs --@local_config_cuda//:cuda_compiler=clang --action_env=CLANG_CUDA_COMPILER_PATH=/usr/lib/llvm-16/bin/clang --action_env=TF_CUDNN_VERSION=8 --action_env=TF_CUDA_VERSION=11.2 tensorflow/tools/lib_package:libtensorflow
```
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fix compile error in case CUDA_VERSION < 11060
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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/62911/checks?check_run_id=21305080859) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request."
] | 2024-02-07T06:36:33 | 2024-02-07T06:41:35 | 2024-02-07T06:41:35 |
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I met and tried to fix this error in building process with CUDA_VERSION < 11060:
external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:2177:34: error: ‘TF_CUPTI_HAS_CHANNEL_ID’ was not declared in this scope
2177 | AddKernelActivityEvent<TF_CUPTI_HAS_CHANNEL_ID>(
| ^~~~~~~~~~~~~~~~~~~~~~~
external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:2178:76: error: no matching function for call to ‘AddKernelActivityEvent<<expression error> >(xla::profiler::CuptiTraceCollector*&, xla::profiler::{anonymous}::CuptiActivityKernelTy*)’
2178 | collector_, reinterpret_cast<CuptiActivityKernelTy *>(record));
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How to create libtensorflow.a file for static linking during go build ?
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"@johntharian28 Could you please try creating a libtensorflow.a file for static linking with Go's `go build` command is not officially supported by TensorFlow and is generally considered quite challenging. Using dynamic linking is the recommended and officially supported approach by TensorFlow. You can install the shared libraries (`libtensorflow.so or .dll`) and link them dynamically with your Go code using `-ldflags `options with `go build`. Thank you!",
"Thanks @sushreebarsa . So to install the `libtensorflow.so` file , do I just do \r\n`bazel test --config opt //tensorflow/tools/lib_package:libtensorflow_test\r\nbazel build --config opt //tensorflow/tools/lib_package:libtensorflow`",
"@johntharian28 Please proceed with caution, as it's not officially supported and might not work reliably.\r\nThe commands you provided (bazel test and bazel build) wouldn't directly generate a usable libtensorflow.a for Go.\r\n Thank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"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/62910\">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/62910\">No</a>\n"
] | 2024-02-07T00:47:45 | 2024-02-23T01:46:46 | 2024-02-23T01:46:42 |
NONE
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### System information
- OS Platform and Distribution: Linux Ubuntu 22.04
- TensorFlow installation: Built from source
- TensorFlow library: tf 2.15
- Python version 3.10.12
### Issue
I am trying to use tfgo for some ml applications. I had created a go executbale using go build, but the executable requires tensorflow c api to be installed. I am trying to package libtensorflow so that the executable does not require any external packages. I saw many github issues on creating libtensrflow.a file for tf lite but not for this. Is there some way to do this ?
Thanks
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Manylinux wheel issue using clang
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"@ellie-jan Please refer to the official TensorFlow Manylinux wheel building guide (https://github.com/pypa/manylinux: https://github.com/pypa/manylinux)\r\nProvide the complete auditwheel error message. Could you consider a clean virtual environment and let us know ?\r\nThank you!",
"We are building tensorflow and running auditwheel repair within the docker container tensorflow/build:2.16-python3.11. I have attached the full auditwheel repair verbose output. Thank you!\r\n[auditwheel_repair_verbose.txt](https://github.com/tensorflow/tensorflow/files/14215860/auditwheel_repair_verbose.txt)\r\n",
"@sachinprasadhs Hi, I was wondering if there are any updates regarding this issue. Thank you!",
"A temporary fix for case 2 is to add `--copt=-Wno-error=unused-command-line-argument` to the bazel command. It will suppress the command not used error. ",
"I have tried to add --copt=-Wno-error=unused-command-line-argument as well as --crosstool_top=\"@sigbuild-r2.16-clang_config_cuda//crosstool:toolchain\" to the bazel command to build with clang17, python3.11 and 2.17 version of GLIBC. However, after tensorflow is built successfully, I ran ldd --version && getconf GNU_LIBC_VERSION and saw that the version of glibc used is still 2.31. \r\n\r\nI have attached the output of the bazel command below. \r\n\r\nbuild --copt=-O3 --copt=-Wno-gnu-offsetof-extensions --crosstool_top=\"@sigbuild-r2.16-clang_config_cuda//crosstool:toolchain\" --copt=-Wno-error=unused-command-line-argument --copt=-Wformat --copt=-Wformat-security --copt=-fstack-protector --copt=-fPIC --copt=-fpic --linkopt=-Wl,-z,noexecstack --linkopt=-Wl,-z,relro --linkopt=-Wl,-z,now --linkopt=-fstack-protector --verbose_failures --copt=-march=sandybridge \r\n\r\nAre there additional steps needed in order to downgrade glibc to be compatible with manylinux2014? \r\n\r\n[build_tf_output.txt](https://github.com/tensorflow/tensorflow/files/14466084/build_tf_output.txt)\r\n",
"I'm not able to reproduce the error that you are seeing.\r\n\r\nCan you check if the following steps work for you?\r\n```\r\n1. docker pull tensorflow/build:2.16-python3.11 \r\n2. docker run -itd --name tf_ml2014 tensorflow/build:2.16-python3.11 && docker attach tf_ml2014\r\n3. git clone https://github.com/tensorflow/tensorflow.git && cd tensorflow\r\n4. bazel build --copt=-O3 --copt=-Wno-gnu-offsetof-extensions --crosstool_top=\"@sigbuild-r2.16-clang_config_cuda//crosstool:toolchain\" --copt=-Wno-error=unused-command-line-argument --copt=-Wformat --copt=-Wformat-security --copt=-fstack-protector --copt=-fPIC --copt=-fpic --linkopt=-Wl,-z,noexecstack --linkopt=-Wl,-z,relro --linkopt=-Wl,-z,now --linkopt=-fstack-protector --verbose_failures --copt=-march=sandybridge //tensorflow/tools/pip_package:build_pip_package\r\n5. ./bazel-bin/tensorflow/tools/pip_package/build_pip_package /tf/pkg\r\n6. python3 -m auditwheel repair --plat manylinux2014_x86_64 --wheel-dir /tf/pkg/ /tf/pkg/tensorflow-2.17.0-cp311-cp311-linux_x86_64.whl \r\n```",
"@nitins17 I was able to build the manylinux wheel after following the steps you gave above. I will close this issue. Thank you!",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62909\">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/62909\">No</a>\n"
] | 2024-02-06T21:51:35 | 2024-03-11T18:24:05 | 2024-03-11T18:24:02 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf2.16
### Custom code
No
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
6.1.0
### GCC/compiler version
Clang 17
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Case 1 - We can successfully build tensorflow with clang17; however, we faced the error below when trying to repair the wheel using auditwheel package to make it manylinux2014 compatible.
Case 2 - We have tried to reference this previous issue #60608 and add this flag --crosstool_top="@sigbuild-r2.16-clang_config_cuda//crosstool:toolchain" as a build option. However, this addition causes an error during the tensorflow build.
### Standalone code to reproduce the issue
```shell
Case 1 - Bazel build option
build --copt=-O3 --copt=-Wno-gnu-offsetof-extensions --copt=-Wformat --copt=-Wformat-security --copt=-fstack-protector --copt=-fPIC --copt=-fpic --linkopt=-Wl,-z,noexecstack --linkopt=-Wl,-z,relro --linkopt=-Wl,-z,now --linkopt=-fstack-protector --linkopt=-Wl,--undefined-version --verbose_failures --copt=-march=sandybridge
Case 2 - Bazel build option
build --copt=-O3 --copt=-Wno-gnu-offsetof-extensions --crosstool_top="@sigbuild-r2.16-clang_config_cuda//crosstool:toolchain" --copt=-Wformat --copt=-Wformat-security --copt=-fstack-protector --copt=-fPIC --copt=-fpic --linkopt=-Wl,-z,noexecstack --linkopt=-Wl,-z,relro --linkopt=-Wl,-z,now --linkopt=-fstack-protector --linkopt=-Wl,--undefined-version --verbose_failures --copt=-march=sandybridge
```
### Relevant log output
```shell
Case 1
tensorflow/libtensorflow_cc.so.2 is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libc.so.6 offending versions: GLIBC_2.28, GLIBC_2.27
libm.so.6 offending versions: GLIBC_2.23, GLIBC_2.27, GLIBC_2.29
libtensorflow_framework.so.2
tensorflow/libtensorflow_framework.so.2 is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libm.so.6 offending versions: GLIBC_2.27, GLIBC_2.29
libc.so.6 offending versions: GLIBC_2.27, GLIBC_2.18
libpthread.so.0 offending versions: GLIBC_2.30
tensorflow/compiler/mlir/quantization/tensorflow/calibrator/pywrap_calibration.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
_pywrap_tensorflow_internal.so
libtensorflow_framework.so.2
tensorflow/compiler/mlir/quantization/tensorflow/python/pywrap_function_lib.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/compiler/mlir/quantization/tensorflow/python/pywrap_quantize_model.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
_pywrap_tensorflow_internal.so
libtensorflow_framework.so.2
tensorflow/compiler/mlir/stablehlo/stablehlo_extension.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libc.so.6 offending versions: GLIBC_2.18
tensorflow/compiler/tf2tensorrt/_pywrap_py_utils.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
tensorflow/compiler/tf2xla/ops/_xla_ops.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
tensorflow/core/kernels/libtfkernel_sobol_op.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libm.so.6 offending versions: GLIBC_2.29
libpthread.so.0 offending versions: GLIBC_2.30
libtensorflow_framework.so.2
tensorflow/include/external/ml_dtypes/_ml_dtypes_ext.so is manylinux_2_17(aka manylinux2014) compliant.
tensorflow/include/ml_dtypes/_ml_dtypes_ext.so is manylinux_2_17(aka manylinux2014) compliant.
tensorflow/lite/experimental/microfrontend/python/ops/_audio_microfrontend_op.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libm.so.6 offending versions: GLIBC_2.27
libtensorflow_framework.so.2
tensorflow/lite/python/analyzer_wrapper/_pywrap_analyzer_wrapper.so is manylinux_2_17(aka manylinux2014) compliant.
tensorflow/lite/python/interpreter_wrapper/_pywrap_tensorflow_interpreter_wrapper.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
libm.so.6 offending versions: GLIBC_2.29, GLIBC_2.27
tensorflow/lite/python/metrics/_pywrap_tensorflow_lite_metrics_wrapper.so is manylinux_2_17(aka manylinux2014) compliant.
tensorflow/lite/python/optimize/_pywrap_tensorflow_lite_calibration_wrapper.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libm.so.6 offending versions: GLIBC_2.29, GLIBC_2.27
libtensorflow_framework.so.2
tensorflow/python/_pywrap_dtensor_device.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/_pywrap_mlir.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
_pywrap_tensorflow_internal.so
libtensorflow_framework.so.2
tensorflow/python/_pywrap_parallel_device.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
_pywrap_tensorflow_internal.so
libtensorflow_framework.so.2
tensorflow/python/_pywrap_py_exception_registry.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_cc.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/_pywrap_quantize_training.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/_pywrap_sanitizers.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/_pywrap_tensorflow_internal.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
libtensorflow_cc.so.2
tensorflow/python/_pywrap_tfcompile.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
_pywrap_tensorflow_internal.so
libtensorflow_cc.so.2
tensorflow/python/_pywrap_tfe.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_cc.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/_pywrap_toco_api.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
_pywrap_tensorflow_internal.so
libtensorflow_framework.so.2
tensorflow/python/flags_pybind.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/autograph/impl/testing/pybind_for_testing.so is manylinux_2_17(aka manylinux2014) compliant.
tensorflow/python/client/_pywrap_debug_events_writer.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
_pywrap_tensorflow_internal.so
libtensorflow_framework.so.2
tensorflow/python/client/_pywrap_device_lib.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/client/_pywrap_events_writer.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/client/_pywrap_tf_session.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
_pywrap_tensorflow_internal.so
libtensorflow_framework.so.2
tensorflow/python/data/experimental/service/_pywrap_server_lib.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/data/experimental/service/_pywrap_snapshot_utils.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
_pywrap_tensorflow_internal.so
libtensorflow_framework.so.2
tensorflow/python/data/experimental/service/_pywrap_utils.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
_pywrap_tensorflow_internal.so
libtensorflow_framework.so.2
libm.so.6 offending versions: GLIBC_2.29
tensorflow/python/framework/_dtypes.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/framework/_op_def_library_pybind.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/framework/_op_def_registry.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/framework/_proto_comparators.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
_pywrap_tensorflow_internal.so
libtensorflow_framework.so.2
tensorflow/python/framework/_python_memory_checker_helper.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/framework/_pywrap_python_api_dispatcher.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/framework/_pywrap_python_op_gen.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/framework/_test_metrics_util.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/framework/lib_native_proto_caster.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
libpthread.so.0 offending versions: GLIBC_2.30
libm.so.6 offending versions: GLIBC_2.29
tensorflow/python/grappler/_pywrap_tf_cluster.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
_pywrap_tensorflow_internal.so
libtensorflow_framework.so.2
tensorflow/python/grappler/_pywrap_tf_item.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/grappler/_pywrap_tf_optimizer.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/lib/core/_pywrap_py_func.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
_pywrap_tensorflow_internal.so
libtensorflow_framework.so.2
tensorflow/python/lib/io/_pywrap_file_io.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/lib/io/_pywrap_record_io.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/platform/_pywrap_cpu_feature_guard.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
_pywrap_tensorflow_internal.so
libtensorflow_framework.so.2
tensorflow/python/platform/_pywrap_stacktrace_handler.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
_pywrap_tensorflow_internal.so
libtensorflow_framework.so.2
tensorflow/python/platform/_pywrap_tf2.so is manylinux_2_17(aka manylinux2014) compliant.
tensorflow/python/profiler/internal/_pywrap_profiler.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
_pywrap_tensorflow_internal.so
libtensorflow_framework.so.2
tensorflow/python/profiler/internal/_pywrap_traceme.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/saved_model/pywrap_saved_model.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/tpu/_pywrap_tpu_embedding.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libm.so.6 offending versions: GLIBC_2.29
_pywrap_tensorflow_internal.so
libtensorflow_framework.so.2
tensorflow/python/util/_pywrap_checkpoint_reader.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/util/_pywrap_determinism.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
_pywrap_tensorflow_internal.so
libtensorflow_framework.so.2
tensorflow/python/util/_pywrap_kernel_registry.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/util/_pywrap_nest.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/util/_pywrap_stat_summarizer.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
_pywrap_tensorflow_internal.so
libtensorflow_framework.so.2
tensorflow/python/util/_pywrap_tensor_float_32_execution.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/util/_pywrap_tfprof.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/util/_pywrap_transform_graph.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/util/_pywrap_util_port.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/util/_pywrap_utils.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/util/_tf_stack.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/util/fast_module_type.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
libtensorflow_framework.so.2
_pywrap_tensorflow_internal.so
tensorflow/python/util/pywrap_xla_ops.so is not manylinux_2_17(aka manylinux2014) compliant because it links the following forbidden libraries:
_pywrap_tensorflow_internal.so
libtensorflow_framework.so.2
Case 2
ERROR: /root/.cache/bazel/_bazel_root/a8f8ba237b1db7e181d878a91ad9136d/external/boringssl/BUILD:133:11: Compiling src/crypto/blake2/blake2.c failed: (Exit 1): clang failed: error executing command (from target @boringssl//:crypto)
(cd /root/.cache/bazel/_bazel_root/a8f8ba237b1db7e181d878a91ad9136d/execroot/org_tensorflow && \
exec env - \
CLANG_COMPILER_PATH=/usr/lib/llvm-17/bin/clang \
DOCKER_CACHEBUSTER=1706400736283930747 \
LD_LIBRARY_PATH=/usr/local/lib64:/usr/local/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64 \
PATH=/usr/local/bin:/dt9/usr/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin \
PWD=/proc/self/cwd \
PYTHON_BIN_PATH=/usr/bin/python3.11 \
PYTHON_LIB_PATH=/usr/lib/python3/dist-packages \
TF2_BEHAVIOR=1 \
/usr/lib/llvm-17/bin/clang -MD -MF bazel-out/k8-opt/bin/external/boringssl/_objs/crypto/blake2.pic.d '-frandom-seed=bazel-out/k8-opt/bin/external/boringssl/_objs/crypto/blake2.pic.o' '-DBAZEL_CURRENT_REPOSITORY="boringssl"' -iquote external/boringssl -iquote bazel-out/k8-opt/bin/external/boringssl -isystem external/boringssl/src/include -isystem bazel-out/k8-opt/bin/external/boringssl/src/include -Wno-builtin-macro-redefined '-D__DATE__="redacted"' '-D__TIMESTAMP__="redacted"' '-D__TIME__="redacted"' -fPIC -U_FORTIFY_SOURCE '-D_FORTIFY_SOURCE=1' -fstack-protector -Wall -Wno-invalid-partial-specialization -fno-omit-frame-pointer -no-canonical-prefixes -DNDEBUG -g0 -O2 -ffunction-sections -fdata-sections '--cuda-path=/usr/local/cuda-12.3' -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 -Wno-gnu-offsetof-extensions -O3 -Wno-gnu-offsetof-extensions -Wformat -Wformat-security -fstack-protector -fPIC -fpic '-march=sandybridge' -DBORINGSSL_IMPLEMENTATION -Wa,--noexecstack -Wall -Werror '-Wformat=2' -Wsign-compare -Wmissing-field-initializers -Wwrite-strings -Wshadow -fno-common '-D_XOPEN_SOURCE=700' '-std=c11' -Wmissing-prototypes -Wold-style-definition -Wstrict-prototypes '--sysroot=/dt9' -c external/boringssl/src/crypto/blake2/blake2.c -o bazel-out/k8-opt/bin/external/boringssl/_objs/crypto/blake2.pic.o)
# Configuration: f2215bf157b278d7b19f73aa38c637c640032d970a2eae7fdce0ea6de3f47b95
# Execution platform: @local_execution_config_platform//:platform
clang: error: argument unused during compilation: '--cuda-path=/usr/local/cuda-12.3' [-Werror,-Wunused-command-line-argument]
```
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I_kwDOArmXAs5-dQZx
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inf outputs with an OpenCL delegate for a pattern with a sequence of Dense/FullyConnected layers
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[
"@MinaBabahaji-ML,\r\nThank you for the issue. Could you please provide the complete error log which helps to analyse and debug the root cause of the issue. Thank you!",
"Sure, this is the complete log message:\r\n```\r\nINFO: Created TensorFlow Lite delegate for GPU.\r\nINFO: Initialized TensorFlow Lite runtime.\r\nVERBOSE: Replacing 20 out of 20 node(s) with delegate (TfLiteGpuDelegateV2) node, yielding 1 partitions for the whole graph.\r\nINFO: Initialized OpenCL-based API.\r\nINFO: Created 1 GPU delegate kernels.\r\nINFO: Created TensorFlow Lite XNNPACK delegate for CPU.\r\nVERBOSE: Replacing 20 out of 20 node(s) with delegate (TfLiteXNNPackDelegate) node, yielding 1 partitions for the whole graph.\r\nOpenCL output:\r\n-32560, -39104, -48128, 24512, -inf, -9808, -inf, -41344, -22960, -54048, -49792, -inf, inf, -1512, -inf, -28368, -inf, -inf, -9864, -14296, -inf, 863, -11792, -48832, -inf, -51040, -20864, -32176, -inf, -10928, -26560, -inf, -21344, -inf, -41632, -22544, -60128, -44832, -37856, -37152, 21216, -inf, -38304, -inf, -15312, 17344, -inf, -18816, -40960, -14112, -44416, -7144, 25568, 18880, -54496, -50400, -23456, -18336, -31072, -inf, inf, -13680, -inf, 784, -52544, -19488, -37856, 36224, -50880, -14416, -13944, -inf, -37952, -41856, 3120, -50176, -38272, -45728, -inf, 9872, -inf, -52768, -40512, -inf, -57664, -64192, inf, -61120, -inf, 11736, -inf, -29488, -35200, -inf, -inf, -inf, -36288, -inf, -43648, -inf, -inf, -14968, -50624, -15208, -inf, 8320, -21344, -65472, 51008, -18768, inf, -9824, -32832, 15624, 14648, -37280, -34688, 42432, -19264, -22688, inf, -34, -32896, -inf, -46272, -62176, -50848, -29344, -inf, 9248, -2568, 16152, -22912, -28880, -4408, -17696, inf, -17632, 32960, -1622, -41536, -36928, -inf, -31872, -62080, -inf, -47296, -inf, -54336, -63904, -35776, 32064, -623.5, -inf, -2802, -46464, -inf, -inf, -inf, -17408, -63360, -10992, -inf, -28960, -56256, -33152, -16296, -inf, 1054, 27456, -57312, -17760, -29312, -50368, -7928, -38368, inf, -inf, -33376, -57664, -20592, 1857, 96, -50560, -11512, -936, 808, -10944, 10376, -44736, -23456, -36320, -51392, -inf, -25760, 14320, -inf, 2280, -52928, -18944, -24928, 3820, -63776, 13120, 17712, -60416, -inf, 10096, -26656, -46688, inf, -3528, -22256, -30512, -61728, 3274, -16232, -18912, -39552, -15896, 1952, -57280, 45216, -3566, -10440, -inf, -18320, -2868, -40800, -29600, -20640, -52928, -inf, 4104, -160, -46240, -32800, -51392, -inf, -inf, 480, -34208, -inf, -5936, -49376, -11184, -37312, 18464, 9872, 20816, 62240, inf, -35360, -12288, -18624, -25728, -23328, 18128, -4580, -inf, 1204, -inf, -52384, -20416, -24928, -22832, -48224, -inf, -8328, -inf, 35168, -53440, -52512, -29024, -39264, -28992, 7340, -5240, -4692, -inf, -60384, 9720, -12272, -47616, -20128, -28832, -10816, -36096, -28944, -31872, -34624, -48000, -inf, -18336, inf, -11232, -22880, -49632, -22400, -inf, 11888, -31712, -29312, -44512, -10112, -49440, -20736, -45088, -30080, -inf, -18624, -15440, -14032, -22080, -4492, -43776, -43456, -inf, -39520, -8040, -16256, -49472, -inf, 480, -38496, -inf, -29920, -33696, -inf, -13808, -inf, -4096, -44480, -inf, -41920, -27280, 8424, -18800, -31120, 20352, -14320, -34336, inf, -22208, -inf, -11120, -38048, -inf, -29584, -17728, 2448, 8904, -40384, -32672, inf, -25312, -19904, -62272, -53216, -21472, -5024, 14048, -inf, -53568, -24832, 12704, 21696, -15600, -inf, -35840, -7208, -52160, 7224, 23984, -inf, -55680, -22816, -313, -19280, -inf, -29952, -57024, -12320, -57056, -32000, inf, 264, -15184, -25824, -12704, inf, 5488, -48128, -34592, 16864, -27840, -inf, inf, -21056, -30432, -63104, -32864, -52480, -29728, -14736, -53088, -inf, -5648, -51648, -12912, -17392, -22880, -inf, -24944, -inf, -35616, -18272, 2776, 33536, -inf, 13592, -56032, -38976, -inf, -36320, -inf, -15936, -5452, -12832, inf, 2688, -inf, -inf, -45696, 4600, 10560, 39840, -44928, -24480, -15496, -13728, -23840, -8152, inf, 44800, -50112, -34816, -45248, 4348, -60672, -inf, -38912, -24640, 45376, -43008, -inf, -22592, -inf, -49024, -8752, -26368, -59520, -inf, inf, -19232, -26976, 6864, -inf, -inf, -10544, 2048, -54144, 12688, -14896, -52352, -28672, -6476, -inf, -22304, -50272, -572, -inf, -63136, -32368, -29088, -inf, -20592, 38272, -32928, -1964, 6436, -63232, -49216, inf, -inf, -46144, 8664, -16736, -6252, -10080, -37344, -inf, -40224, -9184, -63232, 31040, 48512, -inf, -25936, -16960, -38400, -inf, \r\n```",
"Hi @MinaBabahaji-ML, in attempting to reproduce your issue I ran into this issue:\r\n\r\n```\r\nadb shell \"cd /data/local/tmp && LD_LIBRARY_PATH=. ./model_test --model=model_files/sample.tflite --input_shape=1,105 --output_shape=1,512\"\r\nCANNOT LINK EXECUTABLE \"./model_test\": library \"libc++_shared.so\" not found: needed by main executable\r\n```\r\n\r\nI'm using an emulator and NDK=25.2.9519653. Are we missing some steps?\r\n\r\nThanks for your help.",
"Hello @pkgoogle . I added `libc++_shared.so` to the tensorflow_lite_c_2_15_0 package, and please push it to the device with `adb push ./tensorflow_lite_c_2_15_0/lib/aarch64/libc++_shared.so /data/local/tmp` . I also added this to the build instructions. However, I am afraid you may not be able to regenerate the problem with an emulator. I tested it on a Galaxy S20 device. ",
"HI @MinaBabahaji-ML, I was able to continue with the new instructions but am running into a different issue probably due to using an emulator.\r\n\r\nHi @sirakiin, can you please take a look? Thanks.\r\n",
"Hi @MinaBabahaji-ML, I already commented on [AI-Edge-Torch](https://github.com/google-ai-edge/ai-edge-torch), on your other issue. Can you try this out with this script and let us know if it resolves your issue.... you may need to train a bit to get reasonable outputs.\r\n\r\n```py\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport ai_edge_torch\r\n\r\n\r\nclass FCResidualBlock(nn.Module):\r\n def __init__(self):\r\n super().__init__()\r\n self.dense1 = nn.Linear(105, 512)\r\n self.dense2 = nn.Linear(512, 512)\r\n self.dense3 = nn.Linear(512, 512)\r\n self.dense4 = nn.Linear(512, 512)\r\n self.dense5 = nn.Linear(512, 512)\r\n\r\n def forward(self, x):\r\n x = self.dense1(x)\r\n y = F.dropout(F.relu(self.dense2(x)), p=0.2)\r\n y = F.dropout(F.relu(self.dense3(y)), p=0.2)\r\n x = x + y\r\n x = F.dropout(F.relu(self.dense4(x)), p=0.2)\r\n return self.dense5(x)\r\n\r\n\r\nmodel = FCResidualBlock()\r\nsample_inputs = (torch.randn(1, 105),)\r\n\r\nedge_model = ai_edge_torch.convert(model.eval(), sample_inputs)\r\nedge_model.export(\"fc_res_block.tflite\")\r\n```"
] | 2024-02-06T20:19:21 | 2024-06-11T21:34:30 | null |
NONE
| null | null | null |
### Issue Summary:
When running a TensorFlow Lite model ( a sequence of Dense/FullyConnected layers) on an Android 13 device with TensorFlow version 2.15 built from source, using the OpenCL delegate, I am experiencing 'inf' outputs. This issue can't be reproduced with the XNNPACK delegate. The model is shared in the github repository.
### 1. System information
- Android 13
- TensorFlow version 2.15 built from source
### 2. Code
I did not encounter any issues in converting the model, but when running it on the phone with the OpenCL delegate, I am consistently getting 'inf' outputs. The code and steps to reproduce the problem can be found in the following GitHub repository:
[GitHub Repository Link](https://github.com/MinaBabahaji-ML/GPU_problem.git)
### 3. logs:
INFO: Created TensorFlow Lite delegate for GPU.
INFO: Initialized TensorFlow Lite runtime.
... (other log entries)
inf, -25936, -16960, -38400, -inf,
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"Hey @markub3327,\r\n\r\nIt seems like the issue arose because you used `color_mode=\"grayscale\"` with `tf.keras.utils.image_dataset_from_directory`, but your dataset has RGBA images. Try switching it to `color_mode=\"rgba\"`\r\n\r\nCheck out the documentation [here](https://www.tensorflow.org/api_docs/python/tf/keras/utils/image_dataset_from_directory) for more info.\r\n\r\nHope this clears things up!",
"Hello, @aditya02shah \r\n\r\n loaded images are combination of RGBA and Grayscale images. I need to unified it to grayscale during loading by `tf.keras.utils.image_dataset_from_directory`. \r\n\r\n| Args | |\r\n| ----------- | ----------- |\r\n| color_mode | One of \"grayscale\", \"rgb\", \"rgba\". Defaults to \"rgb\". Whether the images will be converted to have 1, 3, or 4 channels. |\r\n\r\nThe `color_mode` set as `grayscale` should be convert all types of images to 1 channel (grayscale) !!!\r\n\r\nThanks for understanding.\r\n",
"@markub3327 I would recommend converting all the images to the same format, preferably Grayscale, to ensure uniformity. This can be achieved by implementing a preprocessing function to convert the images during loading. \r\nOnce all images are in the same format, you can use them as needed for your requirements.",
"@aditya02shah \r\nThe preprocessing function is applied after creating the dataset by `tf.keras.utils.image_dataset_from_directory`. After that I can use `.map` API call.",
"@markub3327,\r\nI was facing a different issue while executing the above provided code. Could you please provide the complete code and the dependencies which helps e=debug the issue in an effective way. Kindly find the [gist](https://colab.research.google.com/gist/tilakrayal/d9e4362fd2620f3c99535d0aeb1f6c0d/untitled1726.ipynb). Thank you",
"The provided code is a part of huge project. I cannot send more, but the error what you mention is:\r\n\r\n> /usr/local/lib/python3.10/dist-packages/tensorflow/python/lib/io/file_io.py in list_directory_v2(path)\r\n> 766 \"\"\"\r\n> 767 if not is_directory(path):\r\n> --> 768 raise errors.NotFoundError(\r\n> 769 node_def=None,\r\n> 770 op=None,\r\n> \r\n> NotFoundError: Could not find directory /code/dataset/train_dataset\r\n\r\nThis error is about path to folder with images contained in dataset. The images are combinations of RGBA and Grayscale (mode = L and mode = RGBA by Pillow). I cannot provide the whole dataset of images here. Please try to model this situation.\r\n\r\nThanks.",
"Certainly! The code you've provided appears to be related to setting up a **TensorFlow image dataset** for training and validation. Let's break it down:\r\n\r\n1. **`tf.keras.utils.image_dataset_from_directory`**:\r\n - This function creates a dataset from image files in a directory.\r\n - It takes the following parameters:\r\n - `\"/code/dataset/train_dataset\"`: The path to the directory containing your training images.\r\n - `validation_split=0.1`: Specifies that 10% of the data will be used for validation.\r\n - `subset=\"both\"`: Includes both training and validation subsets.\r\n - `image_size=(128, 128)`: Resizes the images to 128x128 pixels.\r\n - `interpolation=\"bicubic\"`: Interpolation method for resizing.\r\n - `batch_size=None`: The batch size (you can set this to a specific value).\r\n - `color_mode=\"grayscale\"`: Converts images to grayscale.\r\n - `shuffle=True`: Shuffles the dataset.\r\n - `seed=123`: Sets a random seed for reproducibility.\r\n\r\n2. **`preprocessing` function**:\r\n - This function normalizes the pixel values of the images.\r\n - It clips the values between 0 and 1.\r\n - The function is applied to each image in the dataset using `map`.\r\n\r\n3. **Data Processing Steps**:\r\n - The dataset is shuffled (`train_ds.shuffle(1024)`).\r\n - Batches are created (`train_ds.batch(config.batch_size, drop_remainder=True)`).\r\n - Data is prefetched for efficient loading (`train_ds.prefetch(tf.data.AUTOTUNE)`).\r\n\r\n4. **Model Training**:\r\n - The `model.fit` function trains the model using the training dataset (`train_ds`).\r\n - It specifies the number of epochs (`config.epochs`) and uses the validation dataset (`val_ds`) for validation during training.\r\n\r\nRemember to replace `config.batch_size` and `config.epochs` with actual values from your configuration. If you have any further questions or need additional assistance, feel free to ask! 😊",
"import tensorflow as tf\r\n\r\n# Assume you have a dataset of image files in \"/path/to/images\"\r\nimage_dir = \"/path/to/images\"\r\n\r\n# Create a dataset from image files\r\ntrain_ds = tf.keras.utils.image_dataset_from_directory(\r\n image_dir,\r\n validation_split=0.1, # 10% for validation\r\n subset=\"training\",\r\n image_size=(128, 128),\r\n batch_size=32,\r\n color_mode=\"grayscale\",\r\n shuffle=True,\r\n seed=123,\r\n)\r\n\r\n# Define a preprocessing function\r\ndef preprocessing(image, label):\r\n # Normalize pixel values to [0, 1]\r\n image = tf.clip_by_value(tf.cast(image, dtype=tf.float32) / 255.0, 0.0, 1.0)\r\n return image, label\r\n\r\n# Apply the preprocessing function to each image in the dataset\r\ntrain_ds = train_ds.map(preprocessing, num_parallel_calls=tf.data.AUTOTUNE)\r\n\r\n# Shuffle the dataset\r\ntrain_ds = train_ds.shuffle(1024)\r\n\r\n# Create batches\r\nbatch_size = 64\r\ntrain_ds = train_ds.batch(batch_size, drop_remainder=True)\r\n\r\n# Prefetch for efficient loading\r\ntrain_ds = train_ds.prefetch(tf.data.AUTOTUNE)\r\n\r\n# Now you can use this preprocessed dataset for training your model!\r\nIn this example:\r\n\r\nWe load image files from a directory using image_dataset_from_directory.\r\nThe preprocessing function normalizes pixel values.\r\nWe shuffle the dataset, create batches, and prefetch data for training.\r\nFeel free to adapt this code to your specific use case! 😊\r\n\r\n\r\n\r\n\r\n\r\n",
"@ChrisnaMishra \r\n\r\nYes, you explain it correctly. The only one issues is a warning: `W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: profile 'ICC Profile': 'GRAY': Gray color space not permitted on RGB PNG`",
"@markub3327,\r\nApologies for the delay. looks like this issue is more related to Keras. Could you please raise the issue in [keras-team/Keras](https://github.com/keras-team/keras/issues) repo for the keras3.0 or [keras-team/tf-keras](https://github.com/keras-team/tf-keras/issues) for the tf-keras. Thank you!"
] | 2024-02-06T19:13:24 | 2024-06-05T03:36:28 | null |
CONTRIBUTOR
| 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
Ubuntu 22.04
### 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 created dataset using `tf.keras.utils.image_dataset_from_directory`. When I fitting the model with this dataset I gets a warning about color space. My images have Pillow mode: **L** and **RGBA** and the resulted dataset is grayscale.
Where is the issue?
### Standalone code to reproduce the issue
```shell
train_ds, val_ds = tf.keras.utils.image_dataset_from_directory(
"/code/dataset/train_dataset",
validation_split=0.1, # 90% training, 10% validation
subset="both",
image_size=(128, 128),
interpolation="bicubic",
batch_size=None,
color_mode="grayscale",
shuffle=True,
seed=123,
)
def preprocessing(image, label):
# normalize
image = tf.clip_by_value(tf.cast(image, dtype=tf.float32) / 255.0, 0.0, 1.0)
return image, label
train_ds = train_ds.map(
preprocessing, num_parallel_calls=tf.data.AUTOTUNE
)
train_ds = train_ds.shuffle(1024)
train_ds = train_ds.batch(config.batch_size, drop_remainder=True)
train_ds = train_ds.prefetch(tf.data.AUTOTUNE)
model.fit(
train_ds,
epochs=config.epochs,
validation_data=val_ds,
)
```
### Relevant log output
```shell
W tensorflow/core/lib/png/png_io.cc:88] PNG warning: iCCP: profile 'ICC Profile': 'GRAY': Gray color space not permitted on RGB PNG
```
|
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PR_kwDOArmXAs5mLkff
| 62,906 |
Comments and presentation of WIP Windows support for CI
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[
"Hi @angerson This PR is in draft, any update on this? Please. Thank you!",
"Hi @angerson This PR is in draft, any update on this? Please. Thank you!",
"This was just for information purposes. I think it can be closed"
] | 2024-02-06T18:59:32 | 2024-04-27T14:52:48 | 2024-04-27T14:52:48 |
CONTRIBUTOR
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As far as I can tell, this encapsulates what I had in-progress while I was working on getting Windows builds to work better in Docker containers. I had a Windows Kokoro machine running Docker that I used to test this, and was able to get builds working, but wasn't feeling certain about the configuration, and hadn't integrated anything with the CI system yet. I've included a lot of comments explaining what I was working on.
- One detail that's left out of the comments is that I'm not sure how the recent changes to the Pip package build process factor in to this.
- I removed a lot of bits from the Dockerfile that weren't necessary (this Dockerfile is based on Kokoro's Windows Dockerfile)
- I wasn't able to explicitly test this PR as-is, so it will probably need tweaking before it works.
DO NOT MERGE -- for demonstration only.
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Test internal config change
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Updated the release notes with two major changes
1. The transition of the compiler from MSVC to CLANG on the Windows Platform to build TensorFlow-CPU builds.
2. Updating release notes to reflect changes in Keras. With 2.16 Keras 3.0 release will be default keras. Keras 3.0 has many changes compared to the previous version: Keras 2.x. The changes can break customer models. Adding information in release notes for customers who want to keep using Keras 2.x version and those who want to move to Keras 3.0.
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Padding type for the pooling layer 'PoolingLayerBuilder (MEAN)_57' is not set
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[
"@Varfalamei The error message \"Error reading protobuf spec. validator error: Padding type for the pooling layer 'PoolingLayerBuilder (MEAN)_57' is not set.\" indicates that you haven't specified the padding behavior for the pooling layer with index 57 in your TensorFlow model. Please refer to the compatibility guide for supported operators and layers: https://www.tensorflow.org/lite/guide/ops_compatibility.\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/62902\">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/62902\">No</a>\n"
] | 2024-02-06T12:43:51 | 2024-02-23T01:46:49 | 2024-02-23T01:46:44 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
20.04
### Mobile device
Iphone 15
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I’m trying to convert the tf efficientnetv2s model to tflite format so that I can then use it on iOS with the CoreML delegate, but I encountered the problem:
`Error compiling model compiler error: Error reading protobuf spec. validator error: Padding type for the pooling layer 'PoolingLayerBuilder (MEAN)_57' is not set.`
Is there a normal way to solve this problem? So far, only translating directly into CoreML has helped, but I still want to understand the problem and how to solve it.
It is also worth clarifying that the model runs on Android with different delegates and on iOS with the Metal delegate
### Standalone code to reproduce the issue
```shell
# code for convert model
from tensorflow.keras.applications.efficientnet_v2 import EfficientNetV2S
from albumentations import Compose, LongestMaxSize, Normalize, PadIfNeeded
import tensorflow as tf
from typing import Dict, List, Any, Optional, Tuple
from pathlib import Path
import numpy as np
mean = (0.485, 0.456, 0.406)
std = (0.229, 0.224, 0.225)
input_size = (128, 128)
def preprocess_input(
input: List[np.ndarray],
image_size: Tuple[int, int] = (128, 128),
mean: Tuple[float, float, float] = (0.485, 0.456, 0.406),
std: Tuple[float, float, float] = (0.229, 0.224, 0.225),
) -> np.ndarray:
width, height = image_size
transforms = Compose(
[
LongestMaxSize(max_size=max(image_size)),
PadIfNeeded(width, height, border_mode=cv2.BORDER_CONSTANT, value=0),
Normalize(mean=mean, std=std),
]
)
res = np.array([transforms(image=np.array(item))["image"] for item in input])
return res
def normalize_func(
image: np.ndarray,
mean: Optional[Tuple[float, float, float]] = mean,
std: Optional[Tuple[float, float, float]] = std,
max_pixel_value: float = 255.0,
) -> np.ndarray:
if mean is not None and std is not None:
mean = tf.convert_to_tensor(mean, dtype=tf.float32)
std = tf.convert_to_tensor(std, dtype=tf.float32)
mean *= max_pixel_value
std *= max_pixel_value
denominator = tf.math.reciprocal(std)
image -= mean
image *= denominator
return image
preprocess_input_img_size = lambda x: preprocess_input(x, input_size) # noqa
base_model = EfficientNetV2S(input_shape=(128, 128, 3), include_top=False)
model = tf.keras.Sequential(
[
base_model,
tf.keras.layers.GlobalAveragePooling2D(),
tf.keras.layers.Dropout(rate=0.5),
tf.keras.layers.Dense(1500, activation="softmax"),
]
)
@tf.function(input_signature=[tf.TensorSpec(shape=(1, 128, 128, 3), dtype=tf.uint8)])
def tf_model(image):
image = tf.cast(image, tf.float32)
image = normalize_func(image)
predictions = model(image)
return predictions
tf_model_func = tf_model.get_concrete_function()
converter = tf.lite.TFLiteConverter.from_concrete_functions([tf_model_func])
tflite_model = converter.convert()
tflite_model_file = Path('models/20240201_efficientnetv2s_model.tflite')
tflite_model_file.write_bytes(tflite_model)
# Then I use the model in swift app with CoreML Delegate
```
### Relevant log output
```shell
TensorFlow Lite Error: Failed to Compile and save Model.
TensorFlow Lite Error: CoreMl Kernel was not initialized
TensorFlow Lite Error: Node number 505 (TfLiteCoreMlDelegate) failed to prepare.
TensorFlow Lite Error: Restored original execution plan after delegate application failure.
"Temp folder items count after clean: 0"
TensorFlow Lite Error: keep_dims should be true for Mean op.
CoreML delegate: 500 nodes delegated out of 505 nodes, with 2 partitions.
INFO: CoreML delegate: 500 nodes delegated out of 505 nodes, with 2 partitions.
Error compiling model compiler error: Error reading protobuf spec. validator error: Padding type for the pooling layer 'PoolingLayerBuilder (MEAN)_57' is not set.
TensorFlow Lite Error: Failed to Compile and save Model.
TensorFlow Lite Error: CoreMl Kernel was not initialized
TensorFlow Lite Error: Node number 505 (TfLiteCoreMlDelegate) failed to prepare.
TensorFlow Lite Error: Restored original execution plan after delegate application failure.
"Temp folder items count after clean: 0"
TensorFlow Lite Error: keep_dims should be true for Mean op.
CoreML delegate: 500 nodes delegated out of 505 nodes, with 2 partitions.
INFO: CoreML delegate: 500 nodes delegated out of 505 nodes, with 2 partitions.
Error compiling model compiler error: Error reading protobuf spec. validator error: Padding type for the pooling layer 'PoolingLayerBuilder (MEAN)_57' is not set.
TensorFlow Lite Error: Failed to Compile and save Model.
TensorFlow Lite Error: CoreMl Kernel was not initialized
TensorFlow Lite Error: Node number 505 (TfLiteCoreMlDelegate) failed to prepare.
TensorFlow Lite Error: Restored original execution plan after delegate application failure.
"Temp folder items count after clean: 0"
TensorFlow Lite Error: keep_dims should be true for Mean op.
CoreML delegate: 500 nodes delegated out of 505 nodes, with 2 partitions.
INFO: CoreML delegate: 500 nodes delegated out of 505 nodes, with 2 partitions.
Error compiling model compiler error: Error reading protobuf spec. validator error: Padding type for the pooling layer 'PoolingLayerBuilder (MEAN)_57' is not set.
TensorFlow Lite Error: Failed to Compile and save Model.
TensorFlow Lite Error: CoreMl Kernel was not initialized
TensorFlow Lite Error: Node number 505 (TfLiteCoreMlDelegate) failed to prepare.
TensorFlow Lite Error: Restored original execution plan after delegate application failure.
"Temp folder items count after clean: 0"
TensorFlow Lite Error: keep_dims should be true for Mean op.
CoreML delegate: 500 nodes delegated out of 505 nodes, with 2 partitions.
INFO: CoreML delegate: 500 nodes delegated out of 505 nodes, with 2 partitions.
Error compiling model compiler error: Error reading protobuf spec. validator error: Padding type for the pooling layer 'PoolingLayerBuilder (MEAN)_57' is not set.
TensorFlow Lite Error: Failed to Compile and save Model.
TensorFlow Lite Error: CoreMl Kernel was not initialized
TensorFlow Lite Error: Node number 505 (TfLiteCoreMlDelegate) failed to prepare.
TensorFlow Lite Error: Restored original execution plan after delegate application failure.
"Temp folder items count after clean: 0"
Runner/ModelLoader.swift:71: Assertion failed
```
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[
"@QiyasovH Please make sure CocoaPods is correctly installed and configured in your project. You may refer to the official guide for your system: https://guides.cocoapods.org/using/getting-started\r\nCould you double-check your Podfile:\r\nEnsure you have the correct version of TensorFlowLiteSwift specified:\r\n```\r\npod 'TensorFlowLiteSwift', '~> 2.6.0'\r\n\r\n```\r\nAlso avoid including both TensorFlowLiteSwift and TensorFlowLiteObjC as they share the same base (TensorFlowLiteC). Please choose the language compatible with your project.\r\nKindly run pod install again to re-download and link dependencies.\r\nThank you!",
"Just update CocoaPods. Version is 1.15.2.\r\nI get the same error No such models 'TensorFlowLiteC'. \r\nWhen entered to folder Pods->TensorFlowLiteC , this folder is empty\r\n",
"@QiyasovH \r\nIn order to expedite the trouble-shooting process, please provide TF version you are using and a code snippet to reproduce the issue reported here. Thank you!",
"\r\n<img width=\"573\" alt=\"Снимок экрана 2024-02-07 в 15 07 52\" src=\"https://github.com/tensorflow/tensorflow/assets/85330968/8c1b5f2d-3fd7-42c8-b23a-c5f4178fbd12\">\r\n<img width=\"1397\" alt=\"Снимок экрана 2024-02-07 в 15 08 40\" src=\"https://github.com/tensorflow/tensorflow/assets/85330968/ab8e7f16-7ddf-4dd4-9a4e-0d7a1e5840d1\">\r\n\r\n<img width=\"536\" alt=\"Снимок экрана 2024-02-07 в 15 09 23\" src=\"https://github.com/tensorflow/tensorflow/assets/85330968/0c577676-708e-43ee-a3e3-5354b3f66c7c\">\r\n",
"@QiyasovH Could you please provide the code details in colab gist or notebook as it would be helpful to analyze the issue?\r\nThank you!",
"Did anyone get a solution for this?"
] | 2024-02-06T10:25:56 | 2024-04-29T07:35:30 | 2024-02-09T03:25:44 |
NONE
| null | null | null |
Hello. I try to install pod 'TensorFlowLiteSwift' , when terminal write me that installation is done. During building the project, I get errors "no such models 'TensorFlowLiteC' or 'TensorFlowLiteSwift'"
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I_kwDOArmXAs5-YUY-
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TFlite 2.4.2 build flex delegate error
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[
"@yide1235,\r\nTensorFlow 2.4 is pretty older version. Could you please update TensorFlow to the latest stable version v2.15 and check if you are facing the same issue.\r\n\r\nAlso I suspect you have missed to submit the CMakeLists Info:\r\n\r\n```\r\n\r\nproject(load_model)\r\nSET(TENSORFLOW_CORE_PATH ${CMAKE_SOURCE_DIR}/tensorflow/core)\r\nMESSAGE(STATUS \"TENSORFLOW_CORE_PATH ${TENSORFLOW_CORE_PATH}\")\r\nSET(TENSORFLOW_LIBARY ${CMAKE_SOURCE_DIR}/tensorflow/lib/libtensorflow-core.a)\r\nMESSAGE(STATUS \"TENSORFLOW_LIBARY ${TENSORFLOW_LIBARY}\")\r\nSET(TENSORFLOW_PROTOBUF_INCLUDE_PATH ${CMAKE_SOURCE_DIR}/tensorflow/protobuf/include)\r\nSET(TENSORFLOW_PROTOBUF_LIBRARY_PATH ${CMAKE_SOURCE_DIR}/tensorflow/protobuf/lib)\r\nMESSAGE(STATUS \"TENSORFLOW_PROTOBUF_INCLUDE_PATH ${TENSORFLOW_PROTOBUF_INCLUDE_PATH}\")\r\nMESSAGE(STATUS \"TENSORFLOW_PROTOBUF_LIBRARY_PATH ${TENSORFLOW_PROTOBUF_LIBRARY_PATH}\")\r\nSET(TENSORFLOW_PROTOBUF_LIBRARY ${TENSORFLOW_PROTOBUF_LIBRARY_PATH}/libprotobuf.a)\r\nSET(TENSORFLOW_PROTOC_LIBRARY ${TENSORFLOW_PROTOBUF_LIBRARY_PATH}/libprotoc.a)\r\nMESSAGE(STATUS \"TENSORFLOW_NSYNC_INCLUDE_PATH ${TENSORFLOW_PROTOBUF_LIBRARY}\")\r\nMESSAGE(STATUS \"TENSORFLOW_NSYNC_LIBRARY_PATH ${TENSORFLOW_PROTOC_LIBRARY}\")\r\nSET(TENSORFLOW_NSYNC_INCLUDE_PATH ${CMAKE_SOURCE_DIR}/tensorflow/nsyc/include)\r\nSET(TENSORFLOW_NSYNC_LIBRARY_PATH ${CMAKE_SOURCE_DIR}/tensorflow/nsyc/lib)\r\nMESSAGE(STATUS \"TENSORFLOW_NSYNC_INCLUDE_PATH ${TENSORFLOW_NSYNC_INCLUDE_PATH}\")\r\nMESSAGE(STATUS \"TENSORFLOW_NSYNC_LIBRARY_PATH ${TENSORFLOW_NSYNC_LIBRARY_PATH}\")\r\nSET(TENSORFLOW_NSYNC_LIBRARY ${TENSORFLOW_NSYNC_LIBRARY_PATH}/libnsync.a)\r\nMESSAGE(STATUS \"TENSORFLOW_NSYNC_LIBRARY ${TENSORFLOW_NSYNC_LIBRARY}\")\r\nSET(TENSORFLOW_PROTO_INCLUDE_PATH ${CMAKE_SOURCE_DIR}/tensorflow/proto)\r\nSET(TENSORFLOW_PROTO_TEXT_INCLUDE_PATH ${CMAKE_SOURCE_DIR}/tensorflow/proto_text)\r\nSET(TENSORFLOW_HOST_OBJ_INCLUDE_PATH ${CMAKE_SOURCE_DIR}/tensorflow/host_obj/)\r\nSET(TENSORFLOW_EIGEN_INCLUDE_PATH ${CMAKE_SOURCE_DIR}/tensorflow/eigen3)\r\nSET(TENSORFLOW_ABSL_INCLUDE_PATH ${CMAKE_SOURCE_DIR}/tensorflow/absl)\r\nSET(TENSORFLOW_THIRD_PARTY_INCLUDE_PATH ${CMAKE_SOURCE_DIR}/tensorflow/tensorflow_third_party)\r\nMESSAGE(STATUS \"TENSORFLOW_PROTO_INCLUDE_PATH ${TENSORFLOW_PROTO_INCLUDE_PATH}\")\r\nMESSAGE(STATUS \"TENSORFLOW_PROTO_TEXT_INCLUDE_PATH ${TENSORFLOW_PROTO_TEXT_INCLUDE_PATH}\")\r\nMESSAGE(STATUS \"TENSORFLOW_HOST_OBJ_INCLUDE_PATH ${TENSORFLOW_HOST_OBJ_INCLUDE_PATH}\")\r\nMESSAGE(STATUS \"TENSORFLOW_EIGEN_INCLUDE_PATH ${TENSORFLOW_EIGEN_INCLUDE_PATH}\")\r\nMESSAGE(STATUS \"TENSORFLOW_ABSL_INCLUDE_PATH ${TENSORFLOW_ABSL_INCLUDE_PATH}\")\r\nMESSAGE(STATUS \"TENSORFLOW_THIRD_PARTY_INCLUDE_PATH ${TENSORFLOW_THIRD_PARTY_INCLUDE_PATH}\")\r\nINCLUDE_DIRECTORIES(${TENSORFLOW_CORE_PATH})\r\nINCLUDE_DIRECTORIES(${TENSORFLOW_PROTOBUF_INCLUDE_PATH})\r\nINCLUDE_DIRECTORIES(${TENSORFLOW_PROTO_INCLUDE_PATH})\r\nINCLUDE_DIRECTORIES(${TENSORFLOW_PROTO_TEXT_INCLUDE_PATH})\r\nINCLUDE_DIRECTORIES(${TENSORFLOW_HOST_OBJ_INCLUDE_PATH})\r\nINCLUDE_DIRECTORIES(${TENSORFLOW_EIGEN_INCLUDE_PATH})\r\nINCLUDE_DIRECTORIES(${TENSORFLOW_ABSL_INCLUDE_PATH}) \r\nINCLUDE_DIRECTORIES(${TENSORFLOW_THIRD_PARTY_INCLUDE_PATH})\r\nINCLUDE_DIRECTORIES(${TENSORFLOW_NSYNC_INCLUDE_PATH})\r\nINCLUDE_DIRECTORIES(${TENSORFLOW_NSYNC_LIBRARY_PATH})\r\nADD_EXECUTABLE(load_model test.cpp)\r\nSET(LOAD_MODEL_LIBRARIES\r\n ${TENSORFLOW_NSYNC_LIBRARY}\r\n ${TENSORFLOW_PROTOC_LIBRARY}\r\n ${TENSORFLOW_PROTOBUF_LIBRARY}\r\n )\r\nSET(LDFLAGS \"-std=c++11 -msse4.1 -fPIC -O3 -march=native -Wall -finline-functions -undefined\")\r\nSET(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS}${LDFLAGS}\")\r\nadd_compile_options(-Wl,--whole-archive -lpthread -ldl)\r\nTARGET_LINK_LIBRARIES(load_model -Wl,--whole-archive ${TENSORFLOW_LIBARY} -Wl,--no-whole-archive ${LOAD_MODEL_LIBRARIES} ${CMAKE_CXX_FLAGS})\r\n```\r\n\r\nand try to add a declare `class tensorflow::error::Code`\r\n\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/62900\">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/62900\">No</a>\n"
] | 2024-02-06T09:16:15 | 2024-02-07T17:52:13 | 2024-02-07T17:52:09 |
NONE
| null | null | null |
Hi,
deso anyone know when i try to rebuild the tflite 2.4.2 with the flex delegate open for support the hitnet model, i have met this error:
this is my code:
#include <iostream>
#include <vector>
#include <chrono>
#include "tensorflow/lite/interpreter.h"
#include "tensorflow/lite/kernels/register.h"
#include "tensorflow/lite/string_util.h"
#include "tensorflow/lite/examples/label_image/get_top_n.h"
#include "tensorflow/lite/model.h"
// // //add the delegate
#include "tensorflow/lite/delegates/flex/delegate.h"
// // #include "tensorflow/lite/delegates/flex/converter.h"
#define H 256
#define W 256
#define THREADS 4
int main() {
const char* MODEL = "eth3d";
const int CHANNEL = 2;
// const char* model_path = "eth3d/saved_model_720x1280/model_float32.tflite"; // Replace with the actual path to your TFLite model
std::unique_ptr<tflite::FlatBufferModel> model =tflite::FlatBufferModel::BuildFromFile("./eth3d/saved_model_720x1280/model_float32.tflite");
// std::unique_ptr<tflite::FlatBufferModel> model =tflite::FlatBufferModel::BuildFromFile("./yolov8s_integer_quant.tflite");//this one works, means the issue is the flex delegate
// std::unique_ptr<tflite::delegate> flex_delegate = tflite::FlexDelegate::Create();
tflite::ops::builtin::BuiltinOpResolver resolver;
std::unique_ptr<tflite::Interpreter> interpreter;
tflite::InterpreterBuilder(*model, resolver)(&interpreter);
tflite::TfLiteDelegateUniquePtr flex_delegate = tflite::FlexDelegate::Create();
if (flex_delegate != nullptr) {
if (interpreter->ModifyGraphWithDelegate(std::move(flex_delegate)) != kTfLiteOk) {
std::cerr << "Failed to modify graph with flex delegate." << std::endl;
return 1;
}
}
std::vector<int> input_shape = {1, H, W, CHANNEL};
std::vector<float> input_tensor(1 * H * W * CHANNEL, 1.0f);
// if (!model) {
// std::cerr << "Failed to load the model." << std::endl;
// return 1;
// }
// tflite::InterpreterBuilder builder(*model, resolver);
// if (builder(&interpreter) != kTfLiteOk) {
// std::cerr << "Failed to build interpreter." << std::endl;
// return 1;
// }
// // Allocate tensors
interpreter->AllocateTensors();
// Get input and output details
const auto* input_details = interpreter->input_tensor(0);
const auto* output_details = interpreter->output_tensor(0);
// Perform inference
int roop_count = 10;
float* reference_output_disparity = nullptr;
auto start = std::chrono::high_resolution_clock::now();
for (int i = 0; i < roop_count; ++i) {
memcpy(input_details->data.f, input_tensor.data(), input_tensor.size() * sizeof(float));
interpreter->Invoke();
reference_output_disparity = interpreter->typed_output_tensor<float>(0);
}
auto end = std::chrono::high_resolution_clock::now();
double inference_time = std::chrono::duration_cast<std::chrono::microseconds>(end - start).count() / static_cast<double>(roop_count) / 1000.0;
// Print results
std::cout << "Model: " << MODEL << std::endl;
std::cout << "Input resolution: " << H << "x" << W << std::endl;
std::cout << "Number of Threads: " << THREADS << std::endl;
std::cout << "Average of " << roop_count << " times inference: " << inference_time << "ms" << std::endl;
return 0;
}
and this is my CMakeLists.txt:
cmake_minimum_required(VERSION 3.10)
project(TFLiteImageClassification)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
# OpenCV Integration
find_package(OpenCV 4.7.0 REQUIRED)
include_directories(${OpenCV_INCLUDE_DIRS})
# TensorFlow Lite Integration
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/../tflite-dist/include/)
include_directories(/home/myd/Downloads/tensorflow-2.4.2/)
# Include the directory for generated protobuf headers
include_directories(/home/myd/Downloads/tensorflow-2.4.2/bazel-bin/)
# Define the TensorFlow Lite library and Flex delegate library
add_library(tensorflowlite SHARED IMPORTED)
add_library(flexdelegate SHARED IMPORTED)
# Set the location of the TensorFlow Lite library
set_property(TARGET tensorflowlite PROPERTY IMPORTED_LOCATION /home/myd/Desktop/Stereo-reconstruct-C++/tflite/tflite-dist/libs/linux_x64/libtensorflowlite.so)
# Set the location of the Flex delegate library
set_property(TARGET flexdelegate PROPERTY IMPORTED_LOCATION /home/myd/Downloads/tensorflow-2.4.2/bazel-out/k8-opt/bin/tensorflow/lite/delegates/flex/libdelegate_so.so)
# Add executable target
add_executable(${PROJECT_NAME} main.cpp)
target_link_libraries(${PROJECT_NAME} PRIVATE ${OpenCV_LIBS} tensorflowlite flexdelegate )
# Abseil
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/third_party/abseil-cpp)
link_directories(${CMAKE_CURRENT_SOURCE_DIR}/third_party/abseil-cpp/build)
# Find Eigen3 package
find_package(Eigen3 3.4.0 REQUIRED NO_MODULE)
# Link Eigen3 (if it is found)
if(EIGEN3_FOUND)
target_link_libraries(${PROJECT_NAME} PRIVATE Eigen3::Eigen)
endif()
and this is what i got: Files/ && cmake .. && make && ./TFLiteImageClassification
-- The C compiler identification is GNU 9.4.0
-- The CXX compiler identification is GNU 9.4.0
-- Check for working C compiler: /usr/bin/cc
-- Check for working C compiler: /usr/bin/cc -- works
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Detecting C compile features
-- Detecting C compile features - done
-- Check for working CXX compiler: /usr/bin/c++
-- Check for working CXX compiler: /usr/bin/c++ -- works
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Found OpenCV: /usr/local (found suitable version "4.7.0", minimum required is "4.7.0")
-- Configuring done
-- Generating done
-- Build files have been written to: /home/myd/Desktop/Stereo-reconstruct-C++/tflite/pose_stereovision/build
Scanning dependencies of target TFLiteImageClassification
[ 50%] Building CXX object CMakeFiles/TFLiteImageClassification.dir/main.cpp.o
[100%] Linking CXX executable TFLiteImageClassification
/usr/bin/ld: CMakeFiles/TFLiteImageClassification.dir/main.cpp.o: in function `tensorflow::core::RefCounted::~RefCounted()':
main.cpp:(.text._ZN10tensorflow4core10RefCountedD2Ev[_ZN10tensorflow4core10RefCountedD5Ev]+0xf8): undefined reference to `tensorflow::internal::LogMessageFatal::LogMessageFatal(char const*, int)'
/usr/bin/ld: main.cpp:(.text._ZN10tensorflow4core10RefCountedD2Ev[_ZN10tensorflow4core10RefCountedD5Ev]+0x120): undefined reference to `tensorflow::internal::LogMessageFatal::~LogMessageFatal()'
/usr/bin/ld: CMakeFiles/TFLiteImageClassification.dir/main.cpp.o: in function `tflite::FlexDelegate::Create()':
main.cpp:(.text._ZN6tflite12FlexDelegate6CreateEv[_ZN6tflite12FlexDelegate6CreateEv]+0x40): undefined reference to `tflite::FlexDelegate::Create(std::unique_ptr<tflite::FlexDelegate, std::default_delete<tflite::FlexDelegate> >)'
/usr/bin/ld: CMakeFiles/TFLiteImageClassification.dir/main.cpp.o: in function `std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >* tensorflow::internal::MakeCheckOpString<long, int>(long const&, int const&, char const*)':
main.cpp:(.text._ZN10tensorflow8internal17MakeCheckOpStringIliEEPNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKT_RKT0_PKc[_ZN10tensorflow8internal17MakeCheckOpStringIliEEPNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKT_RKT0_PKc]+0x37): undefined reference to `tensorflow::internal::CheckOpMessageBuilder::CheckOpMessageBuilder(char const*)'
/usr/bin/ld: main.cpp:(.text._ZN10tensorflow8internal17MakeCheckOpStringIliEEPNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKT_RKT0_PKc[_ZN10tensorflow8internal17MakeCheckOpStringIliEEPNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKT_RKT0_PKc]+0x61): undefined reference to `tensorflow::internal::CheckOpMessageBuilder::ForVar2()'
/usr/bin/ld: main.cpp:(.text._ZN10tensorflow8internal17MakeCheckOpStringIliEEPNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKT_RKT0_PKc[_ZN10tensorflow8internal17MakeCheckOpStringIliEEPNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKT_RKT0_PKc]+0x7f): undefined reference to `tensorflow::internal::CheckOpMessageBuilder::NewString[abi:cxx11]()'
/usr/bin/ld: main.cpp:(.text._ZN10tensorflow8internal17MakeCheckOpStringIliEEPNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKT_RKT0_PKc[_ZN10tensorflow8internal17MakeCheckOpStringIliEEPNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKT_RKT0_PKc]+0x8f): undefined reference to `tensorflow::internal::CheckOpMessageBuilder::~CheckOpMessageBuilder()'
/usr/bin/ld: main.cpp:(.text._ZN10tensorflow8internal17MakeCheckOpStringIliEEPNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKT_RKT0_PKc[_ZN10tensorflow8internal17MakeCheckOpStringIliEEPNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKT_RKT0_PKc]+0xb6): undefined reference to `tensorflow::internal::CheckOpMessageBuilder::~CheckOpMessageBuilder()'
collect2: error: ld returned 1 exit status
make[2]: *** [CMakeFiles/TFLiteImageClassification.dir/build.make:154: TFLiteImageClassification] Error 1
make[1]: *** [CMakeFiles/Makefile2:68: CMakeFiles/TFLiteImageClassification.dir/all] Error 2
make: *** [Makefile:84: all] Error 2.
if anyone understand this and can help me, i really appreciated!
best
yide
|
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TensorFlow 2.15 cannot be imported after Poetry install on Windows
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[
"cc: @mraunak ",
"Hi @RRiva, the issue has been addressed in https://github.com/tensorflow/tensorflow/issues/58674#issuecomment-1593891706\r\nI just successfully imported TF 2.15 on the Windows using the Poetry tool.\r\nBefore running the command poetry add tensorflow==2.15.0\r\nplease run the command poetry add tensorflow-io-gcs-filesystem==0.31.0\r\nTF 2.15 supports Python 3.9-3.11, please add it in the pyproject.toml\r\n\r\n\r\n\r\n\r\n",
"Hi @mraunak, thanks for trying, but it doesn't work yet. To demonstrate it, I made a new clean repo, with the pyproject.toml that you have written. It's available at\r\n\r\nhttps://gitlab.windenergy.dtu.dk/surrogate-models/test-poetry-and-tensorflow\r\n\r\nAs you can see, the last pipeline still fails with `ModuleNotFoundError: No module named 'tensorflow'`.\r\n\r\nhttps://gitlab.windenergy.dtu.dk/surrogate-models/test-poetry-and-tensorflow/-/jobs/238494",
"Hi @RRiva, please run the command below to fix your issue. \r\npoetry add tensorflow-intel \r\n\r\n",
"Hi @RRiva, we are working to fix the issues of separate poetry installation of dependent packages",
"Hi @mraunak, thanks a lot! It is now possible to import TensorFlow on both Windows and Linux 🙂 The only difference is that I had to specify that `tensorflow-intel` is only available for Windows, since Poetry cannot infer it from the metadata. The pyproject.toml now looks like\r\n```\r\npython = \">=3.9,<3.12\"\r\ntensorflow-io-gcs-filesystem = \"0.31.0\"\r\ntensorflow = \"2.15.0\"\r\ntensorflow-intel = {version=\"^2.15.0\", platform = \"win32\"}\r\n```\r\nIt should be noted though that `tensorflow-io-gcs-filesystem` 0.31.0 is not compatible with `tensorflow` 2.15.\r\n",
"Thank you @RRiva, yes Intel releases tensorflow-intel for the Windows platform. In pip installation, TensorFlow-intel is automatically installed while installing TensorFlow on the Windows platform. We are working to fix it for Poetry Installation as well which should be available in the TF 2.16 release",
"> Thank you @RRiva, yes Intel releases tensorflow-intel for the Windows platform. In pip installation, TensorFlow-intel is automatically installed while installing TensorFlow on the Windows platform. We are working to fix it for Poetry Installation as well which should be available in the TF 2.16 release\r\n\r\nWill this also fix all the other metadata issues? Can't install tensorflow-io-gcs-filesystem on alpine linux (I know not ideal but it's a specific environment requirement) Sorry if this is unrelated, wondering if the metadata will fix all",
"Hi @ZachHandley, thank you for letting us know about the issue with Alpine Linux. Request you to raise a separate Github issue to TensorFlow. We will work to fix it.",
"Hi, I just checked if the installation improved with TF 2.16, but unfortunately nothing has changed for Windows:\r\n\r\n- `tensorflow-intel` still needs to be specified as a requirement, since it's not found automatically.\r\n- `tensorflow-io-gcs-filesystem` does not provide recent Windows wheels, and therefore does not support python 3.12.\r\n\r\nI hope that the fix that you mentioned will be available with the next release 🙂 ",
"Hi @RRiva, yes the fix for automatic installation of tensorflow-intel using poetry, will be available with the next release. I will contact tensorflow/io team to check the latest release of tensorflow-io-gcs-filesystem on the Windows platform",
"Hey @mraunak do you have any idea on the status of tensorflow-io-gcs-filesystem on windows? Can't seem to locate an answer in any of the issues, or at least find the correct one.",
"Hi @Nicba1010 I have raised an issue https://github.com/tensorflow/io/issues/1966. I haven't heard anything yet. If you are getting any errors please let me know."
] | 2024-02-06T07:52:58 | 2024-06-01T05:55:51 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.15.0
### Custom code
No
### OS platform and distribution
Windows
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I expect to be able to install and import TensorFlow on the `python:3.11-windowsservercore` Docker container, using Poetry.
I made a new project with the following pyproject.toml, copied from [Poetry issue 8271](https://github.com/python-poetry/poetry/issues/8271#issuecomment-1712020965)
```
[tool.poetry]
name = "project-tf"
version = "0.1.0"
description = ""
authors = [""]
readme = "README.md"
[tool.poetry.dependencies]
python = "^3.10,<3.12"
# Issue between poetry and tensorflow metadata since >=2.11
# This is a temporary workaround
# related to https://github.com/python-poetry/poetry/issues/8271
# Inspired from https://github.com/tensorflow/tensorflow/blob/adb39b04e9cb116df4659a7e2de9eea27e62f25c/tensorflow/tools/pip_package/setup.py#L148-L162
tensorflow = {version = "^2.13.0" }
tensorflow-macos = { version = "^2.13.0", platform = "darwin", markers = "platform_machine=='arm64'" }
tensorflow-intel = { version = "^2.13.0", platform = "win32" }
tensorflow-cpu = [
{ version = "^2.13.0", platform = "linux", markers = "platform_machine!='arm64' and platform_machine!='aarch64'" },
{ version = "^2.13.0", platform = "darwin", markers = "platform_machine!='arm64' and platform_machine!='aarch64'" },]
tensorflow-cpu-aws = { version = "^2.13.0", platform = "linux", markers = "platform_machine=='arm64' or platform_machine=='aarch64'" }
# https://github.com/tensorflow/tensorflow/blob/adb39b04e9cb116df4659a7e2de9eea27e62f25c/tensorflow/tools/pip_package/setup.py#L107-L108
# https://github.com/python-poetry/poetry/issues/8271#issuecomment-1697740447
tensorflow-io-gcs-filesystem = [
{ version = ">= 0.23.1", markers = "platform_machine!='arm64' or platform_system!='Darwin'" },
{ version = "< 0.32.0", markers = "platform_system == 'Windows'" }
]
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
```
Then, I generate the lock file and on the Docker container `python:3.11-windowsservercore`, and install my package with
```
# Allow long file paths, or otherwise installing TensorFlow fails.
Set-ItemProperty -Path HKLM:\SYSTEM\CurrentControlSet\Control\FileSystem -Name LongPathsEnabled -Value 1
# Install Microsoft Visual C++ Redistributable, or otherwise importing TensorFlow fails.
Invoke-WebRequest -Uri https://aka.ms/vs/17/release/vc_redist.x64.exe -OutFile .\VC_redist.x64.exe
.\VC_redist.x64.exe /install /quiet /norestart
# Install Poetry.
pip install poetry==1.7.1
# Install this package and run the tests.
poetry install
```
Finally, `pytest` fails as soon as it tries to import TensorFlow.
The reason for this behavior is described at [Poetry issue 8271](https://github.com/python-poetry/poetry/issues/8271#issuecomment-1928038659).
### Standalone code to reproduce the issue
```shell
See above.
```
### Relevant log output
_No response_
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PR_kwDOArmXAs5mDtiy
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Solved issue #62645 CI build fails on install pip packages
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"@angerson @gbaned No tests are found inside `tensorflow/tools/ci_build` but BUILD was successful.\r\n\r\n<details>\r\ntensorflow/tools/ci_build:*\r\nWORKSPACE: /workspaces/tensorflow\r\nCI_DOCKER_BUILD_EXTRA_PARAMS: \r\nCI_DOCKER_EXTRA_PARAMS: \r\nCOMMAND: bazel test //tensorflow/tools/ci_build:*\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 1.0s (18/18) FINISHED 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: 702B 0.0s\r\n => [internal] load metadata for docker.io/library/ubuntu:16.04 0.5s\r\n => [ 1/13] FROM docker.io/library/ubuntu:16.04@sha256:1f1a2d56de1d604801a9671f301190704c25d604a416f59e03c04f5c6ffee0d6 0.0s\r\n => [internal] load build context 0.1s\r\n => => transferring context: 1.73kB 0.0s\r\n => CACHED [ 2/13] COPY install/*.sh /install/ 0.0s\r\n => CACHED [ 3/13] RUN /install/install_bootstrap_deb_packages.sh 0.0s\r\n => CACHED [ 4/13] RUN add-apt-repository -y ppa:openjdk-r/ppa && add-apt-repository -y ppa:george-edison55/cmake-3. 0.0s\r\n => CACHED [ 5/13] RUN /install/install_deb_packages.sh 0.0s\r\n => CACHED [ 6/13] RUN /install/build_and_install_python.sh 3.9.18 0.0s\r\n => CACHED [ 7/13] RUN /install/install_pip_packages.sh 0.0s\r\n => CACHED [ 8/13] RUN /install/install_bazel.sh 0.0s\r\n => CACHED [ 9/13] RUN /install/install_proto3.sh 0.0s\r\n => CACHED [10/13] RUN /install/install_buildifier.sh 0.0s\r\n => CACHED [11/13] RUN /install/install_auditwheel.sh 0.0s\r\n => CACHED [12/13] RUN /install/install_golang.sh 0.0s\r\n => CACHED [13/13] COPY install/.bazelrc /etc/bazel.bazelrc 0.0s\r\n => exporting to image 0.0s\r\n => => exporting layers 0.0s\r\n => => writing image sha256:7c5a8e70f3dbc138e6dc4b80287af162145e156a8606179861da4692a1063196 0.0s\r\n => => naming to docker.io/library/tf_ci.cpu 0.0s\r\nRunning 'bazel test //tensorflow/tools/ci_build:*' inside tf_ci.cpu...\r\nReading package lists...\r\nBuilding dependency tree...\r\nReading state information...\r\nsudo is already the newest version (1.8.16-0ubuntu1.10).\r\n0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded.\r\nAdding group `codespace' (GID 1000) ...\r\nDone.\r\n/workspace /workspace\r\nYou have bazel 6.5.0 installed.\r\nFound possible Python library paths:\r\n /usr/lib/python2.7/dist-packages\r\n /usr/local/lib/python2.7/dist-packages\r\nPlease input the desired Python library path to use. Default is [/usr/lib/python2.7/dist-packages]\r\nDo you wish to build TensorFlow with ROCm support? [y/N]: No ROCm support will be enabled for TensorFlow.\r\n\r\nDo you wish to build TensorFlow with CUDA support? [y/N]: No CUDA support will be enabled for TensorFlow.\r\n\r\nDo you want to use Clang to build TensorFlow? [Y/n]: Clang will be used to compile TensorFlow.\r\n\r\nPlease specify the path to clang executable. [Default is /usr/bin/clang]: \r\n\r\nYou have Clang 3.8.0-2ubuntu4 installed.\r\n\r\nPlease specify optimization flags to use during compilation when bazel option \"--config=opt\" is specified [Default is -Wno-sign-compare]: \r\n\r\nWould you like to interactively configure ./WORKSPACE for Android builds? [y/N]: Not configuring the WORKSPACE for Android builds.\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 --config=mkl # Build with MKL support.\r\n --config=mkl_aarch64 # Build with oneDNN and Compute Library for the Arm Architecture (ACL).\r\n --config=monolithic # Config for mostly static monolithic build.\r\n --config=numa # Build with NUMA support.\r\n --config=dynamic_kernels # (Experimental) Build kernels into separate shared objects.\r\n --config=v1 # Build with TensorFlow 1 API instead of TF 2 API.\r\nPreconfigured Bazel build configs to DISABLE default on features:\r\n --config=nogcp # Disable GCP support.\r\n --config=nonccl # Disable NVIDIA NCCL support.\r\n/workspace\r\nTF_BUILD_INFO = {container_type: \"cpu\", command: \"bazel test //tensorflow/tools/ci_build:*\", source_HEAD: \"258780cb34bfa9fc03cffe2371eeebdb974d8df4\", source_remote_origin: \"https://github.com/giuliocn/tensorflow\", OS: \"Linux\", kernel: \"6.2.0-1019-azure\", architecture: \"x86_64\", processor: \"AMD EPYC 7763 64-Core Processor\", processor_count: \"2\", memory_total: \"8120292 kB\", swap_total: \"0 kB\", Bazel_version: \"Build label: 6.5.0\", Java_version: \"1.8.0_292\", Python_version: \"2.7.12\", gpp_version: \"g++ (Ubuntu 5.4.0-6ubuntu1~16.04.12) 5.4.0 20160609\", swig_version: \"\", NVIDIA_driver_version: \"\", CUDA_device_count: \"0\", CUDA_device_names: \"\", CUDA_toolkit_version: \"\"}\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=0 --terminal_columns=80\r\nINFO: Reading rc options for 'test' from /etc/bazel.bazelrc:\r\n Inherited 'common' options: --color=yes\r\nINFO: Reading rc options for 'test' from /workspace/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'test' from /etc/bazel.bazelrc:\r\n Inherited 'build' options: --verbose_failures --spawn_strategy=standalone --strategy=Genrule=standalone\r\nINFO: Reading rc options for 'test' from /workspace/.bazelrc:\r\n Inherited 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --features=-force_no_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility\r\nINFO: Reading rc options for 'test' from /workspace/.tf_configure.bazelrc:\r\n Inherited 'build' options: --host_force_python=PY2 --action_env PYTHON_BIN_PATH=/usr/bin/python --action_env PYTHON_LIB_PATH=/usr/lib/python2.7/dist-packages --python_path=/usr/bin/python --action_env CLANG_COMPILER_PATH=/usr/lib/llvm-3.8/bin/clang --repo_env=CC=/usr/lib/llvm-3.8/bin/clang --repo_env=BAZEL_COMPILER=/usr/lib/llvm-3.8/bin/clang\r\nINFO: Reading rc options for 'test' from /etc/bazel.bazelrc:\r\n 'test' options: --spawn_strategy=standalone --verbose_failures --test_output=errors --test_verbose_timeout_warnings\r\nINFO: Reading rc options for 'test' from /workspace/.tf_configure.bazelrc:\r\n 'test' options: --test_size_filters=small,medium\r\nINFO: Found applicable config definition build:short_logs in file /workspace/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /workspace/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition test:v2 in file /workspace/.tf_configure.bazelrc: --test_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-gpu,-oss_serial,-v1only --build_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-gpu,-v1only\r\nINFO: Found applicable config definition build:linux in file /workspace/.bazelrc: --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wno-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-array-bounds --copt=-Wunused-result --copt=-Werror=unused-result --copt=-Wswitch --copt=-Werror=switch --copt=-Wno-error=unused-but-set-variable --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --experimental_guard_against_concurrent_changes\r\nINFO: Found applicable config definition build:dynamic_kernels in file /workspace/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS\r\nLoading: \r\nLoading: \r\nLoading: \r\nLoading: 0 packages loaded\r\nAnalyzing: 35 targets (1 packages loaded, 0 targets configured)\r\nINFO: Analyzed 35 targets (1 packages loaded, 35 targets configured).\r\nINFO: Found 35 targets and 0 test targets...\r\n[0 / 1] [Prepa] BazelWorkspaceStatusAction stable-status.txt\r\nINFO: Elapsed time: 4.206s, Critical Path: 0.09s\r\nINFO: 1 process: 1 internal.\r\nINFO: Build completed successfully, 1 total action\r\nERROR: No test targets were found, yet testing was requested\r\n</details>",
"Hi @giuliocn This PR is in draft, any update on this? Please. Thank you!",
"> Hi @giuliocn This PR is in draft, any update on this? Please. Thank you!\r\n\r\n@gbaned Apparently, CPU build runs successfully. Then, Bazel fails raising the error reported above.\r\nSince I could not test my PR, I believe that further work is needed to merge it safely.\r\nWould you like to take care of it ? Otherwise I need to learn how Bazel works... ",
"Hi @angerson Can you please assist on above[ comments](https://github.com/tensorflow/tensorflow/pull/62898#issuecomment-1984228895) from @giuliocn. Thank you!",
"> Hi @angerson Can you please assist on above[ comments](https://github.com/tensorflow/tensorflow/pull/62898#issuecomment-1984228895) from @giuliocn. Thank you!\r\n\r\nThe build failure is related to an invalid path configuration for the Python interpreter on the Windows 1803 system with Python 3.8 (denoted as `win_1803/py38`).\r\n\r\n@gbaned Here's a breakdown of the error message generated by **Gemini**:\r\n\r\n* **Error Message:** \r\n ```\r\n ERROR: /workspace/tensorflow/tools/toolchains/win_1803/py38/BUILD:9:11: in interpreter_path attribute of py_runtime rule //tensorflow/tools/toolchains/win_1803/py38:py3_runtime: must be an absolute path.\r\n ``` \r\n* [Link to BUILD file referenced above on TF master branch](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/toolchains/win_1803/py38/BUILD)\r\n \r\n* **Explanation:** The error points to a specific line (line 9, column 11) in a build file (`BUILD`). This line defines a rule named `py_runtime` which specifies the path to the Python interpreter used during the build process. The error message states that the provided path in the `interpreter_path` attribute is not a valid absolute path. An absolute path starts with the drive letter (e.g., `C:`) or a network location and specifies the complete directory structure to reach the desired file.\r\n\r\n**Possible Causes:**\r\n\r\n* **Incorrect Path:** The path configured in the `interpreter_path` attribute might be incorrect or relative. It should be the complete path to the Python 3.8 executable on the Windows system (e.g., `C:\\Python38\\python.exe`).\r\n* **Missing Configuration:** The build configuration might not be set up to automatically locate the Python interpreter. You might need to explicitly specify the path during the build process.\r\n\r\n**Resolving the Issue:**\r\n\r\n1. **Locate Python Executable:** Find the absolute path to the Python 3.8 executable on your Windows 1803 system. It's typically installed in a directory like `C:\\Python38` or `C:\\Users\\<username>\\AppData\\Local\\Programs\\Python\\Python38`.\r\n2. **Update Build Configuration:** Modify the `BUILD` file (`/workspace/tensorflow/tools/toolchains/win_1803/py38/BUILD`) and update the `interpreter_path` attribute with the absolute path you located in step 1.\r\n3. **Rerun Build:** After updating the path, rerun the build command (`bazel test //tensorflow/...`) to attempt building TensorFlow again.\r\n\r\nBy providing a valid absolute path to the Python interpreter, the build process should be able to locate the correct executable and proceed successfully.\r\n"
] | 2024-02-05T18:19:19 | 2024-06-07T16:46:24 | null |
CONTRIBUTOR
| null | true |
{
"url": "https://api.github.com/repos/tensorflow/tensorflow/pulls/62898",
"html_url": "https://github.com/tensorflow/tensorflow/pull/62898",
"diff_url": "https://github.com/tensorflow/tensorflow/pull/62898.diff",
"patch_url": "https://github.com/tensorflow/tensorflow/pull/62898.patch",
"merged_at": null
}
|
Here is a technical summary #62645 :
1. Use a script to build and install python from source
2. Edit `Dockerfile.cpu` to execute `/install/build_and_install_python.sh 3.9.18`
3. Edit `install_deb_packages.sh` to update and install packages
- clang
- gcc
- libffi-dev liblapack-dev libblas-dev
- make
- python3-numpy
4. Edit `install_pip_packages.sh` to install pip
- 21, use python3.9
- 25, upgrade pip with python3.9
5. Edit `install_pip_packages.sh` to upgrade pip packages
- setuptools
- future
- numpy scipy joblib threadpoolctl scikit-learn pandas
IMPORTANT NOTES:
Running all tests with `bazel test` fails on **win_1803/py38** because of its `interpreter_path`
<details>
Running 'bazel test //tensorflow/...' inside tf_ci.cpu...
Reading package lists...
Building dependency tree...
Reading state information...
sudo is already the newest version (1.8.16-0ubuntu1.10).
0 upgraded, 0 newly installed, 0 to remove and 2 not upgraded.
Adding group `codespace' (GID 1000) ...
Done.
/workspace /workspace
You have bazel 6.1.0 installed.
Found possible Python library paths:
/usr/lib/python2.7/dist-packages
/usr/local/lib/python2.7/dist-packages
Please input the desired Python library path to use. Default is [/usr/lib/python2.7/dist-packages]
Do you wish to build TensorFlow with ROCm support? [y/N]: No ROCm support will be enabled for TensorFlow.
Do you wish to build TensorFlow with CUDA support? [y/N]: No CUDA support will be enabled for TensorFlow.
Do you want to use Clang to build TensorFlow? [Y/n]: Clang will be used to compile TensorFlow.
Please specify the path to clang executable. [Default is /usr/bin/clang]:
You have Clang 3.8.0-2ubuntu4 installed.
Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -Wno-sign-compare]:
Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: Not configuring the WORKSPACE for Android builds.
Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See .bazelrc for more details.
--config=mkl # Build with MKL support.
--config=mkl_aarch64 # Build with oneDNN and Compute Library for the Arm Architecture (ACL).
--config=monolithic # Config for mostly static monolithic build.
--config=numa # Build with NUMA support.
--config=dynamic_kernels # (Experimental) Build kernels into separate shared objects.
--config=v1 # Build with TensorFlow 1 API instead of TF 2 API.
Preconfigured Bazel build configs to DISABLE default on features:
--config=nogcp # Disable GCP support.
--config=nonccl # Disable NVIDIA NCCL support.
/workspace
TF_BUILD_INFO = {container_type: "cpu", command: "bazel test //tensorflow/...", source_HEAD: "483ba60a5afa9e3c77ffc76a3d6eceadfd328bf2", source_remote_origin: "https://github.com/giuliocn/tensorflow", OS: "Linux", kernel: "6.2.0-1018-azure", architecture: "x86_64", processor: "AMD EPYC 7763 64-Core Processor", processor_count: "2", memory_total: "8120288 kB", swap_total: "0 kB", Bazel_version: "Build label: 6.1.0", Java_version: "1.8.0_292", Python_version: "2.7.12", gpp_version: "g++ (Ubuntu 5.4.0-6ubuntu1~16.04.12) 5.4.0 20160609", swig_version: "", NVIDIA_driver_version: "", CUDA_device_count: "0", CUDA_device_names: "", CUDA_toolkit_version: ""}
INFO: Options provided by the client:
Inherited 'common' options: --isatty=0 --terminal_columns=80
INFO: Reading rc options for 'test' from /etc/bazel.bazelrc:
Inherited 'common' options: --color=yes
INFO: Reading rc options for 'test' from /workspace/.bazelrc:
Inherited 'common' options: --experimental_repo_remote_exec
INFO: Reading rc options for 'test' from /etc/bazel.bazelrc:
Inherited 'build' options: --verbose_failures --spawn_strategy=standalone --strategy=Genrule=standalone
INFO: Reading rc options for 'test' from /workspace/.bazelrc:
Inherited '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 'test' from /workspace/.tf_configure.bazelrc:
Inherited 'build' options: --host_force_python=PY2 --action_env PYTHON_BIN_PATH=/usr/bin/python --action_env PYTHON_LIB_PATH=/usr/lib/python2.7/dist-packages --python_path=/usr/bin/python --action_env CLANG_COMPILER_PATH=/usr/lib/llvm-3.8/bin/clang --repo_env=CC=/usr/lib/llvm-3.8/bin/clang --repo_env=BAZEL_COMPILER=/usr/lib/llvm-3.8/bin/clang
INFO: Reading rc options for 'test' from /etc/bazel.bazelrc:
'test' options: --spawn_strategy=standalone --verbose_failures --test_output=errors --test_verbose_timeout_warnings
INFO: Reading rc options for 'test' from /workspace/.tf_configure.bazelrc:
'test' options: --test_size_filters=small,medium
INFO: Found applicable config definition build:short_logs in file /workspace/.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file /workspace/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition test:v2 in file /workspace/.tf_configure.bazelrc: --test_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-gpu,-oss_serial,-v1only --build_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-gpu,-v1only
INFO: Found applicable config definition build:linux in file /workspace/.bazelrc: --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wno-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-array-bounds --copt=-Wunused-result --copt=-Werror=unused-result --copt=-Wswitch --copt=-Werror=switch --copt=-Wno-error=unused-but-set-variable --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --experimental_guard_against_concurrent_changes
INFO: Found applicable config definition build:dynamic_kernels in file /workspace/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS
Loading:
Loading:
Loading:
Loading: 0 packages loaded
Loading: 14 packages loaded
currently loading: tensorflow/compiler/mlir/tfrt/tests/tfrt_fallback ... (2 packages)
Loading: 33 packages loaded
currently loading: tensorflow/compiler/mlir/tfrt/ir/mlrt ... (3 packages)
Loading: 58 packages loaded
currently loading: tensorflow/compiler/mlir/tfrt/ir/mlrt ... (10 packages)
Loading: 152 packages loaded
currently loading: tensorflow/security/fuzzing/cc/core/function
Loading: 212 packages loaded
currently loading: tensorflow/core/kernels ... (2 packages)
Loading: 261 packages loaded
currently loading: tensorflow/core/kernels ... (2 packages)
Loading: 351 packages loaded
currently loading: tensorflow/core/transforms/toposort
Loading: 484 packages loaded
currently loading: tensorflow/python/ops ... (2 packages)
Loading: 615 packages loaded
currently loading: tensorflow/lite/testing ... (2 packages)
Loading: 654 packages loaded
currently loading: tensorflow/lite/testing ... (2 packages)
Loading: 743 packages loaded
currently loading: tensorflow/lite/acceleration/configuration ... (2 packages)
WARNING: /workspace/tensorflow/compiler/mlir/lite/stablehlo/BUILD:581:13: target '//tensorflow/compiler/mlir/lite/stablehlo:odml_to_stablehlo' is deprecated: odml_to_stablehlo is being deprecated, please use TFlite converter with flag: converter.target_spec.supported_ops = [tf.lite.OpsSet.EXPERIMENTAL_STABLEHLO_OPS]
WARNING: /workspace/tensorflow/core/kernels/BUILD:973:18: target '//tensorflow/core/kernels:bitcast_op' is deprecated: use //third_party/tensorflow/c/kernels:bitcast_op instead
WARNING: /workspace/tensorflow/lite/core/async/testing/BUILD:27:23: target '//tensorflow/lite/core/async/testing:mock_async_kernel' is deprecated: Use //tensorflow/lite/async/testing:mock_async_kernel instead.
WARNING: /workspace/tensorflow/lite/core/async/BUILD:87:23: target '//tensorflow/lite/core/async:backend_async_kernel_interface' is deprecated: Use //tensorflow/lite/async:backend_async_kernel_interface instead.
WARNING: /workspace/tensorflow/python/eager/BUILD:696:18: target '//tensorflow/python/eager:framework_for_generated_wrappers' is deprecated: Depending on this target can cause build dependency cycles. Depend on the fine-grained sub-targets instead.
WARNING: /workspace/tensorflow/python/ops/distributions/BUILD:9:18: target '//tensorflow/python/ops/distributions:distributions' is deprecated: TensorFlow Distributions has migrated to TensorFlow Probability (https://github.com/tensorflow/probability). Deprecated copies remaining in tf.distributions will not receive new features, and will be removed by early 2019. You should update all usage of `tf.distributions` to `tfp.distributions`.
Analyzing: 19154 targets (797 packages loaded)
Analyzing: 19154 targets (798 packages loaded, 0 targets configured)
Analyzing: 19154 targets (814 packages loaded, 79 targets configured)
Analyzing: 19154 targets (816 packages loaded, 393 targets configured)
Analyzing: 19154 targets (817 packages loaded, 4468 targets configured)
Analyzing: 19154 targets (820 packages loaded, 9559 targets configured)
Analyzing: 19154 targets (823 packages loaded, 12199 targets configured)
ERROR: /workspace/tensorflow/tools/toolchains/win_1803/py38/BUILD:9:11: in interpreter_path attribute of py_runtime rule //tensorflow/tools/toolchains/win_1803/py38:py3_runtime: must be an absolute path.
ERROR: /workspace/tensorflow/tools/toolchains/win_1803/py38/BUILD:9:11: Analysis of target '//tensorflow/tools/toolchains/win_1803/py38:py3_runtime' failed
INFO: Repository tflite_mobilenet_float instantiated at:
/workspace/WORKSPACE:84:14: in <toplevel>
/workspace/tensorflow/workspace2.bzl:936:21: in workspace
/workspace/tensorflow/workspace2.bzl:645:20: in _tf_repositories
/workspace/third_party/repo.bzl:136:21: in tf_http_archive
Repository rule _tf_http_archive defined at:
/workspace/third_party/repo.bzl:89:35: in <toplevel>
INFO: Repository go_sdk instantiated at:
/workspace/WORKSPACE:92:14: in <toplevel>
/workspace/tensorflow/workspace0.bzl:135:20: in workspace
/workspaces/tensorflow/bazel-ci_build-cache/.cache/bazel/_bazel_codespace/eab0d61a99b6696edb3d2aff87b585e8/external/com_github_grpc_grpc/bazel/grpc_extra_deps.bzl:36:27: in grpc_extra_deps
/workspaces/tensorflow/bazel-ci_build-cache/.cache/bazel/_bazel_codespace/eab0d61a99b6696edb3d2aff87b585e8/external/io_bazel_rules_go/go/private/sdk.bzl:431:28: in go_register_toolchains
/workspaces/tensorflow/bazel-ci_build-cache/.cache/bazel/_bazel_codespace/eab0d61a99b6696edb3d2aff87b585e8/external/io_bazel_rules_go/go/private/sdk.bzl:130:21: in go_download_sdk
Repository rule _go_download_sdk defined at:
/workspaces/tensorflow/bazel-ci_build-cache/.cache/bazel/_bazel_codespace/eab0d61a99b6696edb3d2aff87b585e8/external/io_bazel_rules_go/go/private/sdk.bzl:117:35: in <toplevel>
ERROR: Analysis of target '//tensorflow/tools/toolchains/win_1803/py38:py3_runtime' failed; build aborted:
INFO: Elapsed time: 23.466s
INFO: 0 processes.
FAILED: Build did NOT complete successfully (823 packages loaded, 12233 targets configured)
ERROR: Couldn't start the build. Unable to run tests
</details>
|
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| 62,897 |
Skip those parts of tests that do not pass on AARCH64
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Due to performance reasons the remapper behaves differently on AARCH64 and this consequently leads to test failures when this is checked. Disable the parts of tests that fail on AARCH64 due to this expected difference.
Fixes: #62882
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Failed to load the native TensorFlow runtime when loading DeepLabCut
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[
"@Caarvaa Verify the exact TensorFlow version required for DeepLabCut 2.2.2. If necessary, install the compatible version using conda install tensorflow==<version> or pip install tensorflow==<version>. Kindly consider using virtual environments that help to isolate project-specific dependencies and avoid conflicts.\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/62896\">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/62896\">No</a>\n"
] | 2024-02-05T17:04:44 | 2024-02-22T01:46:36 | 2024-02-22T01:46:33 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.10.0
### Custom code
No
### OS platform and distribution
Windows 10
### Mobile device
_No response_
### Python version
3.8.18
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Attempting to load DeepLabCut (version 2.2.2) via anaconda with python version 3.8.18 but i get a response saying it 'Failed to load the native TensorFlow runtime'. (full output below)
I've tried to uninstall and reinstall tensorflow but that doesn't resolve the issue (instead get other issues, the output code from which i can provide if its helpful)
I have 0 programming experience so i apologise in advance for that
Thank you for any response!
### Standalone code to reproduce the issue
```shell
Not sure what to put here
```
### Relevant log output
```shell
(DEEPLABCUT) C:\Users\Samuel>python -m deeplabcut
Traceback (most recent call last):
File "D:\DLCStartup\DLCSetup\anaconda\envs\DEEPLABCUT\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: The specified module could not be found.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "D:\DLCStartup\DLCSetup\anaconda\envs\DEEPLABCUT\lib\runpy.py", line 185, in _run_module_as_main
mod_name, mod_spec, code = _get_module_details(mod_name, _Error)
File "D:\DLCStartup\DLCSetup\anaconda\envs\DEEPLABCUT\lib\runpy.py", line 144, in _get_module_details
return _get_module_details(pkg_main_name, error)
File "D:\DLCStartup\DLCSetup\anaconda\envs\DEEPLABCUT\lib\runpy.py", line 111, in _get_module_details
__import__(pkg_name)
File "D:\DLCStartup\DLCSetup\anaconda\envs\DEEPLABCUT\lib\site-packages\deeplabcut\__init__.py", line 14, in <module>
import tensorflow as tf
File "D:\DLCStartup\DLCSetup\anaconda\envs\DEEPLABCUT\lib\site-packages\tensorflow\__init__.py", line 37, in <module>
from tensorflow.python.tools import module_util as _module_util
File "D:\DLCStartup\DLCSetup\anaconda\envs\DEEPLABCUT\lib\site-packages\tensorflow\python\__init__.py", line 36, in <module>
from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
File "D:\DLCStartup\DLCSetup\anaconda\envs\DEEPLABCUT\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 77, in <module>
raise ImportError(
ImportError: Traceback (most recent call last):
File "D:\DLCStartup\DLCSetup\anaconda\envs\DEEPLABCUT\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: The specified module could not be found.
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.
```
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CUPTI_ERROR_INVALID_DEVICE when trying to profile model performance
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"@lrohlfs Please ensure the environment variable CUDA_VISIBLE_DEVICES is not set to exclude your RTX 4060Ti. You can check its value using the following:\r\n\r\n```echo $CUDA_VISIBLE_DEVICES```\r\n\r\nPlease double-check if you have installed the appropriate NVIDIA drivers for your WSL2 instance. You can find instructions for different distributions on the NVIDIA website: https://developer.nvidia.com/embedded/linux-tegra. And also make sure the drivers are compatible with your WSL2 version and CUDA Toolkit version.\r\n\r\n",
"I have found a working solution. The issue can be closed, but here are my findings for future reference/others:\r\n\r\nProfiling on WSL2 was added with CUDA 12, so everything before tf 2.15 does not work. \r\nFor tf 2.15 the packaged CUDNN Version (from [and-cuda] does not work with profiling, but a custom install of the Toolkit (12.2) and CUDNN (8.9) finally works. Here it is important to correctly set the LD_LIBRARY_PATH and XLA_FLAGS environmental variable so that the tensorflow installation finds the correct CUDA libraries.\r\n\r\nSo if someone wants to investigate this further, I would start by comparing the libraries installed by [and-cuda] with the custom install and check if something is missing/different here.\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/62895\">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/62895\">No</a>\n"
] | 2024-02-05T14:34:46 | 2024-02-08T09:23:56 | 2024-02-08T09:23:53 |
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
WSL 2 Ubuntu 22_04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.2/8.9
### GPU model and memory
RTX 4060TI 16GB
### Current behavior?
When trying to profile model performance on my fresh WSL2 install of tensorflow (using the pip [and-cuda] approach), I am encountering a CUPTI error.
The installation works perfectly fine for just training models on the GPU (RTX 4060Ti, 16gb), but whenever I enable the profiler, the log states the following error:
"external/local_xla/xla/backends/profiler/gpu/cupti_error_manager.cc:194] cuptiSubscribe: error 2: CUPTI_ERROR_INVALID_DEVICE"
I reproduced the error on the same machine with tf 2.14 as well as a custom install of cuda using the recommended versions from the install guide.
Is there something I am doing obviously wrong or is there possibly an issue with my GPU?
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import tensorflow_datasets as tfds
tfds.disable_progress_bar()
(ds_train, ds_test), ds_info = tfds.load(
'mnist',
split=['train', 'test'],
shuffle_files=True,
as_supervised=True,
with_info=True,
)
def normalize_img(image, label):
"""Normalizes images: `uint8` -> `float32`."""
return tf.cast(image, tf.float32) / 255., label
ds_train = ds_train.map(normalize_img)
ds_train = ds_train.batch(128)
ds_test = ds_test.map(normalize_img)
ds_test = ds_test.batch(128)
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28, 1)),
tf.keras.layers.Dense(128,activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(
loss='sparse_categorical_crossentropy',
optimizer=tf.keras.optimizers.Adam(0.001),
metrics=['accuracy']
)
# Create a TensorBoard callback
logs = "logs/" + datetime.now().strftime("%Y%m%d-%H%M%S")
tboard_callback = tf.keras.callbacks.TensorBoard(log_dir = logs,
histogram_freq = 1,
profile_batch = '500,520')
model.fit(ds_train,
epochs=2,
validation_data=ds_test,
callbacks = [tboard_callback])
```
### Relevant log output
```shell
2024-02-05 15:24:10.044502: 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
2024-02-05 15:24:10.044549: 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
2024-02-05 15:24:10.045378: 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
2024-02-05 15:24:10.050035: 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.
2024-02-05 15:24:10.810462: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
TensorFlow version: 2.15.0
2024-02-05 15:24:11.786337: 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.
2024-02-05 15:24:11.808853: 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.
2024-02-05 15:24:11.808928: 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.
2024-02-05 15:24:11.947877: 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.
2024-02-05 15:24:11.947963: 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.
2024-02-05 15:24:11.947991: 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.
2024-02-05 15:24:11.948032: 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.
2024-02-05 15:24:11.948051: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /device:GPU:0 with 13689 MB memory: -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.9
Found GPU at: /device:GPU:0
2024-02-05 15:24:12.312891: W external/local_tsl/tsl/platform/cloud/google_auth_provider.cc:184] All attempts to get a Google authentication bearer token failed, returning an empty token. Retrieving token from files failed with "NOT_FOUND: Could not locate the credentials file.". Retrieving token from GCE failed with "FAILED_PRECONDITION: Error executing an HTTP request: libcurl code 6 meaning 'Couldn't resolve host name', error details: Could not resolve host: metadata.google.internal".
Downloading and preparing dataset 11.06 MiB (download: 11.06 MiB, generated: 21.00 MiB, total: 32.06 MiB) to /home/lennart/tensorflow_datasets/mnist/3.0.1...
Dataset mnist downloaded and prepared to /home/lennart/tensorflow_datasets/mnist/3.0.1. Subsequent calls will reuse this data.
2024-02-05 15:24:14.863787: 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.
2024-02-05 15:24:14.863867: 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.
2024-02-05 15:24:14.863897: 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.
2024-02-05 15:24:14.864203: 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.
2024-02-05 15:24:14.864270: 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.
2024-02-05 15:24:14.864318: 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.
2024-02-05 15:24:14.864583: 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.
2024-02-05 15:24:14.864612: 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.
2024-02-05 15:24:14.864647: 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.
2024-02-05 15:24:14.864678: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13689 MB memory: -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.9
2024-02-05 15:24:15.372290: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:104] Profiler session initializing.
2024-02-05 15:24:15.372326: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:119] Profiler session started.
2024-02-05 15:24:15.372353: I external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:1883] Profiler found 1 GPUs
2024-02-05 15:24:15.381500: E external/local_xla/xla/backends/profiler/gpu/cupti_error_manager.cc:194] cuptiSubscribe: error 2: CUPTI_ERROR_INVALID_DEVICE
2024-02-05 15:24:15.381539: E external/local_xla/xla/backends/profiler/gpu/cupti_error_manager.cc:459] cuptiGetResultString: ignored due to a previous error.
2024-02-05 15:24:15.381547: E external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:1935] function cupti_interface_->Subscribe( &subscriber_, (CUpti_CallbackFunc)ApiCallback, this)failed with error
2024-02-05 15:24:15.381607: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:131] Profiler session tear down.
2024-02-05 15:24:15.381636: E external/local_xla/xla/backends/profiler/gpu/cupti_error_manager.cc:142] cuptiFinalize: ignored due to a previous error.
2024-02-05 15:24:15.381657: E external/local_xla/xla/backends/profiler/gpu/cupti_error_manager.cc:459] cuptiGetResultString: ignored due to a previous error.
2024-02-05 15:24:15.381663: E external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:2026] function cupti_interface_->Finalize()failed with error
2024-02-05 15:24:15.948766: I external/local_tsl/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory
Epoch 1/2
2024-02-05 15:24:16.798373: I external/local_xla/xla/service/service.cc:168] XLA service 0x7f333486bbd0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2024-02-05 15:24:16.798410: I external/local_xla/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2024-02-05 15:24:16.802065: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2024-02-05 15:24:16.816408: 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 00:00:1707143056.887060 23655 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
469/469 [==============================] - 4s 6ms/step - loss: 0.3633 - accuracy: 0.8988 - val_loss: 0.1990 - val_accuracy: 0.9438
Epoch 2/2
34/469 [=>............................] - ETA: 2s - loss: 0.2058 - accuracy: 0.93542024-02-05 15:24:20.091243: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:104] Profiler session initializing.
2024-02-05 15:24:20.091282: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:119] Profiler session started.
2024-02-05 15:24:20.091298: E external/local_xla/xla/backends/profiler/gpu/cupti_error_manager.cc:135] cuptiGetTimestamp: ignored due to a previous error.
2024-02-05 15:24:20.091319: E external/local_xla/xla/backends/profiler/gpu/cupti_error_manager.cc:186] cuptiSubscribe: ignored due to a previous error.
2024-02-05 15:24:20.091326: E external/local_xla/xla/backends/profiler/gpu/cupti_error_manager.cc:459] cuptiGetResultString: ignored due to a previous error.
2024-02-05 15:24:20.091331: E external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:1935] function cupti_interface_->Subscribe( &subscriber_, (CUpti_CallbackFunc)ApiCallback, this)failed with error
43/469 [=>............................] - ETA: 2s - loss: 0.2060 - accuracy: 0.93802024-02-05 15:24:20.220329: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:70] Profiler session collecting data.
2024-02-05 15:24:20.221421: E external/local_xla/xla/backends/profiler/gpu/cupti_error_manager.cc:142] cuptiFinalize: ignored due to a previous error.
2024-02-05 15:24:20.221455: E external/local_xla/xla/backends/profiler/gpu/cupti_error_manager.cc:459] cuptiGetResultString: ignored due to a previous error.
2024-02-05 15:24:20.221463: E external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:2026] function cupti_interface_->Finalize()failed with error
2024-02-05 15:24:20.260723: E external/local_xla/xla/backends/profiler/gpu/cupti_error_manager.cc:135] cuptiGetTimestamp: ignored due to a previous error.
2024-02-05 15:24:20.260773: E external/local_xla/xla/backends/profiler/gpu/cupti_error_manager.cc:135] cuptiGetTimestamp: ignored due to a previous error.
2024-02-05 15:24:20.260782: I external/local_xla/xla/backends/profiler/gpu/cupti_collector.cc:541] GpuTracer has collected 0 callback api events and 0 activity events.
2024-02-05 15:24:20.283953: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:131] Profiler session tear down.
2024-02-05 15:24:20.293886: I external/local_tsl/tsl/profiler/rpc/client/save_profile.cc:144] Collecting XSpace to repository: logs/20240205-152415/plugins/profile/2024_02_05_15_24_20/LENNART-UNIPC.xplane.pb
469/469 [==============================] - 3s 5ms/step - loss: 0.1723 - accuracy: 0.9512 - val_loss: 0.1449 - val_accuracy: 0.9569
```
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"@SteefR,\r\nHave you tried the above query after trying the **bazel clean --expunge** command. Could you try once and try repeating the above steps. And also make sure you follow the steps mentioned [here](https://www.tensorflow.org/install/source).\r\n\r\nThank you for the details and usually users get this error `PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested` target due to the system firewall. The system firewall restricts the application to connect to external systems. The firewall requires a valid certificate to allow access to the external systems.\r\n\r\nCould you please confirm, whether you're installing [Bazel](https://bazel.build/install) normally or via Bazelisk because [Bazelisk](https://github.com/bazelbuild/bazelisk) is an easy way to install Bazel and automatically downloads the correct Bazel version for TensorFlow and if you're using Bazelisk then please download manually and please follow below steps :\r\n\r\nwget https://github.com/bazelbuild/bazelisk/releases/download/v1.16.0/bazelisk-darwin-arm64 (If you get any error with respect to certificate then you can use this command wget --no-check-certificate https://github.com/bazelbuild/bazelisk/releases/download/v1.16.0/bazelisk-darwin-arm64\r\n\r\n```\r\nchmod +x bazelisk-darwin-arm64\r\n\r\nsudo mv bazelisk-darwin-arm64 /usr/local/bin/bazel\r\n```\r\n\r\nIf you have already done the above steps and installed Bazel correctly then please execute below steps to see the Bazel or Bazelisk location and if possible please help me with those details\r\n\r\n```\r\n1. whereis bazel\r\n2. whereis bazelisk\r\n```\r\n\r\nAlso Could you please have a look at https://bazel.build/install/compile-source#bootstrap-unix.\r\n and comment out 'for' statement and rebuild a binary version of bazel:\r\nhttps://github.com/bazelbuild/bazel/blob/master/src/main/cpp/blaze.cc#L1015\r\nThen use this to build tensorflow. Thank you!",
"The way I fixed it was removing my bazel installation, and installing using apt, and adding\r\n\r\nstartup --host_jvm_args=-Djavax.net.ssl.trustStore=/etc/ssl/certs/java/cacerts \\\r\n --host_jvm_args=-Djavax.net.ssl.trustStorePassword=changeit\r\n\r\nto ~/.bazelrc",
"@SteefR,\r\nGlad the issue was fixed. Could you please feel free to move this issue to closed status. Thank you!",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62894\">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/62894\">No</a>\n"
] | 2024-02-05T08:56:49 | 2024-02-06T13:43:01 | 2024-02-06T13:42:57 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.16
### Custom code
No
### OS platform and distribution
Linux Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
6.5.0
### GCC/compiler version
Clang 14.0
### CUDA/cuDNN version
-
### GPU model and memory
-
### Current behavior?
I am trying to install Tensorflow from source on Linux Ubuntu 22.04 following the guide, and I managed to get to configure the bazel build just fine (I am building without gpu support).
When trying to build it can download all packages except when trying to download from [https://golang.org/dl/?mode=json&include=all, https://golang.google.cn/dl/?mode=json&include=all]
i get the error java.io.IOException: Error downloading [https://golang.org/dl/?mode=json&include=all, https://golang.google.cn/dl/?mode=json&include=all] to /home/name/.cache/bazel/_bazel_name/53019e683ff9cb9992223629eccf493b/external/go_sdk/versions.json: PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target
I tried to install the certification manually and found some places to do it, but still no luck. Can someone help me?
### Standalone code to reproduce the issue
```shell
bazel build //tensorflow/tools/pip_package:build_pip_package
```
### Relevant log output
```shell
bazel build //tensorflow/tools/pip_package:build_pip_package
INFO: Reading 'startup' options from /home/name/Documents/tensorflow_git/.bazelrc: --windows_enable_symlinks
INFO: Options provided by the client:
Inherited 'common' options: --isatty=1 --terminal_columns=165
INFO: Reading rc options for 'build' from /home/name/Documents/tensorflow_git/.bazelrc:
Inherited 'common' options: --experimental_repo_remote_exec
INFO: Reading rc options for 'build' from /home/name/Documents/tensorflow_git/.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/name/Documents/tensorflow_git/.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 CLANG_COMPILER_PATH=/usr/lib/llvm-14/bin/clang --repo_env=CC=/usr/lib/llvm-14/bin/clang --repo_env=BAZEL_COMPILER=/usr/lib/llvm-14/bin/clang
INFO: Found applicable config definition build:short_logs in file /home/name/Documents/tensorflow_git/.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file /home/name/Documents/tensorflow_git/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:linux in file /home/name/Documents/tensorflow_git/.bazelrc: --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wno-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-array-bounds --copt=-Wunused-result --copt=-Werror=unused-result --copt=-Wswitch --copt=-Werror=switch --copt=-Wno-error=unused-but-set-variable --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --experimental_guard_against_concurrent_changes
INFO: Found applicable config definition build:dynamic_kernels in file /home/name/Documents/tensorflow_git/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS
INFO: Build options --action_env and --python_path have changed, discarding analysis cache.
INFO: Repository go_sdk instantiated at:
/home/name/Documents/tensorflow_git/WORKSPACE:92:14: in <toplevel>
/home/name/Documents/tensorflow_git/tensorflow/workspace0.bzl:135:20: in workspace
/home/name/.cache/bazel/_bazel_name/53019e683ff9cb9992223629eccf493b/external/com_github_grpc_grpc/bazel/grpc_extra_deps.bzl:36:27: in grpc_extra_deps
/home/name/.cache/bazel/_bazel_name/53019e683ff9cb9992223629eccf493b/external/io_bazel_rules_go/go/private/sdk.bzl:431:28: in go_register_toolchains
/home/name/.cache/bazel/_bazel_name/53019e683ff9cb9992223629eccf493b/external/io_bazel_rules_go/go/private/sdk.bzl:130:21: in go_download_sdk
Repository rule _go_download_sdk defined at:
/home/name/.cache/bazel/_bazel_name/53019e683ff9cb9992223629eccf493b/external/io_bazel_rules_go/go/private/sdk.bzl:117:35: in <toplevel>
WARNING: Download from https://golang.org/dl/?mode=json&include=all failed: class javax.net.ssl.SSLHandshakeException PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target
WARNING: Download from https://golang.google.cn/dl/?mode=json&include=all failed: class javax.net.ssl.SSLHandshakeException PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target
ERROR: An error occurred during the fetch of repository 'go_sdk':
Traceback (most recent call last):
File "/home/name/.cache/bazel/_bazel_name/53019e683ff9cb9992223629eccf493b/external/io_bazel_rules_go/go/private/sdk.bzl", line 71, column 21, in _go_download_sdk_impl
ctx.download(
Error in download: java.io.IOException: Error downloading [https://golang.org/dl/?mode=json&include=all, https://golang.google.cn/dl/?mode=json&include=all] to /home/name/.cache/bazel/_bazel_name/53019e683ff9cb9992223629eccf493b/external/go_sdk/versions.json: PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target
ERROR: /home/name/Documents/tensorflow_git/WORKSPACE:92:14: fetching _go_download_sdk rule //external:go_sdk: Traceback (most recent call last):
File "/home/name/.cache/bazel/_bazel_name/53019e683ff9cb9992223629eccf493b/external/io_bazel_rules_go/go/private/sdk.bzl", line 71, column 21, in _go_download_sdk_impl
ctx.download(
Error in download: java.io.IOException: Error downloading [https://golang.org/dl/?mode=json&include=all, https://golang.google.cn/dl/?mode=json&include=all] to /home/name/.cache/bazel/_bazel_name/53019e683ff9cb9992223629eccf493b/external/go_sdk/versions.json: PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target
ERROR: Analysis of target '//tensorflow/tools/pip_package:build_pip_package' failed; build aborted: java.io.IOException: Error downloading [https://golang.org/dl/?mode=json&include=all, https://golang.google.cn/dl/?mode=json&include=all] to /home/name/.cache/bazel/_bazel_name/53019e683ff9cb9992223629eccf493b/external/go_sdk/versions.json: PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target
INFO: Elapsed time: 27.591s
INFO: 0 processes.
FAILED: Build did NOT complete successfully (323 packages loaded, 7971 targets configured)
```
|
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I_kwDOArmXAs5-Oth2
| 62,893 |
Need Privacy Manifest(iOS)
|
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[
"Hi @yishuangP, can you please take a look? Thanks.",
"any updates?\r\nI also need a tensorflow PrivacyManifest for our iOS application. we detected LibTensorFlowLiteC contains following rrapis:\r\n\r\n```\r\nlibTensorFlowLiteC.a:util.o: (undefined) external _stat\r\nlibTensorFlowLiteC.a:util.o: (undefined) external _stat\r\nlibTensorFlowLiteC.a:allocation.o: (undefined) external _fstat\r\nlibTensorFlowLiteC.a:mmap_allocation.o: (undefined) external _fstat\r\nlibTensorFlowLiteC.a:allocation.o: (undefined) external _fstat\r\nlibTensorFlowLiteC.a:mmap_allocation.o: (undefined) external _fstat\r\n```\r\n\r\nWould please provide reasons for using them?",
"Another voice hoping that this on your radar before the Apple deadline",
"Hey there following up on this as we're nearing closer to the deadline for Privacy Manifest Compliance (ie May 1st). Are there any new updates here?",
"I don't think TensorFlow will do anything, I have rolled out my own Unofficial SPM fork and I'll probably add the privacy manifest there https://github.com/tareksabry1337/TensorFlowLiteC",
"I added the manifest myself.",
"Hi really sorry for the delay, merging https://github.com/tensorflow/tensorflow/pull/66030 which adds the privacy manifests.",
"Hi @juntec, please check to see if your issue is now resolved with that PR https://github.com/tensorflow/tensorflow/pull/66030, 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/62893\">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/62893\">No</a>\n"
] | 2024-02-05T06:34:36 | 2024-05-09T01:48:55 | 2024-05-09T01:48:50 |
NONE
| null | null | null |
### Issue type
Others
### 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
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Hello.
I'm incorporating tensorflow light into my iOS application.
From Fall 2023 you’ll receive an email from Apple if you upload an app to App Store Connect that uses required reason API without describing the reason in its privacy manifest file. From Spring 2024, apps that don’t describe their use of required reason API in their privacy manifest file won’t be accepted by App Store Connect.
It's described here in detail.
https://developer.apple.com/news/?id=r1henawx
https://developer.apple.com/documentation/bundleresources/privacy_manifest_files/describing_data_use_in_privacy_manifests
Is it necessary to add PrivacyManifest in tensorflow light? If necessary, please add a PrivacyManifest.
Thank you.
### Standalone code to reproduce the issue
```shell
```
### Relevant log output
_No response_
|
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I_kwDOArmXAs5-OkOq
| 62,892 |
How to change color of specific body point in Pose estimation? eg. If I want to change color of Hip, Knee & Ankle then is it possible?
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[
"@vnanaware111 Changing the color of specific body points like Hip, Knee, and Ankle in Pose estimation with TensorFlow Lite (TFLite) is achievable, but it might require some post-processing steps since TFLite itself primarily focuses on model inference. Here is an approach you can consider:\r\n`Inference with TFLite:` Please run your TFLite Pose estimation model to get the predicted keypoints for each body part.\r\n`Post-processing script:`\r\nThen access the keypoints for the desired body parts (Hip, Knee, Ankle) based on their indices or names in the output tensor. Please load the original image you used for inference and iterate through the keypoints and draw circles or shapes on the image at their respective locations using an image editing library like OpenCV or Pillow. You can assign specific colors to Hip, Knee, and Ankle while drawing. Finally save or display the modified image with the highlighted body points.\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."
] | 2024-02-05T06:03:20 | 2024-02-21T01:47:02 | 2024-02-21T01:47:01 |
NONE
| null | null | null |
### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
'TensorFlowLiteSwift', '~> 0.0.1-nightly', :subspecs => ['CoreML', 'Metal']
### 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?
Unable to change specific body point color.
### Standalone code to reproduce the issue
```shell
eg. I want to change color of Hip, Knee & Ankle then is it possible?
```
### Relevant log output
_No response_
|
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I_kwDOArmXAs5-LFtt
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Model inference crashed
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[
"I think this may related to #62701",
"@SchweitzerGAO This error likely indicates an issue with a Mean operation within the TFLite model. It might be due to Incompatibility between the Mean operation in the original model and its implementation in TFLite also has incorrect input or output shapes for the Mean operation. It can be because of the issues with quantization if the model is quantized.\r\n\r\nThank you!",
"Thank you! @sushreebarsa I downgraded to Python 3.8 and tensorflow 2.13.0 and the error disappeared",
"It works with version 2.14.1 on Python too. However, I'm encountering the same issue when trying to use it on Android. If anyone has successfully converted Whisper to tflite for Android, could you please share the versions of the libraries you used, or any modifications you made?\r\n\r\nIn my experiments, I observed that adding multiple tf.print statements to monitor intermediate results leads to the expected outcomes. What could be the reason for this?",
"> It works with version 2.14.1 on Python too. However, I'm encountering the same issue when trying to use it on Android. If anyone has successfully converted Whisper to tflite for Android, could you please share the versions of the libraries you used, or any modifications you made?\r\n\r\nI needed to remove the app to change the assets of the app.",
"This still happens on Android, but only with the base.en model."
] | 2024-02-04T02:18:05 | 2024-04-04T18:59:14 | 2024-02-05T09:25:50 |
NONE
| null | null | null |
### 1. System information
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 10
- TensorFlow installation (pip package or built from source): pip
- TensorFlow library (version, if pip package or github SHA, if built from source): 2.15.0
### 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
```py
from abc import ABC
import tensorflow as tf
from datasets import load_dataset
from transformers import WhisperProcessor, TFWhisperForConditionalGeneration, WhisperFeatureExtractor, WhisperTokenizer
lang_dict = {
tf.constant('en').ref(): 50259,
tf.constant('zh').ref(): 50260,
tf.constant('de').ref(): 50261,
tf.constant('es').ref(): 50262,
tf.constant('ru').ref(): 50263,
tf.constant('ko').ref(): 50264,
tf.constant('fr').ref(): 50265,
tf.constant('ja').ref(): 50266,
tf.constant('pt').ref(): 50267,
tf.constant('tr').ref(): 50268,
tf.constant('ar').ref(): 50272,
tf.constant('it').ref(): 50274,
tf.constant('ur').ref(): 50290,
tf.constant('fa').ref(): 50300,
tf.constant('th').ref(): 50289,
tf.constant('id').ref(): 50275
}
def save_tf_model(model):
processor = WhisperProcessor.from_pretrained("./whisper-base")
feature_extractor = WhisperFeatureExtractor.from_pretrained("./whisper-base")
forced_decoder_ids = processor.get_decoder_prompt_ids(language="en", task="transcribe")
tokenizer = WhisperTokenizer.from_pretrained("./whisper-base", predict_timestamps=True)
processor = WhisperProcessor(feature_extractor, tokenizer)
# Loading dataset
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
inputs = processor(
ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["sampling_rate"], return_tensors="tf"
)
input_features = inputs.input_features
# Generating Transcription
generated_ids = model.generate(input_features=input_features, forced_decoder_ids=forced_decoder_ids)
print(generated_ids)
transcription = processor.tokenizer.decode(generated_ids[0])
print(transcription)
model.save('./content/tf_whisper_saved')
def convert_tflite(model):
saved_model_dir = './content/tf_whisper_saved'
tflite_model_path = './whisper-base.tflite'
generate_model = GenerateModel(model=model)
tf.saved_model.save(generate_model, saved_model_dir, signatures={"serving_default": generate_model.serving})
# Convert the model
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.
tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.
]
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()
# Save the model
with open(tflite_model_path, 'wb') as f:
f.write(tflite_model)
class GenerateModel(tf.Module):
def __init__(self, model):
super(GenerateModel, self).__init__()
self.model = model
self.lang_dict = {
tf.constant(50259).ref(): 50259,
tf.constant(50260).ref(): 50260,
tf.constant(50261).ref(): 50261,
tf.constant(50262).ref(): 50262,
tf.constant(50263).ref(): 50263,
tf.constant(50264).ref(): 50264,
tf.constant(50265).ref(): 50265,
tf.constant(50266).ref(): 50266,
tf.constant(50267).ref(): 50267,
tf.constant(50268).ref(): 50268,
tf.constant(50272).ref(): 50272,
tf.constant(50274).ref(): 50274,
tf.constant(50290).ref(): 50290,
tf.constant(50300).ref(): 50300,
tf.constant(50289).ref(): 50289,
tf.constant(50275).ref(): 50275
}
@tf.function(
# shouldn't need static batch size, but throws exception without it (needs to be fixed)
input_signature=[
tf.TensorSpec((1, 80, 3000), tf.float32, name="input_features"),
tf.TensorSpec((), tf.int32, name="lang")
],
)
def serving(self, input_features, lang):
outputs = self.model.generate(
input_features,
max_new_tokens=450, # change as needed
return_dict_in_generate=True,
forced_decoder_ids=[(1, self.lang_dict.get(lang.ref(), 50259)), (2, 50359), (3, 50363)]
)
return {"sequences": outputs["sequences"]}
def test():
tflite_model_path = 'whisper-base.tflite'
feature_extractor = WhisperFeatureExtractor.from_pretrained("./whisper-base")
tokenizer = WhisperTokenizer.from_pretrained("./whisper-base", predict_timestamps=True)
processor = WhisperProcessor(feature_extractor, tokenizer)
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
inputs = processor(
ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["sampling_rate"], return_tensors="tf"
)
input_features = inputs.input_features
interpreter = tf.lite.Interpreter(tflite_model_path)
tflite_generate = interpreter.get_signature_runner()
generated_ids = tflite_generate(input_features=input_features, lang=tf.constant(50259))["sequences"]
transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(transcription)
if __name__ == '__main__':
model = TFWhisperForConditionalGeneration.from_pretrained("./whisper-base")
# forced_decoder_ids = processor.get_decoder_prompt_ids(language="zh", task="transcribe")
# print(forced_decoder_ids)
save_tf_model(model)
convert_tflite(model)
test()
```
### 3. Failure after conversion
If the conversion is successful, but the generated model is wrong, then state what is wrong:
Inference crashed with the following error:
```bash
Traceback (most recent call last):
File "E:\tflite_convert\convert.py", line 137, in <module>
test()
File "E:\tflite_convert\convert.py", line 125, in test
generated_ids = tflite_generate(input_features=input_features, lang=tf.constant(50259))["sequences"]
File "E:\miniconda\envs\tflite_convert\lib\site-packages\tensorflow\lite\python\interpreter.py", line 249, in __call__
self._interpreter_wrapper.Invoke(self._subgraph_index)
RuntimeError: tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.
gather index out of boundsNode number 32 (GATHER) failed to invoke.Node number 618 (WHILE) failed to invoke.
```
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