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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/59812/checks?check_run_id=11605653066) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request."
] | 2023-02-26T12:02:45 | 2023-02-26T15:31:12 | 2023-02-26T15:31:12 | NONE | spam | false | {
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"guy is a misunderstood genius"
] | 2023-02-25T20:07:39 | 2023-03-12T21:13:03 | 2023-03-12T21:13:03 | NONE | spam | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Others
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
Any
### 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 Behaviour?
```shell
WARNING! C++ is deprecated, use Rust instead
```
### Standalone code to reproduce the issue
```shell
..
```
### Relevant log output
_No response_</details> | {
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"Hi, @scottsmallwood \r\n\r\nApologize for the delay and it seems like this issue is more related with Microsoft C Compiler. Make sure MSVC 2019 PATH is properly set where the python is installed, so that `cl.exe` can be found and I found [similar issue](https://github.com/tensorflow/tensorflow/issues/54578), you can refer this [comment](https://github.com/tensorflow/tensorflow/issues/54578#issuecomment-1058568622) and you can refer this [documentation](https://code.visualstudio.com/docs/python/environments#:~:text=From%20within%20VS%20Code%2C%20you,environment%20types%3A%20Venv%20or%20Conda.)\r\n\r\nConfigure build environment by using below workaround if you're using `Miniconda` , I hope it will resolve your issue. \r\n\r\nYou have to create, activate and configure Python environment. Run inside `VS2019 x64 Native Tools Command Prompt`shell command below. You have to correct paths according your Miniconda3 location.\r\n\r\n```\r\n%windir%\\System32\\cmd.exe \"/K\" \r\nC:\\Users\\gaikwadrahul8\\Miniconda3\\Scripts\\activate.bat \r\nC:\\Users\\gaikwadrahul8\\Miniconda3\r\n```\r\nIf issue still persists please let us know ? or Could you please confirm if this issue is resolved for you ? Please feel free to close the issue if it is resolved ? Thank you!\r\n",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59810\">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/59810\">No</a>\n"
] | 2023-02-25T18:21:06 | 2023-03-20T02:00:40 | 2023-03-20T02:00:37 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Build/Install
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
tf2.11
### Custom Code
No
### OS Platform and Distribution
windows visual studio 2019
### Mobile device
no
### Python version
3.7
### Bazel version
5.3
### GCC/Compiler version
not sure
### CUDA/cuDNN version
none
### GPU model and memory
no
### Current Behaviour?
```shell
A bug happened!
```
### Standalone code to reproduce the issue
```shell
here is the command that i am running
bazel build //tensorflow::install_headers > w:\tmp\bazel_out.txt
any help much appreciated!!
```
### Relevant log output
```shell
In the file that i piped the output to i get this:
tensorflow/core/framework/tensor.cc(744): error C2248: 'tensorflow::TensorShapeBase<tensorflow::TensorShape>::TensorShapeBase': cannot access protected member declared in class 'tensorflow::TensorShapeBase<tensorflow::TensorShape>'
.\tensorflow/core/framework/tensor_shape.h(324): note: see declaration of 'tensorflow::TensorShapeBase<tensorflow::TensorShape>::TensorShapeBase'
.\tensorflow/core/framework/tensor_shape.h(359): note: see declaration of 'tensorflow::TensorShapeBase<tensorflow::TensorShape>'
here is the screen output that i am getting:
W:\bb\git\tensorflow_old>bazel build //tensorflow:install_headers > w:\tmp\bazel_out.txt
Starting local Bazel server and connecting to it...
INFO: Options provided by the client:
Inherited 'common' options: --isatty=1 --terminal_columns=80
INFO: Reading rc options for 'build' from w:\bb\git\tensorflow_old\.bazelrc:
Inherited 'common' options: --experimental_repo_remote_exec
INFO: Options provided by the client:
'build' options: --python_path=C:/Program Files/Python/Python3.7/python.exe
INFO: Reading rc options for 'build' from w:\bb\git\tensorflow_old\.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 --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 --incompatible_enforce_config_setting_visibility
INFO: Reading rc options for 'build' from w:\bb\git\tensorflow_old\.tf_configure.bazelrc:
'build' options: --action_env PYTHON_BIN_PATH=C:/Program Files/Python/Python3.7/python.exe --action_env PYTHON_LIB_PATH=C:/Program Files/Python/Python3.7/Lib --python_path=C:/Program Files/Python/Python3.7/python.exe --copt=/d2ReducedOptimizeHugeFunctions --host_copt=/d2ReducedOptimizeHugeFunctions --define=override_eigen_strong_inline=true
INFO: Reading rc options for 'build' from w:\bb\git\tensorflow_old\.bazelrc:
'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils
INFO: Found applicable config definition build:short_logs in file w:\bb\git\tensorflow_old\.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file w:\bb\git\tensorflow_old\.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:windows in file w:\bb\git\tensorflow_old\.bazelrc: --copt=/W0 --host_copt=/W0 --copt=/Zc:__cplusplus --host_copt=/Zc:__cplusplus --copt=/D_USE_MATH_DEFINES --host_copt=/D_USE_MATH_DEFINES --features=compiler_param_file --copt=/d2ReducedOptimizeHugeFunctions --host_copt=/d2ReducedOptimizeHugeFunctions --cxxopt=/std:c++17 --host_cxxopt=/std:c++17 --config=monolithic --copt=-DWIN32_LEAN_AND_MEAN --host_copt=-DWIN32_LEAN_AND_MEAN --copt=-DNOGDI --host_copt=-DNOGDI --copt=/Zc:preprocessor --host_copt=/Zc:preprocessor --linkopt=/DEBUG --host_linkopt=/DEBUG --linkopt=/OPT:REF --host_linkopt=/OPT:REF --linkopt=/OPT:ICF --host_linkopt=/OPT:ICF --verbose_failures --features=compiler_param_file --distinct_host_configuration=false
INFO: Found applicable config definition build:monolithic in file w:\bb\git\tensorflow_old\.bazelrc: --define framework_shared_object=false --define tsl_protobuf_header_only=false
INFO: Analyzed target //tensorflow:install_headers (265 packages loaded, 13791 targets configured).
INFO: Found 1 target...
ERROR: W:/bb/git/tensorflow_old/tensorflow/core/framework/BUILD:769:16: Compiling tensorflow/core/framework/tensor.cc failed: (Exit 2): cl.exe failed: error executing command
cd /d C:/users/scott.ad/_bazel_scott/24njqhju/execroot/org_tensorflow
SET INCLUDE=C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.24.28314\ATLMFC\include;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.24.28314\include;C:\Program Files (x86)\Windows Kits\10\include\10.0.18362.0\ucrt;C:\Program Files (x86)\Windows Kits\10\include\10.0.18362.0\shared;C:\Program Files (x86)\Windows Kits\10\include\10.0.18362.0\um;C:\Program Files (x86)\Windows Kits\10\include\10.0.18362.0\winrt;C:\Program Files (x86)\Windows Kits\10\include\10.0.18362.0\cppwinrt
SET PATH=C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.24.28314\bin\HostX64\x64;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\VC\VCPackages;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\CommonExtensions\Microsoft\TestWindow;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\CommonExtensions\Microsoft\TeamFoundation\Team Explorer;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\MSBuild\Current\bin\Roslyn;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Team Tools\Performance Tools\x64;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Team Tools\Performance Tools;C:\Program Files (x86)\Microsoft Visual Studio\Shared\Common\VSPerfCollectionTools\vs2019\\x64;C:\Program Files (x86)\Microsoft Visual Studio\Shared\Common\VSPerfCollectionTools\vs2019\;C:\Program Files (x86)\Windows Kits\10\bin\10.0.18362.0\x64;C:\Program Files (x86)\Windows Kits\10\bin\x64;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\\MSBuild\Current\Bin;C:\Windows\Microsoft.NET\Framework64\v4.0.30319;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\Tools\;;C:\Windows\system32;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\CommonExtensions\Microsoft\CMake\CMake\bin;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\CommonExtensions\Microsoft\CMake\Ninja
SET PWD=/proc/self/cwd
SET PYTHON_BIN_PATH=C:/Program Files/Python/Python3.7/python.exe
SET PYTHON_LIB_PATH=C:/Program Files/Python/Python3.7/Lib
SET RUNFILES_MANIFEST_ONLY=1
SET TEMP=C:\Users\scott.AD\AppData\Local\Temp
SET TF2_BEHAVIOR=1
SET TMP=C:\Users\scott.AD\AppData\Local\Temp
C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.24.28314\bin\HostX64\x64\cl.exe @bazel-out/x64_windows-opt/bin/tensorflow/core/framework/_objs/tensor/tensor.obj.params
# Configuration: 548e4eb7c3a997dbe854b942bd4ad3607774b15376fbc38c5c147beb568995f0
# Execution platform: @local_execution_config_platform//:platform
cl : Command line warning D9002 : ignoring unknown option '/Zc:preprocessor'
Target //tensorflow:install_headers failed to build
ERROR: W:/bb/git/tensorflow_old/tensorflow/BUILD:1548:8 Executing genrule //tensorflow:install_headers failed: (Exit 2): cl.exe failed: error executing command
cd /d C:/users/scott.ad/_bazel_scott/24njqhju/execroot/org_tensorflow
SET INCLUDE=C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.24.28314\ATLMFC\include;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.24.28314\include;C:\Program Files (x86)\Windows Kits\10\include\10.0.18362.0\ucrt;C:\Program Files (x86)\Windows Kits\10\include\10.0.18362.0\shared;C:\Program Files (x86)\Windows Kits\10\include\10.0.18362.0\um;C:\Program Files (x86)\Windows Kits\10\include\10.0.18362.0\winrt;C:\Program Files (x86)\Windows Kits\10\include\10.0.18362.0\cppwinrt
SET PATH=C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.24.28314\bin\HostX64\x64;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\VC\VCPackages;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\CommonExtensions\Microsoft\TestWindow;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\CommonExtensions\Microsoft\TeamFoundation\Team Explorer;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\MSBuild\Current\bin\Roslyn;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Team Tools\Performance Tools\x64;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Team Tools\Performance Tools;C:\Program Files (x86)\Microsoft Visual Studio\Shared\Common\VSPerfCollectionTools\vs2019\\x64;C:\Program Files (x86)\Microsoft Visual Studio\Shared\Common\VSPerfCollectionTools\vs2019\;C:\Program Files (x86)\Windows Kits\10\bin\10.0.18362.0\x64;C:\Program Files (x86)\Windows Kits\10\bin\x64;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\\MSBuild\Current\Bin;C:\Windows\Microsoft.NET\Framework64\v4.0.30319;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\Tools\;;C:\Windows\system32;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\CommonExtensions\Microsoft\CMake\CMake\bin;C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\CommonExtensions\Microsoft\CMake\Ninja
SET PWD=/proc/self/cwd
SET PYTHON_BIN_PATH=C:/Program Files/Python/Python3.7/python.exe
SET PYTHON_LIB_PATH=C:/Program Files/Python/Python3.7/Lib
SET RUNFILES_MANIFEST_ONLY=1
SET TEMP=C:\Users\scott.AD\AppData\Local\Temp
SET TF2_BEHAVIOR=1
SET TMP=C:\Users\scott.AD\AppData\Local\Temp
C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.24.28314\bin\HostX64\x64\cl.exe @bazel-out/x64_windows-opt/bin/tensorflow/core/framework/_objs/tensor/tensor.obj.params
# Configuration: 548e4eb7c3a997dbe854b942bd4ad3607774b15376fbc38c5c147beb568995f0
# Execution platform: @local_execution_config_platform//:platform
INFO: Elapsed time: 213.717s, Critical Path: 36.87s
INFO: 1962 processes: 484 internal, 1478 local.
FAILED: Build did NOT complete successfully
```
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"Could you please try to providing more debug details on this issue, it would help us to investigate further on this issue. \r\nFor more details regarding `tf.data` visit https://www.tensorflow.org/api_docs/python/tf/data\r\nFor pipeline related details visit https://www.tensorflow.org/guide/data\r\nThanks!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue has been marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.",
"Closing due to lack of recent activity. Please update the issue when new information becomes available, and we will reopen the issue. Thanks!",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59809\">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/59809\">No</a>\n"
] | 2023-02-25T12:12:59 | 2023-04-05T20:56:49 | 2023-04-05T20:54:20 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
No
### Source
source
### Tensorflow Version
2.11
### Custom Code
Yes
### OS Platform and Distribution
tpu-vm-tf-2.11.0-pod
### 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 Behaviour?
```shell
It appears that when using `tf.data.Dataset` with `libtpu.so 1.5.0` and `tpu-vm-tf-2.11.0-pod` runtime, only half the system resources are being able to get utilized. For a `tpu-v4` if a dataset uses more than 200gb system ram, it will get killed (with just a missing context id provided, no detailed error even gets generated). This problem does not occur using `tpu-vm-tf-2.10.1-pod`. I am sorry I am unable to provide a standalone example but please review build flags / runtime image for `tpu-vm-tf-2.11.0-pod`.
```
### Standalone code to reproduce the issue
```shell
Difficult to provide at this point
```
### Relevant log output
_No response_</details> | {
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"@fcoUnda @learning-to-play Could you help review this one please? "
] | 2023-02-25T12:05:12 | 2023-03-02T18:30:34 | 2023-03-02T18:30:34 | CONTRIBUTOR | null | false | {
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"@AdarshJain2305,\r\nTensorFlow Lite models may come with different associated files. For example, natural language models usually have vocab files that map word pieces to word IDs; classification models may have label files that indicate object categories. Without the associated files (if there are), a model will not function well.\r\n\r\nThe associated files can now be bundled with the model through the metadata Python library. The new TensorFlow Lite model becomes a zip file that contains both the model and the associated files. It can be unpacked with common zip tools. This new model format keeps using the same file extension, .tflite. It is compatible with existing TFLite framework and Interpreter. \r\nAlso please take a look at this official doc link for the [reference](https://www.tensorflow.org/lite/models/convert/metadata#label_output).\r\n\r\nhttps://www.tensorflow.org/lite/models/convert/metadata#adding_metadata_using_flatbuffers_python_api\r\n\r\nThank you!",
"@tilakrayal \r\nin which part of the pose classification do I need make changes to use my classifiers.tflite and label.txt files",
"*pose classification folder\r\n\r\n",
"@AdarshJain2305,\r\n\r\n- Preprocess the pose classification training data into a CSV file that specifies the landmarks (body keypoints) detected by the MoveNet model, along with the ground truth pose labels.\r\n\r\n- Build and train a pose classification model that takes the landmark coordinates from the CSV file as input, and outputs the predicted labels.\r\n\r\n- Convert the pose classification model to TFLite.\r\n\r\nThe detailed explanation was available at this official document **pose_classification.ipynb** for reference.\r\nhttps://github.com/tensorflow/tensorflow/blob/2823f9082cc69174bab5f5478999258139de8610/tensorflow/lite/g3doc/tutorials/pose_classification.ipynb\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.",
"> \r\n\r\n\r\n@tilakrayal What is the label.txt file format? Is there a case?",
"@Cranky-cat,\r\nLooks like the similar issue was raised https://github.com/tensorflow/tensorflow/issues/59968. We can track the issue there. 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/59807\">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/59807\">No</a>\n"
] | 2023-02-25T07:38:38 | 2023-03-28T01:57:43 | 2023-03-28T01:57:41 | NONE | null | null | null | Please go to Stack Overflow for help and support:
https://stackoverflow.com/questions/tagged/tensorflow
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------------------------
### System information
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### Describe the problem
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"Hi, It seems like the missing header files under different directories has been handled in the commit here https://github.com/tensorflow/tensorflow/commit/f167889d58830671d9891b70eac56f075e7e11ee.\r\nCould you please test again and let us know if you still face an issue. Thanks!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59806\">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/59806\">No</a>\n"
] | 2023-02-25T05:06:36 | 2023-04-06T01:53:34 | 2023-04-06T01:53:32 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Build/Install
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
latest master (commit [1f747f5](https://github.com/tensorflow/tensorflow/commit/1f747f5b52003896c5fe6723ff95f84f00def94a))
### Custom Code
No
### OS Platform and Distribution
Ubuntu 22.04
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/Compiler version
gcc 11.3.0
### CUDA/cuDNN version
_No response_
### GPU model and memory
Nvidia GTX 1650 Mobile
### Current Behaviour?
```shell
After building the TFLite installable package from source using CMake, according to the tutorial from the [documentation](https://www.tensorflow.org/lite/guide/build_cmake), when trying to compile code that has `#include "utils/neural_network/NeuralNetwork.h"`, the compiler throws an error, since many files required for the inclusion do not exist at `/usr/local/include/tensorflow/lite` (which is where the TFLite files ended up after running `cmake --install .`).
After checking the CMakeLists file at `tensorflow/lite`, it seems that the inclusion of certain directories is not done recursively (e.g. the inclusion of the files in the directory `core/async`, by the command `populate_tflite_source_vars`, leaves out the sub-folders `c`, `interop`, and `testing`), which seems to be the source of the issue, since the header files are not exported during the installation process.
Copying the missing files mentioned by the compiler to their appropriate locations seems to solve the problem, however, I have not completely tested this, since there is a great amount of files that have to be copied over.
Any help would be greatly appreciated.
```
### Standalone code to reproduce the issue
```shell
#include "utils/neural_network/NeuralNetwork.h"
int main() {
utils::neural_network::NeuralNetwork nn;
return 0;
}
```
### Relevant log output
```shell
/usr/local/include/tensorflow/lite/core/interpreter.h:56:10: fatal error: tensorflow/lite/profiling/telemetry/c/telemetry_setting_internal.h: No such file or directory
56 | #include "tensorflow/lite/profiling/telemetry/c/telemetry_setting_internal.h"
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
compilation terminated.
```
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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/59805/checks?check_run_id=11592499032) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request."
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"CC @reedwm.",
"@philipphack Can you please fix merge conflicts?",
"It looks like you also restored the tests removed in #59515. I didn't look over these very carefully, since I assume they are almost identical to the ones that were removed.",
"Yes, only the contracting dimensions should have changed."
] | 2023-02-25T01:52:20 | 2023-02-28T22:40:59 | 2023-02-28T22:40:58 | CONTRIBUTOR | null | false | {
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"Hi @ngc92, I was able to replicate the issue in Colab using TF v2.11 and tf-nightly(). Please find the gists [here(2.11)](https://colab.sandbox.google.com/gist/synandi/63fb7378e6e2adb8a8e9d8d076693248/untitled305.ipynb) and [here(nightly)](https://colab.sandbox.google.com/gist/synandi/3cfee1ebf013bca0ec9da3c327a7c1ea/59803_nightly.ipynb).\r\n@tilakrayal, Could you please look into this issue? \r\nThank you!",
"There is active work on basic support for int32 as a numeric type on GPU. (This will be opt-in to avoid breaking existing software that relies on reserving int32 for shape computations.) While this is a prerequisite for int32 variables, it is unknown if there are additional specific issues related to the implementation of Variables might also have to be addressed.\r\n\r\n> If that is still a concern, maybe automatically placing any int32 Tensors with e.g. less than 1KiB size on the CPU, and larger ones on the GPU, might be the way to go?\r\n\r\nThank you for the suggestion. Having a way for shape computations to remain on host/cpu is critical for managing data transfer to/from GPU. The current plan is to represent the difference between \"int32 as shape\" and \"int32 as a numeric type\" in the internals of TensorFlow used for placement decisions by using different \"full type information\", `TFT_SHAPE_TENSOR` vs. `TFT_TENSOR` (refer to [full_type.proto](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/full_type.proto)). Full type information has previously been used to fix some placement inconsistencies with ragged tensors. The API for users is not yet decided. So while using a threshold based size of int32 tensors could be considered, my guess is that the final design for the part exposed to the user won't include a threshold.\r\n"
] | 2023-02-25T01:11:55 | 2023-03-09T22:30:14 | null | CONTRIBUTOR | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Feature Request
### Have you reproduced the bug with TF nightly?
No
### Source
binary
### Tensorflow Version
2.11
### 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 Behaviour?
Tensorflow currently does not support `int32` variables being placed on the GPU.
I've found an old [stackoverflow](https://stackoverflow.com/questions/37439299/no-gpu-kernel-for-an-int32-variable-op) post that argued this was because typically, `int32` Tensors often represent sizes of shapes, which would be needed in host memory.
If that is still a concern, maybe automatically placing any int32 Tensors with e.g. less than 1KiB size on the CPU, and larger ones on the GPU, might be the way to go?
For large integer tensors (e.g. indices for sparse operations), the current situation requires a trade-off between two undesireable situations:
* Either use `int64`, which wastes both GPU memory, and also memory bandwidth during kernel calls
* Or use `int32`, but then each step has to invoke a host-to-device data transfer.
Declaring the Variable as `float32` and bitcasting to `int32` before each use also does not allow to work around this issue, as the `bitcast` op also lacks a GPU kernel, and thus requires two memory transfers.
This request is somewhat related to #51728, as supporting int32 as index type for `SparseTensor` would run into the aforementioned data transfer problem if the index Tensor was backed by a Variable.
Even now, [`SparseTensorDenseMatMul`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/sparse_tensor_dense_matmul_op_gpu.cu.cc#L151) provides a kernel for `int32`, but again it cannot be used efficiently with indices supplies by a Variable.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
tf.config.set_soft_device_placement(False)
with tf.device("gpu:0"):
v = tf.Variable(initial_value=tf.zeros((50, 50), dtype=tf.int32))
```
### Relevant log output
```shell
tensorflow.python.framework.errors_impl.InvalidArgumentError: Could not satisfy device specification '/job:localhost/replica:0/task:0/device:GPU:0'. enable_soft_placement=0. Supported device types [CPU].
```
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"@eapolinario Thanks for reporting the issue.\r\n\r\nI have tried it on Mac M1 with arm64 and I was successfully able to install tf-nightly==2.13.0.dev20230224. Please refer to the below screenshots.\r\n\r\n<img width=\"566\" alt=\"Screenshot 2023-02-27 at 2 36 51 PM\" src=\"https://user-images.githubusercontent.com/118897289/221521838-f7f0a63c-b955-4707-b908-b95121fbb5d5.png\">\r\n\r\n<img width=\"564\" alt=\"Screenshot 2023-02-27 at 2 37 34 PM\" src=\"https://user-images.githubusercontent.com/118897289/221521925-f229bb7e-fb3a-4bc9-a266-a344fa961d5c.png\">\r\n\r\nCan you please check for different nightly versions and let us know if the issue persists?\r\n",
"@pjpratik , the screenshot above is pointing to the x86_64 wheel, right?",
"@eapolinario AFAIK, arm64 don't have nightly wheels. I did check with 2.12 nightly and 2.11 as well but the nightly was pointing to x86_64 wheels. The [documentation](https://www.tensorflow.org/install/pip#package_location) for stable release for CPU on MacOS also has x86_64 wheels as well.\r\n\r\nHave you tried with different nightly versions and observed the same behaviour? \r\n\r\nThanks.",
"@pjpratik , yeah, I observe the same behavior. Can you clarify why arm64 wheels are not being built? I tried to search for a [github issue](https://github.com/tensorflow/tensorflow/issues) but couldn't find any.",
"@eapolinario Please find the similar issue [#47782](https://github.com/tensorflow/tensorflow/issues/47782). As per this [comment](https://github.com/tensorflow/tensorflow/issues/47782#issuecomment-799601893), the official pip packages are built for x86_64. For ARM, we have to build from source.\r\n\r\nPlease refer to [this](https://www.tensorflow.org/install/source) for source installation on MacOS.\r\n\r\nThanks.",
"Closing as stale. Please reopen if you'd like to work on this further. Thanks!\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/59801\">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/59801\">No</a>\n"
] | 2023-02-24T22:10:21 | 2023-03-17T09:28:12 | 2023-03-17T09:28:09 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Build/Install
### Have you reproduced the bug with TF nightly?
Yes
### Source
binary
### Tensorflow Version
tf-nightly 2.13.0.dev20230224
### Custom Code
No
### OS Platform and Distribution
Macos M1
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
```shell
I'm not able to install tf-nightly on Macs running arm.
```
### Standalone code to reproduce the issue
```shell
> pip install tf-nightly==2.13.0.dev20230224
ERROR: Could not find a version that satisfies the requirement tf-nightly==2.13.0.dev20230224 (from versions: none)
ERROR: No matching distribution found for tf-nightly==2.13.0.dev20230224
```
### Relevant log output
_No response_</details> | {
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"sorry, I messed up, pulled in another commit unintentionally. will abandon this PR and do over"
] | 2023-02-24T19:37:49 | 2023-03-09T03:21:39 | 2023-02-24T19:42:27 | CONTRIBUTOR | null | true | {
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Signed-off-by: Tai Ly [tai.ly@arm.com](mailto:tai.ly@arm.com)
Change-Id: I6e3929873ea2b1f12fe635b4e953c14ed5bdf526 | {
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"Hi, @dgscharan \r\n\r\nApologize for the delayed response and you can refer our official documentation for [tf.distribute.MultiWorkerMirroredStrategy](https://www.tensorflow.org/api_docs/python/tf/distribute/MultiWorkerMirroredStrategy) and [TF_CONFIG and distributed training](https://cloud.google.com/ai-platform/training/docs/distributed-training-details#chief-versus-master) guide which may help you to configure the `TF_CONFIG`.\r\n\r\n@SuryanarayanaY, Could you please look into this issue ? Thank you!",
"Hi @dgscharan ,\r\n\r\nFor multi-worker training, as mentioned before, you need to set up the 'TF_CONFIG' environment variable for each binary running in your cluster. For more details please refer to the source [here](https://www.tensorflow.org/guide/distributed_training#setting_up_the_tf_config_environment_variable).\r\n\r\nPlease also refer to this [tutorial](https://www.tensorflow.org/tutorials/distribute/multi_worker_with_ctl) on how to setup multi-workers with custom training.",
"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/59799\">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/59799\">No</a>\n"
] | 2023-02-24T19:30:14 | 2023-03-28T01:57:46 | 2023-03-28T01:57:43 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Support
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
2.6
### Custom Code
Yes
### OS Platform and Distribution
Linux HPC
### Mobile device
_No response_
### Python version
3.8.3
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
11.3.1
### GPU model and memory
4 RTX 2080 Ti workers each having 8 GPUs
### Current Behaviour?
```shell
I am trying to run tensorflow distributed training code on multiple worker nodes. I initially tried it using the Mirrored strategy using a single worker with multiple GPUs and the code was working fine and the training process was getting distributed among multiple GPUs. when i have tried it with multiple worker nodes, the training process is actually executing seperately on different workers rather than the load getting distributed among the workers.
My Linux systems has 4 worker nodes with each node having 8 2080Ti GPUs and the GPUs are connected through PCIe system. I also not sure How to configure the TF_config. can anyone help me on this ?
```
### Standalone code to reproduce the issue
```shell
if __name__ == "__main__":
# Get mpi rank
from getOneHot import getOneHot
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
# Load in the parameter files
from json import load as loadf
with open("params.json", 'r') as inFile:
params = loadf(inFile)
# Get data files and prep them for the generator
import tensorflow
from tensorflow import distribute as D
callbacks = []
devices = getDevices()
print(devices)
set_tf_config_mpi()
strat = D.experimental.MultiWorkerMirroredStrategy(
communication=D.experimental.CollectiveCommunication.NCCL)
# Create network
from sys import argv
resume_training = False
print(argv)
if "resume_latest" in argv:
resume_training = True
with strat.scope():
# Scheduler
if isinstance(params["learning_rate"], str):
# Get the string for the importable function
lr = params["learning_rate"]
from tensorflow.keras.callbacks import LearningRateScheduler
# Use a dummy learning rate
params["learning_rate"] = 0.1
# model = create_model(**params)
# Get the importable function
lr = lr.split(".")
baseImport = __import__(lr[0], globals(), locals(), [lr[1]], 0)
lr = getattr(baseImport, lr[1])
# Make a schedule
lr = LearningRateScheduler(lr)
callbacks.append(lr)
# Resume Model?
model_name = None
if resume_training:
initial_epoch, model_name = getInitialEpochsAndModelName(rank)
if model_name is None:
initial_epoch=0
model = create_model(**params)
resume_training = False
else:
from tensorflow.keras.models import load_model
model = load_model(model_name)
# Load data from disk
import numpy
if "root" in params.keys():
root = params['root']
else:
root = "./"
if "filename" in params.keys():
filename = params["filename"]
else:
filename = "150MeV_all_shuffled_normed.csv"
restricted = [
'euc1', 'e1', 'x1', 'y1', 'z1',
'euc2', 'e2', 'x2', 'y2', 'z2',
'euc3', 'e3', 'x3', 'y3', 'z3',
]
x, y = getOneHot("{}/{}".format(root, filename), restricted=restricted, **params)
# val_filename = "150MeV_180kMUmin-stdCC_stitched_triples_dtot_trip_only.csv"
# val_x, val_y = getOneHot("{}/{}".format(root, val_filename), restricted=restricted)
val_x, val_y = None, None
params["gbatch_size"] = params['batch_size'] * len(devices)
print("x.shape =", x.shape)
print("y.shape =", y.shape)
print("epochs =", params['epochs'], type(params['epochs']))
print("batch =", params['batch_size'], type(params['batch_size']))
print("gbatch =", params['gbatch_size'], type(params['gbatch_size']))
# Load data into a distributed dataset
# Dataset object does nothing in place:
# https://stackoverflow.com/questions/55645953/shape-of-tensorflow-dataset-data-in-keras-tensorflow-2-0-is-wrong-after-conver
from tensorflow.data import Dataset
data = Dataset.from_tensor_slices((x, y))
# Create validation set
v = params['validation']
if val_x is not None:
vrecord = val_x.shape[0]
val = Dataset.from_tensor_slices((val_x, val_y))
validation = val # data.take(vrecord)
else:
vrecord = int(x.shape[0]*v)
validation = data.take(vrecord)
validation = validation.batch(params['gbatch_size'])
validation = validation.repeat(params['epochs'])
# Validation -- need to do kfold one day
# This set should NOT be distributed
vsteps = vrecord // params['gbatch_size']
if vrecord % params['gbatch_size'] != 0:
vsteps += 1
data = data.batch(params['gbatch_size'])
data = data.repeat(params['epochs'])
records = x.shape[0] # - vrecord
steps = records // params['gbatch_size']
if records % params['gbatch_size']:
steps += 1
print("steps =", steps)
# Note that if we are resuming that the number of _remaining_ epochs has
# changed!
# The number of epochs * steps is the numbers of samples to drop
print("initial cardinality = ", data.cardinality())
print("initial v cardinality = ", data.cardinality())
data = data.skip(initial_epoch*steps)
validation = validation.skip(initial_epoch*vsteps)
print("final cardinality = ", data.cardinality())
print("final v cardinality = ", data.cardinality())
# data = strat.experimental_distribute_dataset(data)
# Split into validation and training
callbacks = createCallbacks(params, callbacks, rank, resume_training)
print(callbacks)
print("fitting model")
print(data)
import tensorflow as tf
options = tf.data.Options()
options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF
train_data = data.with_options(options)
val_data = validation.with_options(options)
history = model.fit(train_data, epochs=params['epochs'],
batch_size=params['gbatch_size'],
steps_per_epoch=steps,
verbose=0,
initial_epoch=initial_epoch,
validation_data=val_data,
validation_steps=vsteps,
callbacks=callbacks)
print("fitting model done")
if rank == 0:
model.save("model-final")
else:
model.save("checkpoints/model-tmp")
############### slurm script :
#!/bin/bash
#SBATCH --job-name=job1 # Job name
#SBATCH --mem=30000 # Job memory request
#SBATCH --gres=gpu:4 # Number of requested GPU(s)
#SBATCH --time=3-23:00:00 # Time limit days-hrs:min:sec
#SBATCH --constraint=rtx_2080 # Specific hardware constraint
#SBATCH --error=slurm.err # Error file name
#SBATCH --output=slurm.out # Output file name
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=1
#SBATCH --array=1-2%1
if [ -d "model-final" ]
then
scancel $SLURM_ARRAY_JOB_ID
else
module load Anaconda3/2020.07
module load TensorFlow/2.6.0-foss-2021a-CUDA-11.3.1
mpirun python -u main.py resume_latest
fi
```
### Relevant log output
```shell
I log each and every epoch in a csv file. I see two csv files created and each one has different workers running.
```
</details> | {
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"Hi @cantonios ,\r\n\r\nI added a space before `\"//tensorflow/core/util/tensor_bundle:byteswaptensor\"` in `tensorflow/core/kernels/BUILD` as per the format checking result from the latest `Code Check - Changed Files`. Please take a look when you have some time.\r\n\r\nThank you very much!",
"Hi @cantonios ,\r\n\r\nCould you please take a look at the PR update when you have some time?\r\n\r\nThank you very much!",
"Thanks @cantonios !"
] | 2023-02-24T18:45:40 | 2023-03-07T20:42:16 | 2023-03-07T20:09:38 | CONTRIBUTOR | null | false | {
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} | Sub-test `tensorflow.python.ops.io_ops.serialize_tensor` in test case `//tensorflow/tools/docs:tf_doctest` failed on s390x (big-endian arch), because the serialized binary strings which represent the tensor data are different between little-endian and big-endian platforms.
This PR adds the endianness conversion procedure for big-endian systems in the implementation of ParseTensorOp and SerializeTensorOp (in `tensorflow/core/kernels/parse_tensor_op.cc`), so that the serialized binary strings generated on big-endian systems will be in little-endian format as well and identical to the ones generated on little-endian systems.
The code change in `tensorflow/python/kernel_tests/io_ops/parsing_ops_test.py` ensures the serialized proto string in `ParseTensorOpTest.testToFloat32()` would be in little-endian format on big-endian platforms, which is consistent with the updated code flow in `SerializeTensorOp`.
`//tensorflow/tools/docs:tf_doctest` will pass on big-endian systems after applying the code change. It won't affect the functionality and performance on little-endian systems.
This PR also updated the api documentation for `tf.io.serialize_tensor` and `tf.io.parse_tensor` to indicate the serialized binary strings are in little-endian format.
Signed-off-by: Kun-Lu <kun.lu@ibm.com> | {
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] | null | [
"@RonakPandya072 \r\nWhen I tried to execute the given code on Colab using TF 2.11 but got a different error. Could you please provide code with all dependencies and find the gist [here](https://colab.research.google.com/gist/tiruk007/f1505208ade18763a553af88da4f91bc/untitled142.ipynb) for reference.\r\n\r\nThank you !",
"sure, I am trying to run the following github repository\nhttps://github.com/mediumboat/FAiR where I got stuck.\n\nOn Mon, Feb 27, 2023 at 11:44 PM Tirumalesh ***@***.***>\nwrote:\n\n> @RonakPandya072 <https://github.com/RonakPandya072>\n> When I tried to execute the given code on Colab using TF 2.11 but got a\n> different error. Could you please provide code with all dependencies and\n> find the gist here\n> <https://colab.research.google.com/gist/tiruk007/f1505208ade18763a553af88da4f91bc/untitled142.ipynb>\n> for reference.\n>\n> Thank you !\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/tensorflow/tensorflow/issues/59797#issuecomment-1446819443>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AP2UHNQ6NEHGFZS3U7KWT2TWZTVI3ANCNFSM6AAAAAAVHDHE7A>\n> .\n> You are receiving this because you were mentioned.Message ID:\n> ***@***.***>\n>\n",
"@SuryanarayanaY \r\n\r\nI was able to reproduce the issue on Colab using TF v2.11. Please find the gist [here](https://colab.research.google.com/gist/tiruk007/bd93d124d48b6333827a28093b7c1507/untitled142.ipynb) for reference.\r\n\r\nThank you !",
"Hi @RonakPandya072 ,\r\n\r\nIt seems the data source link is breaking. I am getting the below error.\r\n\r\n`FileNotFoundError: [Errno 2] No such file or directory: '../data/movielens/u.data'\r\n`\r\n\r\nPlease refer to attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/54016704be79c3f30b958059500958e2/59797.ipynb).\r\n\r\nFrom the tested gist by @tiruk007 it seems the problem is due to serialization. The code provided by you have lot of things to debug.If possible could you provide minimal code snippet that causing the error ? It helps for us to debug the issue as quick as possible.\r\n\r\nThanks!",
"follow this link for code and dataset:\nhttps://drive.google.com/drive/folders/1LLrEy7na2cS3Hq49MZpaJKUKJg_KqkwN?usp=sharing\nThank you.\n\nOn Fri, Mar 3, 2023 at 6:27 PM SuryanarayanaY ***@***.***>\nwrote:\n\n> Hi @RonakPandya072 <https://github.com/RonakPandya072> ,\n>\n> It seems the data source link is breaking. I am getting the below error.\n>\n> FileNotFoundError: [Errno 2] No such file or directory:\n> '../data/movielens/u.data'\n>\n> Please refer to attached gist\n> <https://colab.research.google.com/gist/SuryanarayanaY/54016704be79c3f30b958059500958e2/59797.ipynb>\n> .\n>\n> From the tested gist by @tiruk007 <https://github.com/tiruk007> it seems\n> the problem is due to serialization. The code provided by you have lot of\n> things to debug.If possible could you provide minimal code snippet that\n> causing the error ? It helps for us to debug the issue as quick as possible.\n>\n> Thanks!\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/tensorflow/tensorflow/issues/59797#issuecomment-1453494344>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AP2UHNTOT2NFGKWYH4A2E4DW2HTD7ANCNFSM6AAAAAAVHDHE7A>\n> .\n> You are receiving this because you were mentioned.Message ID:\n> ***@***.***>\n>\n",
"Hi @RonakPandya072 ,\r\n\r\nApologies for the delay. I tried to access the resource that you provided.I can't access it now. If possible can you please provide access or some dummy data(minimal required for replication). 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/59797\">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/59797\">No</a>\n"
] | 2023-02-24T16:39:03 | 2023-04-05T01:48:12 | 2023-04-05T01:48:09 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
2.2.3
### Custom Code
Yes
### OS Platform and Distribution
_No response_
### Mobile device
_No response_
### Python version
3.6
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
```shell
A bug happened!
```
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/15WKG30ORtNUcaRDUMnMc-oc2XdjNCoWc?authuser=1#scrollTo=SXlXqqFHwkqa
```
### Relevant log output
100%|██████████| 943/943 [00:00<00:00, 1567.84it/s]
n users- 943, n_items- 1682, user_group- 943, item_group- 1682
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Training Epoch 0: 100%|██████████| 50/50 [00:04<00:00, 10.76it/s]
Training Epoch 1: 100%|██████████| 50/50 [00:03<00:00, 12.92it/s]
Training Epoch 2: 100%|██████████| 50/50 [00:02<00:00, 20.62it/s]
Training Epoch 3: 100%|██████████| 50/50 [00:02<00:00, 20.51it/s]
Training Epoch 4: 100%|██████████| 50/50 [00:03<00:00, 14.40it/s]
Training Epoch 5: 100%|██████████| 50/50 [00:04<00:00, 11.95it/s]
Training Epoch 6: 100%|██████████| 50/50 [00:03<00:00, 12.75it/s]
Training Epoch 7: 100%|██████████| 50/50 [00:02<00:00, 20.19it/s]
Training Epoch 8: 100%|██████████| 50/50 [00:02<00:00, 20.61it/s]
Training Epoch 9: 100%|██████████| 50/50 [00:02<00:00, 20.55it/s]
Training Epoch 10: 100%|██████████| 50/50 [00:00<00:00, 326.32it/s]
Training Epoch 11: 100%|██████████| 50/50 [00:00<00:00, 322.16it/s]
WARNING:tensorflow:Skipping full serialization of Keras layer <drive.MyDrive.FAiR.Model.Discriminator object at 0x7fc62233cdc0>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <drive.MyDrive.FAiR.Model.Discriminator object at 0x7fc620a9ea60>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <drive.MyDrive.FAiR.Model.Discriminator object at 0x7fc624006f40>, because it is not built.
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WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dense object at 0x7fc622da1c70>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dense object at 0x7fc622da11c0>, because it is not built.
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WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dense object at 0x7fc6241bb370>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dense object at 0x7fc624025b80>, because it is not built.
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---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
[<ipython-input-15-9ec0e19149d9>](https://localhost:8080/#) in <module>
111 parser.add_argument('-f')
112 flag = parser.parse_args()
--> 113 main(flag)
114
9 frames
[/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/load.py](https://localhost:8080/#) in _finalize_saved_model_layers(layers)
673 _set_network_attributes_from_metadata(layer)
674
--> 675 call_fn = _get_keras_attr(layer).call_and_return_conditional_losses
676 if call_fn.input_signature is None:
677 inputs = infer_inputs_from_restored_call_function(call_fn)
AttributeError: '_UserObject' object has no attribute 'call_and_return_conditional_losses'</details> | {
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https://api.github.com/repos/tensorflow/tensorflow/issues/59796 | https://api.github.com/repos/tensorflow/tensorflow | https://api.github.com/repos/tensorflow/tensorflow/issues/59796/labels{/name} | https://api.github.com/repos/tensorflow/tensorflow/issues/59796/comments | https://api.github.com/repos/tensorflow/tensorflow/issues/59796/events | https://github.com/tensorflow/tensorflow/issues/59796 | 1,598,712,345 | I_kwDOArmXAs5fSmoZ | 59,796 | AutoGraph did convert this function: NameError: name 'Tuple' is not defined | {
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"Hi @albertz, thank you for reporting the issue.\r\n@tilakrayal, I was able to replicate the issue in colab using TF v2.11 and tf-nightly(2.13.0-dev20230228). Please find the gists for the same [here-TF v2.11](https://colab.sandbox.google.com/gist/synandi/e0493a9e1179200485969ff31ed61354/59796.ipynb) and [here-tf-nightly](https://colab.sandbox.google.com/gist/synandi/f78dec08ce6f12d5fe0415849058418d/59796_nightly.ipynb). Thanks! ",
"Hi @albertz ,\r\nIt seems we need to import Tuple outside the function.Please refer to the attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/06436d52cd7bbc2c00baaa63c4e19c00/59796_r1.ipynb).\r\nBy importing the Tuple at Global level it is working fine.",
"I know.\n\nBut this is a bug. This should but be necessary.\n",
"Hi @albertz ,\r\n\r\nYou need to wrap the entire function `f() ` with `tf.function` decorator and inside that you can import Tuple and make it workable.This is applicable for both 1.x and 2.x versions.Please refer attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/8318af1219eeda9d47176aba9887ee45/59796_r2-2-x-v.ipynb).\r\n\r\nWhenever we call `f()` with some input,as it is having `local_func` under `tf.function` decorator and `tf.function` when called will have two stages normally.First stage is tracing for creation of tf.Graph and the second stage is execution of graph(only TF operations).\r\n\r\nIf we enclose the complete function under tf.function then this works as intended.\r\n\r\n",
"In my case, I cannot do that. The outer function must be a regular (non-TF) Python function.\r\n\r\nBut in any case, this is still a TF bug. It should not be necessary to use such workarounds.\r\n"
] | 2023-02-24T14:12:46 | 2023-03-08T05:34:30 | null | CONTRIBUTOR | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
No
### Source
source
### Tensorflow Version
2.11.0
### Custom Code
Yes
### OS Platform and Distribution
Mac, Linux, 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_
</details>
### Current Behaviour?
`tf.function` fails when type annotations are used which are locally imported. I get:
```
Cause: name 'Tuple' is not defined
```
Note:
* It seems important that `Tuple` is locally imported. If it is imported globally in the module, there does not seem to be a problem.
* When I use `from __future__ import annotations`, there is also no error. But I assume because this will just not evaluate it directly, but it still lacks the `Tuple` reference, although it's maybe really not relevant then.
### Standalone code to reproduce the issue
```python
import tensorflow as tf
print("TensorFlow:", tf.__version__)
tf.compat.v1.disable_eager_execution()
session = tf.compat.v1.Session()
# https://www.tensorflow.org/guide/function
def f(x: tf.Tensor):
from typing import Tuple
@tf.function
def local_func(x: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:
while tf.reduce_sum(x) > 1:
tf.print(x)
x = tf.tanh(x)
return x, x
return local_func(x)
session.run(f(tf.random.uniform([5])))
```
Colab link: https://colab.research.google.com/drive/1K69XH_RsU-Ux-RBfUd0B4eR8iJFTXoFM?usp=sharing
### Relevant log output
```
Traceback (most recent call last):
File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py", line 427, in converted_call
converted_f = _convert_actual(target_entity, program_ctx)
File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py", line 269, in _convert_actual
transformed, module, source_map = _TRANSPILER.transform(entity, program_ctx)
File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/transpiler.py", line 282, in transform
return self.transform_function(obj, user_context)
File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/transpiler.py", line 490, in transform_function
transformed_fn = factory.instantiate(
File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/transpiler.py", line 213, in instantiate
new_fn = bound_factory(**self._extra_locals) # pylint:disable=not-callable
File "/tmp/__autograph_generated_filem5lq6_hq.py", line 6, in inner_factory
def tf__local_func(x: tf.Tensor) -> Tuple[(tf.Tensor, tf.Tensor)]:
NameError: name 'Tuple' is not defined
WARNING:tensorflow:AutoGraph could not transform <function f.<locals>.local_func at 0x7f15f5ff3af0> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: name 'Tuple' is not defined
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
TensorFlow: 2.11.0
Converted call: <function f.<locals>.local_func at 0x7f15f5ff3af0>
args: (<tf.Tensor 'x:0' shape=(5,) dtype=float32>,)
kwargs: {}
<function f.<locals>.local_func at 0x7f15f5ff3af0> is not cached for subkey ConversionOptions[{}]
Source code of <function f.<locals>.local_func at 0x7f15f5ff3af0>:
@tf.function
def local_func(x: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:
while tf.reduce_sum(x) > 1:
tf.print(x)
x = tf.tanh(x)
return x, x
Transformed <function f.<locals>.local_func at 0x7f15f5ff3af0>:
# coding=utf-8
def tf__local_func(x: tf.Tensor) -> Tuple[(tf.Tensor, tf.Tensor)]:
with ag__.FunctionScope('local_func', 'fscope', ag__.ConversionOptions(recursive=True, user_requested=True, optional_features=(), internal_convert_user_code=True)) as fscope:
do_return = False
retval_ = ag__.UndefinedReturnValue()
def get_state():
return (x,)
def set_state(vars_):
nonlocal x
(x,) = vars_
def loop_body():
nonlocal x
ag__.converted_call(ag__.ld(tf).print, (ag__.ld(x),), None, fscope)
x = ag__.converted_call(ag__.ld(tf).tanh, (ag__.ld(x),), None, fscope)
def loop_test():
return (ag__.converted_call(ag__.ld(tf).reduce_sum, (ag__.ld(x),), None, fscope) > 1)
ag__.while_stmt(loop_test, loop_body, get_state, set_state, ('x',), {})
try:
do_return = True
retval_ = (ag__.ld(x), ag__.ld(x))
except:
do_return = False
raise
return fscope.ret(retval_, do_return)
Error transforming entity <function f.<locals>.local_func at 0x7f15f5ff3af0>
WARNING: AutoGraph could not transform <function f.<locals>.local_func at 0x7f15f5ff3af0> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: name 'Tuple' is not defined
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
```
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"It seems that the error caused by below code in `tensorflow/tensorflow/lite/delegates/gpu/BUILD`:\r\n```\r\nios_static_framework(\r\n name = \"tensorflow_lite_gpu_framework\",\r\n hdrs = [\r\n \"metal_delegate.h\",\r\n \"metal_delegate_internal.h\",\r\n ],\r\n minimum_os_version = \"11.4\",\r\n deps = [\":metal_delegate\"],\r\n)\r\n``` \r\n\r\nWhen I disable this code, the problem is gone. But I think it need to be checked.",
"I had a different but similar issue that builds for tflite but failed for GPU delegates\n\nIt is the final linkage step that raise an error stating `can't find -lnativewindow`..\n\nI remember it worked before but then I pulled the latest commits and forgot where I was before. \n\n... ",
"From the readme in `https://github.com/bazelbuild/rules_apple`, you need to upgrade `build_bazel_rules_apple` to 2.* for it to work with bazel 6.x. In this case any bazel command that loads `//tensorflow/lite/delegates/gpu/BUILD` would fail.\r\n\r\nTested the upgrade with the following changes and it seems to fix it.\r\n```\r\ntensorflow ❯ git diff\r\ndiff --git a/.bazelversion b/.bazelversion\r\nindex f53152b50eb..8648bac098e 100644\r\n--- a/.bazelversion\r\n+++ b/.bazelversion\r\n@@ -1,2 +1,2 @@\r\n-5.3.0\r\n-# NOTE: Update Bazel version in tensorflow/tools/ci_build/release/common.sh.oss\r\n\\ No newline at end of file\r\n+6.0.0\r\n+# NOTE: Update Bazel version in tensorflow/tools/ci_build/release/common.sh.oss\r\ndiff --git a/tensorflow/workspace2.bzl b/tensorflow/workspace2.bzl\r\nindex 7bccbe873e5..8418596017f 100644\r\n--- a/tensorflow/workspace2.bzl\r\n+++ b/tensorflow/workspace2.bzl\r\n@@ -822,8 +822,10 @@ def _tf_repositories():\r\n # https://github.com/bazelbuild/rules_apple/releases\r\n tf_http_archive(\r\n name = \"build_bazel_rules_apple\",\r\n- sha256 = \"36072d4f3614d309d6a703da0dfe48684ec4c65a89611aeb9590b45af7a3e592\",\r\n- urls = tf_mirror_urls(\"https://github.com/bazelbuild/rules_apple/releases/download/1.0.1/rules_apple.1.0.1.tar.gz\"),\r\n+ # sha256 = \"36072d4f3614d309d6a703da0dfe48684ec4c65a89611aeb9590b45af7a3e592\",\r\n+ # urls = tf_mirror_urls(\"https://github.com/bazelbuild/rules_apple/releases/download/1.0.1/rules_apple.1.0.1.tar.gz\"),\r\n+ sha256 = \"3e2c7ae0ddd181c4053b6491dad1d01ae29011bc322ca87eea45957c76d3a0c3\",\r\n+ urls = tf_mirror_urls(\"https://github.com/bazelbuild/rules_apple/releases/download/2.1.0/rules_apple.2.1.0.tar.gz\"),\r\n )\r\n\r\n # https://github.com/bazelbuild/rules_swift/releases\r\n```",
"@sohaiberrabii, Thanks for the workaround. \r\n@jyh2378 , Our latest Bazel support in Tensorflow is `5.3.0`, until we publish the tested build configuration for the latest version, it is suggested to use 5.3.0.\r\nFor now you can consider the above workaround in your case, but it may break the actual behavior of framework. ",
"This issue has been marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.",
"Closing due to lack of recent activity. Please update the issue when new information becomes available, and we will reopen the issue. Thanks!",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59794\">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/59794\">No</a>\n",
"@shelperI had the same issue that builds for tflite but failed for GPU delegates\r\n\r\nIt is the final linkage step that raise an error stating can't find -lnativewindow..\r\n\r\nIf you solved this problem, can you tell me how?",
"Hi @shelper I had the same issue that builds for tflite but failed for GPU delegates\r\n\r\nIt is the final linkage step that raise an error stating can't find -lnativewindow..\r\n\r\nIf you solved this problem, can you tell me how?\r\n\r\nThanks!\r\n",
"Hi @shelper \r\nThe following is my error report：\r\n/usr/bin/ld: cannot find -lnativewindow\r\n/usr/bin/ld: cannot find -lnativewindow\r\ncollect2: error: ld returned 1 exit status",
"Hi @wqy123456, I encountered the same problem, did you solve it?"
] | 2023-02-24T07:34:22 | 2023-11-15T11:07:01 | 2023-04-05T20:59:15 | NONE | null | null | null | ### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
tf 2.11.0
### OS Platform and Distribution
Linux RHEL 8
### Python version
3.10.8
### Bazel version
6.0.0
### GCC/Compiler version
8.5.0
### Android NDK version
android-ndk-r21e
### Current Behaviour?
I tried to build tflite gpu library for android using bazel. There is no problem when building `libtensorflow.so`, but error is occurred when building `libtensorflowlite_gpu_delegate.so`.
### Standalone code to reproduce the issue
- build `libtensorflowlite_gpu_delegate.so`
```shell
bazel build --config android_arm -c opt //tensorflow/lite:libtensorflowlite.so
```
- build `libtensorflowlite_gpu_delegate.so`
```shell
bazel build --config android_arm -c opt //tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_delegate.so
```
### Relevant log output
```
ERROR: Traceback (most recent call last):
File "/home1/irteam/users/yeonghwa.jin/AR.MobileDL/tensorflow_src/tensorflow/lite/delegates/gpu/BUILD", line 153, column 21, in <toplevel>
ios_static_framework(
File "/home1/irteam/.cache/bazel/_bazel_irteam/326ce39c70196ccdfc072be4c665d440/external/build_bazel_rules_apple/apple/ios.bzl", line 62, column 11, in ios_static_framework
native.apple_static_library(
Error: no native function or rule 'apple_static_library'
Available attributes: aar_import, action_listener, alias, android_binary, android_device, android_device_script_fixture, android_host_service_fixture, android_instrumentation_test, android_library, android_local_test, android_sdk, android_tools_defaults_jar, apple_cc_toolchain, available_xcodes, cc_binary, cc_host_toolchain_alias, cc_import, cc_libc_top_alias, cc_library, cc_proto_library, cc_shared_library, cc_shared_library_permissions, cc_test, cc_toolchain, cc_toolchain_alias, cc_toolchain_suite, config_feature_flag, config_setting, constraint_setting, constraint_value, environment, existing_rule, existing_rules, exports_files, extra_action, fdo_prefetch_hints, fdo_profile, filegroup, genquery, genrule, glob, j2objc_library, java_binary, java_import, java_library, java_lite_proto_library, java_package_configuration, java_plugin, java_plugins_flag_alias, java_proto_library, java_runtime, java_test, java_toolchain, label_flag, label_setting, objc_import, objc_library, package, package_group, package_name, platform, propeller_optimize, proto_lang_toolchain, proto_library, py_binary, py_library, py_runtime, py_test, repository_name, sh_binary, sh_library, sh_test, subpackages, test_suite, toolchain, toolchain_type, xcode_config, xcode_config_alias, xcode_version
ERROR: Skipping '//tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_delegate.so': Error evaluating '//tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_delegate.so': error loading package 'tensorflow/lite/delegates/gpu': Package 'tensorflow/lite/delegates/gpu' contains errors
WARNING: Target pattern parsing failed.
ERROR: Error evaluating '//tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_delegate.so': error loading package 'tensorflow/lite/delegates/gpu': Package 'tensorflow/lite/delegates/gpu' contains errors
INFO: Elapsed time: 0.234s
INFO: 0 processes.
FAILED: Build did NOT complete successfully (1 packages loaded)
``` | {
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"testing bot",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59793\">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/59793\">No</a>\n"
] | 2023-02-24T06:56:58 | 2023-03-14T11:47:44 | 2023-03-14T11:47:41 | CONTRIBUTOR | null | null | null | Please go to Stack Overflow for help and support:
https://stackoverflow.com/questions/tagged/tensorflow
If you open a GitHub issue, here is our policy:
1. It must be a bug, a feature request, or a significant problem with the
documentation (for small docs fixes please send a PR instead).
2. The form below must be filled out.
3. It shouldn't be a TensorBoard issue. Those go
[here](https://github.com/tensorflow/tensorboard/issues).
**Here's why we have that policy**: TensorFlow developers respond to issues. We want to focus on work that benefits the whole community, e.g., fixing bugs and adding features. Support only helps individuals. GitHub also notifies thousands of people when issues are filed. We want them to see you communicating an interesting problem, rather than being redirected to Stack Overflow.
------------------------
### System information
- **Have I written custom code (as opposed to using a stock example script
provided in TensorFlow)**:
- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**:
- **Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue
happens on a mobile device**:
- **TensorFlow installed from (source or binary)**:
- **TensorFlow version (use command below)**:
- **Python version**:
- **Bazel version (if compiling from source)**:
- **GCC/Compiler version (if compiling from source)**:
- **CUDA/cuDNN version**:
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- **Exact command to reproduce**:
You can collect some of this information using our environment capture script:
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You can obtain the TensorFlow version with:
```bash
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```
### Describe the problem
Describe the problem clearly here. Be sure to convey here why it's a bug in TensorFlow or a feature request.
### Source code / 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. Try to provide a reproducible test case that is the bare minimum necessary to generate the problem.
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"@reedwm This PR is essential to test performance on GPT transformer models.",
"Hi @wenscarl Can you please check @reedwm's comments and resolve conflicts?. Thank you!",
"I think https://github.com/tensorflow/tensorflow/pull/59971 replaces this PR, so closing this PR."
] | 2023-02-24T04:29:37 | 2023-03-15T17:36:39 | 2023-03-15T17:36:32 | CONTRIBUTOR | null | false | {
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matmul <- bitcast <- copy <- bitcast <- multiply
matmul <- bitcast <- copy <- multiply
matmul <- bitcast <- copy <- reshape <- multiply
matmul <- copy <- bitcast <- multiply | {
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"@panhu \r\nWe can calculate the gradient value of each iteration in the model with a custom training loop.\r\n\r\n- Calling a model inside a GradientTape scope enables you to retrieve the gradients of the trainable weights of the layer with respect to a loss value. Using an optimizer instance, you can use these gradients to update these variables (which you can retrieve using model.trainable_weights)\r\n\r\nCould you please refer to the [doc](https://www.tensorflow.org/guide/keras/writing_a_training_loop_from_scratch#using_the_gradienttape_a_first_end-to-end_example) for more information.\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.",
"Closing as stale. Please reopen if you'd like to work on this further.\r\n\r\nThanks !",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59790\">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/59790\">No</a>\n",
"Hi:\r\nIf have some examples or methods about Channel pruning of model in TF."
] | 2023-02-24T02:16:56 | 2023-05-15T03:02:31 | 2023-03-25T10:37:03 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
tf 2.X
### 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 Behaviour?
```shell
Hi:
If i want to Slimming Network
```
### Standalone code to reproduce the issue
```shell
I want to ask how I can get the gradient value of each iteration in the model training process.
```
### Relevant log output
_No response_</details> | {
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"Hi @dongyaoli10x, kindly let us know the OS Platform and Distribution used to exceute the above code. I tried executing it in Ubuntu 22.04. Please find the output below.\r\n\r\nThank you!",
"> Hi @dongyaoli10x, kindly let us know the OS Platform and Distribution used to exceute the above code. I tried executing it in Ubuntu 22.04. Please find the output below.  Thank you!\r\n\r\nThank you @synandi for your quick response! The OS is centos 7.9.2009. But it looks like the error message you got with ubuntu 22.04 is the same",
"Thank you for sharing the details. I replicated the issue in CentOS. Please find the complete error below.\r\n```\r\nRunning tests under Python 3.9.16: /home/ynandi/miniconda3/envs/tf/bin/python\r\n[ RUN ] TestDummy.test_dummy_confusing_bug\r\n2023-02-27 11:35:49.001558: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\nINFO:tensorflow:time(__main__.TestDummy.test_dummy_confusing_bug): 0.0s\r\nI0227 11:35:49.005805 140260578801472 test_util.py:2458] time(__main__.TestDummy.test_dummy_confusing_bug): 0.0s\r\n[ FAILED ] TestDummy.test_dummy_confusing_bug\r\n[ RUN ] TestDummy.test_dummy_confusing_error\r\nINFO:tensorflow:time(__main__.TestDummy.test_dummy_confusing_error): 0.0s\r\nI0227 11:35:49.007651 140260578801472 test_util.py:2458] time(__main__.TestDummy.test_dummy_confusing_error): 0.0s\r\n[ FAILED ] TestDummy.test_dummy_confusing_error\r\n[ RUN ] TestDummy.test_dummy_no_error\r\nINFO:tensorflow:time(__main__.TestDummy.test_dummy_no_error): 0.0s\r\nI0227 11:35:49.008224 140260578801472 test_util.py:2458] time(__main__.TestDummy.test_dummy_no_error): 0.0s\r\n[ OK ] TestDummy.test_dummy_no_error\r\n[ RUN ] TestDummy.test_session\r\n[ SKIPPED ] TestDummy.test_session\r\n======================================================================\r\nERROR: test_dummy_confusing_bug (__main__.TestDummy)\r\nTestDummy.test_dummy_confusing_bug\r\n----------------------------------------------------------------------\r\nTraceback (most recent call last):\r\n File \"/home/ynandi/59789.py\", line 15, in test_dummy_confusing_bug\r\n self.assertAlmostEqual(a, b, places=2)\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/unittest/case.py\", line 882, in assertAlmostEqual\r\n if round(diff, places) == 0:\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/ops/numpy_ops/np_array_ops.py\", line 733, in around\r\n return a.astype(dtype)\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/framework/ops.py\", line 440, in __getattr__\r\n raise AttributeError(\r\nAttributeError: EagerTensor object has no attribute 'astype'. \r\n If you are looking for numpy-related methods, please run the following:\r\n from tensorflow.python.ops.numpy_ops import np_config\r\n np_config.enable_numpy_behavior()\r\n \r\n\r\n======================================================================\r\nERROR: test_dummy_confusing_error (__main__.TestDummy)\r\nTestDummy.test_dummy_confusing_error\r\n----------------------------------------------------------------------\r\nTraceback (most recent call last):\r\n File \"/home/ynandi/59789.py\", line 9, in test_dummy_confusing_error\r\n self.assertAlmostEqual(a, b)\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/unittest/case.py\", line 882, in assertAlmostEqual\r\n if round(diff, places) == 0:\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/ops/numpy_ops/np_array_ops.py\", line 733, in around\r\n return a.astype(dtype)\r\n File \"/home/ynandi/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/framework/ops.py\", line 440, in __getattr__\r\n raise AttributeError(\r\nAttributeError: EagerTensor object has no attribute 'astype'. \r\n If you are looking for numpy-related methods, please run the following:\r\n from tensorflow.python.ops.numpy_ops import np_config\r\n np_config.enable_numpy_behavior()\r\n \r\n\r\n----------------------------------------------------------------------\r\nRan 4 tests in 0.008s\r\n\r\nFAILED (errors=2, skipped=1)\r\n```\r\n\r\n- The first test case `test_dummy_confusing_bug` fails with an error message \r\n```\r\nAttributeError: EagerTensor object has no attribute 'astype'.\r\n```\r\nThis error occurs because the `self.assertAlmostEqual` function is not able to perform the numpy operation 'astype' on the EagerTensor objects a and b.\r\n- The second test case `test_dummy_confusing_error` also fails with the same error message as the first test case.",
"To fix the issue, as a workaround you can modify the assertion to compare the numpy values of the tensors instead of the tensors themselves. Here's the modified code:\r\n```\r\nimport tensorflow as tf\r\n\r\nclass TestDummy(tf.test.TestCase):\r\n\r\n def test_dummy_confusing_error(self):\r\n # this test should fail. It fails as expected.\r\n a = tf.constant(1.00002, dtype=tf.float32)\r\n b = tf.constant(1.00001, dtype=tf.float32)\r\n self.assertAlmostEqual(a.numpy(), b.numpy())\r\n\r\n def test_dummy_confusing_bug(self):\r\n # this test passes with places=2.\r\n a = tf.constant(1.00002, dtype=tf.float32)\r\n b = tf.constant(1.00001, dtype=tf.float32)\r\n self.assertAlmostEqual(a.numpy(), b.numpy(), places=2)\r\n\r\n def test_dummy_no_error(self):\r\n # this test passes \r\n a = tf.constant(1.000000002, dtype=tf.float32)\r\n b = tf.constant(1.000000001, dtype=tf.float32)\r\n self.assertAlmostEqual(a.numpy(), b.numpy())\r\n\r\nif __name__ == \"__main__\":\r\n tf.test.main()\r\n```\r\nIn these modified tests, the `numpy()` method is called on the tensors a and b to obtain their numpy values, and the `assertAlmostEqual()` method is used to compare the numpy values instead of the tensors themselves.\r\n\r\nThank you!\r\n\r\n",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59789\">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/59789\">No</a>\n"
] | 2023-02-24T01:56:25 | 2023-03-19T02:03:43 | 2023-03-19T02:03:40 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
No
### Source
source
### Tensorflow Version
tf 2.9
### Custom Code
Yes
### OS Platform and Distribution
_No response_
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/Compiler version
_No response_
</details>
### Current Behaviour?
I seem to observe to issues with `assertAlmostEqual` of `tf.test.TestCase`
1. When using `assertAlmostEqual` and when it fails, the error message is confusing. Instead of saying the test fails, it shows the following error message:
```python
File "...", line ..., in test_dummy
self.assertAlmostEqual(a, b)
File ".../python3.9/unittest/case.py", line 874, in assertAlmostEqual
if round(diff, places) == 0:
File ".../tensorflow/python/ops/numpy_ops/np_array_ops.py", line 733, in around
return a.astype(dtype)
File ".../tensorflow/python/framework/ops.py", line 440, in __getattr__
raise AttributeError(
AttributeError: EagerTensor object has no attribute 'astype'.
If you are looking for numpy-related methods, please run the following:
from tensorflow.python.ops.numpy_ops import np_config
np_config.enable_numpy_behavior()
```
2. when I set the `places` to a smaller value that I think should allow the test to pass, the test still fails with the above error message.
When the difference is indeed smaller than the default `places=7`, the test passes.
### Standalone code to reproduce the issue
```python
import tensorflow as tf
class TestDummy(tf.test.TestCase):
def test_dummy_confusing_error(self):
# this test should fail. But it raises the above confusing error message.
a = tf.constant(1.00002, dtype=tf.float32)
b = tf.constant(1.00001, dtype=tf.float32)
self.assertAlmostEqual(a, b)
def test_dummy_confusing_bug(self):
# this test should pass with places=2. But it still raises the above confusing error message.
a = tf.constant(1.00002, dtype=tf.float32)
b = tf.constant(1.00001, dtype=tf.float32)
self.assertAlmostEqual(a, b, places=2)
def test_dummy_no_error(self):
# this test passes
a = tf.constant(1.000000002, dtype=tf.float32)
b = tf.constant(1.000000001, dtype=tf.float32)
self.assertAlmostEqual(a, b)
if __name__ == "__main__":
tf.test.main()
``` | {
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"@philipphack Can you please resolve conflicts? Thank you!"
] | 2023-02-23T20:35:53 | 2023-03-06T21:09:46 | 2023-03-06T21:09:46 | CONTRIBUTOR | null | false | {
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} | Introduces performance options for sparse embedding lookups that can appreciably speed up the training of recommendation systems. Sparse lookups alternatively accept inputs described by RaggedTensors which are more memory efficient. Performance is further increased by the optional use of a simplified and typically faster embedding lookup.
In the sparse embedding micro benchmarks in tensorflow/python/eager/benchmarks_test.py, the number of examples per second on a DGX A100 system increases from approx. 1,300 with SparseTensor and without simplified lookup to approx. 11,200 with RaggedTensor inputs and simplified lookup (+760%). The combination of SparseTensor inputs and simplified lookup yields approx. 3,000 examples per second (+130%). | {
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"A better solution might be to clamp to `[-1, 1]` (assuming `clamp(NaN, [-1, 1])` is still `NaN`), then round (away from zero).",
"Looks like we do not have any MHLO->TF conversion pattern for `mhlo.round_nearest_afz` yet. For now converting `mhlo.sign` to `tf.sign` might actually be the best option."
] | 2023-02-23T20:04:23 | 2023-03-02T00:11:42 | null | MEMBER | null | null | null | ### System information
This issue applies to all systems on all TF versions since 2020-03-28.
### Describe the problem
MHLO_SignOp is defined to [return -0.0](https://github.com/tensorflow/tensorflow/blob/007ef257793b6ea0464361d2fc6a5a5b57403ce5/tensorflow/compiler/xla/mlir_hlo/mhlo/IR/hlo_ops.td#L562) when input is -0.0, but TF_SignOp and [TFL_SignOp](https://github.com/tensorflow/tensorflow/blob/007ef257793b6ea0464361d2fc6a5a5b57403ce5/tensorflow/compiler/mlir/lite/ir/tfl_ops.td#L5036) both return +0.0 on -0.0. For example:
> \>\>\> tf.math.sign([-0.0, 0.0, 1, -1])
> <tf.Tensor: shape=(4,), dtype=float32, numpy=array([ 0., 0., 1., -1.], dtype=float32)>
### Source code / logs
https://github.com/tensorflow/tensorflow/blob/6b167daef1d6953fd908c1ced6f347e1a7758138/tensorflow/compiler/mlir/tensorflow/transforms/legalize_hlo_patterns.td#L132
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"Hi @jurahul Can you please review this PR ? Thank you!",
"Hi @jurahul Can you please review this PR ? Thank you!",
"Hi @cheshire sorry to ping you again, because this PR is pending too long and it's caused conflict after you approved. So I resolved it, which means it needs you approve again.\r\n\r\n"
] | 2023-02-23T16:10:38 | 2023-05-26T14:12:40 | 2023-05-26T14:12:40 | CONTRIBUTOR | null | false | {
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} | Performance optimization on rocBLAS broadcasting based on fold and fp32 kernel, this has existed in our repo for a long time and we hope it can sync with upstream.
Thanks in advance! @cheshire | {
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"This is true of absolutely every op in TensorFlow. If the op itself says it takes a Tensor, but you pass a numpy array, it will convert the numpy array to a tensor.\r\n\r\nWe should _not_ modify the documentation here."
] | 2023-02-23T15:04:09 | 2023-10-12T09:43:32 | 2023-02-23T17:41:48 | COLLABORATOR | null | false | {
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} | At present as per documentation of `tf.math.cumsum` for the argument `input(x)` the supported type is `Tensor`. But it also accepts python list,tuple and numpy arrays also which have registered Tensor conversion function.Even in the code of `tf.math.cumsum` it converts the` input(x)` to a tensor explicitly.
Also for the argument `axis` the supported type is `Tensor of int32`.But it accepts also `Python integer` with range `[-rank(x), rank(x))`.
Please refer to attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/4a44261fadc8e2a137a99df4e429133c/tf-math-cumsum.ipynb) for the testing on same.
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"FYI, we've removed replay_computation. run_hlo_module has reached feature parity and beyond",
"close it due to replay_computation is removed."
] | 2023-02-23T14:40:58 | 2023-03-14T16:13:42 | 2023-03-14T13:20:21 | CONTRIBUTOR | null | false | {
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"I'm sorry to hear that you're encountering an error when trying to get the value for bert_model in the \"Classify text with BERT\" tutorial. Here are some possible solutions:\r\n\r\nMake sure you have installed the necessary packages. The tutorial requires tensorflow, tensorflow-text, and tensorflow-hub. You can install them using pip: \r\n`pip install tensorflow tensorflow-text tensorflow-hub\r\n`\r\nCheck that you're using the correct BERT model. The tutorial uses the bert_en_uncased_L-12_H-768_A-12 model, which is a smaller and faster version of BERT. Make sure you've downloaded and unzipped this model to the correct directory. You can download it using the following command:\r\n`!wget https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip\r\n!unzip uncased_L-12_H-768_A-12.zip\r\n`\r\nVerify that the path to the BERT model is correct. In the tutorial, the path is set to ./bert_en_uncased_L-12_H-768_A-12/. Double-check that this path is correct and that you're not missing any files.\r\n\r\nTry clearing the TensorFlow session and reloading the BERT model. You can do this using the following code:\r\n`import tensorflow as tf\r\nimport tensorflow_hub as hub\r\n\r\ntf.keras.backend.clear_session()\r\nbert_layer = hub.KerasLayer(\"./bert_en_uncased_L-12_H-768_A-12\")\r\n`\r\nIf none of the above solutions work, try restarting your Python kernel and rerunning the code from the beginning of the tutorial.\r\nI hope one of these solutions helps you resolve the error. Let me know if you have any further questions or concerns!",
"I successfully completed the tutorial, and the code worked. It just took longer than expected. Unfortunately, I did not record how long it took for the line of code to run. \r\n\r\nI am closing this issue because I no longer have any questions. ",
"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/59781\">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/59781\">No</a>\n"
] | 2023-02-23T10:00:02 | 2023-02-24T06:50:43 | 2023-02-24T06:24:24 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
No
### Source
source
### Tensorflow Version
2.4.4
### 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 Behaviour?
```shell
I am trying to run the "Classify text with BERT" tutorial using Jupyter Notebook on a remote server. When I tried to implement 'bert_model = hub.KerasLayer(tfhub_handle_encoder)', it never returned a result and it is still executing with the asterik (*) next to the cell.
Is this supposed to happen, and is it normal for that line to take a long time to execute?
```
### Standalone code to reproduce the issue
```shell
https://www.tensorflow.org/text/tutorials/classify_text_with_bert
The code is this line:
'bert_model = hub.KerasLayer(tfhub_handle_encoder)'
```
### Relevant log output
_No response_</details> | {
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"Hi, @albertz \r\n\r\nApologize for the delay and I was able to reproduce the issue, I was also getting the same error which you've mentioned above and as per [official documentation](https://www.tensorflow.org/install/pip#macos), There is currently no official GPU support for MacOS. It's better to post this issue [here](https://developer.apple.com/forums/tags/tensorflow-metal) for fast resolution. Thank you!\r\n\r\n```\r\n(tf-macos-2.11) gaikwadrahul-macbookpro:returnn gaikwadrahul$ python3 tests/test_TFUtil.py test_get_variable_grad_from_update_ops\r\nCreating converter from 7 to 5\r\nCreating converter from 5 to 7\r\nCreating converter from 7 to 5\r\nCreating converter from 5 to 7\r\ninstallLibSigSegfault exception: libSegFault not found\r\ntests/test_TFUtil.py:12: DeprecationWarning: Importing from numpy.testing.utils is deprecated since 1.15.0, import from numpy.testing instead.\r\n from numpy.testing.utils import assert_almost_equal, assert_allclose\r\nTF version: 2.11.0\r\nMetal device set to: Apple M1 Pro\r\n\r\nsystemMemory: 16.00 GB\r\nmaxCacheSize: 5.33 GB\r\n\r\n2023-02-25 19:29:40.487656: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\r\n2023-02-25 19:29:40.487681: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:272] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\r\nExecuting: test_get_variable_grad_from_update_ops\r\nOptimizer: <tensorflow.python.training.adam.AdamOptimizer object at 0x14e550670>\r\nupdate ops: [<tf.Operation 'test_get_variable_grad_from_update_ops/Adam/update_test_get_variable_grad_from_update_ops/var/ResourceApplyAdam' type=ResourceApplyAdam>]\r\nupdate op keys: ['_has_manual_control_dependencies', 'use_locking', 'T', '_class', 'use_nesterov']\r\nupdate op inputs by name: ['var', 'm', 'v', 'beta1_power', 'beta2_power', 'lr', 'beta1', 'beta2', 'epsilon', 'grad']\r\n2023-02-25 19:29:40.513885: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:357] MLIR V1 optimization pass is not enabled\r\n2023-02-25 19:29:40.517933: W tensorflow/c/c_api.cc:291] Operation '{name:'test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Adam_1/Assign' id:60 op device:{requested: '', assigned: ''} def:{{{node test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Adam_1/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Adam_1, test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Adam_1/Initializer/zeros)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\r\ngrad: Tensor(\"test_get_variable_grad_from_update_ops/gradients/test_get_variable_grad_from_update_ops/sub_grad/tuple/control_dependency:0\", shape=(), dtype=float32)\r\nOptimizer: <tensorflow.python.training.gradient_descent.GradientDescentOptimizer object at 0x14e5506a0>\r\nupdate ops: [<tf.Operation 'test_get_variable_grad_from_update_ops/GradientDescent/update_test_get_variable_grad_from_update_ops/var/ResourceApplyGradientDescent' type=ResourceApplyGradientDescent>]\r\nupdate op keys: ['_has_manual_control_dependencies', 'use_locking', 'T', '_class']\r\nupdate op inputs by name: ['var', 'alpha', 'delta']\r\n2023-02-25 19:29:40.602980: W tensorflow/c/c_api.cc:291] Operation '{name:'test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Adam_1/Assign' id:60 op device:{requested: '', assigned: ''} def:{{{node test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Adam_1/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Adam_1, test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Adam_1/Initializer/zeros)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\r\ngrad: Tensor(\"test_get_variable_grad_from_update_ops/gradients_1/test_get_variable_grad_from_update_ops/sub_grad/tuple/control_dependency:0\", shape=(), dtype=float32)\r\nOptimizer: <tensorflow.python.training.momentum.MomentumOptimizer object at 0x14e550730>\r\nupdate ops: [<tf.Operation 'test_get_variable_grad_from_update_ops/Momentum/update_test_get_variable_grad_from_update_ops/var/ResourceApplyMomentum' type=ResourceApplyMomentum>]\r\nupdate op keys: ['_has_manual_control_dependencies', 'use_locking', 'T', '_class', 'use_nesterov']\r\nupdate op inputs by name: ['var', 'accum', 'lr', 'grad', 'momentum']\r\n2023-02-25 19:29:40.635616: W tensorflow/c/c_api.cc:291] Operation '{name:'test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Momentum/Assign' id:138 op device:{requested: '', assigned: ''} def:{{{node test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Momentum/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Momentum, test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Momentum/Initializer/zeros)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\r\ngrad: Tensor(\"test_get_variable_grad_from_update_ops/gradients_2/test_get_variable_grad_from_update_ops/sub_grad/tuple/control_dependency:0\", shape=(), dtype=float32)\r\nFatal Python error: Segmentation fault\r\n\r\nThread 0x00000001e7798100 (most recent call first):\r\n File \"/Users/gaikwadrahul/miniconda/envs/tf-macos-2.11/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1454 in _call_tf_sessionrun\r\n File \"/Users/gaikwadrahul/miniconda/envs/tf-macos-2.11/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1361 in _run_fn\r\n File \"/Users/gaikwadrahul/miniconda/envs/tf-macos-2.11/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1378 in _do_call\r\n File \"/Users/gaikwadrahul/miniconda/envs/tf-macos-2.11/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1371 in _do_run\r\n File \"/Users/gaikwadrahul/miniconda/envs/tf-macos-2.11/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 1191 in _run\r\n File \"/Users/gaikwadrahul/miniconda/envs/tf-macos-2.11/lib/python3.8/site-packages/tensorflow/python/client/session.py\", line 968 in run\r\n File \"tests/test_TFUtil.py\", line 3529 in test_get_variable_grad_from_update_ops\r\n File \"tests/test_TFUtil.py\", line 4559 in <module>\r\nSegmentation fault: 11\r\n(tf-macos-2.11) gaikwadrahul-macbookpro:returnn gaikwadrahul$ \r\n```",
"Ok, I posted it here: https://developer.apple.com/forums/thread/725592",
"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/59780\">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/59780\">No</a>\n"
] | 2023-02-23T09:13:00 | 2023-02-26T00:29:19 | 2023-02-26T00:29:17 | CONTRIBUTOR | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
No
### Source
source
### Tensorflow Version
tensorflow-macos 2.11.0
### Custom Code
Yes
### OS Platform and Distribution
MacOS 12, tensorflow-macos and tensorflow-metal
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
</details>
### GPU model and memory
Apple M1
### Current Behaviour?
It crashes on Apple M1 hardware. It should not crash. It does not crash on other hardware.
I think the code should work. It does work fine on other hardware. But even if there is sth wrong with the code, it still should not crash, but throw some exception instead.
### Standalone code to reproduce the issue
On Apple M1 hardware:
* Checkout https://github.com/rwth-i6/returnn. (Maybe commit 3a67da87c2fd8783c5c2469d72cf1319b5b45837 to be sure.)
* Run: `python3 tests/test_TFUtil.py test_get_variable_grad_from_update_ops`
The relevant code:
* https://github.com/rwth-i6/returnn/blob/3a67da87c2fd8783c5c2469d72cf1319b5b45837/tests/test_TFUtil.py#L3507
* https://github.com/rwth-i6/returnn/blob/3a67da87c2fd8783c5c2469d72cf1319b5b45837/returnn/tf/util/basic.py#L6649
Specifically:
```python
def test_get_variable_grad_from_update_ops():
with tf_compat.v1.variable_scope("test_get_variable_grad_from_update_ops"):
var = tf_compat.v1.get_variable("var", (), initializer=tf.zeros_initializer())
loss = (var - 1.0) ** 2
for opt in [
tf_compat.v1.train.AdamOptimizer(),
tf_compat.v1.train.GradientDescentOptimizer(learning_rate=1.0),
tf_compat.v1.train.MomentumOptimizer(learning_rate=0.1, momentum=0.9),
tf_compat.v1.train.RMSPropOptimizer(learning_rate=0.1),
]:
print("Optimizer:", opt)
minimize_op = opt.minimize(loss=loss, var_list=[var])
assert isinstance(minimize_op, tf.Operation)
update_ops = get_var_update_ops(var, fetches=minimize_op)
print("update ops:", update_ops)
print("update op keys:", get_op_attrib_keys(update_ops[0]))
print("update op inputs by name:", get_op_input_names(update_ops[0]))
session.run(var.initializer) # reset
session.run(tf_compat.v1.global_variables_initializer()) # from Adam or so
assert_equal(session.run(var), 0.0)
grad = get_variable_grad_from_update_ops(var, update_ops)
print("grad:", grad)
_, grad_np = session.run([minimize_op, grad])
assert_equal(grad_np, -2.0)
```
But there are a few helpers `get_variable_grad_from_update_ops`, `get_var_update_ops` etc.
### Relevant log output
```
Executing: test_get_variable_grad_from_update_ops
Optimizer: <tensorflow.python.training.adam.AdamOptimizer object at 0x16bbe3670>
update ops: [<tf.Operation 'test_get_variable_grad_from_update_ops/Adam/update_test_get_variable_grad_from_update_ops/var/ResourceApplyAdam' type=ResourceApplyAdam>]
update op keys: ['use_nesterov', '_has_manual_control_dependencies', 'use_locking', '_class', 'T']
update op inputs by name: ['var', 'm', 'v', 'beta1_power', 'beta2_power', 'lr', 'beta1', 'beta2', 'epsilon', 'grad']
2023-02-23 10:01:13.694539: W tensorflow/c/c_api.cc:291] Operation '{name:'test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Adam_1/Assign' id:60 op device:{requested: '', assigned: ''} def:{{{node test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Adam_1/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Adam_1, test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Adam_1/Initializer/zeros)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.
grad: Tensor("test_get_variable_grad_from_update_ops/gradients/test_get_variable_grad_from_update_ops/sub_grad/tuple/control_dependency:0", shape=(), dtype=float32)
Optimizer: <tensorflow.python.training.gradient_descent.GradientDescentOptimizer object at 0x16bbe36d0>
update ops: [<tf.Operation 'test_get_variable_grad_from_update_ops/GradientDescent/update_test_get_variable_grad_from_update_ops/var/ResourceApplyGradientDescent' type=ResourceApplyGradientDescent>]
update op keys: ['use_locking', '_class', '_has_manual_control_dependencies', 'T']
update op inputs by name: ['var', 'alpha', 'delta']
2023-02-23 10:01:13.768957: W tensorflow/c/c_api.cc:291] Operation '{name:'test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Adam_1/Assign' id:60 op device:{requested: '', assigned: ''} def:{{{node test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Adam_1/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Adam_1, test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Adam_1/Initializer/zeros)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.
grad: Tensor("test_get_variable_grad_from_update_ops/gradients_1/test_get_variable_grad_from_update_ops/sub_grad/tuple/control_dependency:0", shape=(), dtype=float32)
Optimizer: <tensorflow.python.training.momentum.MomentumOptimizer object at 0x16bbe35b0>
update ops: [<tf.Operation 'test_get_variable_grad_from_update_ops/Momentum/update_test_get_variable_grad_from_update_ops/var/ResourceApplyMomentum' type=ResourceApplyMomentum>]
update op keys: ['use_locking', 'T', '_has_manual_control_dependencies', '_class', 'use_nesterov']
update op inputs by name: ['var', 'accum', 'lr', 'grad', 'momentum']
2023-02-23 10:01:13.805807: W tensorflow/c/c_api.cc:291] Operation '{name:'test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Momentum/Assign' id:138 op device:{requested: '', assigned: ''} def:{{{node test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Momentum/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Momentum, test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Momentum/Initializer/zeros)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.
grad: Tensor("test_get_variable_grad_from_update_ops/gradients_2/test_get_variable_grad_from_update_ops/sub_grad/tuple/control_dependency:0", shape=(), dtype=float32)
Fatal Python error: Segmentation fault
Thread 0x0000000103500580 (most recent call first):
File "/Users/az/.local/lib/python3.9/site-packages/tensorflow/python/client/session.py", line 1454 in _call_tf_sessionrun
File "/Users/az/.local/lib/python3.9/site-packages/tensorflow/python/client/session.py", line 1361 in _run_fn
File "/Users/az/.local/lib/python3.9/site-packages/tensorflow/python/client/session.py", line 1378 in _do_call
File "/Users/az/.local/lib/python3.9/site-packages/tensorflow/python/client/session.py", line 1371 in _do_run
File "/Users/az/.local/lib/python3.9/site-packages/tensorflow/python/client/session.py", line 1191 in _run
File "/Users/az/.local/lib/python3.9/site-packages/tensorflow/python/client/session.py", line 968 in run
File "/Users/az/Programmierung/crnn/tests/test_TFUtil.py", line 3529 in test_get_variable_grad_from_update_ops
File "/Users/az/Programmierung/crnn/tests/test_TFUtil.py", line 4559 in <module>
fish: Job 1, 'python3 tests/test_TFUtil.py te…' terminated by signal SIGSEGV (Address boundary error)
```
Stack trace in LLDB in the crashing thread:
```
* thread #28, queue = 'metal gpu stream', stop reason = EXC_BAD_ACCESS (code=1, address=0xbeaddc3f8010)
* frame #0: 0x00000001836ea5a0 libobjc.A.dylib`objc_msgSend + 32
frame #1: 0x000000018df96d38 MPSNDArray`___lldb_unnamed_symbol1550 + 2292
frame #2: 0x000000018df98bbc MPSNDArray`___lldb_unnamed_symbol1567 + 300
frame #3: 0x000000018df991e8 MPSNDArray`___lldb_unnamed_symbol1569 + 176
frame #4: 0x0000000159a7d2b8 libmetal_plugin.dylib`invocation function for block in double dispatchOneKernel<MPSNDArrayIdentity>(MetalStream*, MPSNDArrayIdentity*, NSArray*, MPSNDArray*, char const*, MPSKernelDAGObject*) + 120
frame #5: 0x00000001836a01b4 libdispatch.dylib`_dispatch_client_callout + 20
frame #6: 0x00000001836af414 libdispatch.dylib`_dispatch_lane_barrier_sync_invoke_and_complete + 56
frame #7: 0x0000000159a7d140 libmetal_plugin.dylib`double dispatchOneKernel<MPSNDArrayIdentity>(MetalStream*, MPSNDArrayIdentity*, NSArray*, MPSNDArray*, char const*, MPSKernelDAGObject*) + 120
frame #8: 0x0000000159a7fffc libmetal_plugin.dylib`metal_plugin::MPSApplyMomentumOp<float>::Compute(metal_plugin::OpKernelContext*) + 2768
frame #9: 0x0000000159a7f2fc libmetal_plugin.dylib`void metal_plugin::ComputeOpKernel<metal_plugin::MPSApplyMomentumOp<float> >(void*, TF_OpKernelContext*) + 44
frame #10: 0x000000014cd00028 libtensorflow_framework.2.dylib`tensorflow::PluggableDevice::Compute(tensorflow::OpKernel*, tensorflow::OpKernelContext*) + 148
frame #11: 0x000000014cc847f0 libtensorflow_framework.2.dylib`tensorflow::(anonymous namespace)::ExecutorState<tensorflow::SimplePropagatorState>::Process(tensorflow::SimplePropagatorState::TaggedNode, long long) + 3764
frame #12: 0x000000028a47eb6c _pywrap_tensorflow_internal.so`Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::WorkerLoop(int) + 1496
frame #13: 0x000000028a47e468 _pywrap_tensorflow_internal.so`tsl::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()::operator()() const + 80
frame #14: 0x000000014cb9e878 libtensorflow_framework.2.dylib`tsl::(anonymous namespace)::PThread::ThreadFn(void*) + 120
frame #15: 0x000000018386426c libsystem_pthread.dylib`_pthread_start + 148
```
As you see from the output, the crash happens in the last `session.run([minimize_op, grad])`.
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"Hi @Davidy22, We were able to replicate the issue in Fedora Linux 37 using tf-nightly from our end as well. Please find the screenshot of the same below.\r\n\r\nIt seems like we have to dig more into this issue, we will update soon here. Thank you!",
"Hi @Davidy22 , Thanks for reporting.\r\n\r\nThis problem already brought to our attention.This log is safe to ignore as you can see the below message from log.\r\nIt is also not affecting the functionality here. Please refer to the comment [here](https://github.com/tensorflow/tensorflow/issues/59117#issuecomment-1402908928) that this log may provide context for some other errors for certain use cases.But it should be moved to debug log for which action is under progress.\r\n\r\n> Executor start aborting (this does not indicate an error and you can ignore this message)\r\n",
"Yeah, it didn't actually impact any of my running code, which ran pretty much the same between tf 2.11 and 2.12 with the only visible difference being that unsightly message. The downgrade would be appreciated, I would like to have less harmless error messages filling screens when not needed",
"Hi @Davidy22 ,\r\nIncase If you want to filter out the logs to only Warning and above levels you can use the below code at the top of your code.\r\n\r\n```\r\nimport os\r\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\r\n```",
"Oh that'll be nice. I assume it'll also potentially end up hidiing messages that I might want to see, but this should be good enough until the tf release happens that cleans this up. I'm on 2.12 RC0, I presume I'll see the resolution to this come down in the final release?",
"@Davidy22 ,\r\nThis is already under purview of Developer team.I hope this might be resolved in next release mostly. \r\n\r\nThanks!",
"I had this error on Ubuntu 22.04 as well as Fedora 37",
"> TF_CPP_MIN_LOG_LEVEL\r\n\r\nThis did not work for me. \r\nInstead, in terminal:\r\n`export TF_CPP_MIN_LOG_LEVEL=2`",
"Same issue (a lot of spam messages) starting from tf 2.12",
"Hi @qqfish, Commit [80a4e5f9e4e103f722df3632db88fdb31537bb26](https://github.com/tensorflow/tensorflow/commit/80a4e5f9e4e103f722df3632db88fdb31537bb26) introduced extra log messages (for most Intel Models) and caused performance degradation. Intel would like to know if this change can be rolled back. Could you please share your thoughts?",
"Somebody helpe me please...\r\n\r\n2023-05-22 20:15:25.042266: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\r\n2023-05-22 20:15:25.047489: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\r\n2023-05-22 20:15:33.030265: I tensorflow/core/common_runtime/executor.cc:1210] [/job:localhost/replica:0/task:0/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): CANCELLED: GetNextFromShard was cancelled\r\n\t [[{{node MultiDeviceIteratorGetNextFromShard}}]]\r\n2023-05-22 20:15:33.030866: I tensorflow/core/common_runtime/executor.cc:1210] [/job:localhost/replica:0/task:0/device:GPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): CANCELLED: GetNextFromShard was cancelled\r\n\t [[{{node MultiDeviceIteratorGetNextFromShard}}]]\r\n\t [[RemoteCall]] [type.googleapis.com/tensorflow.DerivedStatus='']",
"> @Davidy22 , This is already under purview of Developer team.I hope this might be resolved in next release mostly.\r\n> \r\n> Thanks!\r\n\r\nany update regarding it @SuryanarayanaY ",
"> \r\n\r\nMake sure you `os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'` before you import tensorflow.",
"It's a bad decision to make unuseful logging and force users to disable it.",
"It isn't very pleasant to see all those messages, but it does not affect training.",
"The problem is that in the flood of many \"fake\" warnings, the real one can get lost.",
"CC ing - @qqfish for update.",
"> Hi @Davidy22 , Incase If you want to filter out the logs to only Warning and above levels you can use the below code at the top of your code.\r\n> \r\n> ```\r\n> import os\r\n> os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\r\n> ```\r\n\r\nThis solved my issue of flooding my terminal with `You must feed a value for placeholder tensor....` warning log. I am using **Google Colab**.",
"Any chance of backporting a fix for this from TF2.13 into TF2.12.x?"
] | 2023-02-23T09:00:19 | 2023-08-17T14:19:01 | null | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
binary
### Tensorflow Version
v1.12.1-89814-gbaac3548e86 2.13.0-dev20230222
### Custom Code
No
### OS Platform and Distribution
Fedora Linux 37
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
CUDA Version: 12.0, CUDNN unavailable, not using gpu anyway
### GPU model and memory
_No response_
### Current Behaviour?
```shell
When training a model defined with the subclass interface, tensorflow throws up many copies of a warning that doesn't appear to have any visible consequences. It does not halt training and model performance seems unaffected, but reporting because somewhat unsightly and may be the source of some insidious error. This error message did not appear when running the same code under TF 2.11, python 3.10. Issue began immediately after upgrade to TF 2.12, python 3.11. Also confirmed present in nightly.
```
### Standalone code to reproduce the issue
```shell
from tensorflow import keras
class build_rnn(keras.Model):
def __init__(self, layer_size = 128, dim = 5, dropout = 0.2):
super().__init__(self)
self.pre = keras.Sequential([keras.layers.Input(shape=(None, dim), ragged=True), keras.layers.GaussianNoise(0.2)])
self.rnn1 = keras.layers.LSTM(layer_size, return_sequences = True, return_state = True, dropout = dropout, kernel_regularizer=l1(1e-6), recurrent_regularizer=l1(1e-6), bias_regularizer=l1(1e-6))
self.bn = keras.layers.BatchNormalization()
self.rnn2 = keras.layers.LSTM(layer_size, return_sequences = True, return_state = True, dropout = dropout, kernel_regularizer=l2(1e-6), recurrent_regularizer=l2(1e-6), bias_regularizer=l2(1e-6))
self.res = keras.layers.Add()
self.rnn3 = keras.layers.LSTM(layer_size, return_sequences = False, return_state = True, dropout = dropout, kernel_regularizer=l1_l2(1e-6), recurrent_regularizer=l1_l2(1e-6), bias_regularizer=l1_l2(1e-6))
self.dense1 = keras.layers.Dense(32, kernel_regularizer=l1(1e-5), bias_regularizer=l2(1e-5))
self.bn2 = keras.layers.BatchNormalization()
self.dense2 = keras.layers.Dense(16, kernel_regularizer=l2(1e-5), bias_regularizer=l1_l2(1e-5))
self.hosp_out = keras.layers.Dense(1, activation = "sigmoid")
self.dense4 = keras.layers.Dense(128, kernel_regularizer=l1(1e-5), bias_regularizer=l2(1e-5))
self.dropout3 = keras.layers.Dropout(0.2)
self.dense5 = keras.layers.Dense(64, kernel_regularizer=l2(1e-6), bias_regularizer=l1_l2(1e-5))
self.row_out = keras.layers.Dense(dim, activation = "linear")
def call(self, inputs, states=None, return_state=False, training=False):
x = inputs
x = self.pre(x, training=training)
if states is not None: s1, s2, s3 = states
else: s1 = s2 = s3 = None
x, h1, s1 = self.rnn1(x, initial_state=s1, training=training)
xr = self.bn(x, training=training)
xr, h2, s2 = self.rnn2(xr, initial_state=s2, training=training)
x = self.res([x, xr])
x, h3, s3 = self.rnn3(x, initial_state=s3, training=training)
y = self.dense1(x, training=training)
y = self.bn2(y, training=training)
y = self.dense2(y, training=training)
y = self.hosp_out(y, training=training)
z = self.dense4(x, training=training)
z = self.dropout3(z, training=training)
z = self.dense5(z, training=training)
z = self.row_out(z, training=training)
if return_state:
return y, z, ([h1, s1], [h2, s2], [h3, s3])
#return x, [h1, s1]
else:
return y, z
rmodel = build_rnn(dim = len(columns))
rmodel.compile(loss = ["binary_crossentropy", 'mse'], optimizer=keras.optimizers.experimental.AdamW())
rmodel.fit(x_train, [y_train, z_train], epochs = 100, callbacks=callbacks.EarlyStopping(monitor="loss", patience=7), verbose = 2)
```
### Relevant log output
```shell
2023-02-23 16:48:07.354061: I tensorflow/core/common_runtime/executor.cc:1214] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32
[[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]
2023-02-23 16:48:07.355191: I tensorflow/core/common_runtime/executor.cc:1214] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32
[[{{node gradients/split_grad/concat/split/split_dim}}]]
2023-02-23 16:48:07.356586: I tensorflow/core/common_runtime/executor.cc:1214] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32
[[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]
2023-02-23 16:48:07.569860: I tensorflow/core/common_runtime/executor.cc:1214] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32
[[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]
2023-02-23 16:48:07.571111: I tensorflow/core/common_runtime/executor.cc:1214] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32
[[{{node gradients/split_grad/concat/split/split_dim}}]]
2023-02-23 16:48:07.572353: I tensorflow/core/common_runtime/executor.cc:1214] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32
[[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]
2023-02-23 16:48:07.762177: I tensorflow/core/common_runtime/executor.cc:1214] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32
[[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]
2023-02-23 16:48:07.763419: I tensorflow/core/common_runtime/executor.cc:1214] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32
[[{{node gradients/split_grad/concat/split/split_dim}}]]
2023-02-23 16:48:07.764506: I tensorflow/core/common_runtime/executor.cc:1214] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32
[[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]
2023-02-23 16:48:09.234512: I tensorflow/core/common_runtime/executor.cc:1214] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32
[[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]
2023-02-23 16:48:09.235990: I tensorflow/core/common_runtime/executor.cc:1214] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32
[[{{node gradients/split_grad/concat/split/split_dim}}]]
2023-02-23 16:48:09.237207: I tensorflow/core/common_runtime/executor.cc:1214] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32
[[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]
2023-02-23 16:48:09.419726: I tensorflow/core/common_runtime/executor.cc:1214] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32
[[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]
2023-02-23 16:48:09.420944: I tensorflow/core/common_runtime/executor.cc:1214] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32
[[{{node gradients/split_grad/concat/split/split_dim}}]]
2023-02-23 16:48:09.422073: I tensorflow/core/common_runtime/executor.cc:1214] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32
[[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]
2023-02-23 16:48:09.593760: I tensorflow/core/common_runtime/executor.cc:1214] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32
[[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]
2023-02-23 16:48:09.595009: I tensorflow/core/common_runtime/executor.cc:1214] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32
[[{{node gradients/split_grad/concat/split/split_dim}}]]
2023-02-23 16:48:09.596323: I tensorflow/core/common_runtime/executor.cc:1214] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32
[[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]
```
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"Hi, @pabloazurduy \r\n\r\nApologize for the delay and if you don't mind could you please help me with the exact steps which you're following for installing the Tensorflow on `rosetta x86_64` ? If possible please help me with below commands output it seems like some issue with setting up environment variable at the moment, meanwhile could you please try with `python 3.8` or `python 3.9` instead of `python 3.7` with stable version of `Tensorflow(2.11) `and check whether is it resolving your issue or not ? Thank you!\r\n\r\n1. conda --version\r\n2. printenv",
"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/59778\">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/59778\">No</a>\n"
] | 2023-02-23T02:47:07 | 2023-03-19T02:03:46 | 2023-03-19T02:03:42 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
2.7.4
### Custom Code
Yes
### OS Platform and Distribution
macos Ventura 13.2.1 (22D68)
### Mobile device
_No response_
### Python version
3.7
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
```shell
A bug happened!
running TF in a rosetta x86_64 conda env with python 3.7 (this is a requirement from a package) I was able to install it using [this answer][https://stackoverflow.com/a/74439006/5318634] but when trying to import it I receive this error.
"""python
Traceback (most recent call last):
File "/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow.py", line 64, in <module>
from tensorflow.python._pywrap_tensorflow_internal import *
ImportError: dlopen(/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so, 0x0006): Library not loaded: @rpath/libgpr.20.dylib
Referenced from: <4CC88E4E-9383-347A-B5FD-2C28611F371E> /Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so
Reason: tried: '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../../_solib_darwin_x86_64/_U_S_Stensorflow_Clibtensorflow_Uframework_Uimport_Ulib___Utensorflow/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../../../../libgpr.20.dylib' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../../_solib_darwin_x86_64/_U_S_Stensorflow_Clibtensorflow_Uframework_Uimport_Ulib___Utensorflow/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../../../../libgpr.20.dylib' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/Users/azurduy/miniconda/envs/iappkg_x86/bin/../lib/libgpr.20.dylib' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/Users/azurduy/miniconda/envs/iappkg_x86/bin/../lib/libgpr.20.dylib' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/System/Volumes/Preboot/Cryptexes/OS@rpath/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../../_solib_darwin_x86_64/_U_S_Stensorflow_Clibtensorflow_Uframework_Uimport_Ulib___Utensorflow/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../../../../libgpr.20.dylib' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../../_solib_darwin_x86_64/_U_S_Stensorflow_Clibtensorflow_Uframework_Uimport_Ulib___Utensorflow/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../../../../libgpr.20.dylib' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/Users/azurduy/miniconda/envs/iappkg_x86/bin/../lib/libgpr.20.dylib' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/Users/azurduy/miniconda/envs/iappkg_x86/bin/../lib/libgpr.20.dylib' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/usr/local/lib/libgpr.20.dylib' (no such file), '/usr/lib/libgpr.20.dylib' (no such file, not in dyld cache)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/__init__.py", line 41, in <module>
from tensorflow.python.tools import module_util as _module_util
File "/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/__init__.py", line 40, in <module>
from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
File "/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow.py", line 80, in <module>
f'{traceback.format_exc()}'
ImportError: Traceback (most recent call last):
File "/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow.py", line 64, in <module>
from tensorflow.python._pywrap_tensorflow_internal import *
ImportError: dlopen(/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so, 0x0006): Library not loaded: @rpath/libgpr.20.dylib
Referenced from: <4CC88E4E-9383-347A-B5FD-2C28611F371E> /Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so
Reason: tried: '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../../_solib_darwin_x86_64/_U_S_Stensorflow_Clibtensorflow_Uframework_Uimport_Ulib___Utensorflow/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../../../../libgpr.20.dylib' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../../_solib_darwin_x86_64/_U_S_Stensorflow_Clibtensorflow_Uframework_Uimport_Ulib___Utensorflow/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../../../../libgpr.20.dylib' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/Users/azurduy/miniconda/envs/iappkg_x86/bin/../lib/libgpr.20.dylib' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/Users/azurduy/miniconda/envs/iappkg_x86/bin/../lib/libgpr.20.dylib' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/System/Volumes/Preboot/Cryptexes/OS@rpath/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../../_solib_darwin_x86_64/_U_S_Stensorflow_Clibtensorflow_Uframework_Uimport_Ulib___Utensorflow/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../../../../libgpr.20.dylib' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../../_solib_darwin_x86_64/_U_S_Stensorflow_Clibtensorflow_Uframework_Uimport_Ulib___Utensorflow/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../../../../libgpr.20.dylib' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/Users/azurduy/miniconda/envs/iappkg_x86/bin/../lib/libgpr.20.dylib' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/Users/azurduy/miniconda/envs/iappkg_x86/bin/../lib/libgpr.20.dylib' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/usr/local/lib/libgpr.20.dylib' (no such file), '/usr/lib/libgpr.20.dylib' (no such file, not in dyld cache)
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.
"""
```
### Standalone code to reproduce the issue
```shell
import tensorflow
```
### Relevant log output
```shell
ImportError Traceback (most recent call last)
<ipython-input-1-d6579f534729> in <module>
----> 1 import tensorflow
~/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/__init__.py in <module>
39 import sys as _sys
40
---> 41 from tensorflow.python.tools import module_util as _module_util
42 from tensorflow.python.util.lazy_loader import LazyLoader as _LazyLoader
43
~/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/__init__.py in <module>
38 # pylint: disable=wildcard-import,g-bad-import-order,g-import-not-at-top
39
---> 40 from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
41 from tensorflow.python.eager import context
42
~/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow.py in <module>
78 except ImportError:
79 raise ImportError(
---> 80 f'{traceback.format_exc()}'
81 f'\n\nFailed to load the native TensorFlow runtime.\n'
82 f'See https://www.tensorflow.org/install/errors '
ImportError: Traceback (most recent call last):
File "/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow.py", line 64, in <module>
from tensorflow.python._pywrap_tensorflow_internal import *
ImportError: dlopen(/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so, 0x0006): Library not loaded: @rpath/libgpr.20.dylib
Referenced from: <4CC88E4E-9383-347A-B5FD-2C28611F371E> /Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so
Reason: tried: '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../../_solib_darwin_x86_64/_U_S_Stensorflow_Clibtensorflow_Uframework_Uimport_Ulib___Utensorflow/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../../../../libgpr.20.dylib' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../../_solib_darwin_x86_64/_U_S_Stensorflow_Clibtensorflow_Uframework_Uimport_Ulib___Utensorflow/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../../../../libgpr.20.dylib' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/Users/azurduy/miniconda/envs/iappkg_x86/bin/../lib/libgpr.20.dylib' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/Users/azurduy/miniconda/envs/iappkg_x86/bin/../lib/libgpr.20.dylib' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/System/Volumes/Preboot/Cryptexes/OS@rpath/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../../_solib_darwin_x86_64/_U_S_Stensorflow_Clibtensorflow_Uframework_Uimport_Ulib___Utensorflow/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../../../../libgpr.20.dylib' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../../_solib_darwin_x86_64/_U_S_Stensorflow_Clibtensorflow_Uframework_Uimport_Ulib___Utensorflow/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../libgpr.20.dylib' (no such file), '/Users/azurduy/miniconda/envs/iappkg_x86/lib/python3.7/site-packages/tensorflow/python/../../../../libgpr.20.dylib' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/Users/azurduy/miniconda/envs/iappkg_x86/bin/../lib/libgpr.20.dylib' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/Users/azurduy/miniconda/envs/iappkg_x86/bin/../lib/libgpr.20.dylib' (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64')), '/usr/local/lib/libgpr.20.dylib' (no such file), '/usr/lib/libgpr.20.dylib' (no such file, not in dyld cache)
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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"Hi, yes, this is a known issue, tf-team's been working on it for a long time. \r\n\r\nThey're working on the 2.12 release which allows newer versions of protobuf:\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/setup.py#L100\r\n\r\ntry `pip install -U tensorflow==2.12.0rc0`",
"Thanks @MarkDaoust ",
"Arg, it still fails:\r\n\r\n`Because tensorflow (2.12.0rc0) depends on numpy (>=1.22,<1.24)`\r\n\r\nie, tf doesn't want the new numpy. ",
"I see the TODO(numpy 1.24) in the setup.py file.\r\nDarn, your other packages won't work with np 1.23?",
"I have been able to install it with 1.23 yes, and it's good per `pip audit`\r\nThanks once again for your help"
] | 2023-02-22T17:54:09 | 2023-02-23T08:54:04 | 2023-02-22T18:29:15 | NONE | null | null | null | ### System information
- **Have I written custom code (as opposed to using a stock example script
provided in TensorFlow)**: no
- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**:
```sw_vers
ProductName: macOS
ProductVersion: 12.6.3
BuildVersion: 21G419
```
- **Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue
happens on a mobile device**: MacBook
- **TensorFlow installed from (source or binary)**: pip (binary)
- **TensorFlow version (use command below)**: the most recent one,
- **Python version**: 3.8.15
- **Bazel version (if compiling from source)**: N/A
- **GCC/Compiler version (if compiling from source)**: N/A
- **CUDA/cuDNN version**: N/A
- **GPU model and memory**: N/A
- **Exact command to reproduce**: see 'Describe the problem' section
### Describe the problem
I have a `pyproject.toml`. I want to upgrade its `onnx`, from 1.12.0 to 1.13.0. When I run `poetry lock`, it tells me
```
Because concrete-ml depends on onnx (1.13.0) which depends on protobuf (>=3.20.2,<4), protobuf is required.
So, because concrete-ml depends on protobuf (3.19.6), version solving failed.
```
so I upgrade `protobuf`, and now, it tells me
```
Because tensorflow (2.11.0) depends on protobuf (>=3.9.2,<3.20)
and concrete-ml depends on protobuf (>3.20.0), tensorflow is forbidden.
So, because concrete-ml depends on tensorflow (2.11.0), version solving failed.
```
which I understand as the most recent version of tf (2.11.0) currently doesn't support a recent version of protobuf.
This is for me a problem since for now, when I run `pip-audit`, it tells me
```
Found 3 known vulnerabilities in 3 packages
Name Version ID Fix Versions
------ ------- ------------------- ------------
mpmath 1.2.1 PYSEC-2021-427
onnx 1.12.0 GHSA-ffxj-547x-5j7c 1.13.0
py 1.11.0 PYSEC-2022-42969
```
so I can't upgrade `onnx`, and in my understanding, it is related to tf
--
Thanks for your hard work, guys! What you do for the ML community is amazing.
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"@mibumi \r\nCould you please import `tensorflow.keras.models` instead of `tensorflow.python.keras.models`. \r\n\r\n> Anything under tf.python.* is private, intended for development only, rather than for public use.\r\n\r\n> Importing from tensorflow.python or any other modules (including import tensorflow_core...) is not supported, and can break unannounced.So, it is suggested not to use anything with tf.python.*.\r\n\r\nI was able to execute the given code without an error on Colab using TF v2.11. Please find the gist [here](https://colab.research.google.com/gist/tiruk007/57ff2c2f3fd68fedb8a4c6a5df357307/untitled140.ipynb) for reference.\r\n\r\nThank you !",
"That looks like the right answer. \r\n\r\ntensorflow.python.keras.models is not the right coide path, we're removing this from the package in 2.12, all of that code comes from the stand-alone keras package now.",
"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.",
"Closing as stale. Please reopen if you'd like to work on this further. \r\n\r\nThanks !",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59776\">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/59776\">No</a>\n"
] | 2023-02-22T17:49:13 | 2023-03-12T19:33:37 | 2023-03-12T19:33:35 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
No
### Source
source
### Tensorflow Version
tf 2.11
### Custom Code
No
### OS Platform and Distribution
Linux Ubuntu 18.04
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
cudatoolkit=11.2.2 cudnn=8.1.0
### GPU model and memory
_No response_
### Current Behaviour?
Hey,
so I stumbled into this while trying to save a more complex custom model than the one I'll discuss here and getting errors that were reported already in other issues but had remained unsolved. I knew from my dealing with the errors in my other model that saving my model worked fine in TF2.3 but crashed in 2.11 and I had my suspicions as to what caused the problem.
So I created a dummy test that would throw an error (TypeError: 'int' object is not iterable) to be able to check how TF goes through the source code. I executed this once with my old TF2.3 installation and once with my current TF 2.11 installation (did this in 2 different virtual environments).
In both version when calling save on a Keras model that then essentially executes
```
save.save_model(self, filepath, overwrite, include_optimizer, save_format,
signatures, options, save_traces)
```
which is in tensorflow.python.keras.engine.training.py->Model class->def save()
In TF2.3 that then calls the save_model function in tensorflow.python.keras.saving.save.py
In TF2.11 that calls the save function in tensorflow.python.keras.saving.saved_model.save.py
which is odd, because the other function still exits and the new function isn't documented nearly as good as the old one is
In TF2.11 the error disappears when setting save_traces=False.
In TF2.3 the error is thrown no matter what.
I am not sure if this is a bug but it very much seems like one and I think it is connected to some other issues people are having, like this one https://github.com/tensorflow/model-optimization/issues/964 or this one https://github.com/tensorflow/tensorflow/issues/47554
I don't know how to fix this, because I don't know why save.save_model would not point to tensorflow.python.keras.saving.save.py when the import is still "from tensorflow.python.keras.saving import save" in trainings.py. I just want someone to tell me if this is a bug or not.
Have a nice day,
Miriam
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import LSTM, Dense, Dropout
model = Sequential([
LSTM(128,input_shape=(4,5)),
Dropout(0,2),
Dense(1)
])
model.compile(loss='mae', optimizer='adam')
model.summary()
#model.save('testmodel', save_traces=False)
model.save('testmodel', save_traces=True)
```
### Relevant log output
```shell
Traceback (most recent call last):
File "/home/miriam/vscode/UNIQORN/uniqorn/source/text.py", line 15, in <module>
model.save('testmodel', save_traces=True)
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 2132, in save
save.save_model(self, filepath, overwrite, include_optimizer, save_format,
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/keras/saving/save.py", line 149, in save_model
saved_model_save.save(model, filepath, overwrite, include_optimizer,
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/save.py", line 90, in save
saved_nodes, node_paths = save_lib.save_and_return_nodes(
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py", line 1267, in save_and_return_nodes
_build_meta_graph(obj, signatures, options, meta_graph_def))
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py", line 1440, in _build_meta_graph
return _build_meta_graph_impl(obj, signatures, options, meta_graph_def)
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py", line 1383, in _build_meta_graph_impl
signatures = signature_serialization.find_function_to_export(
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/saved_model/signature_serialization.py", line 103, in find_function_to_export
for name, child in children:
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/saved_model/save.py", line 177, in list_children
for name, child in super(_AugmentedGraphView, self).list_children(
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/checkpoint/graph_view.py", line 75, in list_children
for name, ref in super(ObjectGraphView,
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/checkpoint/trackable_view.py", line 84, in children
for name, ref in obj._trackable_children(save_type, **kwargs).items():
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/keras/engine/functional.py", line 370, in _trackable_children
super(Functional, self)._trackable_children(save_type, **kwargs))
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 2746, in _trackable_children
children = super(Model, self)._trackable_children(save_type, **kwargs)
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py", line 3047, in _trackable_children
children = self._trackable_saved_model_saver.trackable_children(cache)
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/base_serialization.py", line 55, in trackable_children
children = self.objects_to_serialize(serialization_cache)
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/layer_serialization.py", line 69, in objects_to_serialize
return (self._get_serialized_attributes(
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/layer_serialization.py", line 89, in _get_serialized_attributes
object_dict, function_dict = self._get_serialized_attributes_internal(
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/model_serialization.py", line 53, in _get_serialized_attributes_internal
super(ModelSavedModelSaver, self)._get_serialized_attributes_internal(
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/layer_serialization.py", line 99, in _get_serialized_attributes_internal
functions = save_impl.wrap_layer_functions(self.obj, serialization_cache)
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/save_impl.py", line 204, in wrap_layer_functions
fn.get_concrete_function()
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/contextlib.py", line 120, in __exit__
next(self.gen)
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/save_impl.py", line 367, in tracing_scope
fn.get_concrete_function(*args, **kwargs)
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py", line 1215, in get_concrete_function
concrete = self._get_concrete_function_garbage_collected(*args, **kwargs)
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py", line 1206, in _get_concrete_function_garbage_collected
concrete = self._variable_creation_fn._get_concrete_function_garbage_collected( # pylint: disable=protected-access
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py", line 192, in _get_concrete_function_garbage_collected
concrete_function, _ = self._maybe_define_concrete_function(args, kwargs)
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py", line 157, in _maybe_define_concrete_function
return self._maybe_define_function(args, kwargs)
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py", line 360, in _maybe_define_function
concrete_function = self._create_concrete_function(args, kwargs)
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py", line 284, in _create_concrete_function
func_graph_module.func_graph_from_py_func(
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py", line 1283, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py", line 645, in wrapped_fn
out = weak_wrapped_fn().__wrapped__(*args, **kwds)
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/save_impl.py", line 599, in wrapper
ret = method(*args, **kwargs)
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/utils.py", line 165, in wrap_with_training_arg
return control_flow_util.smart_cond(
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/keras/utils/control_flow_util.py", line 109, in smart_cond
return smart_module.smart_cond(
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/framework/smart_cond.py", line 52, in smart_cond
return true_fn()
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/utils.py", line 166, in <lambda>
training, lambda: replace_training_and_call(True),
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/utils.py", line 163, in replace_training_and_call
return wrapped_call(*args, **kwargs)
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/save_impl.py", line 681, in call
return call_and_return_conditional_losses(inputs, *args, **kwargs)[0]
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/save_impl.py", line 639, in __call__
return self.wrapped_call(*args, **kwargs)
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/miriam/miniconda3/envs/tf/lib/python3.8/site-packages/tensorflow/python/keras/layers/core.py", line 207, in _get_noise_shape
for i, value in enumerate(self.noise_shape):
TypeError: 'int' object is not iterable
```
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"Thanks for the fix!"
] | 2023-02-22T12:06:44 | 2023-02-23T19:37:52 | 2023-02-23T19:37:48 | CONTRIBUTOR | null | false | {
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} | https://github.com/tensorflow/tensorflow/pull/59536#issuecomment-1439039311
Since pevious PR has been rolled back, this one adopted reviewer's feedback to fix Mac build error.
Thanks @reedwm for the feedback and suggestion! | {
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"I believe that this may be masking some unit test failures as well.\r\n//tensorflow/python/kernel_tests/proto:decode_proto_op_test\r\n//tensorflow/python/util/protobuf:protobuf_compare_test\r\n//tensorflow/tools/api/tests:api_compatibility_test\r\nThese fail with protobuf > 3.20.3",
"Can you assign me this issue?I would love to contribute.",
"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/59774\">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/59774\">No</a>\n",
"@elfringham, Thank you for reporting this issue. \r\nIt has been fixed by https://github.com/tensorflow/tensorflow/pull/59795 and hence the issue is closed. Thank you!"
] | 2023-02-22T11:58:35 | 2023-03-03T07:27:29 | 2023-03-02T19:20:57 | CONTRIBUTOR | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Build/Install
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
git HEAD
### Custom Code
No
### OS Platform and Distribution
Ubuntu 20.04
### Mobile device
n/a
### Python version
3.9.13
### Bazel version
5.3.0
### GCC/Compiler version
10.3.0
### CUDA/cuDNN version
n/a
### GPU model and memory
n/a
### Current Behaviour?
```shell
The version of protobuf specified in the docker container is out of date as it does not match the current requirements.
https://github.com/tensorflow/tensorflow/blob/20e0beaeebc1bd96c8eca40bed0e7b0d065d8e0b/tensorflow/tools/tf_sig_build_dockerfiles/devel.requirements.txt#L18 - this is now wrong
It should match this
https://github.com/tensorflow/tensorflow/blob/20e0beaeebc1bd96c8eca40bed0e7b0d065d8e0b/tensorflow/tools/pip_package/setup.py#L100
```
### Standalone code to reproduce the issue
```shell
Look at the links above
```
### Relevant log output
```shell
n/a
```
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"@TensorFlow-MKL ",
"@cfRod @nSircombe ",
"This does not happen with protobuf==3.20.3 installed. However the default install currently will be protobuf==4.22.0",
"@elfringham thanks for reporting, we will take a look at it.",
"Hi @elfringham, we can reproduce this issue internally with commit id - 62dcf7e02c0dae336bfb54aa83a03ddd22ccc9af\r\n\r\nThank you for your report, we will continue to investigate it and update you if any workaround \r\nIn the meanwhile could you also verify if the git commit id is the same since the master branch is updated everyday?\r\n",
"@agramesh1 , Thanks for quick response. Please keep us updated. Thankyou!",
"@nazneenn You can see that the ARM_CI run for that commit has the failing test in it.\r\nhttps://github.com/tensorflow/tensorflow/actions/runs/4246640900/jobs/7383702056\r\nHowever please be aware that since then this test is now being excluded from ARM_CI runs. If you put a PR in to fix the issue then please also remove the line https://github.com/tensorflow/tensorflow/blob/b5154bce3272d6ba430a881c92e017eb15408cad/tensorflow/tools/ci_build/build_scripts/ARM_SKIP_TESTS.sh#L30 so that the test will be run again.",
"@elfringham ,[ PR has been created which fixes this issue #59837 ](https://github.com/tensorflow/tensorflow/pull/59837)",
"Fixed by merge of #59837 ",
"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/59773\">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/59773\">No</a>\n",
"I cross checked and found now the tests are success.\r\n```\r\n\r\n----------------------------------------------------------------------\r\nRan 97 tests in 8.219s\r\n\r\nOK (skipped=61)\r\n/bin/bash: /home/suryanarayanay/miniconda3/lib/libtinfo.so.6: no version information available (required by /bin/bash)\r\n================================================================================\r\nTarget //tensorflow/python/kernel_tests/nn_ops:pooling_ops_test_cpu up-to-date:\r\n bazel-bin/tensorflow/python/kernel_tests/nn_ops/pooling_ops_test_cpu\r\nINFO: Elapsed time: 11.607s, Critical Path: 10.52s\r\nINFO: 11 processes: 1 internal, 10 local.\r\nINFO: Build completed successfully, 11 total actions\r\n//tensorflow/python/kernel_tests/nn_ops:pooling_ops_test_cpu PASSED in 10.5s\r\n Stats over 10 runs: max = 10.5s, min = 2.3s, avg = 4.6s, dev = 3.0s\r\n\r\nExecuted 1 out of 1 test: 1 test passes.\r\nThere were tests whose specified size is too big. Use the --test_verbose_timeout_warnings command li\r\nINFO: Build completed successfully, 11 total actions\r\n\r\n```\r\n\r\nFull log attached below.\r\n[59773_logs.txt](https://github.com/tensorflow/tensorflow/files/11300417/59773_logs.txt)\r\n"
] | 2023-02-22T10:33:06 | 2023-04-22T06:36:06 | 2023-04-19T08:51:23 | CONTRIBUTOR | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
git HEAD
### Custom Code
No
### OS Platform and Distribution
Ubuntu 20.04
### Mobile device
n/a
### Python version
3.9.13
### Bazel version
5.3.0
### GCC/Compiler version
10.3.0
### CUDA/cuDNN version
n/a
### GPU model and memory
n/a
### Current Behaviour?
```shell
//tensorflow/python/kernel_tests/nn_ops:pooling_ops_test_cpu FAILED in 3 out of 12 in 20.7s
```
### Standalone code to reproduce the issue
```shell
bazel test --test_timeout=300,500,-1,-1 --flaky_test_attempts=3 --test_output=all --cache_test_results=no --noremote_accept_cached --test_env=TF_ENABLE_ONEDNN_OPTS=1 --build_tag_filters=-no_oss,-oss_serial,-gpu,-tpu,-benchmark-test,-v1only,-requires-gpu --test_tag_filters=-no_oss,-oss_serial,-gpu,-tpu,-benchmark-test,-v1only,-requires-gpu --verbose_failures --build_tests_only --jobs=16 -- //tensorflow/python/kernel_tests/nn_ops:pooling_ops_test
```
### Relevant log output
```shell
FAIL: testAvgPoolGradOutputMemoryOutOfBounds (__main__.PoolingTest)
PoolingTest.testAvgPoolGradOutputMemoryOutOfBounds
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/builder/.cache/bazel/_bazel_builder/945690c41481150b9aa58576637dd867/execroot/org_tensorflow/bazel-out/aarch64-opt/bin/tensorflow/python/kernel_tests/nn_ops/pooling_ops_test_cpu.runfiles/org_tensorflow/tensorflow/python/kernel_tests/nn_ops/pooling_ops_test.py", line 2348, in testAvgPoolGradOutputMemoryOutOfBounds
self.evaluate(
AssertionError: InvalidArgumentError not raised
```
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"Downgrading to cuda 11.8 did not help.\r\n```\r\nRTX4090 + Cuda 11.8 + Cudnn 8.8 + TF nightly with mixed_fp16\r\ndw=True, jit_compile=True, ETA after 200 steps: 1:17:38\r\n```\r\n\r\nBut when i downgraded to TF 2.11 and cuda 11.2, depthwise conv become just 2 times slower then normal one.\r\n```\r\nRTX4090 + Cuda 11.2 + Cudnn 8.1 + TF 2.11 with mixed_fp16\r\ndw=True, jit_compile=True, ETA after 200 steps: 9:39\r\ndw=False, jit_compile=True, ETA after 200 steps: 4:58\r\ndw=True, jit_compile=False, ETA after 200 steps: 21:10\r\n```\r\n\r\nCompilation from source with TF 2.11 and Cuda 11.2 required to limit compute compatibility with 8.6\r\nAnd now, when running model i get warning:\r\n```\r\n2023-02-22 16:45:51.222824: W tensorflow/compiler/xla/stream_executor/gpu/asm_compiler.cc:234] Falling back to the CUDA driver for PTX compilation; ptxas does not support CC 8.9\r\n2023-02-22 16:45:51.222852: W tensorflow/compiler/xla/stream_executor/gpu/asm_compiler.cc:237] Used ptxas at ptxas\r\n2023-02-22 16:45:51.222934: W tensorflow/compiler/xla/stream_executor/gpu/redzone_allocator.cc:318] UNIMPLEMENTED: ptxas ptxas too old. Falling back to the driver to compile.\r\nRelying on driver to perform ptx compilation. \r\nModify $PATH to customize ptxas location.\r\nThis message will be only logged once.\r\n2023-02-22 16:45:51.225390: W tensorflow/compiler/xla/service/gpu/buffer_comparator.cc:641] UNIMPLEMENTED: ptxas ptxas too old. Falling back to the driver to compile.\r\nRelying on driver to perform ptx compilation. \r\nSetting XLA_FLAGS=--xla_gpu_cuda_data_dir=/path/to/cuda or modifying $PATH can be used to set the location of ptxas\r\n```",
"@shkarupa-alex,\r\nThank you for reporting the issue. This issue is more related to Keras. Development of keras moved to another [repository](https://github.com/keras-team/keras/issues). \r\n\r\nCould you please post this issue on keras-team/keras [repo](https://github.com/keras-team/keras/issues).\r\nTo know more please refer:\r\nhttps://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999\r\nThank you!\r\n",
"@tilakrayal could you please explain why do you think this issue is related to keras?\r\nUnder the hood it uses tf.nn.depthwise_conv2d which is a part of tensorflow core. There is no something unusual in this layer except that.\r\n\r\nSo i think this issue is fully related to TF.",
"@sachinprasadhs,\r\nI was able to reproduce the issue on tensorflow [v2.11](https://colab.research.google.com/gist/tilakrayal/379181981726a53a61e16b1912e13672/untitled24.ipynb) and tf-nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/1b02979b66e15bf3b68c21bb0097c038/untitled1004.ipynb).",
"Hi, TF 2.12 or TF 2.13 is still a development branch, we are planning to migrate the cuDNN, CUDA versions in the upcoming Tensorflow 2.12 release. \r\nTill then could you please test it against the published tested configurations as below.\r\n\r\n\r\nVersion | Python version | Compiler | Build tools | cuDNN | CUDA\r\n-- | -- | -- | -- | -- | --\r\ntensorflow_gpu-2.11.0 | 3.7-3.10 | MSVC 2019 | Bazel 5.3.0 | 8.1 | 11.2\r\n\r\nAs per your findings, `DepthwiseConv2D` is 2x slower than Conv2D, it might be because of the different implementation methods followed in [DepthwiseConv2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/DepthwiseConv2D#used-in-the-notebooks).",
"> Till then could you please test it against the published tested configurations as below.\r\n\r\n@sachinprasadhs , my test with stable release and cuda 11.2 is here https://github.com/tensorflow/tensorflow/issues/59772#issuecomment-1440047780\r\n// ptxas warnings and DWConv is 2x slower",
"More confusing is that DWConv with jit_compile=True is slower then itself without jit_compile\r\n\r\n> RTX4090 + Cuda 12.0 + Cudnn 8.8 + TF nightly with fp32\r\n> dw=True, jit_compile=True, ETA after 200 steps: 3:43:26\r\n> dw=False, jit_compile=True, ETA after 200 steps: 9:13\r\n> dw=True, jit_compile=False, ETA after 200 steps: 26:18\r\n",
"Tested with fresh 2.12 release + cuda 11.8 and saw same numbers.\r\n\r\nConv2D = 4m 31s vs DepthwiseConv2d = 1h 17m 22s"
] | 2023-02-22T09:20:16 | 2023-03-23T19:16:35 | null | CONTRIBUTOR | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Performance
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
'v1.12.1-89750-g9f16e373ca8', '2.13.0'
### Custom Code
No
### OS Platform and Distribution
Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.10.6
### Bazel version
5.3.0
### GCC/Compiler version
11.3.0
### CUDA/cuDNN version
12.0 / 8.8
### GPU model and memory
RTX 4090 Aorus Master
### Current Behaviour?
```shell
After upgrading to RTX4090 i installed cuda 12.0 + cudnn 8.8 and found that some models become slower (e.g. EfficientNets) and some not (SwinTransformer).
I localized the issue to combination of `DepthwiseConv2D + model.compile(jit_compile=True)`.
In my case this combination is 24 times slower then `Conv2D + model.compile(jit_compile=True)` and 8 times slower then `DepthwiseConv2D + model.compile(jit_compile=False)`.
Here are some numbers:
Colab + TF 2.11.0 with fp32
dw=True, jit_compile=True, ETA after 200 steps: 1:18:06
dw=False, jit_compile=True, ETA after 200 steps: 1:18:20
dw=True, jit_compile=False, ETA after 200 steps: 2:29:57
Colab + TF nightly with fp32
dw=True, jit_compile=True, ETA after 200 steps: 1:21:25
dw=False, jit_compile=True, ETA after 200 steps: 1:18:23
dw=True, jit_compile=False, ETA after 200 steps: 2:29:40
RTX4090 + Cuda 12.0 + Cudnn 8.8 + TF nightly with fp32
dw=True, jit_compile=True, ETA after 200 steps: 3:43:26
dw=False, jit_compile=True, ETA after 200 steps: 9:13
dw=True, jit_compile=False, ETA after 200 steps: 26:18
RTX4090 + Cuda 12.0 + Cudnn 8.8 + TF nightly with mixed_fp16
dw=True, jit_compile=True, ETA after 200 steps: 1:17:36
dw=False, jit_compile=True, ETA after 200 steps: 4:33
dw=True, jit_compile=False, ETA after 200 steps: 21:57
```
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1KTMAVhpQLQhRphRfRjHNo8faR9NBOlK6#scrollTo=IEGDQp7G6aUq
```
### Relevant log output
_No response_</details> | {
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"@johnfhima \r\nA TensorFlow Lite model is represented in a special efficient portable format known as [FlatBuffers](https://google.github.io/flatbuffers/) (identified by the .tflite file extension). This provides several advantages over TensorFlow's protocol buffer model format such as reduced size (small code footprint) and faster inference (data is directly accessed without an extra parsing/unpacking step) that enables TensorFlow Lite to execute efficiently on devices with limited compute and memory resources and please refer to this [doc](https://www.tensorflow.org/lite/guide#1_generate_a_tensorflow_lite_model) for more details.\r\nThank you ! \r\n",
"Hi,\r\nTflite GPU delegates by default should use fp16. If on nvidia was used fp32 and in tflite metal was used fp16 it means 2x reduction of data size if everything else the same.\r\nTflite gpu delegates use special algorithms for memory reuse, it can save significant amount of memory for intermediate tensors that can explain smaller memory footprint.\r\nTflite gpu delegates by default trying to show the best inference performance and do not trying to save memory if this is bad for inference latency.\r\n\r\nYou can get some approximate info how much memory was used for constant/intermediate tensors using this methods:\r\nGetIntermediateTensorsSize()\r\nGetConstantTensorsSize()\r\nOf InferenceContext\r\nSample can be found here:\r\nhttps://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/delegates/gpu/metal/benchmarking/main.mm\r\n",
"Hi, \r\nThanks for your help. I am currently experienced another trouble. As I told you before I am making the inference of my model using tflite and the metal delegate in swift.\r\nWhen I try to perform a loop of inference, it seems that the memory never stop to increase until reaching an out of memory (if there is too many input).\r\nWhy does this behaviour happens? \r\nIf I have enough memory to perform one inference is there a way to free the memory used during this inference before going to the next one ? \r\n\r\nThanks in advance for your help\r\n\r\n******************************************************\r\nHere is the code I use for the inference:\r\n\r\n\r\n\r\n\r\n\t var fundusqnet: ModelDataHandler? = ModelDataHandler(modelFileInfo: FundusQNet.modelInfo)\r\n names_array = []\r\n quality_array = []\r\n var i = 0\r\n for url in self.urlArray{\r\n print(url)\r\n if let data = try? Data(contentsOf: url){\r\n let image = UIImage(data: data)!\r\n var quality_ = self.quality(image: image, fundusqnet: fundusqnet)\r\n self.quality_array.append(Float(Int(quality_[0]*10))/10)\r\n self.names_array.append(url.lastPathComponent)\r\n }\r\n }\r\n \r\n\r\n\r\nfunc quality(image :UIImage, fundusqnet: ModelDataHandler?) -> [Float]{\r\n let targetSize = CGSize(width: 224, height: 224)\r\n let renderer = UIGraphicsImageRenderer(size: targetSize)\r\n let scaledImage = renderer.image { _ in\r\n image.draw(in: CGRect(origin: .zero, size: targetSize))\r\n }\r\n print(scaledImage.size)\r\n let pixelBuffer = scaledImage.pixelBuffer()\r\n let results = (fundusqnet?.runModel(onFrame: pixelBuffer!))\r\n return results! \r\n }\r\n \r\n \r\n \r\n \r\nAnd here is my modelDataHandler class:\r\n******************************************\r\n// Copyright 2019 The TensorFlow Authors. All Rights Reserved.\r\n//\r\n// Licensed under the Apache License, Version 2.0 (the \"License\");\r\n// you may not use this file except in compliance with the License.\r\n// You may obtain a copy of the License at\r\n//\r\n// http://www.apache.org/licenses/LICENSE-2.0\r\n//\r\n// Unless required by applicable law or agreed to in writing, software\r\n// distributed under the License is distributed on an \"AS IS\" BASIS,\r\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n// See the License for the specific language governing permissions and\r\n// limitations under the License.\r\n\r\nimport CoreImage\r\nimport TensorFlowLite\r\nimport UIKit\r\n\r\n\r\ntypealias FileInfo = (name: String, extension: String)\r\n\r\nenum FundusQNet {\r\n static let modelInfo: FileInfo2 = (name: \"fundusqnet\", extension: \"tflite\") //512optimized 512Artery\r\n}\r\n\r\n\r\n/// This class handles all data preprocessing and makes calls to run inference on a given frame\r\n/// by invoking the `Interpreter`. It then formats the inferences obtained and returns the top N\r\n/// results for a successful inference.\r\nclass ModelDataHandler {\r\n \r\n // MARK: - Public Properties\r\n \r\n /// The current thread count used by the TensorFlow Lite Interpreter.\r\n let threadCount: Int\r\n \r\n let resultCount = 1\r\n \r\n // MARK: - Model Parameters\r\n \r\n let batchSize = 1\r\n let inputChannels = 3\r\n let inputWidth = 224\r\n let inputHeight = 224\r\n \r\n // MARK: - Private Properties\r\n \r\n \r\n /// TensorFlow Lite `Interpreter` object for performing inference on a given model.\r\n private var interpreter: Interpreter\r\n \r\n /// Information about the alpha component in RGBA data.\r\n private let alphaComponent = (baseOffset: 4, moduloRemainder: 3)\r\n \r\n // MARK: - Initialization\r\n \r\n /// A failable initializer for `ModelDataHandler`. A new instance is created if the model and\r\n /// labels files are successfully loaded from the app's main bundle. Default `threadCount` is 1.\r\n init?(modelFileInfo: FileInfo2, threadCount: Int = 1) {\r\n let modelFilename = modelFileInfo.name\r\n \r\n // Construct the path to the model file.\r\n guard let modelPath = Bundle.main.path(\r\n forResource: modelFilename,\r\n ofType: modelFileInfo.extension\r\n ) else {\r\n print(\"Failed to load the model file with name: \\(modelFilename).\")\r\n return nil\r\n }\r\n \r\n // Specify the options for the `Interpreter`.\r\n self.threadCount = threadCount\r\n var options = Interpreter.Options()\r\n options.threadCount = threadCount\r\n do {\r\n // Create the `Interpreter`.\r\n let delegate = MetalDelegate()\r\n if delegate != nil {\r\n //interpreter = try Interpreter(modelPath: modelPath,\r\n // delegates: [delegate])\r\n interpreter = try Interpreter(modelPath: modelPath, delegates: [delegate])\r\n } else {\r\n interpreter = try Interpreter(modelPath: modelPath)\r\n }\r\n try interpreter.allocateTensors()\r\n } catch let error {\r\n print(\"Failed to create the interpreter with error: \\(error.localizedDescription)\")\r\n return nil\r\n }\r\n \r\n }\r\n \r\n // MARK: - Public Methods\r\n \r\n /// Performs image preprocessing, invokes the `Interpreter`, and process the inference results.\r\n func runModel(onFrame pixelBuffer: CVPixelBuffer) -> ([Float])? {\r\n let sourcePixelFormat = CVPixelBufferGetPixelFormatType(pixelBuffer)\r\n assert(sourcePixelFormat == kCVPixelFormatType_32ARGB ||\r\n sourcePixelFormat == kCVPixelFormatType_32BGRA ||\r\n sourcePixelFormat == kCVPixelFormatType_32RGBA)\r\n \r\n \r\n let imageChannels = 4\r\n assert(imageChannels >= inputChannels)\r\n \r\n // Crops the image to the biggest square in the center and scales it down to model dimensions.\r\n let scaledSize = CGSize(width: inputWidth, height: inputHeight)\r\n guard let thumbnailPixelBuffer = pixelBuffer.centerThumbnail(ofSize: scaledSize) else {\r\n return nil\r\n }\r\n \r\n let outputTensor: Tensor\r\n do {\r\n // Remove the alpha component from the image buffer to get the RGB data.\r\n guard let rgbData = rgbDataFromBuffer(\r\n thumbnailPixelBuffer,\r\n byteCount: batchSize * inputWidth * inputHeight * inputChannels\r\n ) else {\r\n print(\"Failed to convert the image buffer to RGB data.\")\r\n return nil\r\n }\r\n \r\n // Copy the RGB data to the input `Tensor`.\r\n try interpreter.copy(rgbData, toInputAt: 0)\r\n \r\n // Run inference by invoking the `Interpreter`.\r\n try interpreter.invoke()\r\n \r\n // Get the output `Tensor` to process the inference results.\r\n outputTensor = try interpreter.output(at: 0)\r\n } catch let error {\r\n print(\"Failed to invoke the interpreter with error: \\(error.localizedDescription)\")\r\n return nil\r\n }\r\n \r\n var results = [Float32](unsafeData: outputTensor.data) ?? []\r\n \r\n \r\n // Return the inference time and inference results.\r\n return results\r\n }\r\n \r\n // MARK: - Private Methods\r\n \r\n\r\n \r\n\r\n \r\n /// Returns the RGB data representation of the given image buffer with the specified `byteCount`.\r\n ///\r\n /// - Parameters\r\n /// - buffer: The pixel buffer to convert to RGB data.\r\n /// - byteCount: The expected byte count for the RGB data calculated using the values that the\r\n /// model was trained on: `batchSize * imageWidth * imageHeight * componentsCount`.\r\n /// - isModelQuantized: Whether the model is quantized (i.e. fixed point values rather than\r\n /// floating point values).\r\n /// - Returns: The RGB data representation of the image buffer or `nil` if the buffer could not be\r\n /// converted.\r\n private func rgbDataFromBuffer(\r\n _ buffer: CVPixelBuffer,\r\n byteCount: Int\r\n ) -> Data? {\r\n CVPixelBufferLockBaseAddress(buffer, .readOnly)\r\n defer { CVPixelBufferUnlockBaseAddress(buffer, .readOnly) }\r\n guard let mutableRawPointer = CVPixelBufferGetBaseAddress(buffer) else {\r\n return nil\r\n }\r\n let count = CVPixelBufferGetDataSize(buffer)\r\n let bufferData = Data(bytesNoCopy: mutableRawPointer, count: count, deallocator: .none)\r\n var rgbBytes = [Float](repeating: 0, count: byteCount)\r\n var index = 0\r\n for component in bufferData.enumerated() {\r\n let offset = component.offset\r\n let isAlphaComponent = (offset % alphaComponent.baseOffset) == alphaComponent.moduloRemainder\r\n guard !isAlphaComponent else { continue }\r\n rgbBytes[index] = Float(component.element) // 255.0\r\n index += 1\r\n }\r\n \r\n return rgbBytes.withUnsafeBufferPointer(Data.init)\r\n\r\n }\r\n}\r\n\r\n// MARK: - Extensions\r\n\r\nextension Data {\r\n /// Creates a new buffer by copying the buffer pointer of the given array.\r\n ///\r\n /// - Warning: The given array's element type `T` must be trivial in that it can be copied bit\r\n /// for bit with no indirection or reference-counting operations; otherwise, reinterpreting\r\n /// data from the resulting buffer has undefined behavior.\r\n /// - Parameter array: An array with elements of type `T`.\r\n init<T>(copyingBufferOf array: [T]) {\r\n self = array.withUnsafeBufferPointer(Data.init)\r\n }\r\n}\r\n\r\nextension Array {\r\n /// Creates a new array from the bytes of the given unsafe data.\r\n ///\r\n /// - Warning: The array's `Element` type must be trivial in that it can be copied bit for bit\r\n /// with no indirection or reference-counting operations; otherwise, copying the raw bytes in\r\n /// the `unsafeData`'s buffer to a new array returns an unsafe copy.\r\n /// - Note: Returns `nil` if `unsafeData.count` is not a multiple of\r\n /// `MemoryLayout<Element>.stride`.\r\n /// - Parameter unsafeData: The data containing the bytes to turn into an array.\r\n init?(unsafeData: Data) {\r\n \r\n guard unsafeData.count % MemoryLayout<Element>.stride == 0 else { return nil }\r\n #if swift(>=5.0)\r\n self = unsafeData.withUnsafeBytes { .init($0.bindMemory(to: Element.self)) }\r\n #else\r\n self = unsafeData.withUnsafeBytes {\r\n .init(UnsafeBufferPointer<Element>(\r\n start: $0,\r\n count: unsafeData.count / MemoryLayout<Element>.stride\r\n ))\r\n }\r\n #endif // swift(>=5.0)\r\n }\r\n}\r\n",
"@johnfhima, As the primary issue has been addressed, could you please create a new ticket for this [issue](https://github.com/tensorflow/tensorflow/issues/59771#issuecomment-1447992826).\r\n\r\n> Hi, Thanks for your help. I am currently experienced another trouble. As I told you before I am making the inference of my model using tflite and the metal delegate in swift. When I try to perform a loop of inference, it seems that the memory never stop to increase until reaching an out of memory (if there is too many input). Why does this behaviour happens? If I have enough memory to perform one inference is there a way to free the memory used during this inference before going to the next one ?\r\n>...\r\n\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."
] | 2023-02-22T08:40:13 | 2023-03-20T02:00:40 | 2023-03-20T02:00:40 | NONE | null | null | null | Hi,
I have built a custom model using tensorflow/keras.
While looking at the amount of RAM (nvidia-smi) used to process a single input it gives me 8 gb of RAM (it is expected, as the model is purpose really heavy, and the input resolution also).
Nevertheless, after converting the model to tflite (even without any optimization), and running it on my iPadProM1 with metal delegate support for GPU acceleration, the peak of memory I observed is around 2-3 GB of RAM.
The model even succeeds to run for a reasonable amount of time on an iPhone 12.
I was wondering what I am missing. For my specific project, it is really important for us to keep the performance of the model as close as possible to the original one, even if time/RAM will be more expensive.
Why is tflite lowering the RAM of my model while running on an edge device?
Here is the parameter used to convert the model:
converter = tf.lite.TFLiteConverter.from_keras_model(my_model)
converter.optimizations = []
tflite_model = converter.convert()
with open('lunetnoopt.tflite', 'wb') as f:
f.write(tflite_model)
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"@ysong2123,\r\n\r\n- Train and export a TensorFlow model.\r\n- Manage model versioning with TensorFlow Serving ServerCore.\r\n- Configure batching using SavedModelBundleSourceAdapterConfig.\r\n- Serve request with TensorFlow Serving ServerCore.\r\n- Run and test the service.\r\n\r\nAlso please take a look at these official documents for the [reference](https://www.tensorflow.org/tfx/tutorials/serving/rest_simple) where the ML deployment and also TFX is an end-to-end platform for deploying production ML pipelines for the [reference](https://www.tensorflow.org/tfx).\r\nhttps://www.tensorflow.org/tfx/serving/serving_basic\r\nhttps://www.tensorflow.org/tfx/serving/serving_advanced\r\n",
"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/59770\">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/59770\">No</a>\n"
] | 2023-02-22T05:30:38 | 2023-03-28T01:57:52 | 2023-03-28T01:57:49 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Support
### Have you reproduced the bug with TF nightly?
No
### Source
source
### Tensorflow Version
tag: v2.11.0
### Custom Code
No
### OS Platform and Distribution
MacOS Ventura13
### Mobile device
_No response_
### Python version
3.8
### Bazel version
5.3.0
### GCC/Compiler version
clang 14.0
### CUDA/cuDNN version
none
### GPU model and memory
none
### Current Behaviour?
```shell
Currently, we just have Mac as our daily work laptop. But we want to deep dive into tensorflow source code and maybe doing some customized changes, then compile/build and deploy into test/production environment for model training. Meanwhile, as we knew, tensorflow is a typical cross-platform end2end tool, how can we make sure the whole develop/delivery pipeline runs smoothly? Do we have any best practice? Thank you.
```
### Standalone code to reproduce the issue
```shell
None regarding this topic.
```
### Relevant log output
_No response_</details> | {
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"currently, i'm using M1 chip with macOS ventura 13.1",
"Hi @ysong2123 Thanks for reporting this issue.\r\n\r\nSorry for the late response.\r\n\r\nI have tried to reproduce the issue on the same steup but facing different error. Please refer the screenshot below. Need to try out on [rc2.12](https://github.com/tensorflow/tensorflow/tree/r2.12) version.\r\n\r\n<img width=\"565\" alt=\"Screenshot 2023-02-27 at 8 11 08 PM\" src=\"https://user-images.githubusercontent.com/118897289/221851976-a561c979-2ea3-443f-aa31-eb8d18414e17.png\">\r\n\r\nHave you tried on [rc2.12](https://github.com/tensorflow/tensorflow/tree/r2.12) version and observed the same issue?",
"Closing as stale. Please reopen if you'd like to work on this further. Thanks!\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/59769\">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/59769\">No</a>\n"
] | 2023-02-22T05:18:57 | 2023-03-17T09:31:16 | 2023-03-17T09:31:13 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Build/Install
### Have you reproduced the bug with TF nightly?
No
### Source
source
### Tensorflow Version
tag: v2.11.0
### Custom Code
No
### OS Platform and Distribution
MacOS Ventura13
### Mobile device
_No response_
### Python version
3.8
### Bazel version
5.3.0
### GCC/Compiler version
clang 14.0
### CUDA/cuDNN version
none
### GPU model and memory
none
### Current Behaviour?
```shell
ImportError: dlopen(/private/var/tmp/_bazel_username/b326506f60d8e1a7b80e7181271420e8/execroot/org_tensorflow/bazel-out/host/bin/tensorflow/create_tensorflow.python_api_tf_python_api_gen_v2.runfiles/org_tensorflow/tensorflow/python/platform/_pywrap_cpu_feature_guard.so, 0x0002): tried: '/private/var/tmp/_bazel_ysong2/b326506f60d8e1a7b80e7181271420e8/execroot/org_tensorflow/bazel-out/host/bin/tensorflow/create_tensorflow.python_api_tf_python_api_gen_v2.runfiles/org_tensorflow/tensorflow/python/platform/_pywrap_cpu_feature_guard.so' (mach-o file, but is an incompatible architecture (have 'x86_64', need 'arm64')
```
### Standalone code to reproduce the issue
```shell
bazel build //tensorflow/tools/pip_package:build_pip_package
is there any conifg to control the way of "so" file generation?
```
### Relevant log output
_No response_</details> | {
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"CC @reedwm."
] | 2023-02-22T01:40:50 | 2023-02-27T06:51:45 | 2023-02-27T06:51:44 | CONTRIBUTOR | null | false | {
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} | Creates VLOG warnings when a GEMM directly or indirectly operating on FP8 operands is not pattern matched and rewritten into an FP8 Custom Call. | {
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"model download link - https://easyupload.io/mn1i8h",
"@mv96,\r\nThe link provided contains only the `.h5` model link and it doesn't contain any code. Please provide the complete code to debug. \r\n\r\nThere are two formats you can use to save an entire model to disk: the TensorFlow SavedModel format, and the older Keras H5 format. The recommended format is SavedModel. It is the default when you use model.save().\r\n\r\nYou can switch to the H5 format by:\r\n\r\n```\r\nPassing save_format='h5' to save().\r\nPassing a filename that ends in .h5 or .keras to save().\r\n```\r\nAlso refer to [this](https://www.tensorflow.org/guide/keras/save_and_serialize) guide for the reference.\r\nAlternative approach can be followed as below;\r\n\r\n`my_loaded_model = tf.keras.models.load_model('my_models_name.h5', custom_objects={'KerasLayer':hub.KerasLayer , 'AdamWeightDecay': optimizer})`\r\nAlso, can you also try saving the model in 'tf' format as **model.save('MinimalExample_tf', save_format='tf')** and load the model\r\nSaving a Keras model:\r\n\r\n```\r\nmodel = ... # Get model (Sequential, Functional Model, or Model subclass)\r\nmodel.save('path/to/location')\r\n```\r\nLoading the model back:\r\n\r\n```\r\nfrom tensorflow import keras\r\nmodel = keras.models.load_model('path/to/location')\r\n```\r\nAlso the PR [#17547](https://github.com/keras-team/keras/pull/17547) was merged for the similar issue where fixes a bug in the [LayerScale](https://github.com/keras-team/keras/blob/master/keras/applications/convnext.py#L199) layer, which hindered **ConvNeXt** pretrained models to work with mixed precision. The solution was simply to set the appropriate dtype in the gamma variable of the **LayerScale** layer.\r\n\r\nhttps://github.com/andreped/keras/blob/810fd3c203dbae8bb1f888ebc91cdd31ff30cf34/keras/applications/convnext.py#L221\r\n\r\n\r\n```\r\n def build(self, input_shape):\r\n self.gamma = self.add_weight(\r\n shape=(self.projection_dim,),\r\n initializer=initializers.Constant(self.init_values),\r\n trainable=True,\r\n```",
"This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n",
"Closing this as stale. Please reopen if this is still a valid request.",
"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/59767\">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/59767\">No</a>\n",
"I am having the same problem using ConvNeXt models from the Keras application library.\r\n\r\nI have tried using both ConvNeXtLarge and ConvNeXtBase and I receive the same error. It appears to be some problem with the \"LayerScale\" layeres in the provided ConvNeXt network.\r\n\r\nI have tried the saving the model as a 'tf' model (in order to re save it as a .keras model as discussed in @tilakrayal answer above but run into a different error that does not allow the model to be saved in this format (more on this below).\r\n\r\nBellow is some of my code for context. \r\n\r\nIn training (for transfer learning) I build my model using:\r\n\r\n```\r\nbase_model = tf.keras.applications.ConvNeXtBase(\r\n include_top=False, \r\n weights='imagenet', \r\n input_shape=(image_height, image_width, 3) \r\n)\r\nbase_model.trainable = False\r\n\r\nclassification_head = tf.keras.Sequential([\r\n tf.keras.layers.Flatten(),\r\n tf.keras.layers.Dense(512, activation='relu'),\r\n tf.keras.layers.Dropout(0.5),\r\n tf.keras.layers.Dense(1, activation='sigmoid') # Binary classification\r\n])\r\n\r\ntransfer_model = tf.keras.Sequential([\r\n base_model,\r\n classification_head\r\n])\r\n```\r\n\r\nThe model is then compiled and trained (with a few callbacks e.g learning rate decay - but nothing crazy).\r\ntraining runs as expected and appears to give intended results.\r\n\r\nI then save the model in both .keras and .h5 format.\r\n\r\n\r\n```\r\nmodel_filename = os.path.join(save_folder, f\"{model_save_name}_TL.h5\")\r\ntransfer_model.save(model_filename)\r\n\r\nmodel_filename = os.path.join(save_folder, f\"{model_save_name}_TL.keras\")\r\ntransfer_model.save(model_filename)\r\n```\r\n\r\nThese work fine, but when I try and load them later I get two different errors.\r\n\r\nUsing the .keras format I get this error:\r\n\r\n\r\n```\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_api.py](https://localhost:8080/#) in load_model(filepath, custom_objects, compile, safe_mode, **kwargs)\r\n 228 f\"with the native Keras format: {list(kwargs.keys())}\"\r\n 229 )\r\n--> 230 return saving_lib.load_model(\r\n 231 filepath,\r\n 232 custom_objects=custom_objects,\r\n\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py](https://localhost:8080/#) in load_model(filepath, custom_objects, compile, safe_mode)\r\n 273 \r\n 274 except Exception as e:\r\n--> 275 raise e\r\n 276 else:\r\n 277 return model\r\n\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py](https://localhost:8080/#) in load_model(filepath, custom_objects, compile, safe_mode)\r\n 238 # Construct the model from the configuration file in the archive.\r\n 239 with ObjectSharingScope():\r\n--> 240 model = deserialize_keras_object(\r\n 241 config_dict, custom_objects, safe_mode=safe_mode\r\n 242 )\r\n\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/saving/serialization_lib.py](https://localhost:8080/#) in deserialize_keras_object(config, custom_objects, safe_mode, **kwargs)\r\n 702 safe_mode_scope = SafeModeScope(safe_mode)\r\n 703 with custom_obj_scope, safe_mode_scope:\r\n--> 704 instance = cls.from_config(inner_config)\r\n 705 build_config = config.get(\"build_config\", None)\r\n 706 if build_config:\r\n\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/engine/sequential.py](https://localhost:8080/#) in from_config(cls, config, custom_objects)\r\n 471 for layer_config in layer_configs:\r\n 472 use_legacy_format = \"module\" not in layer_config\r\n--> 473 layer = layer_module.deserialize(\r\n 474 layer_config,\r\n 475 custom_objects=custom_objects,\r\n\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/layers/serialization.py](https://localhost:8080/#) in deserialize(config, custom_objects, use_legacy_format)\r\n 267 )\r\n 268 if use_legacy_format:\r\n--> 269 return legacy_serialization.deserialize_keras_object(\r\n 270 config,\r\n 271 module_objects=LOCAL.ALL_OBJECTS,\r\n\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/saving/legacy/serialization.py](https://localhost:8080/#) in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)\r\n 495 if \"custom_objects\" in arg_spec.args:\r\n 496 tlco = object_registration._THREAD_LOCAL_CUSTOM_OBJECTS.__dict__\r\n--> 497 deserialized_obj = cls.from_config(\r\n 498 cls_config,\r\n 499 custom_objects={\r\n\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py](https://localhost:8080/#) in from_config(cls, config, custom_objects)\r\n 3225 # Revive Functional model\r\n 3226 # (but not Functional subclasses with a custom __init__)\r\n-> 3227 inputs, outputs, layers = functional.reconstruct_from_config(\r\n 3228 config, custom_objects\r\n 3229 )\r\n\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/engine/functional.py](https://localhost:8080/#) in reconstruct_from_config(config, custom_objects, created_layers)\r\n 1500 while layer_nodes:\r\n 1501 node_data = layer_nodes[0]\r\n-> 1502 if process_node(layer, node_data):\r\n 1503 layer_nodes.pop(0)\r\n 1504 else:\r\n\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/engine/functional.py](https://localhost:8080/#) in process_node(layer, node_data)\r\n 1440 input_tensors\r\n 1441 )\r\n-> 1442 output_tensors = layer(input_tensors, **kwargs)\r\n 1443 \r\n 1444 # Update node index map.\r\n\r\nTypeError: 'str' object is not callable\r\n```\r\n\r\n\r\nand when trying to load the .h5 model I get the same error as described at the top of this thread.\r\n\r\n\r\n```\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_api.py](https://localhost:8080/#) in load_model(filepath, custom_objects, compile, safe_mode, **kwargs)\r\n 236 \r\n 237 # Legacy case.\r\n--> 238 return legacy_sm_saving_lib.load_model(\r\n 239 filepath, custom_objects=custom_objects, compile=compile, **kwargs\r\n 240 )\r\n\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py](https://localhost:8080/#) in error_handler(*args, **kwargs)\r\n 68 # To get the full stack trace, call:\r\n 69 # `tf.debugging.disable_traceback_filtering()`\r\n---> 70 raise e.with_traceback(filtered_tb) from None\r\n 71 finally:\r\n 72 del filtered_tb\r\n\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/saving/legacy/serialization.py](https://localhost:8080/#) in class_and_config_for_serialized_keras_object(config, module_objects, custom_objects, printable_module_name)\r\n 363 )\r\n 364 if cls is None:\r\n--> 365 raise ValueError(\r\n 366 f\"Unknown {printable_module_name}: '{class_name}'. \"\r\n 367 \"Please ensure you are using a `keras.utils.custom_object_scope` \"\r\n\r\nValueError: Unknown layer: 'LayerScale'. Please ensure you are using a `keras.utils.custom_object_scope` and that this object is included in the scope. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details.\r\n```\r\n\r\n\r\nI don't understand how to do the registration of a custom object when it is a pretrained model pulled from keras applications. Or if this is a bug or something else. \r\n\r\nI'm not sure if this is relevant to this issue but I have tried saving the model as tf format to use the fix described above. My code for this is:\r\n\r\n```\r\nmodel_filename = os.path.join(save_folder, f\"{model_save_name}_TL_tf\")\r\ntransfer_model.save(model_filename,save_format=\"tf\")\r\n```\r\n\r\nI get this error however:\r\n\r\n```\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py](https://localhost:8080/#) in error_handler(*args, **kwargs)\r\n 68 # To get the full stack trace, call:\r\n 69 # `tf.debugging.disable_traceback_filtering()`\r\n---> 70 raise e.with_traceback(filtered_tb) from None\r\n 71 finally:\r\n 72 del filtered_tb\r\n\r\n[/usr/local/lib/python3.10/dist-packages/tensorflow/python/saved_model/save.py](https://localhost:8080/#) in fill_object_graph_proto(self, proto)\r\n 351 child_proto = object_proto.children.add()\r\n 352 child_proto.node_id = self.node_ids[child.ref]\r\n--> 353 child_proto.local_name = child.name\r\n 354 for name, ref in self.augmented_graph_view.list_dependencies(node):\r\n 355 child_proto = object_proto.dependencies.add()\r\n\r\nTypeError: None has type NoneType, but expected one of: bytes, unicode\r\n```\r\n\r\n\r\nAny help on this would be appreciated. This is the only source I have found that has the exact same problem as me.\r\n\r\nIts it relevant the input images are somewhat large at 572x572 (the model is scaled accordingly), it is being used as a binary classifier and I have tried training on a range of different NVIDIA GPUs ",
"I have been playing around and have simplified the code to recreate the same problem. This time with the default ConvNeXt model just downloading it from applications and then saving it locally and trying to load it in with the same results. See bellow:\r\n\r\nHere is the code for downloading the model and saving it as the two file types (NOTE: again I am unable to save as the 'tf' file format)\r\n\r\n\r\n```\r\nimport tensorflow as tf\r\nfrom tensorflow import keras\r\nfrom keras.applications.convnext import ConvNeXtBase, preprocess_input\r\nimport os\r\n\r\nprint(tf.__version__)\r\n\r\nmodel_save_name =\"OG_ConvNeXTBase_V2\"\r\nsave_folder = \"/content/mydata/Models/Test_Space\"\r\n\r\ntest_model = tf.keras.applications.ConvNeXtBase(\r\n model_name=\"convnext_base\",\r\n include_top=True,\r\n include_preprocessing=True,\r\n weights=\"imagenet\",\r\n input_tensor=None,\r\n input_shape=None,\r\n pooling=None,\r\n classes=1000,\r\n classifier_activation=\"softmax\",\r\n)\r\n\r\n\r\nmodel_filename = os.path.join(save_folder, f\"{model_save_name}_TL.h5\")\r\ntest_model.save(model_filename)\r\n\r\nmodel_filename = os.path.join(save_folder, f\"{model_save_name}_TL.keras\")\r\ntest_model.save(model_filename)\r\n```\r\n\r\nThen when I re load it I get the same errors:\r\n\r\n```\r\nload_model_filename = '/content/mydata/Models/Test_Space/OG_ConvNeXTBase_V2_TL.h5'\r\n\r\nload_test = tf.keras.models.load_model(load_model_filename)\r\n```\r\n\r\n```\r\nValueError Traceback (most recent call last)\r\n[<ipython-input-2-371145205fb0>](https://localhost:8080/#) in <cell line: 3>()\r\n 1 load_model_filename = '/content/mydata/Models/Test_Space/OG_ConvNeXTBase_V2_TL.h5'\r\n 2 \r\n----> 3 load_test = tf.keras.models.load_model(load_model_filename)\r\n\r\n\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_api.py](https://localhost:8080/#) in load_model(filepath, custom_objects, compile, safe_mode, **kwargs)\r\n 236 \r\n 237 # Legacy case.\r\n--> 238 return legacy_sm_saving_lib.load_model(\r\n 239 filepath, custom_objects=custom_objects, compile=compile, **kwargs\r\n 240 )\r\n\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py](https://localhost:8080/#) in error_handler(*args, **kwargs)\r\n 68 # To get the full stack trace, call:\r\n 69 # `tf.debugging.disable_traceback_filtering()`\r\n---> 70 raise e.with_traceback(filtered_tb) from None\r\n 71 finally:\r\n 72 del filtered_tb\r\n\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/saving/legacy/serialization.py](https://localhost:8080/#) in class_and_config_for_serialized_keras_object(config, module_objects, custom_objects, printable_module_name)\r\n 363 )\r\n 364 if cls is None:\r\n--> 365 raise ValueError(\r\n 366 f\"Unknown {printable_module_name}: '{class_name}'. \"\r\n 367 \"Please ensure you are using a `keras.utils.custom_object_scope` \"\r\n\r\nValueError: Unknown layer: 'LayerScale'. Please ensure you are using a `keras.utils.custom_object_scope` and that this object is included in the scope. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details.\r\n```\r\n\r\nand . keras version\r\n\r\n```\r\nload_model_filename = '/content/mydata/Models/Test_Space/OG_ConvNeXTBase_V2_TL.keras'\r\n\r\nload_test = tf.keras.models.load_model(load_model_filename)\r\n```\r\n\r\n```\r\nTypeError Traceback (most recent call last)\r\n[<ipython-input-3-9a9c94514126>](https://localhost:8080/#) in <cell line: 3>()\r\n 1 load_model_filename = '/content/mydata/Models/Test_Space/OG_ConvNeXTBase_V2_TL.keras'\r\n 2 \r\n----> 3 load_test = tf.keras.models.load_model(load_model_filename)\r\n\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_api.py](https://localhost:8080/#) in load_model(filepath, custom_objects, compile, safe_mode, **kwargs)\r\n 228 f\"with the native Keras format: {list(kwargs.keys())}\"\r\n 229 )\r\n--> 230 return saving_lib.load_model(\r\n 231 filepath,\r\n 232 custom_objects=custom_objects,\r\n\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py](https://localhost:8080/#) in load_model(filepath, custom_objects, compile, safe_mode)\r\n 273 \r\n 274 except Exception as e:\r\n--> 275 raise e\r\n 276 else:\r\n 277 return model\r\n\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py](https://localhost:8080/#) in load_model(filepath, custom_objects, compile, safe_mode)\r\n 238 # Construct the model from the configuration file in the archive.\r\n 239 with ObjectSharingScope():\r\n--> 240 model = deserialize_keras_object(\r\n 241 config_dict, custom_objects, safe_mode=safe_mode\r\n 242 )\r\n\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/saving/serialization_lib.py](https://localhost:8080/#) in deserialize_keras_object(config, custom_objects, safe_mode, **kwargs)\r\n 702 safe_mode_scope = SafeModeScope(safe_mode)\r\n 703 with custom_obj_scope, safe_mode_scope:\r\n--> 704 instance = cls.from_config(inner_config)\r\n 705 build_config = config.get(\"build_config\", None)\r\n 706 if build_config:\r\n\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py](https://localhost:8080/#) in from_config(cls, config, custom_objects)\r\n 3225 # Revive Functional model\r\n 3226 # (but not Functional subclasses with a custom __init__)\r\n-> 3227 inputs, outputs, layers = functional.reconstruct_from_config(\r\n 3228 config, custom_objects\r\n 3229 )\r\n\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/engine/functional.py](https://localhost:8080/#) in reconstruct_from_config(config, custom_objects, created_layers)\r\n 1500 while layer_nodes:\r\n 1501 node_data = layer_nodes[0]\r\n-> 1502 if process_node(layer, node_data):\r\n 1503 layer_nodes.pop(0)\r\n 1504 else:\r\n\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/engine/functional.py](https://localhost:8080/#) in process_node(layer, node_data)\r\n 1440 input_tensors\r\n 1441 )\r\n-> 1442 output_tensors = layer(input_tensors, **kwargs)\r\n 1443 \r\n 1444 # Update node index map.\r\n\r\nTypeError: 'str' object is not callable\r\n```\r\nI hope this is helpful for re-creating the error",
"> I have been playing around and have simplified the code to recreate the same problem. This time with the default ConvNeXt model just downloading it from applications and then saving it locally and trying to load it in with the same results. See bellow:\r\n> \r\n> Here is the code for downloading the model and saving it as the two file types (NOTE: again I am unable to save as the 'tf' file format)\r\n> \r\n> ```\r\n> import tensorflow as tf\r\n> from tensorflow import keras\r\n> from keras.applications.convnext import ConvNeXtBase, preprocess_input\r\n> import os\r\n> \r\n> print(tf.__version__)\r\n> \r\n> model_save_name =\"OG_ConvNeXTBase_V2\"\r\n> save_folder = \"/content/mydata/Models/Test_Space\"\r\n> \r\n> test_model = tf.keras.applications.ConvNeXtBase(\r\n> model_name=\"convnext_base\",\r\n> include_top=True,\r\n> include_preprocessing=True,\r\n> weights=\"imagenet\",\r\n> input_tensor=None,\r\n> input_shape=None,\r\n> pooling=None,\r\n> classes=1000,\r\n> classifier_activation=\"softmax\",\r\n> )\r\n> \r\n> \r\n> model_filename = os.path.join(save_folder, f\"{model_save_name}_TL.h5\")\r\n> test_model.save(model_filename)\r\n> \r\n> model_filename = os.path.join(save_folder, f\"{model_save_name}_TL.keras\")\r\n> test_model.save(model_filename)\r\n> ```\r\n> \r\n> Then when I re load it I get the same errors:\r\n> \r\n> ```\r\n> load_model_filename = '/content/mydata/Models/Test_Space/OG_ConvNeXTBase_V2_TL.h5'\r\n> \r\n> load_test = tf.keras.models.load_model(load_model_filename)\r\n> ```\r\n> \r\n> ```\r\n> ValueError Traceback (most recent call last)\r\n> [<ipython-input-2-371145205fb0>](https://localhost:8080/#) in <cell line: 3>()\r\n> 1 load_model_filename = '/content/mydata/Models/Test_Space/OG_ConvNeXTBase_V2_TL.h5'\r\n> 2 \r\n> ----> 3 load_test = tf.keras.models.load_model(load_model_filename)\r\n> \r\n> \r\n> [/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_api.py](https://localhost:8080/#) in load_model(filepath, custom_objects, compile, safe_mode, **kwargs)\r\n> 236 \r\n> 237 # Legacy case.\r\n> --> 238 return legacy_sm_saving_lib.load_model(\r\n> 239 filepath, custom_objects=custom_objects, compile=compile, **kwargs\r\n> 240 )\r\n> \r\n> [/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py](https://localhost:8080/#) in error_handler(*args, **kwargs)\r\n> 68 # To get the full stack trace, call:\r\n> 69 # `tf.debugging.disable_traceback_filtering()`\r\n> ---> 70 raise e.with_traceback(filtered_tb) from None\r\n> 71 finally:\r\n> 72 del filtered_tb\r\n> \r\n> [/usr/local/lib/python3.10/dist-packages/keras/src/saving/legacy/serialization.py](https://localhost:8080/#) in class_and_config_for_serialized_keras_object(config, module_objects, custom_objects, printable_module_name)\r\n> 363 )\r\n> 364 if cls is None:\r\n> --> 365 raise ValueError(\r\n> 366 f\"Unknown {printable_module_name}: '{class_name}'. \"\r\n> 367 \"Please ensure you are using a `keras.utils.custom_object_scope` \"\r\n> \r\n> ValueError: Unknown layer: 'LayerScale'. Please ensure you are using a `keras.utils.custom_object_scope` and that this object is included in the scope. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details.\r\n> ```\r\n> \r\n> and . keras version\r\n> \r\n> ```\r\n> load_model_filename = '/content/mydata/Models/Test_Space/OG_ConvNeXTBase_V2_TL.keras'\r\n> \r\n> load_test = tf.keras.models.load_model(load_model_filename)\r\n> ```\r\n> \r\n> ```\r\n> TypeError Traceback (most recent call last)\r\n> [<ipython-input-3-9a9c94514126>](https://localhost:8080/#) in <cell line: 3>()\r\n> 1 load_model_filename = '/content/mydata/Models/Test_Space/OG_ConvNeXTBase_V2_TL.keras'\r\n> 2 \r\n> ----> 3 load_test = tf.keras.models.load_model(load_model_filename)\r\n> \r\n> [/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_api.py](https://localhost:8080/#) in load_model(filepath, custom_objects, compile, safe_mode, **kwargs)\r\n> 228 f\"with the native Keras format: {list(kwargs.keys())}\"\r\n> 229 )\r\n> --> 230 return saving_lib.load_model(\r\n> 231 filepath,\r\n> 232 custom_objects=custom_objects,\r\n> \r\n> [/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py](https://localhost:8080/#) in load_model(filepath, custom_objects, compile, safe_mode)\r\n> 273 \r\n> 274 except Exception as e:\r\n> --> 275 raise e\r\n> 276 else:\r\n> 277 return model\r\n> \r\n> [/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py](https://localhost:8080/#) in load_model(filepath, custom_objects, compile, safe_mode)\r\n> 238 # Construct the model from the configuration file in the archive.\r\n> 239 with ObjectSharingScope():\r\n> --> 240 model = deserialize_keras_object(\r\n> 241 config_dict, custom_objects, safe_mode=safe_mode\r\n> 242 )\r\n> \r\n> [/usr/local/lib/python3.10/dist-packages/keras/src/saving/serialization_lib.py](https://localhost:8080/#) in deserialize_keras_object(config, custom_objects, safe_mode, **kwargs)\r\n> 702 safe_mode_scope = SafeModeScope(safe_mode)\r\n> 703 with custom_obj_scope, safe_mode_scope:\r\n> --> 704 instance = cls.from_config(inner_config)\r\n> 705 build_config = config.get(\"build_config\", None)\r\n> 706 if build_config:\r\n> \r\n> [/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py](https://localhost:8080/#) in from_config(cls, config, custom_objects)\r\n> 3225 # Revive Functional model\r\n> 3226 # (but not Functional subclasses with a custom __init__)\r\n> -> 3227 inputs, outputs, layers = functional.reconstruct_from_config(\r\n> 3228 config, custom_objects\r\n> 3229 )\r\n> \r\n> [/usr/local/lib/python3.10/dist-packages/keras/src/engine/functional.py](https://localhost:8080/#) in reconstruct_from_config(config, custom_objects, created_layers)\r\n> 1500 while layer_nodes:\r\n> 1501 node_data = layer_nodes[0]\r\n> -> 1502 if process_node(layer, node_data):\r\n> 1503 layer_nodes.pop(0)\r\n> 1504 else:\r\n> \r\n> [/usr/local/lib/python3.10/dist-packages/keras/src/engine/functional.py](https://localhost:8080/#) in process_node(layer, node_data)\r\n> 1440 input_tensors\r\n> 1441 )\r\n> -> 1442 output_tensors = layer(input_tensors, **kwargs)\r\n> 1443 \r\n> 1444 # Update node index map.\r\n> \r\n> TypeError: 'str' object is not callable\r\n> ```\r\n> \r\n> I hope this is helpful for re-creating the error\r\n\r\nHas someone found any solution?? I have exactly the same error, I don't know what to do :(",
"The LayerScale layer is not exposed using @keras_export in https://github.com/keras-team/keras/blob/b80dd12da9c0bc3f569eca3455e77762cf2ee8ef/keras/applications/convnext.py#L199 \r\nA work around this error would be to copy the custom layers LayerScale and also StochasticDepth to a local script and import them, and then to load the model as follows:\r\n\r\n`best_model = models.load_model(\r\n <path_to_checkpoint>, compile=False,\r\n custom_objects={\r\n \"LayerScale\": LayerScale,\r\n \"StochasticDepth\": StochasticDepth\r\n }\r\n )`",
"> The LayerScale layer is not exposed using @keras_export in https://github.com/keras-team/keras/blob/b80dd12da9c0bc3f569eca3455e77762cf2ee8ef/keras/applications/convnext.py#L199 A work around this error would be to copy the custom layers LayerScale and also StochasticDepth to a local script and import them, and then to load the model as follows:\r\n> \r\n> `best_model = models.load_model( <path_to_checkpoint>, compile=False, custom_objects={ \"LayerScale\": LayerScale, \"StochasticDepth\": StochasticDepth } )`\r\n\r\ni did this and it is working well. Thanks baniks!",
"Loading VIT model in keras error coming \r\n\r\n[ValueError: Unknown layer: 'ClassToken'. Please ensure you are using a `keras.utils.custom_object_scope` and that this object is included in the scope. See ](https://www.tensorflow.org/guide/keras/save_and_serialize%3C/span%3E%3Cspan)https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details.",
"> The LayerScale layer is not exposed using @keras_export in https://github.com/keras-team/keras/blob/b80dd12da9c0bc3f569eca3455e77762cf2ee8ef/keras/applications/convnext.py#L199 A work around this error would be to copy the custom layers LayerScale and also StochasticDepth to a local script and import them, and then to load the model as follows:\r\n> \r\n> `best_model = models.load_model( <path_to_checkpoint>, compile=False, custom_objects={ \"LayerScale\": LayerScale, \"StochasticDepth\": StochasticDepth } )`\r\n\r\n\r\nThis solution by @baniks works perfectly!"
] | 2023-02-22T01:04:43 | 2024-04-15T00:24:47 | 2023-03-15T12:38:48 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
2.11
### Custom Code
Yes
### OS Platform and Distribution
Linux Ubuntu
### 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
Nvidia A100
### Current Behaviour?
```shell
Can't load tensorflow saved model (.h5) for Convnext model ..
```
### Standalone code to reproduce the issue
```shell
model download link - https://easyupload.io/mn1i8h
```
### Relevant log output
```shell
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[13], line 47
43 models=["EfficientNetv2L_avg.h5",
44 "convnext_tiny_avg.h5","convnext_Large_avg.h5"]
46 for model in models:
---> 47 _f1_score=evaluate_f1_for_tf_model(model_path=model,validation_dataset=sub_sample_validation_dataset,y_true=y_true)
48 print(f"f1 score of the {model} is {_f1_score}")
Cell In[13], line 23, in evaluate_f1_for_tf_model(model_path, validation_dataset, y_true, show_confusion_report)
21 # Wrap the loaded model inside the strategy scope to distribute it across the GPUs
22 with strategy.scope():
---> 23 model = tf.keras.models.load_model(model_path)
25 #show model arch
26 print(model.summary())
File /gpfslocalsup/pub/anaconda-py3/2022.05/envs/tensorflow-gpu-2.11.0+py3.10.8/lib/python3.10/site-packages/keras/utils/traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
67 filtered_tb = _process_traceback_frames(e.__traceback__)
68 # To get the full stack trace, call:
69 # `tf.debugging.disable_traceback_filtering()`
---> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb
File /gpfslocalsup/pub/anaconda-py3/2022.05/envs/tensorflow-gpu-2.11.0+py3.10.8/lib/python3.10/site-packages/keras/saving/legacy/serialization.py:385, in class_and_config_for_serialized_keras_object(config, module_objects, custom_objects, printable_module_name)
381 cls = object_registration.get_registered_object(
382 class_name, custom_objects, module_objects
383 )
384 if cls is None:
--> 385 raise ValueError(
386 f"Unknown {printable_module_name}: '{class_name}'. "
387 "Please ensure you are using a `keras.utils.custom_object_scope` "
388 "and that this object is included in the scope. See "
389 "https://www.tensorflow.org/guide/keras/save_and_serialize"
390 "#registering_the_custom_object for details."
391 )
393 cls_config = config["config"]
394 # Check if `cls_config` is a list. If it is a list, return the class and the
395 # associated class configs for recursively deserialization. This case will
396 # happen on the old version of sequential model (e.g. `keras_version` ==
397 # "2.0.6"), which is serialized in a different structure, for example
398 # "{'class_name': 'Sequential',
399 # 'config': [{'class_name': 'Embedding', 'config': ...}, {}, ...]}".
ValueError: Unknown layer: 'LayerScale'. Please ensure you are using a `keras.utils.custom_object_scope` and that this object is included in the scope. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details.
```
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"Hi @penpornk Can you please review this PR ? Thank you!",
"@penpornk , could you please review this PR? Thanks!",
"Hi @ShengYang1 Can you please check @penpornk's comments and keep us posted ? Thank you!",
"> Hi @ShengYang1 Can you please check @penpornk's comments and keep us posted ? Thank you!\r\n\r\nAll comments point to one same issue. My reply is:\r\nPossibly we can not do like that, as the signature of `AddNVariant` doesn’t match `VariantBinaryAddFunc`, following existing implementation, we have to add a lambda capture for this. It’s same with original code: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/c/kernels_experimental.cc#L485."
] | 2023-02-22T00:29:00 | 2023-04-18T19:31:26 | 2023-04-18T19:31:25 | CONTRIBUTOR | null | false | {
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} | When the input of AddN is nested variant(eg, nested TensorList), for current implementation, `TF_AddNVariant` will leave a variant tensor for `binary_add_func` used by vendor extension, which cannot be handled in extension as variant class is opaque for extension. So recursively handling for variant is needed, and `TF_ZerosLikeVariant` did same work, code [link](https://github.com/tensorflow/tensorflow/blob/e35b2853331f3caed7612f2ae0596fd2fe1353f3/tensorflow/c/kernels_experimental.cc#L529) for `TF_ZerosLikeVariant`. | {
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"The start of these failures was bisected to https://github.com/tensorflow/tensorflow/commit/84f40925e929d05e72ab9234e53c729224e3af38",
"Changing the installed version of the Python protobuf package did not make a difference, 3.20.1, 3.20.3, 4.21.9, 4.21.12 all the same failure. This is perhaps not surprising as the tests are c++ based.",
"This failure is seen inside the manylinux2014_aarch64 docker container which uses gcc 10.2.1. I tested updating gcc to 10.4.0 and the tests then passed. So this issue seems to be an interaction between protobuf and gcc.",
"Possible routes to an expected resolution are\r\n1) Create a new docker container based on manylinux2014_aarch64 container, but with gcc updated to 10.4.0\r\n2) Move build onto new docker container built by Linaro that has been developed to allow building with CXX11 Dual ABI enabled.",
"Solution 1) above is dead end work, it would likely only be used for the 2.12 release branch.\r\n2) needs work to update the Github actions and co-ordinating a release with tensorflow-io as well as work to the Linaro CI jobs.",
"Hi @elfringham ,\r\n\r\nThanks for the tests and inputs. As build configurations for Tf 2.12v not updated in documentation shall we ensure GCC version to be 10.4.0 as it seems anything below this not working as per your tests? ",
"Hi @SuryanarayanaY well apart from suspecting that gcc 10.3.x would also work, but that is not tested.\r\nUnfortunately when I suggested this I had not realised that there was not a suitably patched release of gcc-10 > 10.2.1 that would be easily usable in CentOS 7. So that makes this option much less desirable. I could try gcc-11 but that worries me as despite there being a source package for it, there are no matching AARCH64 binaries, and of course it has not been validated for building TensorFlow.\r\nThe test I did with gcc 10.4.0 was by installing it from conda and produced binaries that were not compatible with manylinux2014_aarch64.",
"Going with the Linaro developed Docker container. https://github.com/tensorflow/tensorflow/pull/59823",
"Hi @sachinprasadhs, thanks for assigning. How can I help with this?",
"These unit test failures are no longer seen on 2.12 with the move the Dual ABI enabled Docker container build for AARCH64.",
"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/59765\">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/59765\">No</a>\n"
] | 2023-02-21T22:35:03 | 2023-03-02T17:00:10 | 2023-03-02T17:00:06 | CONTRIBUTOR | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
2.12.0-rc0
### Custom Code
No
### OS Platform and Distribution
CentOS 7
### Mobile device
n/a
### Python version
3.8.10
### Bazel version
5.3.0
### GCC/Compiler version
10.2.1
### CUDA/cuDNN version
n/a
### GPU model and memory
n/a
### Current Behaviour?
```shell
Executed 3688 out of 3688 tests: 3244 tests pass and 444 fail locally.
```
### Standalone code to reproduce the issue
```shell
bazel test --test_timeout=300,500,-1,-1 \
--flaky_test_attempts=3 \
--test_output=all \
--cache_test_results=no \
--config=nonccl \
--config=mkl_aarch64_threadpool \
--copt="-mtune=generic" \
--copt="-march=armv8-a" \
--copt="-O3" \
--verbose_failures \
--test_env=TF_ENABLE_ONEDNN_OPTS=1 \
--build_tag_filters=-no_oss,-oss_serial,-gpu,-tpu,-benchmark-test,-v1only,-no_aarch64,-no_oss_py38,-no_oss_py39,-no_oss_py310 \
--test_tag_filters=-no_oss,-oss_serial,-gpu,-tpu,-benchmark-test,-v1only,-no_aarch64,-no_oss_py38,-no_oss_py39,-no_oss_py310 \
--build_tests_only \
-- ${DEFAULT_BAZEL_TARGETS} \
-//tensorflow/lite/... -//tensorflow/compiler/mlir/lite/tests:const-fold.mlir.test -//tensorflow/core/distributed_runtime/integration_test:c_api_session_coordination_test_cpu -//tensorflow/core/platform:ram_file_system_test -//tensorflow/python/client:session_list_devices_test -//tensorflow/python/compiler/xla:xla_test_cpu -//tensorflow/python/data/experimental/kernel_tests:checkpoint_input_pipeline_hook_test -//tensorflow/python/data/kernel_tests:iterator_test_cpu -//tensorflow/python/distribute:parameter_server_strategy_test_cpu -//tensorflow/python/distribute:parameter_server_strategy_test_2gpu -//tensorflow/python/kernel_tests/linalg:matrix_triangular_solve_op_test -//tensorflow/python/training:server_lib_test -//tensorflow/tsl/framework/convolution:spatial_convolutions_test \
-//tensorflow/compiler/mlir/tfr/examples/mnist:mnist_ops_test -//tensorflow/core/grappler/optimizers:auto_mixed_precision_test_cpu -//tensorflow/core/grappler/optimizers:remapper_test_cpu -//tensorflow/python/kernel_tests/nn_ops:atrous_conv2d_test_cpu -//tensorflow/python/kernel_tests/nn_ops:conv_ops_test_cpu
```
### Relevant log output
```shell
No useful log output, tests immediately segfault.
```
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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/59764/checks?check_run_id=11502725901) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request."
] | 2023-02-21T21:07:54 | 2023-02-21T21:08:54 | 2023-02-21T21:08:54 | NONE | null | false | {
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"Hi, @fede72bari \r\n\r\nApologize for the delay and I would suggest you to please try to execute below commands and please create new virtual environment with python version 3.8 and check whether is it resolving your issue or not ? If issue still persists please let us know with complete error log to find out the root cause for your issue. Thank you!\r\n\r\nCreate a conda environment:\r\n```\r\nconda create --name tf python=3.8\r\nconda activate tf\r\n```\r\n\r\n```\r\npip install --upgrade pip\r\npip uninstall keras\r\npip install tensorflow\r\n```\r\n",
"It turned out that reinstalling everything was not enough; probably there was a synch problem with the updating of the kernel used fo rthe jupyter notebook. So in case other will come into the same troble I suggest to reinstall tensorflow and then create a new kernel for jupyter in the same new Anaconda environment.",
"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/59763\">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/59763\">No</a>\n"
] | 2023-02-21T17:55:01 | 2023-03-08T09:13:53 | 2023-03-08T09:13:50 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
No
### Source
source
### Tensorflow Version
2.11
### Custom Code
Yes
### OS Platform and Distribution
Windows
### 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 Behaviour?
```shell
I had to update Tensorflow to the currently latest version 2.11. when importing i get "AttributeError: module 'tensorflow._api.v2.compat.v2.internal' has no attribute 'register_load_context_function'". I have also completely reinstalled a full anaconda environment and downgraded Python to the version compatible with the latest of Tensorflow and then "pip3 install Tensorflow==2.11". Got the same error. I have no other ideas.
The full error log is the following
import tensorflow as tf
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_432\3752927832.py in <module>
----> 1 import tensorflow as tf
~\AppData\Roaming\Python\Python310\site-packages\tensorflow\__init__.py in <module>
467 if hasattr(_current_module, "keras"):
468 try:
--> 469 _keras._load()
470 except ImportError:
471 pass
~\AppData\Roaming\Python\Python310\site-packages\tensorflow\python\util\lazy_loader.py in _load(self)
39 """Load the module and insert it into the parent's globals."""
40 # Import the target module and insert it into the parent's namespace
---> 41 module = importlib.import_module(self.__name__)
42 self._parent_module_globals[self._local_name] = module
43
~\anaconda3\envs\mltrade2\lib\importlib\__init__.py in import_module(name, package)
124 break
125 level += 1
--> 126 return _bootstrap._gcd_import(name[level:], package, level)
127
128
~\anaconda3\envs\mltrade2\lib\site-packages\keras\__init__.py in <module>
19 """
20 from keras import distribute
---> 21 from keras import models
22 from keras.engine.input_layer import Input
23 from keras.engine.sequential import Sequential
~\anaconda3\envs\mltrade2\lib\site-packages\keras\models\__init__.py in <module>
16
17
---> 18 from keras.engine.functional import Functional
19 from keras.engine.sequential import Sequential
20 from keras.engine.training import Model
~\anaconda3\envs\mltrade2\lib\site-packages\keras\engine\functional.py in <module>
32 from keras.engine import input_spec
33 from keras.engine import node as node_module
---> 34 from keras.engine import training as training_lib
35 from keras.engine import training_utils
36 from keras.saving.legacy import serialization
~\anaconda3\envs\mltrade2\lib\site-packages\keras\engine\training.py in <module>
43 from keras.saving.experimental import saving_lib
44 from keras.saving.legacy import hdf5_format
---> 45 from keras.saving.legacy import save
46 from keras.saving.legacy import saving_utils
47 from keras.saving.legacy import serialization
~\anaconda3\envs\mltrade2\lib\site-packages\keras\saving\legacy\save.py in <module>
22 from keras.saving.legacy import serialization
23 from keras.saving.legacy.saved_model import load as saved_model_load
---> 24 from keras.saving.legacy.saved_model import load_context
25 from keras.saving.legacy.saved_model import save as saved_model_save
26 from keras.utils import traceback_utils
~\anaconda3\envs\mltrade2\lib\site-packages\keras\saving\legacy\saved_model\load_context.py in <module>
66
67
---> 68 tf.__internal__.register_load_context_function(in_load_context)
AttributeError: module 'tensorflow._api.v2.compat.v2.__internal__' has no attribute 'register_load_context_function'
```
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
```
### Relevant log output
_No response_</details> | {
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"Hi, @ignvinay \r\n\r\nApologize for the delay and I was able to replicate same issue on Google Colab which uses `Ubuntu OS` so it's working as expected, here is [gist-file ](https://colab.research.google.com/gist/gaikwadrahul8/284cad934c3d2456efbe6184af22f682/lang_c.ipynb)so we'll try on Windows 10 and we'll let you know soon and Thank you for noticing this issue. Thank you!",
"Hi, i got the same issue on Win10 and Visual Studio 2019 and the prebuild Tensorflow lib in Version 2.11 (https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-windows-x86_64-2.11.0.zip). \r\n\r\nAlso tried older versions of TF like 2.6 and there is the same problem. \r\n\r\nThere is no tensorflow/c/tf_buffer.h. The file doesnt exist.\r\n\r\nI downloaded the linux version and here the tf_buffer.h exists.\r\n\r\n\r\nPlease help :(",
"Hi Zarzaro and gaikwadrahul,\r\n I could able to get it working. The include folder is missing two folders which are required for building.\r\nThese two folders i picked it from linux version of package as include files are more of header files, which would be independent of OS.\r\n\r\nThe include folder --> tensorflow ---> should look like this :\r\n\r\n\r\nJust in case if you still need whole include folder, i am attaching here for your reference. Please replace this include folder with include folder of TensorFlow.\r\n[include.zip](https://github.com/tensorflow/tensorflow/files/10899294/include.zip)\r\n\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/59762\">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/59762\">No</a>\n",
"Hm okay at least that seems to be a solution. But its not straight forward. Is that a build bug? Are we the first two persons that are using the prebuilt lib from tensorflow for Windows? A prebuilt lib should be straight forward to use in my opinion. \r\n\r\nI even tried to build the dll etc from tensorflow since since 3 days without final success. The bazel build of dll, lib and headers is finally completed successfully but i get errors in Visual Studio as well when i try to run a simple hello world project. \r\n\r\nIt seems Tensorflow is not meant to be used in c++. Very little documentation for this inconvenient build process. ",
"Yeah, it's cool that there's a workaround, but it would be much better if we could find the script that makes these, and fix it. ",
"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/59762\">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/59762\">No</a>\n",
"also missing from the include \"patch\" is file \"tensorflow/tsl/c/tsl_status.h\" (there might be others)"
] | 2023-02-21T17:44:16 | 2023-09-23T17:59:31 | 2023-04-12T01:53:41 | NONE | null | null | null | Please go to Stack Overflow for help and support:
https://stackoverflow.com/questions/tagged/tensorflow
If you open a GitHub issue, here is our policy:
1. It must be a bug, a feature request, or a significant problem with the
documentation (for small docs fixes please send a PR instead).
2. The form below must be filled out.
3. It shouldn't be a TensorBoard issue. Those go
[here](https://github.com/tensorflow/tensorboard/issues).
**Here's why we have that policy**: TensorFlow developers respond to issues. We want to focus on work that benefits the whole community, e.g., fixing bugs and adding features. Support only helps individuals. GitHub also notifies thousands of people when issues are filed. We want them to see you communicating an interesting problem, rather than being redirected to Stack Overflow.
------------------------
### System information
- **Have I written custom code (as opposed to using a stock example script
provided in TensorFlow)**: Its tensorflow provided code as given in https://www.tensorflow.org/install/lang_c
- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**: windows 10
- **Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue
happens on a mobile device**: Laptop
- **TensorFlow installed from (source or binary)**: static library from https://www.tensorflow.org/install/lang_c
- **TensorFlow version (use command below)**: 2.11
- **Python version**: NA
- **Bazel version (if compiling from source)**: NA
- **GCC/Compiler version (if compiling from source)**: 12.2
- **CUDA/cuDNN version**:NA
- **GPU model and memory**: NA
- **Exact command to reproduce**: Took static library from tensorflow ebsite from link below : https://www.tensorflow.org/install/lang_c
Have taken windows version of https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-windows-x86_64-2.11.0.zip (CPU version)
unzipped and created hello_tf.c as mentioned in same webpage.
Compiled the hello_tf.c as below :
gcc hello_tf.c -I D:\tensorflow_lib\include -lD:\tensorflow_lib\lib\tensorflow -o hello_tf
Got error as
_In file included from hello_tf.c:2:
D:\tensorflow_lib\include/tensorflow/c/c_api.h:23:10: fatal error: tensorflow/c/tf_buffer.h: No such file or directory
23 | #include "tensorflow/c/tf_buffer.h"
| ^~~~~~~~~~~~~~~~~~~~~~~~~~
compilation terminated._
Then took tf_buffer.h from github repo , to see if the error is only dependent on tf_buffer.h, but then got another error :
_D:\tensorflow_lib>gcc hello_tf.c -I D:\tensorflow_lib\include -lD:\tensorflow_lib\lib\tensorflow -o hello_tf
In file included from D:\tensorflow_lib\include/tensorflow/c/tf_tstring.h:19,
from D:\tensorflow_lib\include/tensorflow/c/c_api.h:27,
from hello_tf.c:2:
D:\tensorflow_lib\include/tensorflow/core/platform/ctstring.h:19:10: fatal error: tensorflow/tsl/platform/ctstring.h: No such file or directory
19 | #include "tensorflow/tsl/platform/ctstring.h"
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
compilation terminated._
You can collect some of this information using our environment capture script:
https://github.com/tensorflow/tensorflow/tree/master/tools/tf_env_collect.sh
You can obtain the TensorFlow version with:
```bash
python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"
```
### Describe the problem
Describe the problem clearly here. Be sure to convey here why it's a bug in TensorFlow or a feature request.
Took static library from tensorflow ebsite from link below : https://www.tensorflow.org/install/lang_c
Have taken windows version of https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-windows-x86_64-2.11.0.zip (CPU version)
unzipped and created hello_tf.c as mentioned in same webpage.
Compiled the hello_tf.c as below :
gcc hello_tf.c -I D:\tensorflow_lib\include -lD:\tensorflow_lib\lib\tensorflow -o hello_tf
Got error as
_In file included from hello_tf.c:2:
D:\tensorflow_lib\include/tensorflow/c/c_api.h:23:10: fatal error: tensorflow/c/tf_buffer.h: No such file or directory
23 | #include "tensorflow/c/tf_buffer.h"
| ^~~~~~~~~~~~~~~~~~~~~~~~~~
compilation terminated._
Then took tf_buffer.h from github repo , to see if the error is only dependent on tf_buffer.h, but then got another error :
_D:\tensorflow_lib>gcc hello_tf.c -I D:\tensorflow_lib\include -lD:\tensorflow_lib\lib\tensorflow -o hello_tf
In file included from D:\tensorflow_lib\include/tensorflow/c/tf_tstring.h:19,
from D:\tensorflow_lib\include/tensorflow/c/c_api.h:27,
from hello_tf.c:2:
D:\tensorflow_lib\include/tensorflow/core/platform/ctstring.h:19:10: fatal error: tensorflow/tsl/platform/ctstring.h: No such file or directory
19 | #include "tensorflow/tsl/platform/ctstring.h"
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
compilation terminated._
### Source code / 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. Try to provide a reproducible test case that is the bare minimum necessary to generate the problem.
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"@sachinprasadhs,\r\nI was able to reproduce the issue on tensorflow [v2.9](https://colab.research.google.com/gist/tilakrayal/75e568b061d6bafa8abb6593ee68924a/untitled986.ipynb), v2.11 and tf-nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/b11a9fb39f315458e5de55fcee077fa4/untitled984.ipynb)."
] | 2023-02-21T17:38:45 | 2023-04-27T18:37:51 | null | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
tf 2.9.1
### Custom Code
Yes
### OS Platform and Distribution
Linux ubuntu 18.04
### 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 Behaviour?
```shell
I tested the function against numpy and it throws error when the `ndim` of the input tensors is greater than 2.
```
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
try:
a = tf.constant(np.arange(100).reshape(2, 5, 2, 5))
b = tf.constant(np.arange(24).reshape(2, 3, 4))
print(a.ndim) # 4
print(b.ndim) # 3
y = tf.experimental.numpy.kron(a, b)
print(y.shape)
except:
print("Can't use tf.experimental.numpy.kron on multi-dimensional arrays")
x = np.arange(100).reshape(2, 5, 2, 5)
y = np.arange(24).reshape(2, 3, 4)
print(x.ndim) # 4
print(y.ndim) # 3
z = np.kron(x, y)
print(z.shape) # (2, 10, 6, 20)
```
### Relevant log output
```shell
When I remove the try block I get the error saying:
`UnimplementedError: {{function_node __wrapped__Mul_device_/job:localhost/replica:0/task:0/device:CPU:0}} Broadcast between [2,1,5,1,2,1,5,1] and [1,1,1,2,1,3,1,4] is not supported yet. [Op:Mul]`
```
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"Hi @cantonios ,\r\n\r\nCould you please review this PR when you have some time?\r\n\r\nThank you very much!"
] | 2023-02-21T16:59:41 | 2023-03-02T11:30:00 | 2023-03-02T11:30:00 | CONTRIBUTOR | null | false | {
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} | Test case `//tensorflow/python/framework:tensor_util_test` failed on s390x (big-endian arch) because in `testHalf()` function the `tensor_content` data which is used for comparison with the tensor proto string is hard-coded in little-endian format.
This PR followed the existing pattern in `testFloatMutateArray()` function to add an endianness check in `testHalf()` function and choose the corresponding `tensor_content` data which is in consistent with the platform endianness.
The above mentioned test case will pass on both little-endian and big-endian systems after applying the code change. | {
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} | The dtype to be changed to F16 from F8 at line number 85 as the function converts F16 to F32. Merging this closes the issue #59748 | {
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"Hi, thanks for taking the time to providing complete working code to reproduce the issue.\r\n\r\nThe problem here isn't the RNN, it's the shape of your input data:\r\n\r\nhttps://colab.research.google.com/drive/1pkFuU_nMyJnVHF3TvCSQ49e-IORFu3PQ?authuser=1#scrollTo=lIYdn1woOS1n\r\n\r\nKeras, expects RNN inputs to have shape `(batch, time, features)`, your data with shape `(10k, 1, 1)`\r\n\r\nIt only ever sees one time step, so it never has a chance to learn how to handle the previous time-step.\r\n\r\nTry this tool for a fix: https://www.tensorflow.org/api_docs/python/tf/keras/utils/timeseries_dataset_from_array",
"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/59757\">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/59757\">No</a>\n"
] | 2023-02-21T11:51:56 | 2023-02-22T00:23:46 | 2023-02-22T00:23:44 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
No
### Source
binary
### Tensorflow Version
2.7.0
### Custom Code
Yes
### OS Platform and Distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
Python 3.9.15
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
```shell
code generating the issue is reported here:
https://stackoverflow.com/questions/75496612/keras-simplernncell-appears-to-fail-to-distribute-learning-among-all-its-weights.
basically I expect that all weights in my model change when changing the epochs number, while what I observe is that the state weight (in my simple code the state matrix reduces to only one weight) stays stuck to its initialization.
```
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow_addons.layers import ESN
from tensorflow_addons.rnn import ESNCell
from tensorflow.keras.layers import RNN
from tensorflow.keras.layers import SimpleRNN, SimpleRNNCell
from sklearn.preprocessing import MinMaxScaler
from tensorflow import random as rnd
# Fix the seed
rnd.set_seed(0)
# The data can be downloaded from https://mantas.info/wp/wp-content/uploads/simple_esn/MackeyGlass_t17.txt
data = np.loadtxt('MackeyGlass_t17.txt')
# Normalize
scaler = MinMaxScaler(feature_range=(0, 1))
scaled = scaler.fit_transform(data.reshape(-1, 1))
# Split Dataset in Train and Test
train, test = scaled[0:-100], scaled[-100:]
# Split into input and output
train_X, train_y = train[:-1], train[1:]
test_X, test_y = test[:-1], test[1:]
# Reshaping
train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))
test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))
# Batch and epochs
batch_size = 20
epochs = 3
# Design and run the model
model = Sequential()
model.add(RNN(SimpleRNNCell(1)))
#model.add(ESN(units = 12, spectral_radius = spectral_radius, leaky=0.75, connectivity = 0.9)) # this line works exactly like the next one
#model.add(RNN(ESNCell(12, spectral_radius = spectral_radius, leaky=0.75, connectivity = 0.9)))
model.add(Dense(train_y.shape[1]))
model.compile(loss='huber', optimizer='adam')
model.fit(train_X, train_y, epochs=epochs, batch_size=batch_size, validation_data=(test_X, test_y), verbose=0, shuffle=False)
# Print the weights of the dense layer
#print(model.layers[1].get_weights())
#for layer in model.layers: print(layer.get_config(), layer.get_weights())
for layer in model.layers: print(layer.get_weights())
```
### Relevant log output
```shell
f I run this code with 2 epochs I receive the following output:
[array([[-0.8942287]], dtype=float32), array([[1.]], dtype=float32), array([0.05435111], dtype=float32)] [array([[-1.272426]], dtype=float32), array([0.04711587], dtype=float32)]
If I run this code with 3 epochs I receive the following output:
[array([[-0.89395165]], dtype=float32), array([[1.]], dtype=float32), array([0.06734365], dtype=float32)] [array([[-1.2927996]], dtype=float32), array([0.05247825], dtype=float32)]
so the state weight (array([[1.]]) is the only one not changing.
```
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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/59756/checks?check_run_id=11489268079) 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 @Johnoris Can you please sign CLA. Thank you!",
"Please don't spam"
] | 2023-02-21T11:51:38 | 2023-02-26T00:22:03 | 2023-02-26T00:22:03 | NONE | spam | false | {
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} | Images that are represented using floating point values are expected to have values in the range [0,1) for the function `convert_image_dtype()`. But this is not scaled when the case `image `argument is of `float ` and `dtype` argument is `int` arises. Hence modified the code to bring the input values within [0,1). This also shall fix the issue #58749 . | {
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"@tilakrayal, I was able to replicate the issue in ubuntu 22.04 and Google Colab. Please find the gist for the same in [TF v2.11](https://colab.sandbox.google.com/gist/synandi/8823760deb26897ee84953c054ea0070/59803_2-11.ipynb) and [tf-nightly](https://colab.sandbox.google.com/gist/synandi/99f88fa17d0352dd40f850fe08658320/59754_nightly.ipynb). Thank you!",
"Here and standalone code to reproduce the issue, which only happens on GPU:\r\n**not gpu registered kernels for complex numbers ( tf.complex64 and tf.complex128) for the gather_nd method of tf.Variable**\r\n\r\nimport numpy as np\r\nimport tensorflow as tf\r\nprint(tf.__version__)\r\n\r\nwith tf.device(\"gpu:0\"):\r\n v = tf.Variable(np.array([1, 2, 3], np.complex64))\r\n print(v.gather_nd(indices = [0]))",
"@Ivanlh20, Apologies for the delay. You are trying to pass an invalid dtype of an argument to `gather_nd`. complex64 and complex128 are not registered on GPU Kernel hence you are seeing this message. Please use a compatible dtype to avoid the issue. Thank you!\r\n> Op: ResourceGatherNd\r\nNode attrs: dtype=DT_COMPLEX64, Tindices=DT_INT64\r\nRegistered kernels:\r\n device='GPU'; dtype in [DT_INT8]; Tindices in [DT_INT64]\r\n device='GPU'; dtype in [DT_INT8]; Tindices in [DT_INT32]\r\n device='GPU'; dtype in [DT_UINT8]; Tindices in [DT_INT64]\r\n device='GPU'; dtype in [DT_UINT8]; Tindices in [DT_INT32]\r\n device='GPU'; dtype in [DT_INT16]; Tindices in [DT_INT64]\r\n device='GPU'; dtype in [DT_INT16]; Tindices in [DT_INT32]\r\n device='GPU'; dtype in [DT_UINT16]; Tindices in [DT_INT64]\r\n device='GPU'; dtype in [DT_UINT16]; Tindices in [DT_INT32]\r\n device='GPU'; dtype in [DT_UINT32]; Tindices in [DT_INT64]\r\n device='GPU'; dtype in [DT_UINT32]; Tindices in [DT_INT32]\r\n device='GPU'; dtype in [DT_INT64]; Tindices in [DT_INT64]\r\n device='GPU'; dtype in [DT_INT64]; Tindices in [DT_INT32]\r\n device='GPU'; dtype in [DT_UINT64]; Tindices in [DT_INT64]\r\n device='GPU'; dtype in [DT_UINT64]; Tindices in [DT_INT32]\r\n device='GPU'; dtype in [DT_DOUBLE]; Tindices in [DT_INT64]\r\n device='GPU'; dtype in [DT_DOUBLE]; Tindices in [DT_INT32]\r\n device='GPU'; dtype in [DT_FLOAT]; Tindices in [DT_INT64]\r\n device='GPU'; dtype in [DT_FLOAT]; Tindices in [DT_INT32]\r\n device='GPU'; dtype in [DT_HALF]; Tindices in [DT_INT64]\r\n device='GPU'; dtype in [DT_HALF]; Tindices in [DT_INT32]\r\n device='CPU'; dtype in [DT_VARIANT]; Tindices in [DT_INT64]\r\n device='CPU'; dtype in [DT_VARIANT]; Tindices in [DT_INT32]\r\n device='CPU'; dtype in [DT_RESOURCE]; Tindices in [DT_INT64]\r\n device='CPU'; dtype in [DT_RESOURCE]; Tindices in [DT_INT32]\r\n ...",
"Hi, \r\nI understand that, however, that option is available only on CPU and not on GPU:\r\n\r\n device='CPU'; dtype in [DT_COMPLEX128]; Tindices in [DT_INT64]\r\n device='CPU'; dtype in [DT_COMPLEX128]; Tindices in [DT_INT32]\r\n device='CPU'; dtype in [DT_COMPLEX64]; Tindices in [DT_INT64]\r\n device='CPU'; dtype in [DT_COMPLEX64]; Tindices in [DT_INT32]\r\n\r\nand I need to use **gather_nd** on GPU complex tensorflow arrays. I do not understand why it is not implemented on GPU\r\nbecause there is no problem using **scatter_nd_update** on variables with complex data on GPU.\r\n",
"Hi, The decision is to support only limited dtype is due to the below implementation details mentioned.\r\nhttps://github.com/tensorflow/tensorflow/blob/63cb0d3dad0fe3bb1003f9b856812ade592b807a/tensorflow/core/kernels/gather_nd_op.cc#L65-L72",
"This issue has been marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.",
"Hello,\r\n\r\nThank you for your reply. However, I noticed an issue in line 72. While the comment suggests that the same should be applied to the GPU kernel, the code only redirects certain dtypes for the GPU kernel.\r\n\r\nIt appears that the redirection for complex<float> and complex<double> is only implemented for the CPU kernel and not for the GPU kernel. This could be an oversight or a decision made for performance reasons.\r\n\r\nTo ensure consistency, it would be beneficial to redirect these dtypes for the GPU kernel as well. Do you know if there are plans to implement this in a future version of TensorFlow?",
"I want to add that the complex number is widely used in physics or scientific computation in general. The direct creation and manipulation of complex number in the GPU kernel is quite important to us enabling advanced microscopy calculations with Tensorflow, otherwise strongly limiting the performance. Thus, I also hope to see this can be supported."
] | 2023-02-21T09:50:12 | 2023-05-02T13:52:52 | null | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
No
### Source
source
### Tensorflow Version
tf 2.11
### 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 Behaviour?
```shell
not gpu registered kernels for complex number ( tf.complex64 and tf.complex128) for the gather_nd method of tf.Variable. However, it works without problems with CPU
```
### Standalone code to reproduce the issue
```shell
v = tf.Variable(np.array([1, 2, 3], np.complex64))
print(v.gather_nd(indices = [0]))
```
### Relevant log output
```shell
Colocation members, user-requested devices, and framework assigned devices, if any:
resource (_Arg) framework assigned device=/job:localhost/replica:0/task:0/device:GPU:0
ResourceGatherNd (ResourceGatherNd) /job:localhost/replica:0/task:0/device:GPU:0
Op: ResourceGatherNd
Node attrs: dtype=DT_COMPLEX64, Tindices=DT_INT64
Registered kernels:
device='GPU'; dtype in [DT_INT8]; Tindices in [DT_INT64]
device='GPU'; dtype in [DT_INT8]; Tindices in [DT_INT32]
device='GPU'; dtype in [DT_UINT8]; Tindices in [DT_INT64]
device='GPU'; dtype in [DT_UINT8]; Tindices in [DT_INT32]
device='GPU'; dtype in [DT_INT16]; Tindices in [DT_INT64]
device='GPU'; dtype in [DT_INT16]; Tindices in [DT_INT32]
device='GPU'; dtype in [DT_UINT16]; Tindices in [DT_INT64]
device='GPU'; dtype in [DT_UINT16]; Tindices in [DT_INT32]
device='GPU'; dtype in [DT_UINT32]; Tindices in [DT_INT64]
device='GPU'; dtype in [DT_UINT32]; Tindices in [DT_INT32]
device='GPU'; dtype in [DT_INT64]; Tindices in [DT_INT64]
device='GPU'; dtype in [DT_INT64]; Tindices in [DT_INT32]
device='GPU'; dtype in [DT_UINT64]; Tindices in [DT_INT64]
device='GPU'; dtype in [DT_UINT64]; Tindices in [DT_INT32]
device='GPU'; dtype in [DT_DOUBLE]; Tindices in [DT_INT64]
device='GPU'; dtype in [DT_DOUBLE]; Tindices in [DT_INT32]
device='GPU'; dtype in [DT_FLOAT]; Tindices in [DT_INT64]
device='GPU'; dtype in [DT_FLOAT]; Tindices in [DT_INT32]
device='GPU'; dtype in [DT_HALF]; Tindices in [DT_INT64]
device='GPU'; dtype in [DT_HALF]; Tindices in [DT_INT32]
device='CPU'; dtype in [DT_VARIANT]; Tindices in [DT_INT64]
device='CPU'; dtype in [DT_VARIANT]; Tindices in [DT_INT32]
device='CPU'; dtype in [DT_RESOURCE]; Tindices in [DT_INT64]
device='CPU'; dtype in [DT_RESOURCE]; Tindices in [DT_INT32]
device='CPU'; dtype in [DT_STRING]; Tindices in [DT_INT64]
device='CPU'; dtype in [DT_STRING]; Tindices in [DT_INT32]
device='CPU'; dtype in [DT_BOOL]; Tindices in [DT_INT64]
device='CPU'; dtype in [DT_BOOL]; Tindices in [DT_INT32]
device='CPU'; dtype in [DT_COMPLEX128]; Tindices in [DT_INT64]
device='CPU'; dtype in [DT_COMPLEX128]; Tindices in [DT_INT32]
device='CPU'; dtype in [DT_COMPLEX64]; Tindices in [DT_INT64]
device='CPU'; dtype in [DT_COMPLEX64]; Tindices in [DT_INT32]
device='CPU'; dtype in [DT_DOUBLE]; Tindices in [DT_INT64]
device='CPU'; dtype in [DT_DOUBLE]; Tindices in [DT_INT32]
device='CPU'; dtype in [DT_FLOAT]; Tindices in [DT_INT64]
device='CPU'; dtype in [DT_FLOAT]; Tindices in [DT_INT32]
device='CPU'; dtype in [DT_BFLOAT16]; Tindices in [DT_INT64]
device='CPU'; dtype in [DT_BFLOAT16]; Tindices in [DT_INT32]
device='CPU'; dtype in [DT_HALF]; Tindices in [DT_INT64]
device='CPU'; dtype in [DT_HALF]; Tindices in [DT_INT32]
device='CPU'; dtype in [DT_INT32]; Tindices in [DT_INT64]
device='CPU'; dtype in [DT_INT32]; Tindices in [DT_INT32]
device='CPU'; dtype in [DT_INT8]; Tindices in [DT_INT64]
device='CPU'; dtype in [DT_INT8]; Tindices in [DT_INT32]
device='CPU'; dtype in [DT_UINT8]; Tindices in [DT_INT64]
device='CPU'; dtype in [DT_UINT8]; Tindices in [DT_INT32]
device='CPU'; dtype in [DT_INT16]; Tindices in [DT_INT64]
device='CPU'; dtype in [DT_INT16]; Tindices in [DT_INT32]
device='CPU'; dtype in [DT_UINT16]; Tindices in [DT_INT64]
device='CPU'; dtype in [DT_UINT16]; Tindices in [DT_INT32]
device='CPU'; dtype in [DT_UINT32]; Tindices in [DT_INT64]
device='CPU'; dtype in [DT_UINT32]; Tindices in [DT_INT32]
device='CPU'; dtype in [DT_INT64]; Tindices in [DT_INT64]
device='CPU'; dtype in [DT_INT64]; Tindices in [DT_INT32]
device='CPU'; dtype in [DT_UINT64]; Tindices in [DT_INT64]
device='CPU'; dtype in [DT_UINT64]; Tindices in [DT_INT32]
```
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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/59753/checks?check_run_id=11486196179) 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 is spam, hence closing this PR. "
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"@rubbberrabbit Thanks for reporting the issue.\r\n\r\nThe code produces seg fault in TF 2.7 but produces `InvalidArgumentError: {{function_node __wrapped__LearnedUnigramCandidateSampler_device_/job:localhost/replica:0/task:0/device:CPU:0}} `true_candidate` out of range [0, 19), received 250 [Op:LearnedUnigramCandidateSampler]` error in TF 2.11 and TF Nightly. \r\n\r\nPlease find the gist [here](https://colab.research.google.com/gist/pjpratik/36d64d8528852a0e15aa7bdaa8ddc9b2/59752.ipynb).\r\n\r\nAs per the [documentation](https://www.tensorflow.org/api_docs/python/tf/random/learned_unigram_candidate_sampler?version=nightly) and the error suggests, the `true_classes` argument expects between `[0, range_max)`, which is the number of possible classes.\r\n\r\nThe error can be resolved by changing the `true_classes` argument to take values from `[0,range_max)`. \r\n\r\nPlease find the working gist [here](https://colab.research.google.com/gist/pjpratik/e38efa01ce483ea31f91ff2a95553682/59752.ipynb) and let us know if it helps. Thank you.",
"I find this problem still exists in the latest docker images tensorflow/tensorflow:nightly-gpu or in the enviroment using pip to install tf-nightly in my own computer(RTX3090 CUDA 11.6/8.6 Ubuntu 18.04), I am not sure it may be a version release issue. It was confirmed on colab that this issue has been fixed with latest nightly version.",
"tensorflow/tensorflow:nightly-gpu docker enviroment\r\n\r\nthe execution result show the code pass the check but raise Segmentation fault error after.",
"Hi, @rubbberrabbit \r\n\r\nApologize for the delay and I was able to replicate the same issue with `tensorflow/tensorflow:nightly-gpu` docker image and I was getting below error message and it's seems like expected behaviour. \r\n\r\nIf issue still persists please let us know or Could you please confirm if this issue is resolved for you ? Please feel free to close the issue if it is resolved ? Thank you!\r\n\r\n```\r\n2023-02-26 12:01:25.948205: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 14582 MB memory: -> device: 1, name: Tesla V100-SXM2-16GB, pci bus id: 0000:00:05.0, compute capability: 7.0\r\nTraceback (most recent call last):\r\n File \"_59752_test.py\", line 14, in <module>\r\n output = tf.nn.learned_unigram_candidate_sampler(tensor,2,10,True,19)\r\n File \"/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/traceback_utils.py\", line 153, in error_handler\r\n raise e.with_traceback(filtered_tb) from None\r\n File \"/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py\", line 7133, in raise_from_not_ok_status\r\n raise core._status_to_exception(e) from None # pylint: disable=protected-access\r\ntensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__LearnedUnigramCandidateSampler_device_/job:localhost/replica:0/task:0/device:CPU:0}} `true_candidate` out of range [0, 19), received 204 [Op:LearnedUnigramCandidateSampler]\r\n```",
"Hi，I am not sure which image you use，my replicate the script in [image is ](https://hub.docker.com/r/tensorflow/tensorflow/tags) which pushed 17 hours ago by tensorflowpackages using command\r\ndocker pull tensorflow/tensorflow:nightly-gpu and still cause Segment fault.\r\n Is the bug related with some environment setting or the bug handle code did not update with the release yet？",
"```\r\n2023-03-09 07:03:10.340519: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1446] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 22322 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:09:00.0, compute capability: 8.6)\r\n2023-03-09 07:03:10.340743: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-03-09 07:03:10.340852: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1446] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 10484 MB memory) -> physical GPU (device: 1, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:0a:00.0, compute capability: 8.6)\r\nSegmentation fault (core dumped)\r\n```\r\n",
"I notice your environment is Tesla V100-SXM2-16GB with CUDA 7.0. I'm using CUDA 8.6 and an RTX3090, could this be the reason for the bug?",
"I switch to the the latest 2.13.0, and it's seems like expected behaviour.",
"Hi, @rubbberrabbit \r\n\r\nThank you for the confirmation and if it's working as expected with latest nightly docker image then Could you please confirm if this issue is resolved for you ? Please feel free to close the issue if it is resolved ? \r\n\r\nIf you need any further assistance please let us know ? 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/59752\">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/59752\">No</a>\n"
] | 2023-02-21T09:17:01 | 2023-03-31T01:56:40 | 2023-03-31T01:56:38 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
tf2.7,tf2.11.0,tf-nightly
### Custom Code
Yes
### OS Platform and Distribution
Linux Ubuntu18.04
### Mobile device
_No response_
### Python version
3.8.10
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
11.6/8.6
### GPU model and memory
GPU
### Current Behaviour?
```shell
# code trigger bug
import tensorflow as tf
tensor= tf.random.uniform([2, 2], minval=-256, maxval=257, dtype=tf.int64)
output = tf.nn.learned_unigram_candidate_sampler(tensor,2,10,True,19)
print("Anything")
print(output)
```
### Standalone code to reproduce the issue
```shell
In tf2.7.0 the execution will directly lead to segment fault without any error information.
In tf2.11.0 and the latest nightly version, the execution will silently break down at line 4, which means that the "Anything" will not be output, and neither does the next line "print(output)".
```
### Relevant log output
```shell
Segment fault in tf2.7.0.
no output but silently shutdown in tf2.11.0 and nightly version.
```
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"The new optimizers are the default after 2.11.\r\n\r\nYes, there were some breaking changes in the switch. \r\n\r\nSee [the 2.11 release notes for details](https://www.tensorflow.org/api_docs/python/tf/gather#validate_indices)",
"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/59751\">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/59751\">No</a>\n"
] | 2023-02-21T07:51:34 | 2023-02-22T01:19:19 | 2023-02-22T01:19:17 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
No
### Source
binary
### Tensorflow Version
2.11
### Custom Code
No
### OS Platform and Distribution
Ubuntu
### 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 Behaviour?
```shell
I am upgrading to tensorflow 2.11 from 2.6. it seems keras now has multiple classes of optimizers - two of them being `optimzier_v2` and `optimizer_experimental`. It is not clear in the tf version 2.11, which optimizer class is meant to be used as default.
When trying to instantiate optimizers directly in python, we get `optimizer_experimental`.
However, when initiated from the config, it returns the optimzier_v2.
We are trying to log the current learning rate as the training progresses, and the different interface of the two optimizer classes causes things to break.
We can check the class types when getting the learning rate from the optimizer object as a workaround, but it will be nice to have consistent default behavior.
```
### Standalone code to reproduce the issue
```shell
import tensorflow.keras.optimizers as optimizers
type(optimizers.Adam())
```
keras.optimizers.optimizer_experimental.adam.Adam
```
import tensorflow.keras.optimizers as optimizers
cfg = """
class_name: adam
config:
beta_1: 0.9
beta_2: 0.99
epsilon: 1.0e-05
learning_rate:
class_name: ExponentialDecay
config:
decay_rate: 1.0
decay_steps: 10000
initial_learning_rate: 0.01
"""
import yaml
cfg = yaml.safe_load(cfg)
opt = optimizers.get(cfg)
type(opt)
```
keras.optimizers.optimizer_v2.adam.Adam
```
### Relevant log output
_No response_</details> | {
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"Hi, thanks for taking the time to report the issue.\r\n\r\nTensorflow's gather op has inconsistent behavior: On GPU it silently returns zeros (for speed), on CPU it checks the bounds fails if the bounds are exceded. \r\n\r\nSee the wraning here: \r\n\r\nhttps://www.tensorflow.org/api_docs/python/tf/gather#validate_indices\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/59750\">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/59750\">No</a>\n"
] | 2023-02-21T07:14:36 | 2023-02-22T00:27:37 | 2023-02-22T00:27:34 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
2.6
### Custom Code
Yes
### OS Platform and Distribution
windows10
### 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 Behaviour?
```shell
my code can run in tensorflow-gpu==2.6.0,but can`t run in tensorflow-cpu==2.6.0. they use same code and DS .why?
error is :
Traceback (most recent call last):
File "C:\Users\DH\PycharmProjects\four\model_test.py", line 53, in <module>
movie_combine_layer_flat_val = movie_layer_model([np.reshape(item.take(0), [1, 1]), titles, actors, categories])
File "C:\Users\DH\anaconda3\envs\test\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\DH\AppData\Roaming\Python\Python39\site-packages\tensorflow\python\framework\ops.py", line 7215, in raise_from_not_ok_status
raise core._status_to_exception(e) from None # pylint: disable=protected-access
tensorflow.python.framework.errors_impl.InvalidArgumentError: Exception encountered when calling layer "movie_actor_embed_layer" " f"(type Embedding).
{{function_node __wrapped__ResourceGather_device_/job:localhost/replica:0/task:0/device:CPU:0}} indices[0,3] = 2010 is not in [0, 2006) [Op:ResourceGather]
Call arguments received by layer "movie_actor_embed_layer" " f"(type Embedding):
• inputs=tf.Tensor(shape=(1, 30), dtype=int32)
it look like matrix boundary problem, but it can`t happen in tensorflow-gpu-2.6.0
```
### Standalone code to reproduce the issue
```shell
for item in movies.values:
titles = np.zeros([1, title_count])
titles[0] = item.take(1)
actors = np.zeros([1, 30])
actors[0] = item.take(2)
categories = np.zeros([1, 10])
categories[0] = item.take(3)
movie_combine_layer_flat_val = movie_layer_model([np.reshape(item.take(0), [1, 1]), titles, actors, categories])
movie_matrics.append(movie_combine_layer_flat_val)
```
### Relevant log output
```shell
Traceback (most recent call last):
File "C:\Users\DH\PycharmProjects\four\model_test.py", line 53, in <module>
movie_combine_layer_flat_val = movie_layer_model([np.reshape(item.take(0), [1, 1]), titles, actors, categories])
File "C:\Users\DH\anaconda3\envs\test\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\DH\AppData\Roaming\Python\Python39\site-packages\tensorflow\python\framework\ops.py", line 7215, in raise_from_not_ok_status
raise core._status_to_exception(e) from None # pylint: disable=protected-access
tensorflow.python.framework.errors_impl.InvalidArgumentError: Exception encountered when calling layer "movie_actor_embed_layer" " f"(type Embedding).
{{function_node __wrapped__ResourceGather_device_/job:localhost/replica:0/task:0/device:CPU:0}} indices[0,3] = 2010 is not in [0, 2006) [Op:ResourceGather]
Call arguments received by layer "movie_actor_embed_layer" " f"(type Embedding):
• inputs=tf.Tensor(shape=(1, 30), dtype=int32)
```
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"New problem,when I execute this command：sudo su \r\nthen make -f tensorflow/lite/micro/tools/make/Makefile test_hello_world_test\r\nThe output result is：\r\n--2023-02-21 14:23:26-- https://github.com/google/flatbuffers/archive/a66de58af9565586832c276fbb4251fc416bf07f.zip Resolving github.com (github.com)... 20.205.243.166 Connecting to github.com (github.com)|20.205.243.166|:443... connected. HTTP request sent, awaiting response... 302 Found Location: https://codeload.github.com/google/flatbuffers/zip/a66de58af9565586832c276fbb4251fc416bf07f [following] --2023-02-21 14:23:26-- https://codeload.github.com/google/flatbuffers/zip/a66de58af9565586832c276fbb4251fc416bf07f Resolving codeload.github.com (codeload.github.com)... 20.205.243.165 Connecting to codeload.github.com (codeload.github.com)|20.205.243.165|:443... connected. HTTP request sent, awaiting response... 200 OK Length: unspecified [application/zip] Saving to: ‘/tmp/tmp.0PxhI5l73F/a66de58af9565586832c276fbb4251fc416bf07f.zip’ /tmp/tmp.0PxhI5l73F/a66de58af [ <=> ] 2.74M 58.9KB/s in 38s 2023-02-21 14:24:07 (73.1 KB/s) - ‘/tmp/tmp.0PxhI5l73F/a66de58af9565586832c276fbb4251fc416bf07f.zip’ saved [2872508] tensorflow/lite/micro/tools/make/flatbuffers_download.sh: line 66: unzip: command not found tensorflow/lite/micro/tools/make/Makefile:491: *** Something went wrong with the flatbuffers download: . Stop. ",
"Hi @singbys,\r\n\r\nAs per the error it seems like you did not install the unzip package, please try installing it using the following command and let us know if the issue still persists. \r\n```\r\nsudo apt-get install unzip\r\n```\r\n\r\nThank you! ",
"Thank you for your answer. New questions appear again：\r\n\r\n> /bin/sh: 1: tensorflow/lite/micro/tools/make/download_and_extract.sh: Permission denied make: *** [tensorflow/lite/micro/tools/make/Makefile:634: tensorflow/lite/micro/tools/make/downloads/gemmlowp] Error 126\r\n\r\nThese files with the suffix sh always have such problems. I'm a beginner, and I don't know what's wrong",
"@singbys, could you please let us know the steps you have followed and also ensure that you have pip, zip packages installed. Thank you!\r\n ",
"At first, I downloaded the zip of tensorflow lite,Extract it into the ubuntu system folder, and then executed：`make -f tensorflow/lite/micro/tools/make/Makefile test_hello_world_test`\r\nAn error occurred：\r\n\r\n> make: tensorflow/lite/micro/tools/make/flatbuffers_download.sh: Permission denied tensorflow/lite/micro/tools/make/Makefile:491: *** Something went wrong with the flatbuffers download: . Stop.\r\n\r\nThen I execute the command：`chmod tensorflow/lite/micro/tools/make/flatbuffers_download.sh`\r\n\r\nWhen running again, a similar error occurs：\r\n\r\n> /bin/sh: 1: tensorflow/lite/micro/tools/make/download_and_extract.sh: Permission denied make: *** [tensorflow/lite/micro/tools/make/Makefile:634: tensorflow/lite/micro/tools/make/downloads/gemmlowp] Error 126\r\n\r\nNow the mistake is：\r\n\r\n> PermissionError: [Errno 13] Permission denied: 'gen/linux_x86_64_default/genfiles/tensorflow/lite/micro/examples/hello_world/hello_world_model_data.cc' tensorflow/lite/micro/examples/hello_world/Makefile.inc:38: *** Something went wrong: gen/linux_x86_64_default/genfiles/tensorflow/lite/micro/examples/hello_world/hello_world_model_data.cc. Stop. \r\n",
"I did it according to the book TINY ML. What should I do if I start again?",
"I was successfully able to run ` make -f tensorflow/lite/micro/tools/make/Makefile test_hello_world_test` without any error with the following commands.\r\n\r\n\r\nIt is suggested to use a virtual environment, as it is a tool that helps to keep dependencies required by different projects separate by creating isolated python virtual environments.\r\n\r\n\r\n1. mkdir tf_lite (I have first created a new folder and cloned the tflite-micro repo in it )\r\n\r\n2. cd tf_lite\r\n\r\n3. git clone https://github.com/tensorflow/tflite-micro.git\r\n\r\n4. cd tflite-micro\r\n\r\n 5. make -f tensorflow/lite/micro/tools/make/Makefile test_hello_world_test\r\n\r\nPlease find the output below for your reference.\r\n\r\nThank you! ",
"The problem is solved. I ran the following command：\r\n```\r\nsudo make -f tensorflow/lite/micro/tools/make/Makefile clean_downloads\r\nsudo make -f tensorflow/lite/micro/tools/make/Makefile clean\r\nsudo make -f tensorflow/lite/micro/tools/make/Makefile test_hello_world_test\r\n```\r\nThank you again for your answer"
] | 2023-02-21T05:46:19 | 2023-02-21T12:21:51 | 2023-02-21T12:21:51 | NONE | null | null | null | ### 1. System information
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 22.04.2 LTS
- TensorFlow installation (pip package or built from source): built from source
- TensorFlow library (version, if pip package or github SHA, if built from source): 2.11.0
### 2. Code
make -f tensorflow/lite/micro/tools/make/Makefile test_hello_world_test
### 3. Failure after conversion
make: tensorflow/lite/micro/tools/make/flatbuffers_download.sh: Permission denied tensorflow/lite/micro/tools/make/Makefile:491: *** Something went wrong with the flatbuffers download: . Stop.
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"@etolliver Thanks for reporting the error. \r\n\r\nPR #59758 has been opened to fix the issue. The issue will be closed once it is merged.\r\n\r\nThank you.",
"Closing this as the PR #59758 is merged. ",
"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/59748\">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/59748\">No</a>\n"
] | 2023-02-21T02:59:45 | 2023-02-28T12:04:21 | 2023-02-28T12:04:17 | NONE | null | null | null | https://github.com/tensorflow/tensorflow/blob/b308c65397964d74b46a4015d15b6911ac710616/tensorflow/compiler/xla/tests/constants_test.cc#L85
F8 might need to be F16 in this comment | {
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"\r\n\r\nIt is working for me in windows 11 with the above shown versions, please specify more details about your OS, etc.\r\nso that we can reproduce this error.",
"desktop;Windows 10 \r\npip version:23.0.1 \r\npython :3.7 \r\n\r\n\r\nhere is all the libray in the virtual env \r\n------------------------------------\r\n\r\nabsl-py==1.3.0\r\nastunparse==1.6.3\r\nattrs==22.1.0\r\nbackcall==0.2.0\r\ncachetools==5.2.1\r\ncertifi==2022.12.7\r\ncharset-normalizer==2.1.1\r\ncolorama==0.4.6\r\ncycler==0.11.0\r\ndebugpy==1.6.6\r\ndecorator==5.1.1\r\nentrypoints==0.4\r\nflatbuffers==22.11.23\r\nfonttools==4.38.0\r\ngast==0.4.0\r\ngoogle-auth==2.16.0\r\ngoogle-auth-oauthlib==0.4.6\r\ngoogle-pasta==0.2.0\r\ngrpcio==1.51.1\r\nh5py==3.7.0\r\nidna==3.4\r\nimageio==2.25.1\r\nimportlib-metadata==6.0.0\r\nimutils==0.5.4\r\nipykernel==6.16.2\r\nipython==7.34.0\r\njedi==0.18.2\r\njoblib==1.2.0\r\njupyter_client==7.4.9\r\njupyter_core==4.12.0\r\nkaggle==1.5.12\r\nkeras==2.11.0\r\nkiwisolver==1.4.4\r\nlibclang==15.0.6.1\r\nMarkdown==3.4.1\r\nMarkupSafe==2.1.1\r\nmatplotlib==3.5.3\r\nmatplotlib-inline==0.1.6\r\nmediapipe==0.9.0\r\nnest-asyncio==1.5.6\r\nnumpy==1.21.6\r\noauthlib==3.2.2\r\nopencv-contrib-python==4.6.0.66\r\nopt-einsum==3.3.0\r\npackaging==21.3\r\npandas==1.3.5\r\nparso==0.8.3\r\npickleshare==0.7.5\r\nPillow==9.3.0\r\nprompt-toolkit==3.0.36\r\nprotobuf==3.19.6\r\npsutil==5.9.4\r\npyasn1==0.4.8\r\npyasn1-modules==0.2.8\r\nPygments==2.14.0\r\npyparsing==3.0.9\r\npython-dateutil==2.8.2\r\npython-slugify==8.0.0\r\npytz==2022.7.1\r\npywin32==305\r\npyzmq==25.0.0\r\nrequests==2.28.1\r\nrequests-oauthlib==1.3.1\r\nrsa==4.9\r\nscikit-learn==1.0.2\r\nscipy==1.7.3\r\nsix==1.16.0\r\ntensorboard==2.11.0\r\ntensorboard-data-server==0.6.1\r\ntensorboard-plugin-wit==1.8.1\r\ntensorflow==2.11.0\r\ntensorflow-estimator==2.11.0\r\ntensorflow-intel==2.11.0\r\ntensorflow-io-gcs-filesystem==0.29.0\r\ntermcolor==2.2.0\r\ntext-unidecode==1.3\r\nthreadpoolctl==3.1.0\r\ntornado==6.2\r\ntqdm==4.64.1\r\ntraitlets==5.9.0\r\ntyping_extensions==4.4.0\r\nurllib3==1.26.14\r\nwcwidth==0.2.6\r\nWerkzeug==2.2.2\r\nwrapt==1.14.1\r\nzipp==3.11.0\r\n\r\n> \r\n> \r\n> It is working for me in windows 11 with the above shown versions, please specify more details about your OS, etc. so that we can reproduce this error.\r\n\r\n",
"@danyow-cheung,\r\nI tried to execute the code **!pip install -q git+https://github.com/tensorflow/docs** from the mentioned official keras doc on both Windows and colab and it was executed without any issue/error. Kindly find the gist of it [here](https://colab.sandbox.google.com/gist/tilakrayal/8fd4862f3b50b725ddb94e4b687ac2ab/untitled977.ipynb) and the SS for the reference.\r\n\r\n\r\n\r\n \r\nCould you please create a virtual environment and test your code again. Thank you!",
"> @danyow-cheung, I tried to execute the code **!pip install -q git+https://github.com/tensorflow/docs** from the mentioned official keras doc on both Windows and colab and it was executed without any issue/error. Kindly find the gist of it [here](https://colab.sandbox.google.com/gist/tilakrayal/8fd4862f3b50b725ddb94e4b687ac2ab/untitled977.ipynb) and the SS for the reference.\r\n> \r\n>  \r\n> \r\n> Could you please create a virtual environment and test your code again. Thank you!\r\n\r\nstill the same,but i will try my code on colab next time,thank you \r\n\r\n",
"@danyow-cheung,\r\nYeah, please try to execute in colab and update the same. And the error clearly mentioned that it is not the problem with either pip or tensorflow. Also this particular error is caused by not having a C/C++ compiler installed. As said in the error message, either install [MSVC](https://visualstudio.microsoft.com/visual-cpp-build-tools/) or another compiler to compile it.\r\n\r\nPlease try to download Visual Studio from [its main page](https://visualstudio.microsoft.com/).\r\n\r\nIf Visual Studio is already installed, then when you run the installer, you can modify it (by clicking the modify button). Thank you!",
"Closing this as stale. Please reopen if this is still a valid request. 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/59747\">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/59747\">No</a>\n"
] | 2023-02-21T02:58:36 | 2023-03-27T12:48:09 | 2023-03-27T12:48:00 | NONE | null | null | null | seen as below
i got this command from here:<u>https://keras.io/examples/vision/video_classification/</u>
tensorflow version : 2.11.0
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"@Leengit \r\n\r\nCan you please elaborate about your feature and please specify the use cases for this feature.\r\n\r\nThank you!",
"I would like the documentation page for [`tensorflow.Dataset.concatenate`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#concatenate) to mention that if multiple datasets are to be concatenated by repeated use of this two-dataset `concatenate` method then it is safest to accomplish this with a divide-and-conquer approach or, at the very least, the documentation should indicate that the straightforward approach of appending datasets one-by-one can be problematic.\r\n\r\nAlternatively, we can expand the `tensorflow.Dataset` interface so that the argument to the `concatenate` method can be a list (or tuple, etc.) of datasets, and the implementation would then use the divide-and-conquer approach (above) (or it would use a new `tensorflow` data structure) to concatenate them.\r\n\r\nIn my case, I have a function that reads a chunk of a very large whole-slide (medical) image. Because these chunks require different parameters for reading, masking, color normalization, transforming, etc., it is convenient for me to make a single `tensorflow.Dataset` from each. I then need to concatenate all these `tensorflow.Dataset` objects together.",
"I think the better place to mention the best practices to use the data is https://www.tensorflow.org/guide/data.\r\nIn the API document, we mainly mention the usage and it's behavior.",
"Thanks for raising this issue @Leengit. Agreed that the documentation should be updated to warn about performance issues with concatenating a large number of datasets together and suggest an alternative.\r\n\r\nFor what it's worth, the best way to concatenate a large number of datasets is to use `flat_map` instead:\r\n\r\n```python\r\ndef concat_datasets(datasets):\r\n ds = tf.data.Dataset.from_tensor_slices(datasets)\r\n return ds.flat_map(lambda x: x)\r\n```",
"I didn't know that one could give a `List[tf.data.Dataset]` to `tf.data.Dataset.from_tensor_slices`! Please document this approach somewhere where people who are tempted to use the pairwise `concatenate` will see it."
] | 2023-02-20T21:19:16 | 2023-03-03T17:30:34 | null | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Documentation Feature Request
### Have you reproduced the bug with TF nightly?
No
### Source
binary
### Tensorflow Version
2.11.0
### Custom Code
No
### OS Platform and Distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9.7
### Bazel version
_No response_
### GCC/Compiler version
9.4.9
### CUDA/cuDNN version
_No response_
### GPU model and memory
NVIDIA RTX A4000 15.74GiB
### Current Behaviour?
If I have a long list of datasets that I wish to concatenate, it matters whether I concatenate them pairwise with heavy left-hand sides or heavy right-hand sides. I would like to see this in the documentation so that others don't have to learn this on their own. (Additionally, a static tf.Dataset.concatenate_from_list function that does the right thing for the user would be nice.) Specifically, for my large list of datasets that contain large amounts of image data that are read from disk via py_functions, batched, and prefetched, the following code snippets give different performances. `concatenate_by_appending` causes the process to be killed, likely due to an out-of-memory condition. In contrast, `concatenate_by_prepending` and `concatenate_by_splitting` work well. I suspect that if I used an aggressive form of `shuffle` then only `concatenate_by_splitting` would work well.
I should clarify it is not the creation of the `concatenate_by_appending` dataset that causes the process to be killed. The process is killed only once I attempt to `predict` using the combined dataset. The pre-pending approach is right-heavy for each concatenation operation, and the left-most dataset can be found immediately, as the left branch of the top-most concatenation. In contrast, the appending approach is left-heavy for each concatenation operation, requires descending through all concatenations to find the left-most dataset, and causes the process to be killed, likely due to resource exhaustion, specifically likely an out-of-memory condition. At least I think that is what is happening.
### Standalone code to reproduce the issue
```python
def concatenate_by_prepending(dataset_list):
response = None
# Note: list traversal is reversed
for dataset in reversed(dataset_list):
if response is None:
response = dataset
else:
# Note: Right-heavy concatenation
response = dataset.concatenate(response)
return response
def concatenate_by_appending(dataset_list):
response = None
# Note: list traversal is forward
for dataset in dataset_list:
if response is None:
response = dataset
else:
# Note: Left-heavy concatenation
response = response.concatenate(dataset)
return response
def concatenate_by_splitting(dataset_list):
# Note: recursive, rather than list traversing
length = len(dataset_list)
if length == 1:
return dataset_list[0]
if length > 1:
# Note: Left and right are of roughly equal size
return concatenate_by_splitting(dataset_list[0 : length // 2]).concatenate(
concatenate_by_splitting(dataset_list[length // 2 : length])
)
return None
```
### Relevant log output
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"Check out this pull request on <a href=\"https://app.reviewnb.com/tensorflow/tensorflow/pull/59745\"><img align=\"absmiddle\" alt=\"ReviewNB\" height=\"28\" class=\"BotMessageButtonImage\" src=\"https://raw.githubusercontent.com/ReviewNB/support/master/images/button_reviewnb.png\"/></a> \n\n See visual diffs & provide feedback on Jupyter Notebooks. \n\n---\n\n <i>Powered by <a href='https://www.reviewnb.com/?utm_source=gh'>ReviewNB</a></i>",
"Hi @RohitSgh, Please submit multiple typo fixes in a single PR as the CPU/GPU hours are wasted on CI. Hence, we do not encourage one liner grammatical changes as it is an expensive process. Thank you for your contribution!"
] | 2023-02-20T17:56:02 | 2023-02-21T19:41:41 | 2023-02-21T10:40:01 | NONE | null | false | {
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"Thank you for reporting this issue with TensorFlow! It's possible that you are observing slower performance when iterating over a tf.data.Dataset compared to a Python generator that produces the same data.\r\n\r\nOne possible reason for this difference in performance could be due to the additional overhead introduced by tf.data.Dataset. While tf.data.Dataset provides many useful features such as shuffling, batching, and prefetching, these operations can introduce additional overhead that might make it slower than a Python generator in some cases.\r\n\r\nIt's important to note that the performance of tf.data.Dataset can vary based on several factors, such as the size and complexity of the dataset, the specific operations being applied, and the hardware being used. It's possible that in some cases, tf.data.Dataset might be faster than a Python generator.\r\n\r\nTo investigate this issue further, you might consider profiling your code using TensorFlow's built-in profiling tools or a third-party profiling tool such as cProfile. This can help you identify specific operations or areas of your code that might be introducing additional overhead.\r\n\r\nAdditionally, you might consider upgrading to a newer version of TensorFlow (such as TensorFlow 2.7 or later) or using TensorFlow nightly builds to see if this issue has already been resolved or improved in a newer version.\r\n\r\nFinally, if you're still experiencing performance issues with tf.data.Dataset, you might consider using alternative data loading methods such as tf.keras.utils.Sequence or tf.data.experimental.CsvDataset. These methods might offer better performance depending on your specific use case.\r\n\r\nI hope this helps! Let me know if you have any further questions or concerns.\r\n",
"I'm talking here about a difference of two orders of magnitude. It's not just the usual \"in one case one option is better, in another case another option is better\".\r\nThere really seems to be something wrong with the multi-input tf.data.Dataset, which makes it almost unusable. I had to rewrite my code without tf.data, because otherwise the bare iteration - before any processing - over a dataset of 2 million records (about 150 columns, mixed types: strings, integers, floats, booleans) takes more than an hour.",
"Hi @pbav ,\r\n\r\nThanks for bringing this.\r\n\r\nIn my opinion this comparison seems unfair.As you know `tf.data.Dataset` is a `data pipeline` more than a data generator and will have some overheads compared to normal python generator . Your compared only data generator times. It will be fair if you actually pass both of them to a model(large enough for comparison) and compare training time. This will provide more context to look into the issue whether there is really a performance issue. \r\n\r\nThanks!",
"Closing as stale. Please reopen if you'd like to work on this further. Thanks.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59744\">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/59744\">No</a>\n"
] | 2023-02-20T16:58:15 | 2023-03-18T04:22:11 | 2023-03-18T04:22:08 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Performance
### Have you reproduced the bug with TF nightly?
No
### Source
binary
### Tensorflow Version
2.11.0
### Custom Code
Yes
### OS Platform and Distribution
MacOS 12.6.3
### Mobile device
_No response_
### Python version
3.10.6
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
```shell
Iterating tf.data.Dataset with 10k records (consisting of 10 short strings each) is 78 times slower than iterating a Python generator producing the same data
```
### Standalone code to reproduce the issue
```shell
from time import perf_counter
import tensorflow as tf
def gen():
for _ in range(10000):
yield {str(k): 'string value' for k in range(10)}
start = perf_counter()
for _ in gen():
pass
print('Python generator', perf_counter() - start)
ds = tf.data.Dataset.from_generator(
gen,
output_signature={str(k): tf.TensorSpec(shape=(), dtype=tf.string) for k in range(10)}
)
ds.save('/tmp/ds')
ds = tf.data.Dataset.load('/tmp/ds')
start = perf_counter()
for _ in ds:
pass
print('tf.data.Dataset', perf_counter() - start)
```
### Relevant log output
```shell
Python generator 0.018086554016917944
tf.data.Dataset 1.4203055879333988
```
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"@yufang67 As per the guide for [Mixed precision](https://www.tensorflow.org/guide/mixed_precision#setting_the_dtype_policy), the bfloat16 should be used to run on TPUs. Using `mixed_float16` is suggested when running on GPUs, and CPUs.\r\n\r\nI did try with `mixed_float16` and the model works fine. Please find the gist [here](https://colab.research.google.com/gist/pjpratik/86f09732242b0595d6b3b505e230200e/59743.ipynb).\r\n\r\nCan you please elaborate your feature request with use case. Thanks!",
"Thanks @pjpratik . \r\nYes, we are trying to train an ASR model on GPU with bfloat16. We would like to check the benefit of using bfloat16 like speedup and accuracy comparing to the mixed_float16 which we usually use. The activations and layers above are used in the model. when training on GPU, other ops and layers are on GPU, but they are on CPU. it has an huge impact on speed. As a result, they are also expected to supported on GPU.\r\n\r\nsimilar threads:\r\nhttps://github.com/tensorflow/tensorflow/issues/59050#issuecomment-1428862660\r\nhttps://github.com/tensorflow/tensorflow/issues/59728\r\n\r\nThanks",
"Hi @yufang67 Thanks for the information.\r\n\r\n@SuryanarayanaY Could you please look at this feature request? Thanks.",
"Hi @yufang67 ,\r\n\r\n`bfloat16` data type specifically designed for TPUs only and on GPUs this wont work and hence throwing user error as `ValueError:Value passed to parameter 'input' has DataType bfloat16 not in list of allowed values: float16, float32, float64` which is intended behaviour.\r\n\r\nPlease use `mixed_bfloat16` for TPUs environment only. For GPUs you should use `mixed_float16` .\r\n\r\nThanks!\r\n",
"@SuryanarayanaY I dont think mixed_bfloat16 is for TPUs only (A100 support bfloat16). As more and more layers support bfloat16 on GPU, i would like to test the gain for the whole model. And the above activations and layers are blockers. thats why i open this feature request. \r\n\r\nif i understand you correctly, do you means that there is no plan to support bfloat16 for LSTM layer ?\r\n\r\nThanks",
"This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n",
"@SuryanarayanaY Is that possible to handle sigmoid function as done in https://github.com/tensorflow/tensorflow/issues/59050#issuecomment-1428862660 for relu ? thanks",
"> `bfloat16` data type specifically designed for TPUs only and on GPUs this wont work and hence throwing user error as `ValueError:Value passed to parameter 'input' has DataType bfloat16 not in list of allowed values: float16, float32, float64` which is intended behaviour.\r\n\r\nI find this is surprising. PyTorch has enabled bfloat16 support on compatible GPU (e.g. A100) quite some time ago. Does this mean TF prioritize the optimization implementation on TPUs over GPUs?",
"Hi @tengwenxuan ,\r\n\r\nLet me correct myself. On GPUs `bfloat16` may be workable if used with XLA i.e j`it_compile=True` either with `model.compile()` or with `tf.function()` for the Ops that supported on XLA. Support to add 'bfloat16' on GPUs also started recently and in current nightly version few ops might already support `bfloat16` with GPU. Right now I don't have any exclusive list of Ops for same.\r\n\r\n@yufang67 ,\r\n\r\nCould you please compile your model with XLA and use nightly version and let us know the output.\r\n\r\nThanks!\r\n\r\n\r\n\r\n",
"@SuryanarayanaY Thanks for response. Yes, i have already tried XLA and it runs without error, but the training runs extremely slowly because of the variable length of data. ",
"> Hi @tengwenxuan ,\r\n> \r\n> Let me correct myself. On GPUs `bfloat16` may be workable if used with XLA i.e j`it_compile=True` either with `model.compile()` or with `tf.function()` for the Ops that supported on XLA. Support to add 'bfloat16' on GPUs also started recently and in current nightly version few ops might already support `bfloat16` with GPU. Right now I don't have any exclusive list of Ops for same.\r\n> \r\n> @yufang67 ,\r\n> \r\n> Could you please compile your model with XLA and use nightly version and let us know the output.\r\n> \r\n> Thanks!\r\n\r\nThanks. however, XLA is not suitable here due to varying tensor sizes, I think we need native support of bfloat16 in order to have real performance gain. ",
"@trevor-m do you know if it's feasible to add GPU bf16 support to such ops?",
"@reedwm We still plan to support cwise ops like this through MLIR gen which is not yet ready. Until then we can easily add bf16 registrations. I originally thought there was way too many, but I checked and it doesn't look that bad if we were to add gpu registrations for the cwise ops which have bfloat16 cpu implementations already. I can take a look at that soon.\r\n\r\n\r\n",
"Thanks! Adding gpu registrations to cwise ops that have bf16 cpu implementations SGTM.",
"Thank you for the follow-up. re: \"support this thru MLIR gen\", I'm curious, is this what was explained in [this blog post](https://medium.com/tensorflow/mlir-a-new-intermediate-representation-and-compiler-framework-beba999ed18d)? If so, will it still suffer from the performance issue of changing tensor size like in current XLA/JIT implementation? Do you expect the support thru MLIR will provide better performance similar as native kernel support?\r\n",
"I opened a PR for the cwise op support.\r\n\r\nLooks like we will also need bfloat16 support for CudnnRNN as well for this example since it is used by keras - not sure how to tell keras not to use this. This might require casting to float if bf16 is not supported by cudnnRnn.\r\n\r\n> Thank you for the follow-up. re: \"support this thru MLIR gen\", I'm curious, is this what was explained in [this blog post](https://medium.com/tensorflow/mlir-a-new-intermediate-representation-and-compiler-framework-beba999ed18d)? If so, will it still suffer from the performance issue of changing tensor size like in current XLA/JIT implementation? Do you expect the support thru MLIR will provide better performance similar as native kernel support?\r\n\r\nI think this isn't the same as what's described by that blog post. A large number of TF kernel today use MLIR generation by default. It can be either JIT or AOT (ahead of time). I think the perf of mlir generated kernels is expected to be better, but not 100% sure. @kushanam might be able to provide more details.",
"@reedwm Looks like CudnnRNN doesn't support BF16 yet. Should I add a bf16 registration for the op and just cast to float always? I guess the alternative would be to exclude CudnnRNN from the auto mixed precision allowlist for bf16 only - I don't see any place where there is currently a way to have a different list for bf16 vs fp16.",
"@yufang67,\r\nThe related PR(https://github.com/tensorflow/tensorflow/pull/59972) which was raised has been merged and alsoI tried to execute the mentioned code on tensorflow v2.15 (GPU) by importing the keras from the keras_core and the code was executed without any issue/error. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/fdc27fa1701f846a63b859b9dab4132c/untitled1575.ipynb). Thank you!\r\n\r\n",
"Thanks for the update. This issue can be closed.",
"@yufang67,\r\nGlad the issue is resolved for you, Could you please feel free to move this to closed status. Thank you!"
] | 2023-02-20T15:24:06 | 2023-12-06T19:32:03 | 2023-12-06T19:32:03 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Feature Request
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
TensorFlow version 2.13.0-dev20230215
### Custom Code
Yes
### OS Platform and Distribution
ubuntu 20.04
### Mobile device
_No response_
### Python version
3.8
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
11.8/8.6
### GPU model and memory
single A100 80G
### Current Behaviour?
```shell
current behaviour:
when using mixed_bfloat16 policy, sigmoid type activation and dropout layer run on CPU, and LSTM layer does not support bfloat16 input.
expected behavior:
sigmoid/swish activation, dropout and LSTM layers run on GPU with mixed_bfloat16 policy
```
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras import mixed_precision
from tensorflow.keras.layers import LSTM, Embedding
tf.debugging.set_log_device_placement(True)
#from tensorflow.python.framework.ops import disable_eager_execution
#disable_eager_execution()
policy = mixed_precision.Policy('mixed_bfloat16')
# float32
print(policy.name, policy.variable_dtype, policy.compute_dtype)
mixed_precision.set_global_policy(policy)
input_shape = (4, 28, 28, 3)
x = tf.random.normal((4, 28, 28, 3))
#layer = tf.keras.layers.Conv2D(2, 3, activation='relu', input_shape=(28, 28, 3))
layer1 = tf.keras.layers.Activation(activation=tf.keras.activations.swish)
layer2 = tf.keras.layers.Activation(activation=tf.keras.activations.sigmoid)
#layer3 = tf.keras.layers.Activation(activation=tf.keras.activations.relu)
layer4 = tf.keras.layers.Dropout(0.1, name="dropout")
x1 = layer1(x)
x2 = layer2(x1)
y = layer4(x2, training=True)
print(' ====== output ======== ', y.dtype, layer1.dtype, layer2.dtype, layer4.dtype)
vocab_size = 50
input_shape = (4, vocab_size, 1)
x = tf.random.normal(input_shape)
def customer_model():
model = Sequential()
model.add(LSTM(128))
return model
model = customer_model()
model.build(input_shape=input_shape)
model.summary()
y = model(x)
```
### Relevant log output
```shell
2023-02-20 15:15:13.745147: 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.
2023-02-20 15:15:14.363645: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
mixed_bfloat16 float32 bfloat16
2023-02-20 15:15:15.799744: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 78915 MB memory: -> device: 0, name: NVIDIA A100 80GB PCIe, pci bus id: 0001:00:00.0, compute capability: 8.0
input: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:15.809731: I tensorflow/core/common_runtime/placer.cc:114] input: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
_EagerConst: (_EagerConst): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:15.809763: I tensorflow/core/common_runtime/placer.cc:114] _EagerConst: (_EagerConst): /job:localhost/replica:0/task:0/device:GPU:0
output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:15.809775: I tensorflow/core/common_runtime/placer.cc:114] output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:15.813051: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.219908: I tensorflow/core/common_runtime/placer.cc:114] input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
_EagerConst: (_EagerConst): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.219944: I tensorflow/core/common_runtime/placer.cc:114] _EagerConst: (_EagerConst): /job:localhost/replica:0/task:0/device:GPU:0
output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.219952: I tensorflow/core/common_runtime/placer.cc:114] output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.221005: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.221972: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
shape: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.222322: I tensorflow/core/common_runtime/placer.cc:114] shape: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
RandomStandardNormal: (RandomStandardNormal): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.222337: I tensorflow/core/common_runtime/placer.cc:114] RandomStandardNormal: (RandomStandardNormal): /job:localhost/replica:0/task:0/device:GPU:0
output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.222345: I tensorflow/core/common_runtime/placer.cc:114] output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.222865: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op RandomStandardNormal in device /job:localhost/replica:0/task:0/device:GPU:0
x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.223723: I tensorflow/core/common_runtime/placer.cc:114] x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
y: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.223736: I tensorflow/core/common_runtime/placer.cc:114] y: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
Mul: (Mul): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.223750: I tensorflow/core/common_runtime/placer.cc:114] Mul: (Mul): /job:localhost/replica:0/task:0/device:GPU:0
z_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.223761: I tensorflow/core/common_runtime/placer.cc:114] z_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.224202: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Mul in device /job:localhost/replica:0/task:0/device:GPU:0
x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.224907: I tensorflow/core/common_runtime/placer.cc:114] x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
y: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.224922: I tensorflow/core/common_runtime/placer.cc:114] y: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
AddV2: (AddV2): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.224936: I tensorflow/core/common_runtime/placer.cc:114] AddV2: (AddV2): /job:localhost/replica:0/task:0/device:GPU:0
z_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.224944: I tensorflow/core/common_runtime/placer.cc:114] z_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.225386: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op AddV2 in device /job:localhost/replica:0/task:0/device:GPU:0
x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.229609: I tensorflow/core/common_runtime/placer.cc:114] x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
Cast: (Cast): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.229623: I tensorflow/core/common_runtime/placer.cc:114] Cast: (Cast): /job:localhost/replica:0/task:0/device:GPU:0
y_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.229630: I tensorflow/core/common_runtime/placer.cc:114] y_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.230004: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Cast in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.230386: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.230496: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Cast in device /job:localhost/replica:0/task:0/device:GPU:0
x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.230820: I tensorflow/core/common_runtime/placer.cc:114] x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
y: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.230831: I tensorflow/core/common_runtime/placer.cc:114] y: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
Mul: (Mul): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.230843: I tensorflow/core/common_runtime/placer.cc:114] Mul: (Mul): /job:localhost/replica:0/task:0/device:GPU:0
z_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.230850: I tensorflow/core/common_runtime/placer.cc:114] z_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.231215: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Mul in device /job:localhost/replica:0/task:0/device:GPU:0
x: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.231575: I tensorflow/core/common_runtime/placer.cc:114] x: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
Sigmoid: (Sigmoid): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.231596: I tensorflow/core/common_runtime/placer.cc:114] Sigmoid: (Sigmoid): /job:localhost/replica:0/task:0/device:CPU:0
y_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.231608: I tensorflow/core/common_runtime/placer.cc:114] y_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.232198: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Sigmoid in device /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.233223: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Mul in device /job:localhost/replica:0/task:0/device:GPU:0
input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.233727: I tensorflow/core/common_runtime/placer.cc:114] input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
Identity: (Identity): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.233747: I tensorflow/core/common_runtime/placer.cc:114] Identity: (Identity): /job:localhost/replica:0/task:0/device:GPU:0
output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.233759: I tensorflow/core/common_runtime/placer.cc:114] output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.234163: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Identity in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.234923: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Sigmoid in device /job:localhost/replica:0/task:0/device:CPU:0
x: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.235519: I tensorflow/core/common_runtime/placer.cc:114] x: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
Cast: (Cast): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.235536: I tensorflow/core/common_runtime/placer.cc:114] Cast: (Cast): /job:localhost/replica:0/task:0/device:CPU:0
y_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.235546: I tensorflow/core/common_runtime/placer.cc:114] y_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.235889: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Cast in device /job:localhost/replica:0/task:0/device:CPU:0
input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.236126: I tensorflow/core/common_runtime/placer.cc:114] input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
_EagerConst: (_EagerConst): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.236140: I tensorflow/core/common_runtime/placer.cc:114] _EagerConst: (_EagerConst): /job:localhost/replica:0/task:0/device:GPU:0
output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.236147: I tensorflow/core/common_runtime/placer.cc:114] output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.236694: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.237002: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Mul in device /job:localhost/replica:0/task:0/device:GPU:0
input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.237338: I tensorflow/core/common_runtime/placer.cc:114] input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
Shape: (Shape): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.237358: I tensorflow/core/common_runtime/placer.cc:114] Shape: (Shape): /job:localhost/replica:0/task:0/device:GPU:0
output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.237369: I tensorflow/core/common_runtime/placer.cc:114] output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.237779: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Shape in device /job:localhost/replica:0/task:0/device:GPU:0
shape: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.238193: I tensorflow/core/common_runtime/placer.cc:114] shape: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
RandomUniform: (RandomUniform): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.238212: I tensorflow/core/common_runtime/placer.cc:114] RandomUniform: (RandomUniform): /job:localhost/replica:0/task:0/device:GPU:0
output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.238223: I tensorflow/core/common_runtime/placer.cc:114] output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.238690: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op RandomUniform in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.238925: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Cast in device /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.239130: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
x: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.239387: I tensorflow/core/common_runtime/placer.cc:114] x: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
y: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.239404: I tensorflow/core/common_runtime/placer.cc:114] y: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
GreaterEqual: (GreaterEqual): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.239434: I tensorflow/core/common_runtime/placer.cc:114] GreaterEqual: (GreaterEqual): /job:localhost/replica:0/task:0/device:CPU:0
z_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.239442: I tensorflow/core/common_runtime/placer.cc:114] z_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.240101: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op GreaterEqual in device /job:localhost/replica:0/task:0/device:CPU:0
x: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.240458: I tensorflow/core/common_runtime/placer.cc:114] x: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
Cast: (Cast): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.240477: I tensorflow/core/common_runtime/placer.cc:114] Cast: (Cast): /job:localhost/replica:0/task:0/device:CPU:0
y_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.240489: I tensorflow/core/common_runtime/placer.cc:114] y_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.240879: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Cast in device /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.241141: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
condition: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.241482: I tensorflow/core/common_runtime/placer.cc:114] condition: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
t: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.241500: I tensorflow/core/common_runtime/placer.cc:114] t: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
e: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.241508: I tensorflow/core/common_runtime/placer.cc:114] e: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
SelectV2: (SelectV2): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.241516: I tensorflow/core/common_runtime/placer.cc:114] SelectV2: (SelectV2): /job:localhost/replica:0/task:0/device:CPU:0
output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.241527: I tensorflow/core/common_runtime/placer.cc:114] output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.242187: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op SelectV2 in device /job:localhost/replica:0/task:0/device:CPU:0
====== output ======== <dtype: 'bfloat16'> float32 float32 float32
2023-02-20 15:15:16.242688: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.242816: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op RandomStandardNormal in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.242920: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Mul in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.243029: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op AddV2 in device /job:localhost/replica:0/task:0/device:GPU:0
input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.244373: I tensorflow/core/common_runtime/placer.cc:114] input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
_EagerConst: (_EagerConst): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.244402: I tensorflow/core/common_runtime/placer.cc:114] _EagerConst: (_EagerConst): /job:localhost/replica:0/task:0/device:GPU:0
output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.244422: I tensorflow/core/common_runtime/placer.cc:114] output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.245327: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.245736: I tensorflow/core/common_runtime/placer.cc:114] resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.245756: I tensorflow/core/common_runtime/placer.cc:114] VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.246125: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.253589: I tensorflow/core/common_runtime/placer.cc:114] resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.253619: I tensorflow/core/common_runtime/placer.cc:114] value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.253635: I tensorflow/core/common_runtime/placer.cc:114] AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.254140: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.259081: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.259243: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.259523: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.259624: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.266083: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.266318: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.266583: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.266830: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
seed: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.267103: I tensorflow/core/common_runtime/placer.cc:114] seed: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
StatelessRandomGetKeyCounter: (StatelessRandomGetKeyCounter): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.267120: I tensorflow/core/common_runtime/placer.cc:114] StatelessRandomGetKeyCounter: (StatelessRandomGetKeyCounter): /job:localhost/replica:0/task:0/device:GPU:0
key_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.267134: I tensorflow/core/common_runtime/placer.cc:114] key_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
counter_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.267143: I tensorflow/core/common_runtime/placer.cc:114] counter_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.267699: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op StatelessRandomGetKeyCounter in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.286569: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
shape: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.286927: I tensorflow/core/common_runtime/placer.cc:114] shape: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
key: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.286943: I tensorflow/core/common_runtime/placer.cc:114] key: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
counter: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.286957: I tensorflow/core/common_runtime/placer.cc:114] counter: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
alg: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.286966: I tensorflow/core/common_runtime/placer.cc:114] alg: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
StatelessRandomUniformV2: (StatelessRandomUniformV2): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.286977: I tensorflow/core/common_runtime/placer.cc:114] StatelessRandomUniformV2: (StatelessRandomUniformV2): /job:localhost/replica:0/task:0/device:GPU:0
output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.286994: I tensorflow/core/common_runtime/placer.cc:114] output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.287706: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op StatelessRandomUniformV2 in device /job:localhost/replica:0/task:0/device:GPU:0
x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.291713: I tensorflow/core/common_runtime/placer.cc:114] x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
y: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.291736: I tensorflow/core/common_runtime/placer.cc:114] y: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
Sub: (Sub): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.291752: I tensorflow/core/common_runtime/placer.cc:114] Sub: (Sub): /job:localhost/replica:0/task:0/device:GPU:0
z_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.291766: I tensorflow/core/common_runtime/placer.cc:114] z_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.292313: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Sub in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.300187: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Mul in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.300343: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op AddV2 in device /job:localhost/replica:0/task:0/device:GPU:0
resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.300704: I tensorflow/core/common_runtime/placer.cc:114] resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.300720: I tensorflow/core/common_runtime/placer.cc:114] VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.301056: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.301395: I tensorflow/core/common_runtime/placer.cc:114] resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.301410: I tensorflow/core/common_runtime/placer.cc:114] value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.301424: I tensorflow/core/common_runtime/placer.cc:114] AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.301772: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.302407: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.302584: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.302674: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op StatelessRandomGetKeyCounter in device /job:localhost/replica:0/task:0/device:GPU:0
shape: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.303396: I tensorflow/core/common_runtime/placer.cc:114] shape: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
key: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.303409: I tensorflow/core/common_runtime/placer.cc:114] key: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
counter: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.303444: I tensorflow/core/common_runtime/placer.cc:114] counter: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
alg: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.303454: I tensorflow/core/common_runtime/placer.cc:114] alg: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
StatelessRandomNormalV2: (StatelessRandomNormalV2): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.303464: I tensorflow/core/common_runtime/placer.cc:114] StatelessRandomNormalV2: (StatelessRandomNormalV2): /job:localhost/replica:0/task:0/device:GPU:0
output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.303474: I tensorflow/core/common_runtime/placer.cc:114] output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.304084: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op StatelessRandomNormalV2 in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.304383: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Mul in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.304498: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op AddV2 in device /job:localhost/replica:0/task:0/device:GPU:0
input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.304807: I tensorflow/core/common_runtime/placer.cc:114] input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
Qr: (Qr): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.304826: I tensorflow/core/common_runtime/placer.cc:114] Qr: (Qr): /job:localhost/replica:0/task:0/device:GPU:0
q_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.304838: I tensorflow/core/common_runtime/placer.cc:114] q_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
r_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.304845: I tensorflow/core/common_runtime/placer.cc:114] r_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.305406: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Qr in device /job:localhost/replica:0/task:0/device:GPU:0
input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.321530: I tensorflow/core/common_runtime/placer.cc:114] input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
DiagPart: (DiagPart): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.321554: I tensorflow/core/common_runtime/placer.cc:114] DiagPart: (DiagPart): /job:localhost/replica:0/task:0/device:GPU:0
diagonal_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.321568: I tensorflow/core/common_runtime/placer.cc:114] diagonal_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.322028: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op DiagPart in device /job:localhost/replica:0/task:0/device:GPU:0
x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.322425: I tensorflow/core/common_runtime/placer.cc:114] x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
Sign: (Sign): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.322448: I tensorflow/core/common_runtime/placer.cc:114] Sign: (Sign): /job:localhost/replica:0/task:0/device:GPU:0
y_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.322462: I tensorflow/core/common_runtime/placer.cc:114] y_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.322849: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Sign in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.333823: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Mul in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.334332: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.334700: I tensorflow/core/common_runtime/placer.cc:114] x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
perm: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.334718: I tensorflow/core/common_runtime/placer.cc:114] perm: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
Transpose: (Transpose): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.334731: I tensorflow/core/common_runtime/placer.cc:114] Transpose: (Transpose): /job:localhost/replica:0/task:0/device:GPU:0
y_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.334742: I tensorflow/core/common_runtime/placer.cc:114] y_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.335267: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Transpose in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.342633: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
tensor: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.343070: I tensorflow/core/common_runtime/placer.cc:114] tensor: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
shape: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.343097: I tensorflow/core/common_runtime/placer.cc:114] shape: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
Reshape: (Reshape): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.343117: I tensorflow/core/common_runtime/placer.cc:114] Reshape: (Reshape): /job:localhost/replica:0/task:0/device:GPU:0
output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.343132: I tensorflow/core/common_runtime/placer.cc:114] output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.343815: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Reshape in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.347316: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Mul in device /job:localhost/replica:0/task:0/device:GPU:0
resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.348034: I tensorflow/core/common_runtime/placer.cc:114] resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.348058: I tensorflow/core/common_runtime/placer.cc:114] VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.348550: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.348896: I tensorflow/core/common_runtime/placer.cc:114] resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.348912: I tensorflow/core/common_runtime/placer.cc:114] value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.348922: I tensorflow/core/common_runtime/placer.cc:114] AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.349273: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.349657: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.349863: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
dims: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.350162: I tensorflow/core/common_runtime/placer.cc:114] dims: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.350183: I tensorflow/core/common_runtime/placer.cc:114] value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
Fill: (Fill): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.350203: I tensorflow/core/common_runtime/placer.cc:114] Fill: (Fill): /job:localhost/replica:0/task:0/device:GPU:0
output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.350213: I tensorflow/core/common_runtime/placer.cc:114] output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.350732: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Fill in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.354235: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.354513: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.354638: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Fill in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.354780: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.354859: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Fill in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.354995: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
values_0: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.355260: I tensorflow/core/common_runtime/placer.cc:114] values_0: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
values_1: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.355270: I tensorflow/core/common_runtime/placer.cc:114] values_1: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
values_2: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.355276: I tensorflow/core/common_runtime/placer.cc:114] values_2: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
axis: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-20 15:15:16.355285: I tensorflow/core/common_runtime/placer.cc:114] axis: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
ConcatV2: (ConcatV2): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.355299: I tensorflow/core/common_runtime/placer.cc:114] ConcatV2: (ConcatV2): /job:localhost/replica:0/task:0/device:GPU:0
output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.355305: I tensorflow/core/common_runtime/placer.cc:114] output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.355806: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op ConcatV2 in device /job:localhost/replica:0/task:0/device:GPU:0
resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.359677: I tensorflow/core/common_runtime/placer.cc:114] resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.359707: I tensorflow/core/common_runtime/placer.cc:114] VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.360132: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.360527: I tensorflow/core/common_runtime/placer.cc:114] resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.360544: I tensorflow/core/common_runtime/placer.cc:114] value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.360554: I tensorflow/core/common_runtime/placer.cc:114] AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-20 15:15:16.360967: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0
Traceback (most recent call last):
File "/home/azureuser/env_tf_nightly2/lib/python3.8/site-packages/keras/engine/training.py", line 510, in build
self.call(x, **kwargs)
File "/home/azureuser/env_tf_nightly2/lib/python3.8/site-packages/keras/engine/sequential.py", line 427, in call
outputs = layer(inputs, **kwargs)
File "/home/azureuser/env_tf_nightly2/lib/python3.8/site-packages/keras/layers/rnn/base_rnn.py", line 556, in __call__
return super().__call__(inputs, **kwargs)
File "/home/azureuser/env_tf_nightly2/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/azureuser/env_tf_nightly2/lib/python3.8/site-packages/tensorflow/python/framework/op_def_library.py", line 56, in _SatisfiesTypeConstraint
raise TypeError(
TypeError: Exception encountered when calling layer 'lstm' (type LSTM).
Value passed to parameter 'input' has DataType bfloat16 not in list of allowed values: float16, float32, float64
Call arguments received by layer 'lstm' (type LSTM):
• inputs=tf.Tensor(shape=(4, 50, 1), dtype=bfloat16)
• mask=None
• training=None
• initial_state=None
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "test_bfloat16.py", line 72, in <module>
model.build(input_shape=input_shape)
File "/home/azureuser/env_tf_nightly2/lib/python3.8/site-packages/keras/engine/sequential.py", line 383, in build
super().build(input_shape)
File "/home/azureuser/env_tf_nightly2/lib/python3.8/site-packages/keras/engine/training.py", line 512, in build
raise ValueError(
ValueError: You cannot build your model by calling `build` if your layers do not support float type inputs. Instead, in order to instantiate and build your model, call your model on real tensor data (of the correct dtype).
The actual error from `call` is: Exception encountered when calling layer 'lstm' (type LSTM).
Value passed to parameter 'input' has DataType bfloat16 not in list of allowed values: float16, float32, float64
Call arguments received by layer 'lstm' (type LSTM):
• inputs=tf.Tensor(shape=(4, 50, 1), dtype=bfloat16)
• mask=None
• training=None
• initial_state=None.
```
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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/59742/checks?check_run_id=11468280945) 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.",
"Looks good, it's just that it breaks the flow of the security related badges. Can you move it after the fuzzing ones please?",
"ok moved it :) thanks"
] | 2023-02-20T14:32:55 | 2023-02-22T17:51:05 | 2023-02-22T17:51:04 | CONTRIBUTOR | null | false | {
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} | Tensorflow is a top 1% project on OSSRank, and this badge shows Tensorflow's current rank in the world of open source. I hope you accept the PR, love the project :)
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"@ysong2123,\r\n Could you try building with the full Xcode? and also please make sure to set the **DEVELOPER_DIR** environment variable to the Xcode path before running the build, i.e, run something like `export DEVELOPER_DIR=/Applications/Xcode.app/Contents/Developer`(path to Xcode) before running the bazel build command and try running the build again.\r\n\r\nIf using a .bazelrc file, include build --action_env DEVELOPER_DIR=/Applications/Xcode.app/Contents/Developer in it.\r\n\r\nAlso there is an issue opened for the similar error on v2.11 and v2.12 where the developer is involved.\r\nhttps://github.com/tensorflow/tensorflow/issues/58368 Thank you!\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/59741\">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/59741\">No</a>\n",
"i replaced another ld in another place, so we don't need take this, thanks"
] | 2023-02-20T14:08:45 | 2023-02-22T05:15:08 | 2023-02-22T05:14:38 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Build/Install
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
tag: v2.11.0
### Custom Code
No
### OS Platform and Distribution
MacOS Ventura13
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
5.3.0
### GCC/Compiler version
clang 14.0
### CUDA/cuDNN version
none
### GPU model and memory
none
### Current Behaviour?
```shell
ERROR: /Users/ysong2/Downloads/tensorflow/tensorflow/python/BUILD:358:27: Linking tensorflow/python/_pywrap_py_exception_registry.so failed: (Aborted): cc_wrapper.sh failed: error executing command external/local_config_cc/cc_wrapper.sh @bazel-out/darwin-opt/bin/tensorflow/python/_pywrap_py_exception_registry.so-2.params
ld: malformed trie, node past end file 'bazel-out/darwin-opt/bin/_solib_darwin_x86_64/libtensorflow_Spython_S_Upywrap_Utensorflow_Uinternal.so'
clang: error: linker command failed with exit code 1 (use -v to see invocation)
Error in child process '/usr/bin/xcrun'. 1
external/local_config_cc/cc_wrapper.sh: line 69: 23910 Abort trap: 6 "$(/usr/bin/dirname "$0")"/wrapped_clang "$@"
Target //tensorflow/tools/pip_package:build_pip_package failed to build.
I want to compile and build fully success.
```
### Standalone code to reproduce the issue
```shell
execting: "bazel build //tensorflow/tools/pip_package:build_pip_package" under tensorflow root directory then this erorr occurs.
```
### Relevant log output
_No response_</details> | {
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"@gogogwwb \r\nIn sync training, all workers train over different slices of input data in sync, and aggregating gradients at each step. In async training, all workers are independently training over the input data and updating variables asynchronously. Typically sync training is supported via all-reduce and async through parameter server architecture. The final training result of async and sync parallel distributed training depends upon specific implementation, optimization, and training algorithm. Please refer to the [doc_1](https://www.tensorflow.org/guide/distributed_training#types_of_strategies) and [doc_2](https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras#choose_the_right_strategy) for your reference and let us know if it helps.\r\n\r\nThank you !",
"Closing as stale. Please reopen if you'd like to work on this further.\r\n\r\n Thanks",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59740\">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/59740\">No</a>\n"
] | 2023-02-20T14:05:03 | 2023-03-12T19:32:28 | 2023-03-12T19:32:25 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Support
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
tf2.8
### 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 Behaviour?
```shell
When distributed training adopts asynchronous synchronous parallel, the parameters updated by each worker are inconsistent. Is the final result of distributed training the slowest parameter updated by workers?
```
### Standalone code to reproduce the issue
```shell
When distributed training adopts asynchronous synchronous parallel, the parameters updated by each worker are inconsistent. Is the final result of distributed training the slowest parameter updated by workers?
```
### Relevant log output
_No response_</details> | {
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"This happens because vars.bzl is created by the implementation function of llvm_configure() which is not called in the case of a repository override. I think the instructions should be modified to override llvm-raw instead which only requires creating a WORKSPACE and BUILD.bazel in the LLVM repo and does not need to create an overlay. I'm happy to provide a patch for that.",
"Hi, @RoboTux \r\n\r\nApologize for the delay and It seems like there is some issue with the `local_repository` rule in `bazel` is for external bazel repositories only. To use a non-bazel external repository, we need to use `new_local_repository `which takes `build_file` as an argument.\r\n\r\nBazel has the `new_local_repository` check this link `https://bazel.build/reference/be/workspace#new_local_repository` which allows you to use a local directory as a repo.\r\n\r\nYou will need to change the `tf_http_archive(name = \"llvm-project\",...)` section in `tensorflow/workspace.bzl `into `new_local_repository`, and add necessary BUILD files for llvm, mlir, and mlir/tests directory in your `local llvm directory.` you can check this [discussion](https://groups.google.com/a/tensorflow.org/g/mlir/c/EVWnlwPKLT4) and stack overflow [answer](https://stackoverflow.com/questions/42918662/bazel-error-building-tensorflow-with-local-llvm-repository), I hope it will help you to resolve your issue.\r\n\r\nIf something is missing in the instructions and you've workaround for it then PR will be welcomed from your end so please submit PR for it ? Thank you for noticing the issue in our instructions. Thank you!",
"Any reason why we don't override llvm-raw instead of llvm-project? When I tried it seemed to work fine and avoid the issues mentionned here. The patch I have is to change the instructions to override llvm-raw instead of llvm-project.",
"Hi, dude! Did u solve this problem? I ran into the same problem as you. \r\n\r\nI followed the steps given in the [readme](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/compiler/mlir), and roll back the version of llvm-project to [corresponding version](https://github.com/tensorflow/tensorflow/tree/master/third_party/llvm).\r\n\r\n#### When I start build\r\n```shell\r\n bazel clean --expunge\r\n\r\nbazel build --override_repository=llvm-project=$LLVM_BAZEL_OVERLAY \\\r\n -c opt tensorflow/compiler/mlir:tf-opt\r\n\r\nbazel build --override_repository=llvm-project=$LLVM_BAZEL_OVERLAY \\\r\n -c opt tensorflow/compiler/mlir:tf-mlir-translate\r\n```\r\n\r\n#### I got Error output below\r\n```shell\r\nERROR: /private/var/tmp/_bazel_isolateya/af219aaee912b96bcf0876417404d886/external/llvm-project/mlir/BUILD.bazel:5904:11: error loading package '@llvm-project//llvm': at /private/var/tmp/_bazel_isolateya/af219aaee912b96bcf0876417404d886/external/llvm-project/llvm/config.bzl:8:5: cannot load '@llvm-project//:vars.bzl': no such file and referenced by '@llvm-project//mlir:Transforms'\r\nERROR: Analysis of target '//tensorflow/compiler/mlir:tf-opt' failed; build aborted: \r\nINFO: Elapsed time: 38.684s\r\nINFO: 0 processes.\r\nFAILED: Build did NOT complete successfully (122 packages loaded, 463 targets \\\r\nconfigured)\r\n currently loading: @com_google_protobuf// ... (4 packages)\r\n Fetching https://storage.googleapis.com/.../nsync/archive/1.25.0.tar.gz\r\n Fetching https://storage.googleapis.com/.../archive/v2.0.6.tar.gz\r\n Fetching https://storage.googleapis.com/...65082ee350509af1d113344d.tar.gz\r\n```\r\n```shell\r\nERROR: /Volumes/back/mlir_project/tensorflow/tensorflow/compiler/mlir/BUILD:219:13: error loading package '@llvm-project//llvm': at /private/var/tmp/_bazel_isolateya/af219aaee912b96bcf0876417404d886/external/llvm-project/llvm/config.bzl:8:5: cannot load '@llvm-project//:vars.bzl': no such file and referenced by '//tensorflow/compiler/mlir:tf-mlir-translate'\r\nERROR: Analysis of target '//tensorflow/compiler/mlir:tf-mlir-translate' failed; build aborted: Analysis failed\r\nINFO: Elapsed time: 27.395s\r\nINFO: 0 processes.\r\nFAILED: Build did NOT complete successfully (54 packages loaded, 247 targets configured)\r\n currently loading: @llvm-project//llvm\r\n Fetching @local_config_cc; Building xcode-locator\r\n Fetching https://storage.googleapis.com/.../abseil-cpp/archive/273292d1cfc0a94a65082ee350509af1d113344d.tar.gz\r\n```\r\n\r\n#### Standalone code to reproduce the issue\r\n```shell\r\nLLVM_SRC=/Volumes/back/mlir_project/llvm-project\r\nLLVM_BAZEL_OVERLAY=${LLVM_SRC}/bazel # Note: this can be anywhere\r\nmkdir -p ${LLVM_BAZEL_OVERLAY}\r\n# This will symlink your LLVM sources with the BUILD files to be usable by Bazel.\r\npython ${LLVM_SRC}/utils/bazel/overlay_directories.py \\\r\n --src ${LLVM_SRC} \\\r\n --overlay ${LLVM_SRC}/utils/bazel/llvm-project-overlay/ \\\r\n --target ${LLVM_BAZEL_OVERLAY}\r\ntouch ${LLVM_BAZEL_OVERLAY}/BUILD.bazel ${LLVM_BAZEL_OVERLAY}/WORKSPACE\r\necho 'llvm_targets = [\"X86\",\"NVPTX\",\"AMDGPU\",\"AArch64\",\"ARM\"]' > ${LLVM_BAZEL_OVERLAY}/llvm/targets.bzl\r\n```\r\n\r\n#### Environment\r\n```shell\r\nbazel 5.3.0\r\nopenjdk 11.0.16.1\r\npython 3.9.16\r\nllvm 15.0.5\r\nOS mac(M2)\r\n```\r\n\r\n",
"Hi, @isolateFeng \r\n\r\nApologize for the delayed response and May I know have you tried this [workaround](https://github.com/tensorflow/tensorflow/issues/59739#issuecomment-1454792939), If yes is it resolving your issue or not ? I see @RoboTux has submitted one PR [59942](https://github.com/tensorflow/tensorflow/pull/59942) to solve this issue so we'll wait to merge that PR, meanwhile if you did not follow above workaround could you please give it try ? Thank you!",
"Hi, @gaikwadrahul8 \r\n\r\nSorry for replying so late. I did try this, but I'm not sure if I tried it correctly.\r\n\r\nI modified the `tensorflow/workspace.bzl`(https://github.com/tensorflow/tensorflow/blob/master/tensorflow/workspace2.bzl), added the following content to it, but it did't work\r\n\r\n```shell\r\ntf_http_archive(\r\n name = \"llvm-project\",\r\n urls = [\r\n /Volumes/back/mlir_project/llvm-project\r\n ],\r\n build_file = str(Label(\"//third_party/llvm:llvm.BUILD\")),\r\n )\r\n```\r\n\r\nI just don't know how to modify the `tensorflow/workspace.bzl` correctly, for I don't know anything about bazel",
"I saw RoboTux's PR https://github.com/tensorflow/tensorflow/pull/59942 had failed merge test, maybe I'll try it (qwq\r\n\r\nor could someone give me an alternative method that I can try",
"> I saw RoboTux's PR #59942 had failed merge test, maybe I'll try it (qwq\r\n> \r\n> or could someone give me an alternative method that I can try\r\n\r\nI don't think the failure is related to the patch since it fails to find patchelf and the patch only changes the documentation which does not require patchelf to build obviously. I'd be interested to know if the new instructions suggested in the patch fail to work for you.\r\n\r\nBest regards,\r\nThomas",
"Hi, Thomas\r\nI'm sorry to inform you that it(PR https://github.com/tensorflow/tensorflow/pull/59942) didn't work for me ：(\r\nit returns me the ERROR below\r\n```shell\r\nINFO: Found applicable config definition build:short_logs in file /Volumes/back/mlir_project/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /Volumes/back/mlir_project/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:macos in file /Volumes/back/mlir_project/tensorflow/.bazelrc: --apple_platform_type=macos --copt=-DGRPC_BAZEL_BUILD --copt=-w --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\r\nERROR: no such package '@llvm-raw//utils/bazel': The repository's path is \"llvm-raw\" (absolute: \"/Volumes/back/mlir_project/llvm-project/bazel\") but it does not exist or is not a directory.\r\nINFO: Elapsed time: 2.574s\r\nINFO: 0 processes.\r\nFAILED: Build did NOT complete successfully (0 packages loaded)\r\n```\r\nI will try to try other ways I can find, thanks a lot\r\n\r\nBest regards,\r\nTingfeng",
"Why does it mention (absolute: \"/Volumes/back/mlir_project/llvm-project/bazel\") insteal of /Volumes/back/mlir_project/llvm-project? Is your llvm tree checkout out in the bazel directory?",
"I used the command `git checkout 31c39439a894` in the llvm-project main folder ( download at 22:30 3.7.2023)",
"> I used the command `git checkout 31c39439a894` in the llvm-project main folder ( download at 22:30 3.7.2023)\r\n\r\nWhat was the bazel command that you ran? Note: there is a mistake in my patch, the bazel build line should be LLVM_SRC not LLVM_BAZEL_OVERLAY",
"There have been too many things in the course recently, I sincerely apologize for my late answer\r\n\r\n### at the first I used \r\n```shell\r\nLLVM_SRC=/Volumes/back/mlir_project/llvm-project\r\ntouch ${LLVM_SRC}/BUILD.bazel ${LLVM_SRC}/WORKSPACE\r\n\r\n# build\r\nbazel build --override_repository=llvm-raw=$LLVM_BAZEL_OVERLAY \\\r\n-c opt tensorflow/compiler/mlir:tf-opt\r\n```\r\nI was so impetuous that I didn't notice the problem with the bazel build line\r\n\r\n\r\n### just now I tried \r\n```shell\r\nLLVM_SRC=/Volumes/back/mlir_project/llvm-project\r\ntouch ${LLVM_SRC}/BUILD.bazel ${LLVM_SRC}/WORKSPACE\r\n\r\n# build\r\nbazel build --override_repository=llvm-raw=$LLVM_SRC \\\r\n-c opt tensorflow/compiler/mlir:tf-opt\r\n```\r\n\r\nI get Error below\r\n```shell\r\nERROR: /private/var/tmp/_bazel_isolateya/af219aaee912b96bcf0876417404d886/external/com_google_protobuf/BUILD.bazel:459:10: Linking external/com_google_protobuf/protoc failed: (Exit 1): cc_wrapper.sh failed: error executing command external/local_config_cc/cc_wrapper.sh @bazel-out/darwin_arm64-opt-exec-50AE0418/bin/external/com_google_protobuf/protoc-2.params\r\nclang: error: invalid linker name in argument '-fuse-ld=-debugger-tuning=lldb'\r\n```\r\n\r\nI did install llvm, `lldb --version` will output `lldb version 15.0.5`\r\n\r\n### the full build info\r\n```\r\nINFO: Found applicable config definition build:short_logs in file /Volumes/back/mlir_project/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /Volumes/back/mlir_project/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:macos in file /Volumes/back/mlir_project/tensorflow/.bazelrc: --apple_platform_type=macos --copt=-DGRPC_BAZEL_BUILD --copt=-w --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\r\nINFO: Analyzed target //tensorflow/compiler/mlir:tf-opt (369 packages loaded, 25834 targets configured).\r\nINFO: Found 1 target...\r\nERROR: /private/var/tmp/_bazel_isolateya/af219aaee912b96bcf0876417404d886/external/com_google_protobuf/BUILD.bazel:459:10: Linking external/com_google_protobuf/protoc failed: (Exit 1): cc_wrapper.sh failed: error executing command external/local_config_cc/cc_wrapper.sh @bazel-out/darwin_arm64-opt-exec-50AE0418/bin/external/com_google_protobuf/protoc-2.params\r\nclang: error: invalid linker name in argument '-fuse-ld=-debugger-tuning=lldb'\r\nTarget //tensorflow/compiler/mlir:tf-opt failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nINFO: Elapsed time: 447.115s, Critical Path: 66.04s\r\nINFO: 6768 processes: 4338 internal, 2430 local.\r\nFAILED: Build did NOT complete successfully\r\n```\r\n\r\n\r\nThank you for all your help\r\n\r\nBest regards,\r\nTingfeng\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/59739\">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/59739\">No</a>\n"
] | 2023-02-20T12:53:23 | 2023-03-14T21:51:14 | 2023-03-14T21:51:11 | CONTRIBUTOR | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Build/Install
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
2.9
### Custom Code
No
### OS Platform and Distribution
Linux Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
5.3.0
### GCC/Compiler version
11.3
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
```shell
Following instructions from tensorflow/compiler/mlir/README.md with ["X86", "AArch64", "ARM"] in targets.bzl results in a build failure with:
cannot load '@llvm-project//:vars.bzl': no such file and referenced by '//tensorflow/compiler/mlir/tensorflow:tensorflow_test_passes'
```
### Standalone code to reproduce the issue
```shell
LLVM_SRC=/home/ubuntu/src/llvm-project
LLVM_BAZEL_OVERLAY=/home/ubuntu/src/llvm-overlay
mkdir -p ${LLVM_BAZEL_OVERLAY}
python3 ${LLVM_SRC}/utils/bazel/overlay_directories.py \
--src ${LLVM_SRC} \
--overlay ${LLVM_SRC}/utils/bazel/llvm-project-overlay/ \
--target ${LLVM_BAZEL_OVERLAY}
touch ${LLVM_BAZEL_OVERLAY}/BUILD.bazel ${LLVM_BAZEL_OVERLAY}/WORKSPACE
echo 'llvm_targets = ["X86", "AArch64", "ARM"]' > ${LLVM_BAZEL_OVERLAY}/llvm/targets.bzl
```
### Relevant log output
```shell
INFO: Repository stablehlo instantiated at:
/home/ubuntu/src/tensorflow/WORKSPACE:15:14: in <toplevel>
/home/ubuntu/src/tensorflow/tensorflow/workspace2.bzl:960:28: in workspace
/home/ubuntu/src/tensorflow/tensorflow/workspace2.bzl:81:14: in _initialize_third_party
/home/ubuntu/src/tensorflow/third_party/stablehlo/workspace.bzl:11:20: in repo
/home/ubuntu/src/tensorflow/third_party/repo.bzl:136:21: in tf_http_archive
Repository rule _tf_http_archive defined at:
/home/ubuntu/src/tensorflow/third_party/repo.bzl:89:35: in <toplevel>
INFO: Repository nsync instantiated at:
/home/ubuntu/src/tensorflow/WORKSPACE:15:14: in <toplevel>
/home/ubuntu/src/tensorflow/tensorflow/workspace2.bzl:967:21: in workspace
/home/ubuntu/src/tensorflow/tensorflow/workspace2.bzl:470:20: in _tf_repositories
/home/ubuntu/src/tensorflow/third_party/repo.bzl:136:21: in tf_http_archive
Repository rule _tf_http_archive defined at:
/home/ubuntu/src/tensorflow/third_party/repo.bzl:89:35: in <toplevel>
ERROR: /home/ubuntu/src/tensorflow/tensorflow/compiler/mlir/tensorflow/BUILD:1455:11: error loading package '@llvm-project//llvm': at /home/ubuntu/.cache/bazel/_bazel_ubuntu/b813f517b143a3c1665dd902035fe00f/external/llvm-project/llvm/config.bzl:8:5: cannot load '@llvm-project//:vars.bzl': no such file and referenced by '//tensorflow/compiler/mlir/tensorflow:tensorflow_test_passes'
ERROR: Analysis of target '//tensorflow/compiler/mlir:tf-opt' failed; build aborted:
INFO: Elapsed time: 59.508s
INFO: 0 processes.
FAILED: Build did NOT complete successfully (84 packages loaded, 255 targets configured)
currently loading: @llvm-project//mlir ... (3 packages)
Fetching https://storage.googleapis.com/.../github.com/openxla/stablehlo/archive/51f005f0a8ff6e28f535adfec4de936cb4097aa4.zip
Fetching https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/nsync/archive/1.25.0.tar.gz
```
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"Hi @tfeher This PR is in draft, any update on this? Please. Thank you!",
"closing this in favor of #60076 "
] | 2023-02-20T09:53:20 | 2023-03-27T21:09:56 | 2023-03-27T21:09:56 | CONTRIBUTOR | null | true | {
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} | To facilitate conversion of variable ops (used in the experimental `disable_graph_freezing` mode) the converter annotates the graph with the shape of the variable ops.
This PR fixes the mapping between captured input names and variables, so that the graph can be annotated correctly. | {
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"Hi @ShengYang1 This PR is duplicate of [#59499](https://github.com/tensorflow/tensorflow/pull/59499). Hence closing this. Thank you for your contribution. "
] | 2023-02-20T05:52:03 | 2023-02-21T12:48:20 | 2023-02-21T12:48:17 | CONTRIBUTOR | null | false | {
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} | When the input of AddN is nested variant(eg, nested TensorList), for current implementation, `TF_AddNVariant` will leave a variant tensor for `binary_add_func` used by vendor extension, which cannot be handled in extension as variant class is opaque for extension. So recursively handling for variant is needed, and `TF_ZerosLikeVariant` did same work, code [link](https://github.com/tensorflow/tensorflow/blob/e35b2853331f3caed7612f2ae0596fd2fe1353f3/tensorflow/c/kernels_experimental.cc#L529) for `TF_ZerosLikeVariant`. | {
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"@retonym,\r\nUsually Keras SavedModel uses [tf.saved_model.save](https://www.tensorflow.org/api_docs/python/tf/saved_model/save) to save the model and all trackable objects attached to the model (e.g. layers and variables). The model config, weights, and optimizer are saved in the SavedModel. Additionally, for every Keras layer attached to the model, the SavedModel stores:\r\n\r\n- the config and metadata -- e.g. name, dtype, trainable status\r\n- traced call and loss functions, which are stored as TensorFlow subgraphs.\r\n\r\nThe traced functions allow the SavedModel format to save and load custom layers without the original class definition.\r\nAlso tf.keras.initializers.Constant is the Initializer that generates tensors with constant values and please take a look at this issue for the [reference](https://stackoverflow.com/questions/63630875/change-keras-model-variable). 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/59736\">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/59736\">No</a>\n"
] | 2023-02-20T02:51:57 | 2023-03-29T02:02:15 | 2023-03-29T02:02:12 | CONTRIBUTOR | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Performance
### Have you reproduced the bug with TF nightly?
Yes
### Source
binary
### Tensorflow Version
tf 2.11
### 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 Behaviour?
Keras save_model keeps variable status instead of const. It will block some inference opportunities, such as Conv + Batchnormalization folding.
We expect Keras save format can provide similar APIs like [convert_variables_to_constants](https://www.tensorflow.org/api_docs/python/tf/compat/v1/graph_util/convert_variables_to_constants) in TF1 pb format.
With variables replaced by constant in inference, more optimization can be done.
### Standalone code to reproduce the issue
```shell
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
# tf.keras.utils.set_random_seed(34)
np.random.seed(34)
tf.random.set_seed(34)
training_data = np.random.normal(0, 1, size=(32, 5, 5, 3)).astype("float32")
test_data = np.random.normal(0, 1, size=(16, 5, 5, 3)).astype("float32")
label_data = np.random.normal(0, 1, size=(32, 5, 5, 3)).astype("float32")
model = tf.keras.models.Sequential()
model.add(tf.keras.Input(shape=(5, 5, 3)))
model.add(tf.keras.layers.Conv2D(filters=3, kernel_size=3, strides=(1, 1), padding="same", data_format="channels_last"))
model.add(tf.keras.layers.BatchNormalization())
model.summary()
# Compile the model
model.compile(optimizer=keras.optimizers.Adam(1e-3), loss=tf.keras.losses.MeanSquaredError())
# Train the model for 1 epoch from Numpy data
batch_size = 1
print("Fit on NumPy data")
history = model.fit(training_data, label_data, batch_size=batch_size, epochs=3)
print(history.history)
predictions = model.predict(training_data, batch_size=2)
print(predictions)
print(predictions.shape)
```
### Relevant log output
```shell
We expect the Batchnormalization op is folded in above code.
```
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"Standalone code `y = tf.keras.layers.Cropping1D(cropping=(-1,0))(x)` parameter should be -1 ",
"I install tf with `pip install tensorflow-cpu` (version is 2.11)",
"@CuiDonCai,\r\nThe `cropping` argument must be a tuple of 2 integers and does not satisfy the requirement `>= 0`. Also the argument cropping should be Int or tuple of int (length 2) How many units should be trimmed off at the beginning and end of the cropping dimension (axis 1). If a single int is provided, the same value will be used for both.\r\nCase 1:\r\n```\r\nx = np.arange(np.prod(input_shape)).reshape(input_shape)\r\ny = tf.keras.layers.Cropping1D(cropping=(-20, 0))(x)\r\n```\r\n\r\nCase 2:\r\n\r\n```\r\nx = np.arange(np.prod(input_shape)).reshape(input_shape)\r\ny = tf.keras.layers.Cropping1D(cropping=(2, 0))(x)\r\n```\r\n\r\nKindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/39a24c2f0d6ae6050631397cb80906f9/untitled975.ipynb) and also please take a look at this official [document](https://keras.io/api/layers/reshaping_layers/cropping1d/#cropping1d-class) and the PR for the [reference](https://github.com/keras-team/keras/pull/14970). Thank you!\r\n",
"First, `The cropping argument must be a tuple of 2 integers and does not satisfy the requirement >= 0` is only true when `tensorflow` version is earlier than 2.6 (the reason is [here](https://github.com/keras-team/keras/blob/r2.11/keras/utils/conv_utils.py), and line 58). \r\nSecond, `Kindly find the gist of it` [here](https://colab.research.google.com/gist/tilakrayal/39a24c2f0d6ae6050631397cb80906f9/untitled975.ipynb) , The result has an error. \r\n\r\n\r\nfinally, I give two examples.(I'm having problems with my server environment, so I chose tf versions 2.9.0-GPU and 1.15.0 for comparison. And I think it's also illustrative)\r\ncase 1:\r\n\r\n\r\ncase 2:\r\n\r\n",
"So my question is how to use `Cropping1D()` in tensorflow 2.11 consistent with the behavior in tensorflow 1.15. Or is there another way to do it in tensorflow 2.11. Thanks so much for response.",
"@CuiDonCai,\r\nThanks for opening this issue. Development of keras moved to another [repository](https://github.com/keras-team/keras/issues). \r\n\r\n\r\n\r\n\r\nCould you please post this issue on keras-team/keras [repo](https://github.com/keras-team/keras/issues).\r\nTo know more please refer:\r\nhttps://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999\r\nThank you!\r\n",
"ok. Thanks for your reply.",
"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/59735\">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/59735\">No</a>\n"
] | 2023-02-20T02:37:05 | 2023-02-27T02:28:50 | 2023-02-27T02:28:47 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Support
### Have you reproduced the bug with TF nightly?
No
### Source
source
### Tensorflow Version
tf 2.11
### Custom Code
Yes
### OS Platform and Distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.8.10
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
```shell
tf.keras.layers.Cropping1D((-20,0))(out) report error, The 'cropping' argument must be a tuple of 2 interges. The really reson is keras.utils.conv_utils.normalize_tuple() update.
And tf <= 2.6, the error is not exist. Is there a way to fix or keep away from this error?
```
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
input_shape = (2, 3, 2)
x = np.arange(np.prod(input_shape)).reshape(input_shape)
y = tf.keras.layers.Cropping1D(cropping=(-20,0))(x)
```
```
### Relevant log output
_No response_</details> | {
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"Hi @nurskurmanbekov Can you please sign CLA. Thank you!"
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"Hi @MrShadowDev Can you please sign CLA. Thank you!",
"Signed the CLA",
"Hi @sjamesr Can you please review this PR ? Thank you!",
"Hi @sjamesr Can you please review this PR ? Thank you!",
"Hi @sjamesr Can you please review this PR ? Thank you!",
"Hi @sjamesr Can you please review this PR ? Thank you!",
"Hi @sjamesr Can you please review this PR ? Thank you!",
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"> Hi @MrShadowDev Can you please confirm if this PR is still valid? Thank you!\r\n\r\nMaybe",
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"Hi @sjamesr Can you please review this PR ? Thank you!",
"Hi @sjamesr Can you please review this PR ? Thank you!"
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- Made the `FULL_TRACE_RUN_OPTIONS` variable private and added the private keyword.
- Added the final keyword to the `FULL_TRACE_RUN_OPTIONS` variable to indicate that it is a constant.
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"@JackTemaki \r\nCould you please provide a reproducible code or complete code to replicate the issue reported here ?\r\n\r\nThank you !\r\n",
"The two lines of python code I posted are already sufficient to show the mismatch in allocated memory in the \"created device\" log statement. If you want something that shows also the mismatch in `nvidia-smi` usage, I can try to produce a minimal example.",
"Opening an `ipython` session with just:\r\n```python3\r\nIn [1]: import tensorflow as tf\r\nIn [2]: x = tf.constant(1)\r\n```\r\nIs sufficient. This will block the GPU and show the difference in memory allocation in both the log message and in `nvidia-smi`.\r\n\r\nI also tried `export TF_FORCE_GPU_ALLOW_GROWTH=true` but this did not change anything.",
"It seems that TF changed its default memory allocation at some point. I was able to increase the allocated memory correctly using the old `v1` API with e.g.:\r\n```python\r\ngpu_options = tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction=1.0)\r\nconfig = tf.compat.v1.ConfigProto(gpu_options=gpu_options)\r\n\r\nloaded_graph = tf.Graph()\r\nwith tf.compat.v1.Session(graph=loaded_graph, config=config) as sess:\r\n pass\r\n```\r\n\r\n~~Nevertheless, with the \"new\" API this did not work, I tried it with the following:~~\r\nFor the new API it does work using:\r\n```python\r\ngpus = tf.config.list_physical_devices('GPU')\r\nif gpus:\r\n try:\r\n tf.config.set_logical_device_configuration(\r\n gpus[0],\r\n [tf.config.LogicalDeviceConfiguration(memory_limit=10500)]) # force 10.5GB\r\n logical_gpus = tf.config.list_logical_devices('GPU')\r\n print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\r\n except RuntimeError as e:\r\n # Virtual devices must be set before GPUs have been initialized\r\n print(e)\r\n```\r\n",
"Additional note: Manually setting the memory limit to a factor of 0.92 for the old API or to the limit of 10100 for the new API gave us the old behavior again.\r\n\r\nMaybe someone can comment if this is a dangerous solution and newer TF versions need more free space to function properly, but at least for us it seems to work this way.",
"Hi @JackTemaki ,\r\nAs I don't have environment for 2.3v now, I tried to replicate the reported behaviour with Tf2.10,TF2.11 and Tf 2.12v with Ubuntu VM having 2XA100 GPU(40GB) and I observed there is no much difference among Tf2.10,Tf2.11 and Tf2.12 versions.\r\n\r\n**With TF2.9.2 :** Got 38251 MB out of 40000 MB\r\n```\r\n2.9.2\r\n2023-02-22 09:17:40.823117: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-02-22 09:17:42.034974: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 38251 MB memory: -> device: 0, name: NVIDIA A100-SXM4-40GB, pci bus id: 0000:00:04.0, compute capability: 8.0\r\n2023-02-22 09:17:42.036673: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 38251 MB memory: -> device: 1, name: NVIDIA A100-SXM4-40GB, pci bus id: 0000:00:05.0, compute capability: 8.0\r\n(tf2.9) suryanarayanay@surya-ubuntu-22-04:~$ \r\n```\r\n\r\n**With TF2.11v:** Got 38235 MB/ 40000 MB\r\n```\r\n2.11.0\r\n2023-02-22 09:03:54.865804: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-02-22 09:03:56.192602: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 38235 MB memory: -> device: 0, name: NVIDIA A100-SXM4-40GB, pci bus id: 0000:00:04.0, compute capability: 8.0\r\n2023-02-22 09:03:56.194130: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 38235 MB memory: -> device: 1, name: NVIDIA A100-SXM4-40GB, pci bus id: 0000:00:05.0, compute capability: 8.0\r\n(tf2.11) suryanarayanay@surya-ubuntu-22-04:~$ \r\n```\r\n**With TF2.12v:** 38201MB/ 40000MB\r\n```\r\n(tf) suryanarayanay@surya-ubuntu-22-04:~$ python 59732.py\r\n2023-02-22 09:05:10.877697: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n2023-02-22 09:05:11.704764: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-02-22 09:05:13.640413: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2.12.0-rc0\r\n2023-02-22 09:05:22.580744: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 38201 MB memory: -> device: 0, name: NVIDIA A100-SXM4-40GB, pci bus id: 0000:00:04.0, compute capability: 8.0\r\n2023-02-22 09:05:22.583282: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 38201 MB memory: -> device: 1, name: NVIDIA A100-SXM4-40GB, pci bus id: 0000:00:05.0, compute capability: 8.0\r\n(tf) suryanarayanay@surya-ubuntu-22-04:~$ \r\n```\r\nFrom your case it's around 400 MB difference wrt 2.3v and 2.9v which is not much higher and this amount of memory shortage should not lead OOM except very few cases which needs around neck to neck 10 GB RAM. \r\n\r\nThe proposed workaround using logical devices with memory setting to full available memory may works for TF but its not recommended always.Keeping some buffer GPU memory for other processes might be needed depending on individual requirement.Also if you are configuring logical gpus the memory limit configuration will be applied for current process only and for model trainings you may try it.Hope I answered your query. Thanks !\r\n",
"> From your case it's around 400 MB difference wrt 2.3v and 2.9v which is not much higher and this amount of memory shortage should not lead OOM except very few cases which needs around neck to neck 10 GB RAM.\r\n\r\nOur training runs are extremely tuned towards the maximum, so 400mb make the difference between running OOM or not.\r\n\r\n> The proposed workaround using logical devices with memory setting to full available memory may works for TF but its not recommended always.Keeping some buffer GPU memory for other processes might be needed depending on individual requirement.Also if you are configuring logical gpus the memory limit configuration will be applied for current process only and for model trainings you may try it.Hope I answered your query. Thanks !\r\n\r\nYes, for us everything is solved. Taking 100% is certainly not a good idea and lead to crashes. This is why we sticked to 92-93% now, which seems stable and gives us even slightly more memory than before.\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/59732\">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/59732\">No</a>\n"
] | 2023-02-18T13:44:05 | 2023-02-22T13:09:48 | 2023-02-22T13:09:45 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Performance
### Have you reproduced the bug with TF nightly?
No
### Source
binary
### Tensorflow Version
tf 2.9
### Custom Code
No
### OS Platform and Distribution
Ubuntu 22
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
11.2
### GPU model and memory
_No response_
### Current Behaviour?
We recently switched our cluster system from Ubuntu 16.04, TF2.3, CUDA 10.1 to Ubuntu 22.04, TF2.9 and CUDA 11.2. After some trainings crashed with an out-of-memory error we found that Tensorflow is now utilizing less memory than before (ca. 600mb on an RTX 2080 Ti).
The nvidia-driver is 510.85.02 for Ubuntu 22 and 510.54 for Ubuntu 16.
I understand many factors can play a role here (OS version, TF version, Cuda version). I will update the information once I am able to also run TF2.3/Cuda10.1 on Ubuntu 22.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
tf.device("gpu")
```
### Relevant log output
Before (Ubuntu 16.04, TF 2.3, CUDA 10.1):
```
Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10078 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:e1:00.0, compute capability: 7.5)
```
relevant `nvidia-smi` output:
```
|===============================+======================+======================|
| 0 NVIDIA GeForce ... Off | 00000000:01:00.0 Off | N/A |
| 45% 73C P2 215W / 250W | 10838MiB / 11264MiB | 90% Default |
| | | N/A |
```
After (Ubuntu 22.04, TF2.9, CUDA 11.2)
```
Created device /device:GPU:0 with 9650 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:20:00.0, compute capability: 7.5
```
relevant `nvidia-smi` output:
```
|===============================+======================+======================|
| 0 NVIDIA GeForce ... Off | 00000000:1D:00.0 Off | N/A |
| 78% 72C P2 191W / 250W | 10266MiB / 11264MiB | 79% Default |
| | | N/A |
```
```
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"@njzjz, thank you for reporting this issue. We are trying to investigate the issue and will update here soon. Thank you! ",
"@njzjz, Apologies for the delay. Since 2.12 is not the stable version, it seems that the wheel files will be available in the next stable release. As a workaround, kindly use the .whl files of the current stable version provided [here](https://pypi.org/project/tensorflow-cpu-aws/2.11.0/#files). Thank you! ",
"I've noticed that tensorflow-cpu-aws 2.12.0rc1 has resolved this problem.",
"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/59731\">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/59731\">No</a>\n"
] | 2023-02-17T21:30:58 | 2023-03-17T07:28:12 | 2023-03-13T15:53:05 | CONTRIBUTOR | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
No
### Source
binary
### Tensorflow Version
2.12.0rc0
### Custom Code
No
### OS Platform and Distribution
aarch64
### 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 Behaviour?
```shell
See https://pypi.org/project/tensorflow-cpu-aws/2.12.0rc0/#files. There is only one file.
```
### Standalone code to reproduce the issue
```shell
Just go to https://pypi.org/project/tensorflow-cpu-aws/2.12.0rc0/#files.
Or reproduce using `pip` on aarch64 and Python 3.10:
pip install tensorflow==2.12.0rc0
```
### Relevant log output
```shell
Collecting tensorflow==2.12.0rc0
Downloading tensorflow-2.12.0rc0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (2.0 kB)
ERROR: Could not find a version that satisfies the requirement tensorflow-cpu-aws==2.12.0-rc0; platform_system == "Linux" and (platform_machine == "arm64" or platform_machine == "aarch64") (from tensorflow) (from versions: 2.9.1, 2.10.0rc0, 2.10.0rc2, 2.10.0rc3, 2.10.0, 2.10.1, 2.11.0rc0, 2.11.0rc1, 2.11.0rc2, 2.11.0)
ERROR: No matching distribution found for tensorflow-cpu-aws==2.12.0-rc0; platform_system == "Linux" and (platform_machine == "arm64" or platform_machine == "aarch64")
```
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"@yd2102 Thanks for reporting the issue.\r\n\r\nI was able to reproduce this issue in TF 2.11. Please find the gist [here](https://colab.research.google.com/gist/pjpratik/3dbd22039d8453458cc37addfaea7637/59730_2-11.ipynb).\r\n\r\nThe issue seems to be resolved in TF nightly version. I was able to run the quantized bert model with out any error. Please find the gist [here](https://colab.research.google.com/gist/pjpratik/3606190d65dd55f689907ce8bbbfb3f0/59730_tfnightly.ipynb) and let us know if it helps. \r\n\r\nThank you.\r\n\r\n\r\n\r\n",
"@pjpratik Thank you for the update. I tried your suggestion and updated TF to nightly version, on my end it still failed. Although I'm not sure what caused that, it appears there's some invalid memory access? I originally observed this issue on aarch64, but now I'm also seeing the same issue on x86_64 instance.\r\n\r\nTo illustrate the problem, I ran the same python script multiple times. And sometimes it failed on the Gather operation:\r\n```\r\nINFO: Created TensorFlow Lite XNNPACK delegate for CPU.\r\nTraceback (most recent call last):\r\n File \"/home/ubuntu/alexa/tf.py\", line 53, in <module>\r\n interpreter.invoke() # Seg Fault\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/interpreter.py\", line 941, in invoke\r\n self._interpreter.Invoke()\r\nRuntimeError: tensorflow/lite/kernels/gather.cc:132 indices_has_only_positive_elements was not true.gather index out of boundsNode number 17 (GATHER) failed to invoke.\r\n```\r\n\r\nwhile other times the model output just seems to be random (because I've used fixed random seed in the script). Note that the printed elements below are emitted by [this](https://www.tensorflow.org/api_docs/python/tf/debugging/assert_near) API:\r\n```\r\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Expected 'tf.Tensor(False, shape=(), dtype=bool)' to be true. Summarized data: b''\r\nb'x and y not equal to tolerance rtol = tf.Tensor(1.1920929e-06, shape=(), dtype=float32), atol = tf.Tensor(0.05, shape=(), dtype=float32)'\r\nb'x (shape=(1, 10, 768) dtype=float32) = '\r\n0.33638424, -0.8871238, 0.74096835, ...\r\nb'y (shape=(1, 10, 768) dtype=float32) = '\r\n-0.45434994, -0.16998653, 1.0446085, ...\r\n```\r\n```\r\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Expected 'tf.Tensor(False, shape=(), dtype=bool)' to be true. Summarized data: b''\r\nb'x and y not equal to tolerance rtol = tf.Tensor(1.1920929e-06, shape=(), dtype=float32), atol = tf.Tensor(0.05, shape=(), dtype=float32)'\r\nb'x (shape=(1, 10, 768) dtype=float32) = '\r\n-0.4937328, 1.9754344, 0.4033151, ...\r\nb'y (shape=(1, 10, 768) dtype=float32) = '\r\n-1.236057, 0.8411898, 1.2963096, ...\r\n```\r\n\r\nI've also reproduced the issue on your [colab](https://colab.research.google.com/gist/yd2102/df1b6e4904a139b7a2b8386b03915094/59730_tfnightly.ipynb) session by running the script multiple times.\r\n",
"@yd2102 Thanks for the information. I was able to reproduce after multiple runs. Please find the gist [here](https://colab.research.google.com/gist/pjpratik/265e5c9c7698c8dfee41c1db163fb93e/59730_tfnightly.ipynb).\r\n\r\n@sachinprasadhs Could you please look into this? Thanks!",
"Hi @yd2102, It looks like we are calling `resize_tensor_input` on what looks to be output related arguments:\r\n```\r\ninterpreter.resize_tensor_input(output0_index, [BATCH_SIZE, SEQUENCE_LEGNTH, 768], strict=True)\r\ninterpreter.resize_tensor_input(output1_index, [BATCH_SIZE, 768], strict=True)\r\n```\r\n\r\nWe shouldn't need to resize output tensors, this may be a mistake. Could you remove these lines and try again?",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further."
] | 2023-02-17T19:15:18 | 2023-06-02T02:06:13 | 2023-06-02T02:06:12 | NONE | null | null | null | ### 1. System information
- OS Platform and Distribution: Ubuntu 22.04, aarch64
- TensorFlow installation (pip package or built from source): pip
- TensorFlow library (version, if pip package or github SHA, if built from source): tensorflow-cpu-aws==2.11.0
- Model library: transformers==4.26.1
### 2. Code
```
import tensorflow as tf
from transformers import BertConfig, TFBertModel
BATCH_SIZE = 1
SEQUENCE_LEGNTH = 10
VOCAB_SIZE = 150000
tf.random.set_seed(0)
tf.config.threading.set_intra_op_parallelism_threads(4)
bert_config = BertConfig(vocab_size=VOCAB_SIZE,
hidden_size=768,
num_hidden_layers=4,
num_attention_heads=12,
intermediate_size=1200,
hidden_act='gelu',
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
position_embedding_type='absolute',
return_dict=False)
model = TFBertModel(bert_config)
input = [
tf.random.uniform(shape=(BATCH_SIZE, SEQUENCE_LEGNTH), minval=0, maxval=VOCAB_SIZE, dtype=tf.int32),
tf.ones(shape=(BATCH_SIZE, SEQUENCE_LEGNTH), dtype=tf.int32)
]
# Run original model
model(input)
print("1")
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
model2 = converter.convert()
interpreter = tf.lite.Interpreter(model_content=model2)
input0_index = interpreter.get_input_details()[0]["index"]
input1_index = interpreter.get_input_details()[1]["index"]
output0_index = interpreter.get_output_details()[0]["index"]
output1_index = interpreter.get_output_details()[1]["index"]
interpreter.resize_tensor_input(input0_index, input[0].shape, strict=True)
interpreter.resize_tensor_input(input1_index, input[1].shape, strict=True)
interpreter.resize_tensor_input(output0_index, [BATCH_SIZE, SEQUENCE_LEGNTH, 768], strict=True)
interpreter.resize_tensor_input(output1_index, [BATCH_SIZE, 768], strict=True)
interpreter.allocate_tensors()
interpreter.set_tensor(input0_index, input[0])
interpreter.set_tensor(input1_index, input[1])
# Run converted model
interpreter.invoke() # Seg Fault
print("2")
```
### 3. Failure after conversion
The conversion is successful, but interpreter's invoke() fails with segmentation fault.
### 4. (optional) RNN conversion support
N/ A
### 5. (optional) Any other info / logs
I was mainly following the example code [here](https://www.tensorflow.org/lite/performance/post_training_quant#test_the_model_on_one_image), and tried to convert Bert model to a quantized one. The interpreter failed with the following error:
```
/usr/local/lib/python3.10/dist-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/usr/local/lib/python3.10/dist-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']
caused by: ["[Errno 2] The file to load file system plugin from does not exist.: '/usr/local/lib/python3.10/dist-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so'"]
warnings.warn(f"unable to load libtensorflow_io_plugins.so: {e}")
/usr/local/lib/python3.10/dist-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/usr/local/lib/python3.10/dist-packages/tensorflow_io/python/ops/libtensorflow_io.so']
caused by: ['/usr/local/lib/python3.10/dist-packages/tensorflow_io/python/ops/libtensorflow_io.so: cannot open shared object file: No such file or directory']
warnings.warn(f"file system plugins are not loaded: {e}")
1
2023-02-17 18:55:23.466676: I tensorflow/core/grappler/devices.cc:75] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0 (Note: TensorFlow was not compiled with CUDA or ROCm support)
2023-02-17 18:55:23.466855: I tensorflow/core/grappler/clusters/single_machine.cc:358] Starting new session
2023-02-17 18:55:29.150726: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format.
2023-02-17 18:55:29.150768: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:365] Ignored drop_control_dependency.
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
Segmentation fault (core dumped)
```
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"Hey @trickiwoo try this `x_copy = tf.identity(x)` instead of `x_copy = tf.experimental.numpy.copy(x)`.\r\n",
"FYI follow this link :- https://www.tensorflow.org/api_docs/python/tf/identity",
"@trickiwoo,\r\n**tf-nightly v2.13.0.dev20230204** which is not the stable version. I tried to execute the code multiple tiimes with the stable version **tf 2.11** and haven't faced any issue. Kindly find the text of the same and also the image below for the reference.\r\n\r\n\r\n\r\n```\r\n\r\n(tf) tilakrayal@tilak-cpu-instance:~$ python3 59729numpy.py\r\n2023-02-20 10:49:30.254453: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-02-20 10:49:30.466013: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\r\n2023-02-20 10:49:30.466232: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\n2023-02-20 10:49:31.412451: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\r\n2023-02-20 10:49:31.412593: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\r\n2023-02-20 10:49:31.412617: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\r\n2.11.0\r\n2023-02-20 10:49:32.198426: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\r\n2023-02-20 10:49:32.198468: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\r\n2023-02-20 10:49:32.198505: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (tilak-cpu-instance): /proc/driver/nvidia/version does not exist\r\n2023-02-20 10:49:32.198901: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n(tf) tilakrayal@tilak-cpu-instance:~$ python3 59729numpy.py\r\n2023-02-20 10:49:38.750996: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-02-20 10:49:38.937838: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\r\n2023-02-20 10:49:38.937881: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\n2023-02-20 10:49:39.835979: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\r\n2023-02-20 10:49:39.836100: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\r\n2023-02-20 10:49:39.836122: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\r\n2.11.0\r\n2023-02-20 10:49:40.626076: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\r\n2023-02-20 10:49:40.626116: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\r\n2023-02-20 10:49:40.626150: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (tilak-cpu-instance): /proc/driver/nvidia/version does not exist\r\n2023-02-20 10:49:40.626547: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n(tf) tilakrayal@tilak-cpu-instance:~$ python3 59729numpy.py\r\n2023-02-20 10:49:46.471764: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-02-20 10:49:46.641868: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\r\n2023-02-20 10:49:46.641908: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\n2023-02-20 10:49:47.534333: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\r\n2023-02-20 10:49:47.534448: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\r\n2023-02-20 10:49:47.534470: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\r\n2.11.0\r\n2023-02-20 10:49:48.324850: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\r\n2023-02-20 10:49:48.324888: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\r\n2023-02-20 10:49:48.324921: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (tilak-cpu-instance): /proc/driver/nvidia/version does not exist\r\n2023-02-20 10:49:48.325328: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n(tf) tilakrayal@tilak-cpu-instance:~$ python3 59729numpy.py\r\n2023-02-20 10:49:51.697105: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-02-20 10:49:51.902325: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\r\n2023-02-20 10:49:51.902389: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\n2023-02-20 10:49:52.835385: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\r\n2023-02-20 10:49:52.835510: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\r\n2023-02-20 10:49:52.835535: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\r\n2.11.0\r\n2023-02-20 10:49:53.621156: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\r\n2023-02-20 10:49:53.621197: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\r\n2023-02-20 10:49:53.621230: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (tilak-cpu-instance): /proc/driver/nvidia/version does not exist\r\n2023-02-20 10:49:53.621683: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n(tf) tilakrayal@tilak-cpu-instance:~$ python3 59729numpy.py\r\n2023-02-20 10:49:57.771047: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-02-20 10:49:58.005568: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\r\n2023-02-20 10:49:58.005674: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\n2023-02-20 10:49:59.020246: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\r\n2023-02-20 10:49:59.020377: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\r\n2023-02-20 10:49:59.020400: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\r\n2.11.0\r\n2023-02-20 10:49:59.836802: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\r\n2023-02-20 10:49:59.836924: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\r\n2023-02-20 10:49:59.837011: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (tilak-cpu-instance): /proc/driver/nvidia/version does not exist\r\n2023-02-20 10:49:59.837558: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n(tf) tilakrayal@tilak-cpu-instance:~$ python3 59729numpy.py\r\n2023-02-20 10:50:04.571082: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-02-20 10:50:04.760537: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:04.760590: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\n2023-02-20 10:50:05.698994: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:05.699141: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:05.699170: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\r\n2.11.0\r\n2023-02-20 10:50:06.499657: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:06.499711: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\r\n2023-02-20 10:50:06.499751: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (tilak-cpu-instance): /proc/driver/nvidia/version does not exist\r\n2023-02-20 10:50:06.500157: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n(tf) tilakrayal@tilak-cpu-instance:~$ python3 59729numpy.py\r\n2023-02-20 10:50:11.225399: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-02-20 10:50:11.448887: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:11.448949: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\n2023-02-20 10:50:12.396209: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:12.396372: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:12.396396: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\r\n2.11.0\r\n2023-02-20 10:50:13.185562: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:13.185610: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\r\n2023-02-20 10:50:13.185667: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (tilak-cpu-instance): /proc/driver/nvidia/version does not exist\r\n2023-02-20 10:50:13.186108: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n(tf) tilakrayal@tilak-cpu-instance:~$ python3 59729numpy.py\r\n2023-02-20 10:50:18.252309: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-02-20 10:50:18.461471: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:18.461534: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\n2023-02-20 10:50:19.411662: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:19.411806: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:19.411828: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\r\n2.11.0\r\n2023-02-20 10:50:20.215193: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:20.215244: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\r\n2023-02-20 10:50:20.215279: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (tilak-cpu-instance): /proc/driver/nvidia/version does not exist\r\n2023-02-20 10:50:20.215699: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n(tf) tilakrayal@tilak-cpu-instance:~$ python3 59729numpy.py\r\n2023-02-20 10:50:23.607334: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-02-20 10:50:23.804093: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:23.804141: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\n2023-02-20 10:50:24.719589: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:24.719712: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:24.719734: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\r\n2.11.0\r\n2023-02-20 10:50:25.514461: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:25.514590: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\r\n2023-02-20 10:50:25.514671: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (tilak-cpu-instance): /proc/driver/nvidia/version does not exist\r\n2023-02-20 10:50:25.515096: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n(tf) tilakrayal@tilak-cpu-instance:~$ python3 59729numpy.py\r\n2023-02-20 10:50:29.199966: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-02-20 10:50:29.372612: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:29.372657: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\n2023-02-20 10:50:30.303912: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:30.304036: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:30.304060: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\r\n2.11.0\r\n2023-02-20 10:50:31.100249: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:31.100288: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\r\n2023-02-20 10:50:31.100324: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (tilak-cpu-instance): /proc/driver/nvidia/version does not exist\r\n2023-02-20 10:50:31.100720: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n(tf) tilakrayal@tilak-cpu-instance:~$ python3 59729numpy.py\r\n2023-02-20 10:50:37.436578: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-02-20 10:50:37.606332: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:37.606511: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\n2023-02-20 10:50:38.509385: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:38.509661: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:38.509691: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\r\n2.11.0\r\n2023-02-20 10:50:39.302680: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:39.302792: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\r\n2023-02-20 10:50:39.302877: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (tilak-cpu-instance): /proc/driver/nvidia/version does not exist\r\n2023-02-20 10:50:39.303293: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n(tf) tilakrayal@tilak-cpu-instance:~$ python3 59729numpy.py\r\n2023-02-20 10:50:44.109269: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-02-20 10:50:44.275432: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:44.275480: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\n2023-02-20 10:50:45.183550: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:45.183669: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:45.183691: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\r\n2.11.0\r\n2023-02-20 10:50:45.979766: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\r\n2023-02-20 10:50:45.979816: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\r\n2023-02-20 10:50:45.979866: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (tilak-cpu-instance): /proc/driver/nvidia/version does not exist\r\n2023-02-20 10:50:45.980332: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n```",
"@tilakrayal Shouldn't we investigate the issue on the nightly build, to ensure that we don't have a regression waiting for us later?\r\n\r\nThe issue is reproducible using the nightly build -- although **it does not happen on all runs**. You may have to run the code many times to observe it.\r\n\r\nIt seems to reproduce on a much higher percentage of runs on my laptop compared to on Colab.\r\n\r\nHere's a [gist](https://colab.research.google.com/gist/nrwahl2/3b551b2ade7ef493a3c82918283ab3f6/59729.ipynb) reproducing the issue on nightly.\r\n\r\nHere's a [gist](https://colab.research.google.com/gist/nrwahl2/2615eba3331b1777b4e8afad1302f2cf/59729.ipynb) reproducing the issue on 2.11.",
"Thanks for getting back to me! I can still reproduce this issue. Please run it multiple times. Thanks!",
"Hi, @trickiwoo \r\n\r\nApologize for the delayed response and I was able to replicate the issue and I have executed the same code more than 100 times with `tf-nightly`, `tensorflow==2.11`,`tensorflow==2.12.0rc0`, `tensorflow==2.12.0rc1` and in my case I got error with `tf-nightly`, for your reference I have added [gist-file](https://colab.research.google.com/gist/gaikwadrahul8/4b7faccc7669b69f09e6d0161008c32c/-59729.ipynb) so we'll have to dig more into this issue because as you got error with `TF2.11` also and this behaviour is happening after running the same code multiple times so we'll update you soon, Thank you for noticing this issue.\r\n\r\n@sachinprasadhs, Could you please look into this issue? Thank you!"
] | 2023-02-17T17:32:28 | 2023-03-09T19:42:39 | null | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
2.13.0.dev20230204
### 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 Behaviour?
```shell
Modifying the original tensor can change the value of the copied tensor. This buggy behavior is unstable so we run it 100 times to reproduce. The bug only happens on the CPU.
```
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
for _ in range(100):
with tf.device("cpu"):
x = np.arange(10)
x_copy = tf.experimental.numpy.copy(x)
x[3] = 42
assert tf.experimental.numpy.all((x_copy == tf.range(10, dtype=tf.int64))), x_copy
```
### Relevant log output
```shell
AssertionError: tf.Tensor([ 0 1 2 42 4 5 6 7 8 9], shape=(10,), dtype=int64)
```
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"@yufang67 \r\nI was able to execute the given code on GPU without any problem on the Linux virtual machine using tf-nightly 2.13.0-dev20230220 and on colab using TF v2.11. Please find the gist of [2.11](https://colab.research.google.com/gist/tiruk007/64342dba197c2a9fc60c141d0327ea62/2_11.ipynb) and refer to the [screenshot](https://user-images.githubusercontent.com/111861663/220181799-2a4d4b82-112c-4608-921f-a95210473b43.png) for tf-nightly.\r\n\r\nThank you!\r\n",
"@tiruk007 thanks for the response. \r\nBut it seems the cast run on CPU: Executing op Cast in device /job:localhost/replica:0/task:0/device:CPU:0",
"@gaikwadrahul8 \r\nCould you please look into this ?\r\n\r\nThank you!",
"Hi, @yufang67 \r\n\r\nApologize for the delay and I was able to replicate the issue with `Tensorflow==2.11` on Google Colab with `TPU` and I think `bfloat16` runs on TPU and you'll have to use `bfloat16` datatype with `TPU` runtype and it should work so for your reference I have added [gist-file](https://colab.research.google.com/gist/gaikwadrahul8/e443a8c05d2f7419868da2a589c39103/-59728.ipynb) and it's working as expected and even you can check the similar issue [#59624](https://github.com/tensorflow/tensorflow/issues/59624)\r\n\r\nIf issue still persists please let us know or Could you please confirm if this issue is resolved for you ? Please feel free to close the issue if it is resolved ? Thank you!",
"Hi @gaikwadrahul8 ,\r\n\r\nthanks for responds. As A100 GPU supports also bfloat16, im trying to evaluate the whole model on it. Some layers are already support bfloat16 (https://github.com/tensorflow/tensorflow/issues/59050#issuecomment-1428862660), i would like to know if there is plan to support also this cast op ?\r\n\r\nthanks, ",
"Hi, @yufang67 \r\n\r\nApologize for the delay and I hope you checked this [comment](https://github.com/tensorflow/tensorflow/issues/59050#issuecomment-1402914100) and at the moment we don't have the exact estimated time of arrival, if you have any workaround or implementation to cast `int32` to `bfloat16` for `GPU`, PR will be welcomed from your end and if you don't have then developer team will review and validate this feature request and developer team will decide upon the priority whether they will work on this feature request or not. Thank you! ",
"@gaikwadrahul8 Thanks. how to submit the request to the developer team ? ",
"Hi, @yufang67\r\n\r\nThank you for the confirmation so it seems like you don't have workaround or implementation from your end then I'll escalate this issue to developer team as feature request and they'll take care of this issue. Thank you!",
"Hi, @yufang67 \r\n\r\nI think at the moment `bfloat16` only supports `Nvidia A100 GPU's` so could you please try to run your code on `Nvidia A100 GPU's` and please let us know whether is it working or not as expected? Thank you!",
"Hi @gaikwadrahul8,\r\nYes, all my tests and reported issue is based on single A100 80G GPU.",
"@yufang67, Thank you for the confirmation and this issue we'll consider as feature request \r\n\r\nHi, @sachinprasadhs \r\n\r\nCould you please look into this issue ? Thank you!",
"@trevor-m, can you look into this? Looks like we don't register cast kernels to or from bfloat16 for most other types.",
"Hi @reedwm, I spent some time on this but the cast code is a bit confusing.\r\nUsing the macros, I keep getting duplicate registration errors or missing instantiations.\r\n\r\nI also tried to explicitly only add int32, which compiles, but when running the script it still always uses the CPU. Any idea what I'm missing here: https://github.com/trevor-m/tensorflow/commit/ac9a6b134ac022dd520cf0c3a73a222f0824418d ?",
"Running the script in the original post with your patch https://github.com/trevor-m/tensorflow/commit/ac9a6b134ac022dd520cf0c3a73a222f0824418d fixes the issue for me. Without your patch, last Cast log line is\r\n\r\n```\r\n2023-03-24 01:52:55.531962: I tensorflow/core/common_runtime/eager/execute.cc:1588] Executing op Cast in device /job:localhost/replica:0/task:0/device:CPU:0\r\n```\r\n\r\nWith your patch, the last log Cast log line is:\r\n\r\n```\r\n2023-03-24 02:16:08.281971: I tensorflow/core/common_runtime/placer.cc:114] Cast: (Cast): /job:localhost/replica:0/task:0/device:GPU:0\r\n```",
"Oh wait but other Cast ops are still on the CPU, taking a look...",
"With your patch, the CPU casts are casts from float32 to bfloat16. Casts to/from int32 are on the GPU.\r\n\r\nIn the following program, all casts except the cast from float32 to bfloat16 (the third cast) are on the GPU.\r\n\r\n```python\r\nimport tensorflow as tf\r\n\r\ntf.debugging.set_log_device_placement(True)\r\n\r\nx = tf.constant(1, dtype=tf.int32)\r\ny = tf.cast(x, tf.bfloat16)\r\nz = tf.cast(y, tf.int32)\r\n\r\nx = tf.constant(1, dtype=tf.float32)\r\ny = tf.cast(x, tf.bfloat16)\r\nz = tf.cast(y, tf.float32)\r\n```\r\n\r\nIf you're having trouble with duplicate registration errors or missing instantiations, you can share a patch and i can take a look.",
"Thanks @reedwm! I was able to figure it out, I was originally confused because [DEFINE_ALL_FROM](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/cast_op_gpu.cu.cc#L39) and [DEFINE_ALL_TO_*](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/cast_op_gpu.cu.cc#L104) are actually the same and one of them mislabeled the input type... Let me know if you have any suggestions to clean things up in the PR."
] | 2023-02-17T16:37:57 | 2023-03-27T20:30:06 | 2023-03-27T18:53:00 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Feature Request
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
TensorFlow version 2.13.0-dev20230215
### Custom Code
Yes
### OS Platform and Distribution
ubuntu 20.04
### Mobile device
_No response_
### Python version
3.8
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
11.8/8.6
### GPU model and memory
single A100 80G
### Current Behaviour?
```shell
when using tf.cast to cast tf.int32 tensor to tf.bfloat16 tensor, op run on GPU.
when i convert int32->bfloat16, it run on CPU.
when i convert int32->float32->bfloat16, it run on GPU. is it expected ?
```
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
from tensorflow.keras import mixed_precision
tf.debugging.set_log_device_placement(True)
policy = mixed_precision.Policy('mixed_bfloat16')
print(policy.name)
mixed_precision.set_global_policy(policy)
class toy_layer(tf.keras.layers.Layer):
def build(self, input_shape):
self.kernel = self.add_weight('kernel', (input_shape[-1], 10))
def call(self, inputs):
out = tf.linalg.matmul(inputs, self.kernel)
out2 = tf.ones((10, 10), dtype=tf.int32)
#out2 = tf.cast(out2, tf.float32, name="cast_out2_1")
out2 = tf.cast(out2, out.dtype, name="cast_out2_2")
out3 = out * out2
return out3
layer = toy_layer()
y = layer(tf.ones((10, 10)))
```
### Relevant log output
```shell
2023-02-17 16:26:34.748024: 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.
2023-02-17 16:26:35.370677: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
mixed_bfloat16
2023-02-17 16:26:36.876507: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 78915 MB memory: -> device: 0, name: NVIDIA A100 80GB PCIe, pci bus id: 0001:00:00.0, compute capability: 8.0
input: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-17 16:26:36.886233: I tensorflow/core/common_runtime/placer.cc:114] input: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
_EagerConst: (_EagerConst): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:36.886254: I tensorflow/core/common_runtime/placer.cc:114] _EagerConst: (_EagerConst): /job:localhost/replica:0/task:0/device:GPU:0
output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:36.886267: I tensorflow/core/common_runtime/placer.cc:114] output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:36.889118: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.293473: I tensorflow/core/common_runtime/placer.cc:114] input: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
_EagerConst: (_EagerConst): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.293513: I tensorflow/core/common_runtime/placer.cc:114] _EagerConst: (_EagerConst): /job:localhost/replica:0/task:0/device:GPU:0
output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.293521: I tensorflow/core/common_runtime/placer.cc:114] output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.294777: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
dims: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-17 16:26:37.295227: I tensorflow/core/common_runtime/placer.cc:114] dims: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.295238: I tensorflow/core/common_runtime/placer.cc:114] value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
Fill: (Fill): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.295250: I tensorflow/core/common_runtime/placer.cc:114] Fill: (Fill): /job:localhost/replica:0/task:0/device:GPU:0
output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.295257: I tensorflow/core/common_runtime/placer.cc:114] output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.295729: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Fill in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.297303: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.297667: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.297928: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.298227: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
seed: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-17 16:26:37.298533: I tensorflow/core/common_runtime/placer.cc:114] seed: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
StatelessRandomGetKeyCounter: (StatelessRandomGetKeyCounter): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.298547: I tensorflow/core/common_runtime/placer.cc:114] StatelessRandomGetKeyCounter: (StatelessRandomGetKeyCounter): /job:localhost/replica:0/task:0/device:GPU:0
key_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.298561: I tensorflow/core/common_runtime/placer.cc:114] key_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
counter_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.298573: I tensorflow/core/common_runtime/placer.cc:114] counter_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.299212: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op StatelessRandomGetKeyCounter in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.300704: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
shape: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-17 16:26:37.300963: I tensorflow/core/common_runtime/placer.cc:114] shape: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
key: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.300974: I tensorflow/core/common_runtime/placer.cc:114] key: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
counter: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.300979: I tensorflow/core/common_runtime/placer.cc:114] counter: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
alg: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-17 16:26:37.300993: I tensorflow/core/common_runtime/placer.cc:114] alg: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
StatelessRandomUniformV2: (StatelessRandomUniformV2): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.300999: I tensorflow/core/common_runtime/placer.cc:114] StatelessRandomUniformV2: (StatelessRandomUniformV2): /job:localhost/replica:0/task:0/device:GPU:0
output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.301008: I tensorflow/core/common_runtime/placer.cc:114] output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.301526: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op StatelessRandomUniformV2 in device /job:localhost/replica:0/task:0/device:GPU:0
x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.302304: I tensorflow/core/common_runtime/placer.cc:114] x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
y: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.302316: I tensorflow/core/common_runtime/placer.cc:114] y: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
Sub: (Sub): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.302332: I tensorflow/core/common_runtime/placer.cc:114] Sub: (Sub): /job:localhost/replica:0/task:0/device:GPU:0
z_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.302338: I tensorflow/core/common_runtime/placer.cc:114] z_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.302706: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Sub in device /job:localhost/replica:0/task:0/device:GPU:0
x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.303361: I tensorflow/core/common_runtime/placer.cc:114] x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
y: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.303372: I tensorflow/core/common_runtime/placer.cc:114] y: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
Mul: (Mul): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.303379: I tensorflow/core/common_runtime/placer.cc:114] Mul: (Mul): /job:localhost/replica:0/task:0/device:GPU:0
z_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.303391: I tensorflow/core/common_runtime/placer.cc:114] z_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.303748: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Mul in device /job:localhost/replica:0/task:0/device:GPU:0
x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.304205: I tensorflow/core/common_runtime/placer.cc:114] x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
y: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.304216: I tensorflow/core/common_runtime/placer.cc:114] y: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
AddV2: (AddV2): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.304223: I tensorflow/core/common_runtime/placer.cc:114] AddV2: (AddV2): /job:localhost/replica:0/task:0/device:GPU:0
z_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.304229: I tensorflow/core/common_runtime/placer.cc:114] z_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.304663: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op AddV2 in device /job:localhost/replica:0/task:0/device:GPU:0
resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.305249: I tensorflow/core/common_runtime/placer.cc:114] resource_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.305263: I tensorflow/core/common_runtime/placer.cc:114] VarHandleOp: (VarHandleOp): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.305637: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.306104: I tensorflow/core/common_runtime/placer.cc:114] resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.306122: I tensorflow/core/common_runtime/placer.cc:114] value: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.306139: I tensorflow/core/common_runtime/placer.cc:114] AssignVariableOp: (AssignVariableOp): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.306554: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0
x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.307498: I tensorflow/core/common_runtime/placer.cc:114] x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
Cast: (Cast): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.307523: I tensorflow/core/common_runtime/placer.cc:114] Cast: (Cast): /job:localhost/replica:0/task:0/device:GPU:0
y_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.307532: I tensorflow/core/common_runtime/placer.cc:114] y_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.307943: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Cast in device /job:localhost/replica:0/task:0/device:GPU:0
resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.308714: I tensorflow/core/common_runtime/placer.cc:114] resource: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
ReadVariableOp: (ReadVariableOp): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.308734: I tensorflow/core/common_runtime/placer.cc:114] ReadVariableOp: (ReadVariableOp): /job:localhost/replica:0/task:0/device:GPU:0
value_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.308743: I tensorflow/core/common_runtime/placer.cc:114] value_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.309154: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op ReadVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.309366: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Cast in device /job:localhost/replica:0/task:0/device:GPU:0
a: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.309693: I tensorflow/core/common_runtime/placer.cc:114] a: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
b: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.309710: I tensorflow/core/common_runtime/placer.cc:114] b: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
MatMul: (MatMul): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.309727: I tensorflow/core/common_runtime/placer.cc:114] MatMul: (MatMul): /job:localhost/replica:0/task:0/device:GPU:0
product_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.309738: I tensorflow/core/common_runtime/placer.cc:114] product_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.310165: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op MatMul in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.897703: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.897841: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0
dims: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-17 16:26:37.898261: I tensorflow/core/common_runtime/placer.cc:114] dims: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
value: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-17 16:26:37.898272: I tensorflow/core/common_runtime/placer.cc:114] value: (_Arg): /job:localhost/replica:0/task:0/device:CPU:0
Fill: (Fill): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.898280: I tensorflow/core/common_runtime/placer.cc:114] Fill: (Fill): /job:localhost/replica:0/task:0/device:GPU:0
output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.898285: I tensorflow/core/common_runtime/placer.cc:114] output_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.898934: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Fill in device /job:localhost/replica:0/task:0/device:GPU:0
x: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-17 16:26:37.899509: I tensorflow/core/common_runtime/placer.cc:114] x: (_DeviceArg): /job:localhost/replica:0/task:0/device:CPU:0
Cast: (Cast): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-17 16:26:37.899523: I tensorflow/core/common_runtime/placer.cc:114] Cast: (Cast): /job:localhost/replica:0/task:0/device:CPU:0
y_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-17 16:26:37.899530: I tensorflow/core/common_runtime/placer.cc:114] y_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:CPU:0
2023-02-17 16:26:37.899935: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Cast in device /job:localhost/replica:0/task:0/device:CPU:0
x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.900335: I tensorflow/core/common_runtime/placer.cc:114] x: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
y: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.900348: I tensorflow/core/common_runtime/placer.cc:114] y: (_Arg): /job:localhost/replica:0/task:0/device:GPU:0
Mul: (Mul): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.900361: I tensorflow/core/common_runtime/placer.cc:114] Mul: (Mul): /job:localhost/replica:0/task:0/device:GPU:0
z_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.900369: I tensorflow/core/common_runtime/placer.cc:114] z_RetVal: (_Retval): /job:localhost/replica:0/task:0/device:GPU:0
2023-02-17 16:26:37.901006: I tensorflow/core/common_runtime/eager/execute.cc:1514] Executing op Mul in device /job:localhost/replica:0/task:0/device:GPU:0
```
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"@tilakrayal \r\nI was able to replicate the issue in Ubuntu and colab. Please find the gist [here](https://colab.sandbox.google.com/gist/synandi/c7b9d6765441e78b1a374f481b1c7333/59727.ipynb) for TF v2.10 and [here](https://colab.sandbox.google.com/gist/synandi/4612a2921b20e8e081c594a0706d6a0c/59727-nightly.ipynb) for tf-nightly. Thank you!",
"@cbreak-black ,Thanks for reporting this.\r\n\r\nThe error is raised from below code.\r\n```\r\nelif isinstance(grad, indexed_slices.IndexedSlices):\r\n shape_tuple = grad.values._shape_tuple() # pylint: disable=protected-access\r\n```\r\nfrom source:\r\nhttps://github.com/tensorflow/tensorflow/blob/735b325c4ba38da4c5fc265036606ce22838a242/tensorflow/python/eager/backprop.py#L606-L616\r\nThe code indicates `grad` is of `indexed_slices.IndexedSlices` and `grad.values` should return a `Tensor` as per IndexedSlices API. \r\n\r\nIf the reported code snippet failing then the below code from above code block also may fail which needs to be checked.\r\n```\r\nif isinstance(grad, ops.Tensor):\r\n shape_tuple = grad._shape_tuple() \r\n```\r\nIt may need to be digged more for root cause. Thanks!",
"Hi, as far as I was able to find with my limited knowledge of tensorflow's internals is that grad.values is itself an `IndexedSlices` typed object. I did not find out if this is expected (apparently not), and how this came to be."
] | 2023-02-17T16:11:20 | 2023-02-28T17:27:13 | null | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
No
### Source
source
### Tensorflow Version
2.10
### Custom Code
Yes
### OS Platform and Distribution
Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
```shell
Attempting to compute the gradient, both as part of an autographed keras layer, or in eager mode, fails. This seems to happen when concatenating different slices of a ragged tensor.
```
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1kteIaQeDouRH-DEEYYeGE9jiMtgcXUZm#scrollTo=MOhPQNf4JDBB
import tensorflow as tf
values = tf.constant([0, 1,2,3,4,5,6,7,8,9], tf.float32)
values = tf.reshape(values, [-1, 1])
r = tf.RaggedTensor.from_row_lengths(values, [0, 2, 2, 1, 0, 2, 3, 0, 0])
r = tf.RaggedTensor.from_uniform_row_length(r, 3)
r = tf.RaggedTensor.from_uniform_row_length(r, 3)
def crop(raggedImage: tf.RaggedTensor, top, bottom, left, right) -> tf.RaggedTensor:
'''
Crops a ragged tensor, removing 'pixels' from the boundary.
The input is interpreted as being b x h x w x s x c layout.
Only the sample dimension may be ragged.
'''
if bottom > 0:
bottom = -bottom
else:
bottom = None
if right > 0:
right = -right
else:
right = None
cropped = raggedImage[:, top : bottom, left : right, :, :]
return cropped
diameter = 3
with tf.GradientTape() as tape:
tape.watch(r)
l = []
for u in range(diameter):
for v in range(diameter):
l.append(crop(r, u, diameter-1-u, v, diameter-1-v))
s = tf.concat(l, axis=2)
tf.print(tape.gradient(s, [r]))
```
### Relevant log output
```shell
Traceback (most recent call last):
File "venv-2.10/lib/python3.10/site-packages/tensorflow/python/eager/backprop.py", line 663, in _num_elements
shape_tuple = grad.values._shape_tuple() # pylint: disable=protected-access
AttributeError: 'IndexedSlices' object has no attribute '_shape_tuple'
```
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"I was able to reproduce this issue in TF 2.11 and TF Nightly 2.13.0-dev20230219 . Please find the gist [here](https://colab.research.google.com/gist/pjpratik/20cd82f24b7697da535ba222cd066b28/59726.ipynb). \r\n\r\n@SuryanarayanaY Could you please look into this issue. Thanks. ",
"Hi @MegaCreater ,\r\nThe error might be due to python function using inside the Graph for which the shape is unknown.Hence getting the error:Unknown rank error.\r\nThe proposed workaround is you have add this code `inputs.set_shape([None,32,32,3])` after tf.nupmy_function like below.\r\n@tf.function\r\n```\r\ndef sample(inputs:tf.Tensor): # some sample function \r\n\r\n def numpy_method(inputs:np.ndarray):\r\n # some complex method \r\n return np.sqrt(inputs)# example code \r\n\r\n inputs=tf.numpy_function(numpy_method,[inputs],inputs.dtype,name='outputs')\r\n inputs.set_shape([None,32,32,3]) #New line of code\r\n inputs=tf.keras.layers.AvgPool2D()(inputs) \r\n\r\n return inputs\r\n\r\nsample(tf.random.uniform((1,32,32,3)))\r\n```\r\n\r\nPlease refer to the attached gist [here](https://colab.research.google.com/gist/SuryanarayanaY/5c64d66b14b62ca6308674e4b80f6a13/59726.ipynb).\r\n\r\nHope this will solve your purpose. Thanks!",
"@SuryanarayanaY thanks a lot. But this I already know. This solution I have already mentioned here - https://github.com/tensorflow/tensorflow/issues/37193#issuecomment-1434693519. But this issue must be resolved. This issue is very old as stated here - #37193 but till now not solved. Adding `@tf.function` created issue only when with `tf.numpy_function` why cannot be redefine this `tf.numpy_function` function. ",
"Can we update something here -> https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/script_ops.py#L768 \r\nsomthing like this -> \r\n\r\n```python\r\n# ...... \r\ndef numpy_function(func, inp, Tout, outputs_shapes:tuple=None, stateful=True, name=None):\r\n\r\n ''' docs ------ \r\n '''\r\n\r\n outputs = py_func_common(func, inp, Tout, stateful=stateful, name=name)# line 768\r\n if isinstance(outputs,tf.Tensor) and (outputs.shape==None):\r\n if outputs_shapes==None: \r\n if isinstance(inp,(list,tuple)): outputs.set_shape(inp[0].shape)\r\n else: outputs.set_shape(inp.shape)\r\n else: outputs.set_shape(outputs_shapes)\r\n #elif isinstance(outputs,(list,tuple)): ..... \r\n return outputs\r\n\r\n# ..... \r\n```",
"Hi @MegaCreater ,\r\n\r\nI have tested the proposed changes and seems working fine for this case.Please refer to attached tested [gist](https://colab.research.google.com/gist/SuryanarayanaY/f032311e9975716a69fc06d8ba0c06a0/59726_r3-code-gtest.ipynb). At least the changes works fine with and without @tf.function. I am not sure whether this covers all cases or not. Also it involves the structural changes in API as it adds new argument also.This may needs to be reviewed properly for short falls if any.\r\n\r\nThe proposed changes relies completely on input_shape which we are assuming that output_shape also being same as input_shape. What about the case when there is change in shapes that actually happens inside the numpy_method ?\r\n\r\nCould we cover this to all use cases ? Thanks!\r\n",
"Yes we can. When the shapes of output is same then user wont have to pass `output_shape` variable (i.e `output_shape=None`, but when `output_shape` changes then `output_shape` has to be provided. Give me some time to work on it more. I will update you. \r\nBut still with this update `rank` issue is not resolved. I am look for that also. ",
"@MegaCreater ,\r\n\r\nIf `output_shape` shape has to be passed by the user explicitly when output_shape changes based on computation then the same thing can be done by `inputs.set_shape([None,32,32,3]) ` that proposed in earlier workaround right.\r\n\r\nI would like to understand how it is different from earlier workaround. For me the earlier work around using `set_shape` might work for all cases.Is there any exception for this ?\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/59726\">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/59726\">No</a>\n",
"I feel like this should be documented officially. I just wasted a few hours pulling my hairs :("
] | 2023-02-17T13:28:37 | 2023-09-22T18:22:23 | 2023-04-14T01:52:26 | NONE | null | null | null | Issue Type -> Bug
Have you reproduced the bug with TF nightly? -> No
Tensorflow Version -> v2.11.0-rc2-17-gd5b57ca93e5, 2.11.0
Custom Code -> Yes
OS Platform and Distribution -> Google colab
#### Standalone code to reproduce the issue
```python3
import numpy as np,tensorflow as tf
@tf.function
def sample(inputs:tf.Tensor): # some sample function
def numpy_method(inputs:np.ndarray):
# some complex method
return np.sqrt(inputs)# example code
inputs=tf.numpy_function(numpy_method,[inputs],inputs.dtype,name='outputs') # make apply numpy method
inputs=tf.keras.layers.AvgPool2D()(inputs) # ERROR !!!!!
return inputs
sample(tf.random.uniform((1,32,32,3)))
```
#### Relevant log output
```shell
.....
ValueError: in user code:
File "<ipython-input-189-8001acaa2b43>", line 10, in sample *
inputs=tf.keras.layers.AvgPool2D()(inputs) # ERROR !!!!!
File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 70, in error_handler **
raise e.with_traceback(filtered_tb) from None
ValueError: Exception encountered when calling layer 'average_pooling2d' (type AveragePooling2D).
Cannot take the length of shape with unknown rank.
Call arguments received by layer 'average_pooling2d' (type AveragePooling2D):
• inputs=tf.Tensor(shape=<unknown>, dtype=float32)
```
#### But works fine if we reverse the order (i.e. tf.numpy_function cannot properly cast back outputs to tensors)
```python3
import numpy as np,tensorflow as tf
@tf.function
def sample(inputs:tf.Tensor): # some sample function
def numpy_method(inputs:np.ndarray):
# some complex method
return np.sqrt(inputs)# example code
inputs=tf.keras.layers.AvgPool2D()(inputs) # works fine
inputs=tf.numpy_function(numpy_method,[inputs],inputs.dtype,name='outputs') # make apply numpy method
return inputs
sample(tf.random.uniform((1,32,32,3)))
```
#### Output
```shell
<tf.Tensor: shape=(1, 16, 16, 3), dtype=float32, numpy=
array([[[[0.7783269 , 0.6260437 , 0.81261575] ......
```
This issue was also mention here -> #37193 ["Cannot take the length of shape with unknown rank" error](https://github.com/tensorflow/tensorflow/issues/37193#top) in but not resolved. | {
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"Easily reproducible: see [gist](https://colab.research.google.com/gist/nrwahl2/b083eacf2542a2310740e8e14ed6ebb0/59725.ipynb).\r\n\r\nWhen the decorator is used, the local scope appears to be overwritten within the for loop:\r\n```\r\nWith decorator, before for loop\r\ndict_keys(['inputs', 'application_order', 'method', 'fscope', 'ag__'])\r\nIn for loop\r\ndict_keys(['itr', 'application', 'continue_', 'ag__', 'break_', 'fscope'])\r\n\r\nWithout decorator, before for loop\r\ndict_keys(['inputs', 'application_order', 'method'])\r\nIn for loop\r\ndict_keys(['inputs', 'application_order', 'method', 'application'])\r\n```\r\n\r\nThe decorator transforms the function pretty substantially as shown in the gist, so that the loop body itself is a function and has its own scope.",
"@nrwahl2 Yah! that is like c/c++ behaviour, where loops have there own scope (might be because it is compiling it like c/c++ to get ([tf.Graph](https://www.tensorflow.org/api_docs/python/tf/Graph))). (Same in both stable and tf-nightly version). \r\nSo, what can be done to get python like behaviour in decorator? ",
"@MegaCreater Good question :) I'm new to TF and I'm still getting familiar with how the code base is structured. I don't have an idea for a fix yet.\r\n\r\nPerhaps we could parse `locals()` calls so that they refer to the original local variables dict within the transformed for loop (this might be complicated or not feasible); or maybe we need to document it as a limitation of `@tf.function`. I'm interested to hear what the regular contributors think.",
"@sachinprasadhs,\r\nI was able to reproduce the issue on tensorflow v2.11 and nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/a48c82ea8b273a479dfc21824ab0e2c7/59725.ipynb). ",
"@tilakrayal or @sachinprasadhs plz have a look ...",
"Hi, Since the `tf.function` runs in a graph mode, the variables which are not used or watched will not b traced in the graph and hence you are getting an error.\r\nPlease refer the [limitations](https://www.tensorflow.org/guide/function#limitations) and guidelines for the tf.function to understand in detail. Thanks",
"So what can be done to get normal python like mode ? ",
"Currently it falls under the limitations of `tf.function()`, you try try wrapping `tf.py_function ` https://www.tensorflow.org/api_docs/python/tf/py_function inside tf.function. \r\nIf it still fails, you need to execute it outside tf.function which uses eager mode instead of graph mode.",
"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/59725\">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/59725\">No</a>\n"
] | 2023-02-17T12:36:22 | 2023-04-12T01:53:47 | 2023-04-12T01:53:44 | NONE | null | null | null | Issue Type -> Bug
Have you reproduced the bug with TF nightly? -> No
Tensorflow Version -> v2.11.0-rc2-17-gd5b57ca93e5, 2.11.0
Custom Code -> Yes
OS Platform and Distribution -> Google colab
#### Standalone code to reproduce the issue
```python3
import tensorflow as tf
@tf.function
def sample_function(inputs:tf.Tensor,application_order:list=['method_3','method_1','method_2']):
def method_1(inputs:tf.Tensor): return inputs*2 # some complex code
def method_2(inputs:tf.Tensor): return inputs/2 # some complex code
def method_3(inputs:tf.Tensor): return inputs*3 # some complex code
print(locals().keys()) # check all methods ! work fine all are here as dictionary
for application in application_order:
inputs=locals()[application](inputs) # ERROR with tensorflow.function decorator
return inputs
sample_function(tf.random.normal((1,3,3,1))) # call function # ERROR
```
#### Relevant log output
```shell
dict_keys(['application_order', 'do_return', 'retval_', 'method_1', 'method_2', 'method_3', 'fscope', 'ag__', 'inputs'])
.....
KeyError: in user code:
File "<ipython-input-128-82f7957c05a2>", line 12, in sample_function *
inputs=locals()[application](inputs) # ERROR with tensorflow.function decorator
KeyError: 'method_3'
```
#### Work fine without tensorflow.function
```python3
import tensorflow as tf
#@tf.function
def sample_function(inputs:tf.Tensor,application_order:list=['method_3','method_1','method_2']):
def method_1(inputs:tf.Tensor): return inputs*2 # some complex code
def method_2(inputs:tf.Tensor): return inputs/2 # some complex code
def method_3(inputs:tf.Tensor): return inputs*3 # some complex code
print(locals().keys()) # check all methods ! work fine all are here as dictionary
for application in application_order:
inputs=locals()[application](inputs) # working fine as normal function !!!!!!!!!
return inputs
sample_function(tf.random.normal((1,3,3,1))) # call function # works fine
```
#### Relevant log output
```shell
dict_keys(['inputs', 'application_order', 'method_1', 'method_2', 'method_3'])
<tf.Tensor: shape=(1, 3, 3, 1), dtype=float32, numpy=
....
```
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"@SuryanarayanaY \r\nI was able to replicate the issue on Colab using TF v2.11 and tf-nightly-2.13.0.dev20230221. Please find the gist of [2.11](https://colab.research.google.com/gist/tiruk007/a657e62ad14d253d0cd2dd2775bb4f2b/untitled137.ipynb) and [tf.nightly](https://colab.research.google.com/gist/tiruk007/c9e2511f3ed3733828e8f2f200177d2c/untitled138.ipynb).\r\n\r\nThank you!",
"Hi @albertz ,\r\n\r\nThe indices are validated on CPU but not GPU.Hence CPU code throws an User error.This is intended behaviour.\r\nPlease refer attached [source](https://www.tensorflow.org/api_docs/python/tf/gather#:~:text=Deprecated%3A%20SOME%20ARGUMENTS%20ARE%20DEPRECATED%3A%20(validate_indices).%20They%20will%20be%20removed%20in%20a%20future%20version.%20Instructions%20for%20updating%3A%20The%20validate_indices%20argument%20has%20no%20effect.%20Indices%20are%20always%20validated%20on%20CPU%20and%20never%20validated%20on%20GPU.).\r\n\r\nWould you please elaborate how your Feature request beneficial over current behaviour ? That helps in providing more context to consider the request.\r\n\r\nThanks!\r\n\r\n",
"I know that this is the documented behavior.\r\n\r\nBut I request for a feature here where the CPU behavior is just the same as the GPU behavior. I have a use case where the GPU behavior is totally fine, and I don't want the CPU behavior to be inconsistent but rather behave exactly the same. The use case is probably also not too rare: I just happen to have a few indices which might be out-of-range, and I want to get 0s returned for them. Exactly like what the GPU behavior does. But when I run it on CPU, I want it to behave in the same way as the GPU.\r\n",
"Hi @ albertz,\r\n\r\nUnfortunately this can't be considered as Feature now.With invalid data the behaviour is expected and also documented. \r\n\r\nPlease refer the Developer [comment-1439267105](https://github.com/tensorflow/tensorflow/issues/59750#issuecomment-1439267105) regarding similar issue with same Op where it was clearly mentioned that it won't be considered for fix and closed the issue well.",
"Maybe you misunderstood what I wrote. I did not say that the behavior is unexpected or not documented. I know that it is exactly as expected. But I'm not really asking about `tf.gather` here at all.\r\n\r\nI'm asking for a **separate** new `tf.gather_with_fallback` or whatever you want to name it. Or maybe a new **option** for `tf.gather`.\r\n\r\nI don't want to change any existing behavior.\r\n",
"Hi @albertz ,\r\n\r\nCould you please elaborate more ? You want the proposed API should work like same as tf.gather without raising error in validating indices.If Indices is large, is that not resource intensive on CPU ? What are the use cases for this feature and how community will get benefit from this ?",
"The proposed API should work exactly like `tf.gather` does currently on GPU. I.e. on invalid indices, it would not raise an error but just return 0 (or maybe some configurable tensor). Pseudo code (per element):\r\n```python\r\nif 0 <= index < dim:\r\n return x[index]\r\nelse:\r\n return fallback\r\n```\r\nIn most of my (more dynamic) use of `tf.gather`, I actually need to have this behavior. Using an embedding where I can make sure the indices should be valid is actually more like the exception.\r\n\r\nI don't see why this should be resource intensive.\r\n",
"Hi @albertz ,\r\n\r\nI tried a demo example on a 2D tensor using your workaround code but getting error.Please refer the attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/53a7d2cfa5c51567aff97b73934b1909/59724.ipynb) and confirm us the way you want to implement it and revert us with the gist. That provide me more context. Thanks! ",
"I think there was a small error in my workaround code as I posted it here. I corrected it. Anyway, this workaround code was just for demonstration purpose. (In our internal code, the workaround code is a bit more involved, that's why I did not copy it here.)\r\n\r\nBut anyway, I did not say you should use my workaround code. When implementing that inside TF, it is much easier to implement it just following the pseudo code above, directly in C++. I assume your current GPU implementation actually is already like that.",
"@albertz , \r\n\r\nI just want to understand the requirement and implementation thoroughly and provide complete context to the next level. Hence i tried your code. Anyway the implementation has to be done at C++ level only for this API.\r\n\r\nI will escalate the issue to next level and will comeback.\r\n\r\nThanks!",
"We won't do this internally - it's not highly requested, and there are potential work-arounds that use combinations of ops (or a single custom op if it is a performance bottleneck)."
] | 2023-02-17T10:23:10 | 2023-03-13T19:15:49 | 2023-03-13T19:15:49 | CONTRIBUTOR | null | null | null | This is about `tf.gather`. The wanted behavior could be added directly, or maybe via a new option, or maybe via a separate op.
Actually, on GPU (at least where I tested it), the behavior of `tf.gather` is already as I want it:
- It returns 0 for invalid indices.
- I think (not really verified): gradients for those 0s back to the `param` would go nowhere. (This is important though.)
On CPU, the current behavior is:
- An exception is raised.
I basically want the same behavior on CPU as we currently have it on GPU.
My current workaround is sth like:
```python
# need to extend param such that we can lead gradients to nowhere
zeros = tf.zeros([tf.shape(param)[i] if i != axis else 1 for i in range(param.ndim)], dtype=param.dtype)
param = tf.concat([param, zeros], axis=axis)
indices = tf.where(indices >= 0 and indices < tf.shape(param)[axis], indices, tf.shape(param)[axis] - 1)
gather = tf.gather(param, indices, axis=axis)
```
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"Hi, @pjpratik \r\n\r\nCould you please look into this issue ? Thank you!",
"Hi @Nnevalti, as far as I know we don't especially support mac catalyst, can you try doing whatever you want to do with normal iOS/macOS workflows and see if you run into any trouble? If so, we can identify more specifically what is unsupported and possibly make a feature request.",
"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.",
"Hello, so in the end I changed my code so I can use the MacOS library instead of catalyst. I did not succeed in compiling for Mac Catalyst, I lack knowledge for it !",
"Hi @Nnevalti, can you show me how you tried compiling (i.e. what commands you used) and the error you get? 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.",
"It's been a long time so I don't remember everything, all I know is that i tried using bazel but there is no rule for the catalyst build. I don't remember if I tried to build using Cmake (for catalyst at least), I just used it to build the regular MacOS M1 library.\r\nCurrently I'm still a beginner concerning compilation and cross compilation, so I'm not sure about what needs to be done for it to work.",
"Hi @Nnevalti, fair enough, if you can replicate the issue, please let us know.",
"It's not an issue, tensorflow just doesn't support catalyst build. I don't think it's an important feature, but I think it could help people in the future to look into it !"
] | 2023-02-17T09:57:40 | 2023-09-27T19:54:35 | null | NONE | null | null | null | Hi I created a stackoverlfow post already: https://stackoverflow.com/questions/75473220/build-tensorflow-lite-for-mac-catalyst/75480973#75480973
But I thought I should have asked here first.
### System information
- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**: Mac mini M1 Ventura 13.1
- **TensorFlow installed from (source or binary)**: source
- **TensorFlow version (use command below)**: 2.10.0
- **Python version**: 3.10.8
- **Bazel version (if compiling from source)**: 5.3.0
- **GCC/Compiler version (if compiling from source)**: clang 14.0.0
- **Exact command to reproduce**: bazel build --config=catalyst //tensorflow/lite:tensorflowlite.framework
### Describe the problem
So I'm trying to build tensorflow lite from source (github) with bazel for mac catalyst and I wanted to know if it is featured and if not if there is anyway I could build it.
### Source code / logs
For now without modification of any sort I have this error: `ERROR: Config value 'catalyst' is not defined in any .rc file`
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"In LLDB where it hangs:\r\n\r\n<details>\r\n\r\n```\r\n(lldb) thread backtrace all\r\n* thread #1, queue = 'com.apple.main-thread', stop reason = signal SIGSTOP\r\n * frame #0: 0x000000018382a270 libsystem_kernel.dylib`__psynch_cvwait + 8\r\n frame #1: 0x000000018386483c libsystem_pthread.dylib`_pthread_cond_wait + 1236\r\n frame #2: 0x00000001837b3294 libc++.1.dylib`std::__1::condition_variable::wait(std::__1::unique_lock<std::__1::mutex>&) + 28\r\n frame #3: 0x000000014b0e7954 libtensorflow_framework.2.dylib`nsync::nsync_mu_semaphore_p_with_deadline(nsync::nsync_semaphore_s_*, timespec) + 412\r\n frame #4: 0x000000014b0e42f8 libtensorflow_framework.2.dylib`nsync::nsync_cv_wait_with_deadline_generic(nsync::nsync_cv_s_*, void*, void (*)(void*), void (*)(void*), timespec, nsync::nsync_note_s_*) + 372\r\n frame #5: 0x0000000284afeb9c _pywrap_tensorflow_internal.so`tensorflow::DirectSession::WaitForNotification(tsl::Notification*, long long) + 216\r\n frame #6: 0x0000000284af6c38 _pywrap_tensorflow_internal.so`tensorflow::DirectSession::WaitForNotification(tsl::Notification*, tensorflow::DirectSession::RunState*, tsl::CancellationManager*, long long) + 44\r\n frame #7: 0x0000000284af5a90 _pywrap_tensorflow_internal.so`tensorflow::DirectSession::RunInternal(long long, tensorflow::RunOptions const&, tensorflow::CallFrameInterface*, tensorflow::DirectSession::ExecutorsAndKeys*, tensorflow::RunMetadata*, tsl::thread::ThreadPoolOptions const&) + 2752\r\n frame #8: 0x0000000284af76e8 _pywrap_tensorflow_internal.so`tensorflow::DirectSession::Run(tensorflow::RunOptions const&, std::__1::vector<std::__1::pair<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, tensorflow::Tensor>, std::__1::allocator<std::__1::pair<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, tensorflow::Tensor> > > const&, std::__1::vector<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, std::__1::allocator<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > > > const&, std::__1::vector<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, std::__1::allocator<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > > > const&, std::__1::vector<tensorflow::Tensor, std::__1::allocator<tensorflow::Tensor> >*, tensorflow::RunMetadata*, tsl::thread::ThreadPoolOptions const&) + 1800\r\n frame #9: 0x0000000284af6fd4 _pywrap_tensorflow_internal.so`tensorflow::DirectSession::Run(tensorflow::RunOptions const&, std::__1::vector<std::__1::pair<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, tensorflow::Tensor>, std::__1::allocator<std::__1::pair<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, tensorflow::Tensor> > > const&, std::__1::vector<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, std::__1::allocator<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > > > const&, std::__1::vector<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, std::__1::allocator<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > > > const&, std::__1::vector<tensorflow::Tensor, std::__1::allocator<tensorflow::Tensor> >*, tensorflow::RunMetadata*) + 32\r\n frame #10: 0x000000028098ca60 _pywrap_tensorflow_internal.so`tensorflow::SessionRef::Run(tensorflow::RunOptions const&, std::__1::vector<std::__1::pair<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, tensorflow::Tensor>, std::__1::allocator<std::__1::pair<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, tensorflow::Tensor> > > const&, std::__1::vector<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, std::__1::allocator<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > > > const&, std::__1::vector<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, std::__1::allocator<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > > > const&, std::__1::vector<tensorflow::Tensor, std::__1::allocator<tensorflow::Tensor> >*, tensorflow::RunMetadata*) + 248\r\n frame #11: 0x00000002813cf550 _pywrap_tensorflow_internal.so`TF_Run_Helper(tensorflow::Session*, char const*, TF_Buffer const*, std::__1::vector<std::__1::pair<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, tensorflow::Tensor>, std::__1::allocator<std::__1::pair<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, tensorflow::Tensor> > > const&, std::__1::vector<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, std::__1::allocator<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > > > const&, TF_Tensor**, std::__1::vector<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, std::__1::allocator<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > > > const&, TF_Buffer*, TF_Status*) + 508\r\n frame #12: 0x00000002813db464 _pywrap_tensorflow_internal.so`TF_SessionRun + 972\r\n frame #13: 0x000000028098aa38 _pywrap_tensorflow_internal.so`tensorflow::TF_SessionRun_wrapper_helper(TF_Session*, char const*, TF_Buffer const*, std::__1::vector<TF_Output, std::__1::allocator<TF_Output> > const&, std::__1::vector<_object*, std::__1::allocator<_object*> > const&, std::__1::vector<TF_Output, std::__1::allocator<TF_Output> > const&, std::__1::vector<TF_Operation*, std::__1::allocator<TF_Operation*> > const&, TF_Buffer*, TF_Status*, std::__1::vector<_object*, std::__1::allocator<_object*> >*) + 816\r\n frame #14: 0x000000028098aeb4 _pywrap_tensorflow_internal.so`tensorflow::TF_SessionRun_wrapper(TF_Session*, TF_Buffer const*, std::__1::vector<TF_Output, std::__1::allocator<TF_Output> > const&, std::__1::vector<_object*, std::__1::allocator<_object*> > const&, std::__1::vector<TF_Output, std::__1::allocator<TF_Output> > const&, std::__1::vector<TF_Operation*, std::__1::allocator<TF_Operation*> > const&, TF_Buffer*, TF_Status*, std::__1::vector<_object*, std::__1::allocator<_object*> >*) + 56\r\n frame #15: 0x000000014a3bb044 _pywrap_tf_session.so`void pybind11::cpp_function::initialize<pybind11_init__pywrap_tf_session(pybind11::module_&)::$_16, pybind11::object, TF_Session*, TF_Buffer*, pybind11::handle const&, std::__1::vector<TF_Output, std::__1::allocator<TF_Output> > const&, std::__1::vector<TF_Operation*, std::__1::allocator<TF_Operation*> > const&, TF_Buffer*, pybind11::name, pybind11::scope, pybind11::sibling>(pybind11_init__pywrap_tf_session(pybind11::module_&)::$_16&&, pybind11::object (*)(TF_Session*, TF_Buffer*, pybind11::handle const&, std::__1::vector<TF_Output, std::__1::allocator<TF_Output> > const&, std::__1::vector<TF_Operation*, std::__1::allocator<TF_Operation*> > const&, TF_Buffer*), pybind11::name const&, pybind11::scope const&, pybind11::sibling const&)::'lambda'(pybind11::detail::function_call&)::__invoke(pybind11::detail::function_call&) + 648\r\n frame #16: 0x000000014a39d010 _pywrap_tf_session.so`pybind11::cpp_function::dispatcher(_object*, _object*, _object*) + 3580\r\n frame #17: 0x00000001000b3398 python3`cfunction_call + 80\r\n frame #18: 0x000000010005f1e8 python3`_PyObject_MakeTpCall + 340\r\n frame #19: 0x000000010016f6ac python3`call_function + 724\r\n frame #20: 0x000000010016bd44 python3`_PyEval_EvalFrameDefault + 29268\r\n frame #21: 0x000000010005fee4 python3`function_code_fastcall + 116\r\n frame #22: 0x000000010016f614 python3`call_function + 572\r\n frame #23: 0x000000010016bd28 python3`_PyEval_EvalFrameDefault + 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0x00000001001644a8 python3`_PyEval_EvalCode + 2968\r\n frame #39: 0x000000010005fe64 python3`_PyFunction_Vectorcall + 240\r\n frame #40: 0x0000000100062cf0 python3`method_vectorcall + 164\r\n frame #41: 0x000000010016f614 python3`call_function + 572\r\n frame #42: 0x000000010016be40 python3`_PyEval_EvalFrameDefault + 29520\r\n frame #43: 0x00000001001644a8 python3`_PyEval_EvalCode + 2968\r\n frame #44: 0x000000010005fe64 python3`_PyFunction_Vectorcall + 240\r\n frame #45: 0x0000000100062cf0 python3`method_vectorcall + 164\r\n frame #46: 0x000000010016f614 python3`call_function + 572\r\n frame #47: 0x000000010016be40 python3`_PyEval_EvalFrameDefault + 29520\r\n frame #48: 0x000000010005fee4 python3`function_code_fastcall + 116\r\n frame #49: 0x000000010016f614 python3`call_function + 572\r\n frame #50: 0x000000010016bd28 python3`_PyEval_EvalFrameDefault + 29240\r\n frame #51: 0x000000010005fee4 python3`function_code_fastcall + 116\r\n frame #52: 0x000000010016f614 python3`call_function + 572\r\n frame #53: 0x000000010016bd28 python3`_PyEval_EvalFrameDefault + 29240\r\n frame #54: 0x000000010005fee4 python3`function_code_fastcall + 116\r\n frame #55: 0x000000010016f614 python3`call_function + 572\r\n frame #56: 0x000000010016bdc4 python3`_PyEval_EvalFrameDefault + 29396\r\n frame #57: 0x000000010005fee4 python3`function_code_fastcall + 116\r\n frame #58: 0x000000010016f614 python3`call_function + 572\r\n frame #59: 0x000000010016bdc4 python3`_PyEval_EvalFrameDefault + 29396\r\n frame #60: 0x00000001001644a8 python3`_PyEval_EvalCode + 2968\r\n frame #61: 0x00000001001c7834 python3`pyrun_file + 376\r\n frame #62: 0x00000001001c6d48 python3`PyRun_SimpleFileExFlags + 816\r\n frame #63: 0x00000001001e9e84 python3`Py_RunMain + 2916\r\n frame #64: 0x00000001001eb018 python3`pymain_main + 1272\r\n frame #65: 0x0000000100005ddc python3`main + 56\r\n frame #66: 0x00000001003d108c dyld`start + 520\r\n thread #11\r\n frame #0: 0x000000018382a270 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#3: 0x000000028a47f320 _pywrap_tensorflow_internal.so`Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 208\r\n frame #4: 0x000000028a47f03c _pywrap_tensorflow_internal.so`Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::WaitForWork(Eigen::EventCount::Waiter*, tsl::thread::EigenEnvironment::Task*) + 952\r\n frame #5: 0x000000028a47eb4c _pywrap_tensorflow_internal.so`Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::WorkerLoop(int) + 1464\r\n frame #6: 0x000000028a47e468 _pywrap_tensorflow_internal.so`tsl::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()::operator()() const + 80\r\n frame #7: 0x000000014bb9e878 libtensorflow_framework.2.dylib`tsl::(anonymous namespace)::PThread::ThreadFn(void*) + 120\r\n frame #8: 0x000000018386426c libsystem_pthread.dylib`_pthread_start + 148\r\n thread #22\r\n frame #0: 0x000000018382a270 libsystem_kernel.dylib`__psynch_cvwait + 8\r\n frame #1: 0x000000018386483c libsystem_pthread.dylib`_pthread_cond_wait + 1236\r\n frame #2: 0x00000001837b3294 libc++.1.dylib`std::__1::condition_variable::wait(std::__1::unique_lock<std::__1::mutex>&) + 28\r\n frame #3: 0x000000028a47f320 _pywrap_tensorflow_internal.so`Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 208\r\n frame #4: 0x000000028a47f03c _pywrap_tensorflow_internal.so`Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::WaitForWork(Eigen::EventCount::Waiter*, tsl::thread::EigenEnvironment::Task*) + 952\r\n frame #5: 0x000000028a47eb4c _pywrap_tensorflow_internal.so`Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::WorkerLoop(int) + 1464\r\n frame #6: 0x000000028a47e468 _pywrap_tensorflow_internal.so`tsl::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()::operator()() const + 80\r\n frame #7: 0x000000014bb9e878 libtensorflow_framework.2.dylib`tsl::(anonymous namespace)::PThread::ThreadFn(void*) + 120\r\n frame #8: 0x000000018386426c libsystem_pthread.dylib`_pthread_start + 148\r\n thread #23\r\n frame #0: 0x000000018382a270 libsystem_kernel.dylib`__psynch_cvwait + 8\r\n frame #1: 0x000000018386483c libsystem_pthread.dylib`_pthread_cond_wait + 1236\r\n frame #2: 0x00000001837b3294 libc++.1.dylib`std::__1::condition_variable::wait(std::__1::unique_lock<std::__1::mutex>&) + 28\r\n frame #3: 0x000000028a47f320 _pywrap_tensorflow_internal.so`Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 208\r\n frame #4: 0x000000028a47f03c _pywrap_tensorflow_internal.so`Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::WaitForWork(Eigen::EventCount::Waiter*, tsl::thread::EigenEnvironment::Task*) + 952\r\n frame #5: 0x000000028a47eb4c _pywrap_tensorflow_internal.so`Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::WorkerLoop(int) + 1464\r\n frame #6: 0x000000028a47e468 _pywrap_tensorflow_internal.so`tsl::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()::operator()() const + 80\r\n frame #7: 0x000000014bb9e878 libtensorflow_framework.2.dylib`tsl::(anonymous namespace)::PThread::ThreadFn(void*) + 120\r\n frame #8: 0x000000018386426c libsystem_pthread.dylib`_pthread_start + 148\r\n thread #24\r\n frame #0: 0x000000018382a270 libsystem_kernel.dylib`__psynch_cvwait + 8\r\n frame #1: 0x000000018386483c libsystem_pthread.dylib`_pthread_cond_wait + 1236\r\n frame #2: 0x00000001837b3294 libc++.1.dylib`std::__1::condition_variable::wait(std::__1::unique_lock<std::__1::mutex>&) + 28\r\n frame #3: 0x000000028a47f320 _pywrap_tensorflow_internal.so`Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 208\r\n frame #4: 0x000000028a47f03c _pywrap_tensorflow_internal.so`Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::WaitForWork(Eigen::EventCount::Waiter*, tsl::thread::EigenEnvironment::Task*) + 952\r\n frame #5: 0x000000028a47eb4c _pywrap_tensorflow_internal.so`Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::WorkerLoop(int) + 1464\r\n frame #6: 0x000000028a47e468 _pywrap_tensorflow_internal.so`tsl::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()::operator()() const + 80\r\n frame #7: 0x000000014bb9e878 libtensorflow_framework.2.dylib`tsl::(anonymous namespace)::PThread::ThreadFn(void*) + 120\r\n frame #8: 0x000000018386426c libsystem_pthread.dylib`_pthread_start + 148\r\n thread #25\r\n frame #0: 0x000000018382a270 libsystem_kernel.dylib`__psynch_cvwait + 8\r\n frame #1: 0x000000018386483c libsystem_pthread.dylib`_pthread_cond_wait + 1236\r\n frame #2: 0x00000001837b3294 libc++.1.dylib`std::__1::condition_variable::wait(std::__1::unique_lock<std::__1::mutex>&) + 28\r\n frame #3: 0x000000028a47f320 _pywrap_tensorflow_internal.so`Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 208\r\n frame #4: 0x000000028a47f03c _pywrap_tensorflow_internal.so`Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::WaitForWork(Eigen::EventCount::Waiter*, tsl::thread::EigenEnvironment::Task*) + 952\r\n frame #5: 0x000000028a47eb4c _pywrap_tensorflow_internal.so`Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::WorkerLoop(int) + 1464\r\n frame #6: 0x000000028a47e468 _pywrap_tensorflow_internal.so`tsl::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()::operator()() const + 80\r\n frame #7: 0x000000014bb9e878 libtensorflow_framework.2.dylib`tsl::(anonymous namespace)::PThread::ThreadFn(void*) + 120\r\n frame #8: 0x000000018386426c libsystem_pthread.dylib`_pthread_start + 148\r\n thread #26\r\n frame #0: 0x000000018382a270 libsystem_kernel.dylib`__psynch_cvwait + 8\r\n frame #1: 0x000000018386483c libsystem_pthread.dylib`_pthread_cond_wait + 1236\r\n frame #2: 0x00000001837b3294 libc++.1.dylib`std::__1::condition_variable::wait(std::__1::unique_lock<std::__1::mutex>&) + 28\r\n frame #3: 0x000000028a47f320 _pywrap_tensorflow_internal.so`Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 208\r\n frame #4: 0x000000028a47f03c _pywrap_tensorflow_internal.so`Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::WaitForWork(Eigen::EventCount::Waiter*, tsl::thread::EigenEnvironment::Task*) + 952\r\n frame #5: 0x000000028a47eb4c _pywrap_tensorflow_internal.so`Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::WorkerLoop(int) + 1464\r\n frame #6: 0x000000028a47e468 _pywrap_tensorflow_internal.so`tsl::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()::operator()() const + 80\r\n frame #7: 0x000000014bb9e878 libtensorflow_framework.2.dylib`tsl::(anonymous namespace)::PThread::ThreadFn(void*) + 120\r\n frame #8: 0x000000018386426c libsystem_pthread.dylib`_pthread_start + 148\r\n thread #27\r\n frame #0: 0x000000018382a270 libsystem_kernel.dylib`__psynch_cvwait + 8\r\n frame #1: 0x000000018386483c libsystem_pthread.dylib`_pthread_cond_wait + 1236\r\n frame #2: 0x00000001837b3294 libc++.1.dylib`std::__1::condition_variable::wait(std::__1::unique_lock<std::__1::mutex>&) + 28\r\n frame #3: 0x000000028a47f320 _pywrap_tensorflow_internal.so`Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 208\r\n frame #4: 0x000000028a47f03c _pywrap_tensorflow_internal.so`Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::WaitForWork(Eigen::EventCount::Waiter*, tsl::thread::EigenEnvironment::Task*) + 952\r\n frame #5: 0x000000028a47eb4c _pywrap_tensorflow_internal.so`Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::WorkerLoop(int) + 1464\r\n frame #6: 0x000000028a47e468 _pywrap_tensorflow_internal.so`tsl::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()::operator()() const + 80\r\n frame #7: 0x000000014bb9e878 libtensorflow_framework.2.dylib`tsl::(anonymous namespace)::PThread::ThreadFn(void*) + 120\r\n frame #8: 0x000000018386426c libsystem_pthread.dylib`_pthread_start + 148\r\n thread #28\r\n frame #0: 0x000000018382a270 libsystem_kernel.dylib`__psynch_cvwait + 8\r\n frame #1: 0x000000018386483c libsystem_pthread.dylib`_pthread_cond_wait + 1236\r\n frame #2: 0x00000001837b3294 libc++.1.dylib`std::__1::condition_variable::wait(std::__1::unique_lock<std::__1::mutex>&) + 28\r\n frame #3: 0x000000014b0cefdc libtensorflow_framework.2.dylib`Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 208\r\n frame #4: 0x000000014b0cecf8 libtensorflow_framework.2.dylib`Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::WaitForWork(Eigen::EventCount::Waiter*, tsl::thread::EigenEnvironment::Task*) + 952\r\n frame #5: 0x000000014b0ce490 libtensorflow_framework.2.dylib`Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::WorkerLoop(int) + 576\r\n frame #6: 0x000000014b0ce124 libtensorflow_framework.2.dylib`tsl::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()::operator()() const + 80\r\n frame #7: 0x000000014bb9e878 libtensorflow_framework.2.dylib`tsl::(anonymous namespace)::PThread::ThreadFn(void*) + 120\r\n frame #8: 0x000000018386426c libsystem_pthread.dylib`_pthread_start + 148\r\n thread #29\r\n frame #0: 0x000000018382a270 libsystem_kernel.dylib`__psynch_cvwait + 8\r\n frame #1: 0x000000018386483c libsystem_pthread.dylib`_pthread_cond_wait + 1236\r\n frame #2: 0x00000001837b3294 libc++.1.dylib`std::__1::condition_variable::wait(std::__1::unique_lock<std::__1::mutex>&) + 28\r\n frame #3: 0x000000014b0cefdc libtensorflow_framework.2.dylib`Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 208\r\n frame #4: 0x000000014b0cecf8 libtensorflow_framework.2.dylib`Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::WaitForWork(Eigen::EventCount::Waiter*, tsl::thread::EigenEnvironment::Task*) + 952\r\n frame #5: 0x000000014b0ce808 libtensorflow_framework.2.dylib`Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::WorkerLoop(int) + 1464\r\n frame #6: 0x000000014b0ce124 libtensorflow_framework.2.dylib`tsl::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()::operator()() const + 80\r\n frame #7: 0x000000014bb9e878 libtensorflow_framework.2.dylib`tsl::(anonymous namespace)::PThread::ThreadFn(void*) + 120\r\n frame #8: 0x000000018386426c libsystem_pthread.dylib`_pthread_start + 148\r\n thread #30\r\n frame #0: 0x000000018382a06c libsystem_kernel.dylib`__semwait_signal + 8\r\n frame #1: 0x0000000183732fc8 libsystem_c.dylib`nanosleep + 220\r\n frame #2: 0x000000014bb9d7f4 libtensorflow_framework.2.dylib`tsl::(anonymous namespace)::PosixEnv::SleepForMicroseconds(long long) + 152\r\n frame #3: 0x000000014bcfb620 libtensorflow_framework.2.dylib`tensorflow::EventMgr::PollLoop() + 500\r\n frame #4: 0x000000014b0ce828 libtensorflow_framework.2.dylib`Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::WorkerLoop(int) + 1496\r\n frame #5: 0x000000014b0ce124 libtensorflow_framework.2.dylib`tsl::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()::operator()() const + 80\r\n frame #6: 0x000000014bb9e878 libtensorflow_framework.2.dylib`tsl::(anonymous namespace)::PThread::ThreadFn(void*) + 120\r\n frame #7: 0x000000018386426c libsystem_pthread.dylib`_pthread_start + 148\r\n thread #31\r\n frame #0: 0x000000018382a270 libsystem_kernel.dylib`__psynch_cvwait + 8\r\n frame #1: 0x000000018386483c libsystem_pthread.dylib`_pthread_cond_wait + 1236\r\n frame #2: 0x00000001837b3294 libc++.1.dylib`std::__1::condition_variable::wait(std::__1::unique_lock<std::__1::mutex>&) + 28\r\n frame #3: 0x000000014b0cefdc libtensorflow_framework.2.dylib`Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 208\r\n frame #4: 0x000000014b0cecf8 libtensorflow_framework.2.dylib`Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::WaitForWork(Eigen::EventCount::Waiter*, tsl::thread::EigenEnvironment::Task*) + 952\r\n frame #5: 0x000000014b0ce490 libtensorflow_framework.2.dylib`Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::WorkerLoop(int) + 576\r\n frame #6: 0x000000014b0ce124 libtensorflow_framework.2.dylib`tsl::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()::operator()() const + 80\r\n frame #7: 0x000000014bb9e878 libtensorflow_framework.2.dylib`tsl::(anonymous namespace)::PThread::ThreadFn(void*) + 120\r\n frame #8: 0x000000018386426c libsystem_pthread.dylib`_pthread_start + 148\r\n thread #32\r\n frame #0: 0x000000018382a270 libsystem_kernel.dylib`__psynch_cvwait + 8\r\n frame #1: 0x000000018386483c libsystem_pthread.dylib`_pthread_cond_wait + 1236\r\n frame #2: 0x00000001001d96a0 python3`PyThread_acquire_lock_timed + 604\r\n frame #3: 0x00000001002478e4 python3`acquire_timed + 236\r\n frame #4: 0x0000000100247aa8 python3`lock_PyThread_acquire_lock + 72\r\n frame #5: 0x000000010006d284 python3`method_vectorcall_VARARGS_KEYWORDS + 292\r\n frame #6: 0x000000010016f614 python3`call_function + 572\r\n frame #7: 0x000000010016bd28 python3`_PyEval_EvalFrameDefault + 29240\r\n frame #8: 0x00000001001644a8 python3`_PyEval_EvalCode + 2968\r\n frame #9: 0x000000010005fe64 python3`_PyFunction_Vectorcall + 240\r\n frame #10: 0x000000010016f614 python3`call_function + 572\r\n frame #11: 0x000000010016bd28 python3`_PyEval_EvalFrameDefault + 29240\r\n frame #12: 0x00000001001644a8 python3`_PyEval_EvalCode + 2968\r\n frame #13: 0x000000010005fe64 python3`_PyFunction_Vectorcall + 240\r\n frame #14: 0x000000010016f614 python3`call_function + 572\r\n frame #15: 0x000000010016bd28 python3`_PyEval_EvalFrameDefault + 29240\r\n frame #16: 0x000000010005fee4 python3`function_code_fastcall + 116\r\n frame #17: 0x0000000100062d9c python3`method_vectorcall + 336\r\n frame #18: 0x000000010016c078 python3`_PyEval_EvalFrameDefault + 30088\r\n frame #19: 0x000000010005fee4 python3`function_code_fastcall + 116\r\n frame #20: 0x000000010016f614 python3`call_function + 572\r\n frame #21: 0x000000010016bd28 python3`_PyEval_EvalFrameDefault + 29240\r\n frame #22: 0x000000010005fee4 python3`function_code_fastcall + 116\r\n frame #23: 0x000000010016f614 python3`call_function + 572\r\n frame #24: 0x000000010016bd28 python3`_PyEval_EvalFrameDefault + 29240\r\n frame #25: 0x000000010005fee4 python3`function_code_fastcall + 116\r\n frame #26: 0x0000000100062d9c python3`method_vectorcall + 336\r\n frame #27: 0x0000000100246d50 python3`t_bootstrap + 180\r\n frame #28: 0x00000001001d91c8 python3`pythread_wrapper + 48\r\n frame #29: 0x000000018386426c libsystem_pthread.dylib`_pthread_start + 148\r\n thread #33\r\n frame #0: 0x000000018382872c libsystem_kernel.dylib`__workq_kernreturn + 8\r\n```\r\n\r\n</details>",
"When executing on CPU, I get an actual exception:\r\n```\r\nTensorFlow exception: Graph execution error:\r\n\r\nindices[1] = 7 is not in [0, 7)\r\n\t [[{{node output/rec/enc_value_gather/GatherV2}}]]\r\n```\r\nAnd this is probably correct. It's very possible that I have the index off for `tf.gather`. I think I did not do any bound checks.\r\n\r\nSo maybe that is the error here on GPU? Maybe there are no bound checks for `tf.gather` on GPU, and this somehow causes it to access some memory region which hangs?\r\n**Edit** Not sure. I cannot really trigger any strange behavior manually by trial and error using `tf.gather` and invalid indices on GPU. It always returns 0. I assume this is still safe and the problem is elsewhere.\r\n**Edit** I explicitly clipped the values now in all cases, and I still have the same hang, so this seems to be unrelated.",
"@albertz I have tried to reproduce this error in colab on TF 2.11 and did not face any error. The GPU was enabled during the experiment. Please find the gist [here](https://colab.research.google.com/gist/pjpratik/175321e781bacf4dc0540e7b44dea00e/59722.ipynb). \r\n\r\nHave you checked in the latest TF version and see if the issue still exists? Thanks!",
"Thanks for creating the gist.\n\nHowever, as I said, it is crucial to run it on Apple M1 hardware to reproduce it. In your gist, I see that you use an Nvidia GPU.\n\nAlso, you did not exactly use the commits I specified, although this probably should not matter.\n",
"@albertz Thanks for the clarification. I was able to reproduce the error with the commits on Apple M1 hardware. The code runs fine else wise. \r\n\r\n**Hangs here when the specific commits are used**\r\n<img width=\"890\" alt=\"Screenshot 2023-02-20 at 5 25 09 PM\" src=\"https://user-images.githubusercontent.com/118897289/220099655-20e8f0b2-f303-454f-92c7-a0b4afb2dc6a.png\">\r\n\r\n**Works fine else wise**\r\n<img width=\"693\" alt=\"Screenshot 2023-02-20 at 4 27 50 PM\" src=\"https://user-images.githubusercontent.com/118897289/220086396-c2ae8b7f-9f7c-480c-924e-ffcf1efba09b.png\">\r\n\r\n\r\nHi @gaikwadrahul8 Could you please look into this issue. Thanks.",
"Hi, @albertz \r\n\r\nApologize for the delay and I was able to reproduce the issue with specific commits on `Apple M1` and it seems like Tensorflow hangs in session.run() or else it's working fine as expected, For you reference I have added screenshots below and as per [official documentation](https://www.tensorflow.org/install/pip#macos), **There is currently no official GPU support for MacOS.** It's better to post this issue [here](https://github.com/apple/tensorflow_macos/issues) for fast resolution. Thank you!\r\n\r\nWorking fine as expected without specific commits :\r\n\r\n```\r\ndev: score 0.3890726839668453 error 0.1398488128509993\r\ntrain epoch 100, finished after 96 steps, 0:00:11 elapsed (99.5% computing time) \r\nepoch 100 score: 0.3872191429890062 error: 0.13927021657019117 elapsed: 0:00:11\r\nepoch 100 'dev' eval, finished after 10 steps, 0:00:00 elapsed (98.9% computing time) \r\ndev: score 0.4133371474361815 error 0.1389488848403751\r\nFinished training in epoch 100.\r\nelapsed: 0:21:43.9778\r\n```\r\n\r\nHere is screenshot where Tensorflow hangs with specific commits:\r\n\r\n```\r\n2023-02-24 19:59:43.993909: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\r\n2023-02-24 19:59:44.001514: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\r\nCreate optimizer <class 'tensorflow.python.training.adam.AdamOptimizer'> with options {'epsilon': 1e-08, 'learning_rate': <tf.Variable 'learning_rate:0' shape=() dtype=float32>}.\r\nInitialize optimizer (default) with slots ['m', 'v'].\r\nThese additional variable were created by the optimizer: [<tf.Variable 'optimize/beta1_power:0' shape=() dtype=float32>, <tf.Variable 'optimize/beta2_power:0' shape=() dtype=float32>].\r\n2023-02-24 19:59:44.714013: W tensorflow/c/c_api.cc:291] Operation '{name:'global_step' id:1528 op device:{requested: '/device:CPU:0', assigned: ''} def:{{{node global_step}} = VarHandleOp[_class=[\"loc:@global_step\"], _has_manual_control_dependencies=true, allowed_devices=[], container=\"\", dtype=DT_INT64, shape=[], shared_name=\"global_step\", _device=\"/device:CPU:0\"]()}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\r\n2023-02-24 19:59:44.757001: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\r\n2023-02-24 19:59:44.855683: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\r\n2023-02-24 19:59:44.954833: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\r\n```",
"> **There is currently no official GPU support for MacOS.** It's better to post this issue [here](https://github.com/apple/tensorflow_macos/issues) for fast resolution. Thank you!\r\n\r\nIt says:\r\n\r\n> This repository has been archived by the owner on Jun 10, 2021. It is now read-only.\r\n\r\nI cannot post any new issue there.\r\n",
"Hi, @albertz \r\n\r\nYou can post this issue on [Apple Developer Forums ](https://developer.apple.com/forums/tags/tensorflow-metal)for fast resolution. Thank you!",
"I posted it here: https://developer.apple.com/forums/thread/725543",
"Hi, @albertz \r\n\r\nThank you for posting this issue to [Apple Developer Forums ](https://developer.apple.com/forums/tags/tensorflow-metal) and it will be taken care by Apple Developer Team so Could you please close this issue because There is currently no official GPU support for MacOS from our end? 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/59722\">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/59722\">No</a>\n"
] | 2023-02-17T09:25:55 | 2023-02-26T00:29:42 | 2023-02-26T00:29:39 | CONTRIBUTOR | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
No
### Source
binary
### Tensorflow Version
tensorflow-macos 2.11.0
### Custom Code
Yes
### OS Platform and Distribution
MacOS 12, tensorflow-macos and tensorflow-metal
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
M1
</details>
### Current Behaviour?
This is a new neural model I implemented, and I want to do training. It's modified based on an existing attention-based encoder-decoder model, where everything works fine.
In the new model, it just hangs in `session.run` and does not do anything. I also cannot interrupt it. It hangs inside the TensorFlow C++ code.
This seems to be specific for Mac M1 hardware. I cannot reproduce the problem on other hardware or environments.
### Standalone code to reproduce the issue
So far I don't have a minimal example, and this will be quite a big effort to generate one, as this is some very complex model. But here some relevant details:
* This is based on [RETURNN](https://github.com/rwth-i6/returnn).
* We still use graph-mode.
* I tested both with control flow v1 (calling `disable_control_flow_v2`) and control flow v2. It hangs in both cases.
* I tested using tfdbg or `enable_dump_debug_info`. It crashes then with a segfault.
* I get a number of other warnings, which are maybe related. See below.
To reproduce:
* Code: https://github.com/rwth-i6/i6_experiments/blob/81bcef39b5829aa43b84bcab4b4fa03f82fc3bc5/users/zeyer/experiments/exp2023_02_16_chunked_attention/demo_returnn_config.py
* Checkout the [i6_experiments repo](https://github.com/rwth-i6/i6_experiments). (Maybe use commit 81bcef39b5829aa43b84bcab4b4fa03f82fc3bc5 to be sure.)
* Checkout [RETURNN](https://github.com/rwth-i6/returnn). (Maybe commit 2ed598443f22de42599a0fee9bc43fbb5e0abec2 to be sure.)
* Run: `python3 returnn/rnn.py i6_experiments/users/zeyer/experiments/exp2023_02_16_chunked_attention/demo_returnn_config.py`
### Relevant log output
With control flow v2:
```
2023-02-17 10:02:03.997491: W tensorflow/core/common_runtime/type_inference.cc:339] Type inference failed. This indicates an invalid graph that escaped type checking. Error message: INVALID_ARGUMENT: expected compatible input types, but input 1:
type_id: TFT_OPTIONAL
args {
type_id: TFT_PRODUCT
args {
type_id: TFT_TENSOR
args {
type_id: TFT_INT32
}
}
}
is neither a subtype nor a supertype of the combined inputs preceding it:
type_id: TFT_OPTIONAL
args {
type_id: TFT_PRODUCT
args {
type_id: TFT_TENSOR
args {
type_id: TFT_FLOAT
}
}
}
while inferring type of node 'output/rec/while/body/_38/output/rec/prev_target_embed_moved_input/cond/output/_1608'
2023-02-17 10:34:46.595736: W tensorflow/c/c_api.cc:291] Operation '{name:'global_step' id:1961 op device:{requested: '/device:CPU:0', assigned: ''} def:{{{node global_step}} = VarHandleOp[_class=["loc:@global_step"], _has_manual_control_dependencies=true, allowed_devices=[], container="", dtype=DT_INT64, shape=[], shared_name="global_step", _device="/device:CPU:0"]()}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.
2023-02-17 10:35:56.799620+0100 python3[5197:2744697] Execution of the command buffer was aborted due to an error during execution. Caused GPU Timeout Error (00000002:kIOGPUCommandBufferCallbackErrorTimeout)
...
2023-02-17 10:36:01.801307+0100 python3[5197:2744697] Execution of the command buffer was aborted due to an error during execution. Ignored (for causing prior/excessive GPU errors) (00000004:kIOGPUCommandBufferCallbackErrorSubmissionsIgnored)
...
```
(Related: https://github.com/tensorflow/tensorflow/issues/57052)
With control flow v1:
```
2023-02-17 10:10:01.733679: W tensorflow/c/c_api.cc:291] Operation '{name:'global_step' id:1528 op device:{requested: '/device:CPU:0', assigned: ''} def:{{{node global_step}} = VarHandleOp[_class=["loc:@global_step"], _has_manual_control_dependencies=true, allowed_devices=[], container="", dtype=DT_INT64, shape=[], shared_name="global_step", _device="/device:CPU:0"]()}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.
2023-02-17 10:10:14.257716+0100 python3[3727:2732395] Execution of the command buffer was aborted due to an error during execution. Caused GPU Timeout Error (00000002:kIOGPUCommandBufferCallbackErrorTimeout)
2023-02-17 10:10:14.257754+0100 python3[3727:2732582] Execution of the command buffer was aborted due to an error during execution. Caused GPU Timeout Error (00000002:kIOGPUCommandBufferCallbackErrorTimeout)
2023-02-17 10:10:14.258366+0100 python3[3727:2732395] Execution of the command buffer was aborted due to an error during execution. Caused GPU Timeout Error (00000002:kIOGPUCommandBufferCallbackErrorTimeout)
2023-02-17 10:10:14.258504+0100 python3[3727:2732582] Execution of the command buffer was aborted due to an error during execution. Caused GPU Timeout Error (00000002:kIOGPUCommandBufferCallbackErrorTimeout)
2023-02-17 10:10:14.258541+0100 python3[3727:2732395] Execution of the command buffer was aborted due to an error during execution. Caused GPU Timeout Error (00000002:kIOGPUCommandBufferCallbackErrorTimeout)
2023-02-17 10:10:14.258587+0100 python3[3727:2732395] Execution of the command buffer was aborted due to an error during execution. Caused GPU Timeout Error (00000002:kIOGPUCommandBufferCallbackErrorTimeout)
2023-02-17 10:10:19.258726+0100 python3[3727:2732395] Execution of the command buffer was aborted due to an error during execution. Caused GPU Timeout Error (00000002:kIOGPUCommandBufferCallbackErrorTimeout)
2023-02-17 10:10:19.258784+0100 python3[3727:2732395] Execution of the command buffer was aborted due to an error during execution. Caused GPU Timeout Error (00000002:kIOGPUCommandBufferCallbackErrorTimeout)
```
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"@vishnukvmd I have tried to build in MacOS with the command provided and I have received different error. Please refer the below screenshot. Thanks!\r\n\r\n<img width=\"1037\" alt=\"Screenshot 2023-02-20 at 1 23 32 PM\" src=\"https://user-images.githubusercontent.com/118897289/220045388-e4f9f5cf-2dda-437d-86fe-5d0e4434af27.png\">\r\n\r\n",
"Hey @pjpratik, sorry I'm not sure what this error is about. It sounds like your toolchain has not been configured correctly. Please make sure that you're following the steps @ https://www.tensorflow.org/lite/guide/build_ios. You might also want to check out https://developer.apple.com/metal/tensorflow-plugin/ and ensure that the right version of bazel is installed via https://docs.bazel.build/versions/5.3.0/install-bazelisk.html.\r\n\r\nAlso, the \"awaiting response\" label does not correctly reflect the status of the ticket. But thank you for looking into this! :)",
"@vishnukvm Thanks for the information. I did follow the steps provided. However, still getting the same error. Need to dig deep.\r\n\r\nHi @sachinprasadhs , could you please look into this issue. Thanks.",
"Can you try the following command?\r\n`bazel build -c opt --config=ios --ios_multi_cpus=sim_arm64 //tensorflow/lite/ios:TensorFlowLiteSelectTfOps_framework` or\r\n`bazel build -c opt --config=ios --ios_multi_cpus=arm64 //tensorflow/lite/ios:TensorFlowLiteSelectTfOps_framework`",
"Hey @yishuangP!\r\n\r\nDid that, the build succeeded on tag `2.12`, but linking the output binary is failing with the following error:\r\n\r\nBuild output:\r\n<details>\r\n<summary>Click to expand!</summary> \r\n\r\n```\r\nLaunching lib/main.dart on Vishnu’s iPhone in debug mode...\r\nAutomatically signing iOS for device deployment using specified development team in Xcode project: 6Z68YJY9Q2\r\nXcode build done. 114.5s\r\nFailed to build iOS app\r\nCould not build the precompiled application for the device.\r\nError (Xcode): Undefined symbol: _TfLiteFloatArrayCreate\r\nError (Xcode): Undefined symbol: _TfLiteIntArrayCopy\r\nError (Xcode): Undefined symbol: _TfLiteIntArrayCreate\r\n\r\nError (Xcode): Undefined symbol: _TfLiteIntArrayEqual\r\n\r\nError (Xcode): Undefined symbol: _TfLiteIntArrayEqualsArray\r\n\r\nError (Xcode): Undefined symbol: _TfLiteIntArrayFree\r\n\r\nError (Xcode): Undefined symbol: _TfLiteIntArrayGetSizeInBytes\r\nError (Xcode): Undefined symbol: _TfLiteQuantizationFree\r\n\r\nError (Xcode): Undefined symbol: _TfLiteSparsityFree\r\n\r\nError (Xcode): Undefined symbol: _TfLiteTensorCopy\r\n\r\nError (Xcode): Undefined symbol: _TfLiteTensorDataFree\r\n\r\nError (Xcode): Undefined symbol: _TfLiteTensorFree\r\n\r\nError (Xcode): Undefined symbol: _TfLiteTensorRealloc\r\nError (Xcode): Undefined symbol: _TfLiteTensorReset\r\nError (Xcode): Undefined symbol: _TfLiteTensorResizeMaybeCopy\r\n\r\nError (Xcode): Undefined symbol: _TfLiteTypeGetName\r\n\r\nError (Xcode): Undefined symbol: tensorflow::MemoryDump::MemoryDump(google::protobuf::Arena*, bool)\r\n\r\nError (Xcode): Undefined symbol: tensorflow::MemoryDump::~MemoryDump()\r\n\r\nError (Xcode): Undefined symbol: tensorflow::RPCOptions::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&)\r\n\r\nError (Xcode): Undefined symbol: tensorflow::RPCOptions::RPCOptions(tensorflow::RPCOptions const&)\r\n\r\nError (Xcode): Undefined symbol: tensorflow::RPCOptions::~RPCOptions()\r\nError (Xcode): Undefined symbol: tensorflow::HistogramProto::Clear()\r\n\r\nError (Xcode): Undefined symbol: tensorflow::HistogramProto::MergeImpl(google::protobuf::Message&, google::protobuf::Message const&)\r\n\r\nError (Xcode): Undefined symbol: tensorflow::HistogramProto::~HistogramProto()\r\n\r\nError (Xcode): Undefined symbol: tensorflow::_RPCOptions_default_instance_\r\nError (Xcode): Undefined symbol: tensorflow::_HistogramProto_default_instance_\r\n\r\nError (Xcode): Undefined symbol: tensorflow::error::Code_descriptor()\r\nError (Xcode): Undefined symbol: tensorflow::BinSummary* google::protobuf::Arena::CreateMaybeMessage<tensorflow::BinSummary>(google::protobuf::Arena*)\r\n\r\nError (Xcode): Undefined symbol: tensorflow::RPCOptions* google::protobuf::Arena::CreateMaybeMessage<tensorflow::RPCOptions>(google::protobuf::Arena*)\r\n\r\nError (Xcode): Undefined symbol: tensorflow::HistogramProto* google::protobuf::Arena::CreateMaybeMessage<tensorflow::HistogramProto>(google::protobuf::Arena*)\r\n\r\nError (Xcode): Undefined symbol: tensorflow::MemAllocatorStats* google::protobuf::Arena::CreateMaybeMessage<tensorflow::MemAllocatorStats>(google::protobuf::Arena*)\r\n\r\nError (Xcode): Undefined symbol: tensorflow::MemChunk* google::protobuf::Arena::CreateMaybeMessage<tensorflow::MemChunk>(google::protobuf::Arena*)\r\n\r\nError (Xcode): Undefined symbol: tensorflow::RPCOptions::ByteSizeLong() const\r\nError (Xcode): Undefined symbol: tensorflow::HistogramProto::ByteSizeLong() const\r\n\r\nError (Xcode): Undefined symbol: _descriptor_table_tensorflow_2ftsl_2fprotobuf_2fbfc_5fmemory_5fmap_2eproto\r\n\r\nError (Xcode): Undefined symbol: _descriptor_table_tensorflow_2ftsl_2fprotobuf_2ferror_5fcodes_2eproto\r\n\r\nError (Xcode): Undefined symbol: _descriptor_table_tensorflow_2ftsl_2fprotobuf_2fhistogram_2eproto\r\nError (Xcode): Undefined symbol: _descriptor_table_tensorflow_2ftsl_2fprotobuf_2frpc_5foptions_2eproto\r\n\r\nError (Xcode): Undefined symbol: _descriptor_table_tensorflow_2ftsl_2fprotobuf_2ftest_5flog_2eproto\r\n\r\nError launching application on Vishnu’s iPhone.\r\nExited\r\n```\r\n\r\n</details>\r\n\r\nEdit: Just fyi, the output binary is 1,147,338,064 bytes in size",
"> Did that, the build succeeded on tag 2.12, but linking the output binary is failing with the following error:\r\n\r\nDid you also include the c framework? TensorFlowLiteSelectTfOps only has the custom ops. You need to build the TensorFlowLiteC as well. It has the tflite runtime and builtin ops. You can find instructions [here](https://www.tensorflow.org/lite/guide/build_ios#build_tensorflowlitec_dynamic_framework_recommended).\r\n\r\n\r\n> Edit: Just fyi, the output binary is 1,147,338,064 bytes in size\r\n\r\nYes this is normal, TensorFlowLiteSelectTfOps is very large so if your model only use tflite builtin ops, you don't need to build TensorFlowLiteSelectTfOps. Besides, we also release TensorFlowLiteSelectTfOps via cocoapods, so you can install it from cocoapods. \r\n",
"Thank you for the tip @yishuangP!\r\n\r\nThe `Podfile` now contains the following:\r\n```\r\npod 'TensorFlowLiteSwift', '~> 2.11'\r\npod 'TensorFlowLiteSelectTfOps', '~> 2.11'\r\n```\r\n\r\nBut the build is still failing, but with a different error:\r\n\r\n```\r\nFailed to build iOS app\r\nCould not build the precompiled application for the device.\r\nError (Xcode): Undefined symbol: tensorflow::HistogramProto::MergePartialFromCodedStream(google::protobuf::io::CodedInputStream*)\r\n\r\nError (Xcode): Undefined symbol: tensorflow::HistogramProto::Clear()\r\n\r\nError (Xcode): Undefined symbol: tensorflow::HistogramProto::MergeFrom(tensorflow::HistogramProto const&)\r\n\r\nError (Xcode): Undefined symbol: tensorflow::_HistogramProto_default_instance_\r\n\r\nError (Xcode): Undefined symbol: tensorflow::HistogramProto* google::protobuf::Arena::CreateMaybeMessage<tensorflow::HistogramProto>(google::protobuf::Arena*)\r\n\r\nError (Xcode): Undefined symbol: tensorflow::HistogramProto::ByteSizeLong() const\r\n\r\nError (Xcode): Undefined symbol: tensorflow::HistogramProto::InternalSerializeWithCachedSizesToArray(unsigned char*) const\r\n\r\nError (Xcode): Undefined symbol: _descriptor_table_tensorflow_2ftsl_2fprotobuf_2ferror_5fcodes_2eproto\r\n\r\nError (Xcode): Undefined symbol: _descriptor_table_tensorflow_2ftsl_2fprotobuf_2fhistogram_2eproto\r\n\r\nError (Xcode): Undefined symbol: _scc_info_HistogramProto_tensorflow_2ftsl_2fprotobuf_2fhistogram_2eproto\r\n\r\nError launching application on Vishnu’s iPhone.\r\nExited\r\n```\r\n\r\nThanks again!",
"Sorry for the late reply. I can reproduce the issue. Can you try 2.10.0 or a nightly version between 2.10.0 and 2.11.0?",
"Hi I just verified that 0.0.1-nightly.20220915 is working fine. Can you switch to this version?",
"Hey @yishuangP thanks, it is working on this version. I'll close the issue.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59721\">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/59721\">No</a>\n",
"FYI, I just submitted the fixes for the TensorFlowLiteSelectTfOps_framework. They should be in tonight's release.",
"FYI, Just verified TensorFlowLiteSelectTfOps 0.0.1-nightly.20230414 is working.",
"TensorFlowLiteSelectTfOps 0.0.1-nightly.20230414 and TensorFlowLiteSwift 2.11.0 combination is building successfully however crashes when creating an interpreter. ",
"Hi you need to use the same nightly version for all frameworks. So if you are using TensorFlowLiteSelectTfOps 0.0.1-nightly.20230414, you need to use TensorFlowLiteSwift 0.0.1-nightly.20230414",
"Hi thanks a lot for the response. I'll do that.\r\n\r\nOn Fri, Jun 23, 2023 at 9:17 AM yishuangP ***@***.***> wrote:\r\n\r\n> Hi you need to use the same nightly version for all frameworks. So if you\r\n> are using TensorFlowLiteSelectTfOps 0.0.1-nightly.20230414, you need to use\r\n> TensorFlowLiteSwift 0.0.1-nightly.20230414\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/tensorflow/tensorflow/issues/59721#issuecomment-1604507942>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/A6W57LDUUN23BEDEOIOCSO3XMW6S7ANCNFSM6AAAAAAU7DXRWI>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n>\r\n"
] | 2023-02-17T07:59:28 | 2023-06-23T17:15:55 | 2023-04-11T06:31:24 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Build/Install
### Have you reproduced the bug with TF nightly?
Yes
### Source
source
### Tensorflow Version
2.11.0
### Custom Code
No
### OS Platform and Distribution
Macbook Air M1, Ventura 13.1
### Mobile device
_No response_
### Python version
3.10.10
### Bazel version
5.3.0
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
Executing the following command:
```shell
bazel build -c opt --config=ios --ios_multi_cpus=arm64,x86_64 \
//tensorflow/lite/ios:TensorFlowLiteSelectTfOps_framework
```
results in the following error:
```
ERROR: /Users/vishnu/work/ente/tensorflow/tensorflow/lite/ios/BUILD:146:21: Processing and signing TensorFlowLiteSelectTfOps_framework failed: (Exit 2): process-and-sign--932494739.sh failed: error executing command bazel-out/applebin_ios-ios_arm64-opt-ST-82c2c37ad712/bin/tensorflow/lite/ios/TensorFlowLiteSelectTfOps_framework-intermediates/process-and-sign--932494739.sh should_compress
bazel-out/applebin_ios-ios_arm64-opt-ST-82c2c37ad712/bin/tensorflow/lite/ios/TensorFlowLiteSelectTfOps_framework_archive-root/TensorFlowLiteSelectTfOps.framework/TensorFlowLiteSelectTfOps bad CRC a72ee873 (should be a131c8e2)
```
### Standalone code to reproduce the issue
Executing the following command on the current `main` (0c955358f75cc3d2f0e21e60cb9b3a2bd3faa24b) as well as `v2.11.0` reproduces the error:
```shell
bazel build -c opt --config=ios --ios_multi_cpus=arm64,x86_64 \
//tensorflow/lite/ios:TensorFlowLiteSelectTfOps_framework
```
### Relevant log output
```shell
bazel build -c opt --config=ios --ios_multi_cpus=arm64,x86_64 \
//tensorflow/lite/ios:TensorFlowLiteSelectTfOps_framework
INFO: Options provided by the client:
Inherited 'common' options: --isatty=1 --terminal_columns=101
INFO: Reading rc options for 'build' from /Users/vishnu/work/ente/tensorflow/.bazelrc:
Inherited 'common' options: --experimental_repo_remote_exec
INFO: Reading rc options for 'build' from /Users/vishnu/work/ente/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 --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
INFO: Reading rc options for 'build' from /Users/vishnu/work/ente/tensorflow/.tf_configure.bazelrc:
'build' options: --action_env PYTHON_BIN_PATH=/opt/homebrew/opt/python@3.10/bin/python3.10 --action_env PYTHON_LIB_PATH=/opt/homebrew/opt/python@3.10/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages --python_path=/opt/homebrew/opt/python@3.10/bin/python3.10
INFO: Reading rc options for 'build' from /Users/vishnu/work/ente/tensorflow/.bazelrc:
'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/common,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils
INFO: Found applicable config definition build:short_logs in file /Users/vishnu/work/ente/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file /Users/vishnu/work/ente/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:ios in file /Users/vishnu/work/ente/tensorflow/.bazelrc: --apple_platform_type=ios --apple_bitcode=embedded --copt=-fembed-bitcode --copt=-Wno-c++11-narrowing --noenable_platform_specific_config --copt=-w --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --define=with_xla_support=false
INFO: Analyzed target //tensorflow/lite/ios:TensorFlowLiteSelectTfOps_framework (0 packages loaded, 0 targets configured).
INFO: Found 1 target...
ERROR: /Users/vishnu/work/ente/tensorflow/tensorflow/lite/ios/BUILD:146:21: Processing and signing TensorFlowLiteSelectTfOps_framework failed: (Exit 2): process-and-sign--932494739.sh failed: error executing command bazel-out/applebin_ios-ios_arm64-opt-ST-82c2c37ad712/bin/tensorflow/lite/ios/TensorFlowLiteSelectTfOps_framework-intermediates/process-and-sign--932494739.sh should_compress
bazel-out/applebin_ios-ios_arm64-opt-ST-82c2c37ad712/bin/tensorflow/lite/ios/TensorFlowLiteSelectTfOps_framework_archive-root/TensorFlowLiteSelectTfOps.framework/TensorFlowLiteSelectTfOps bad CRC a72ee873 (should be a131c8e2)
Target //tensorflow/lite/ios:TensorFlowLiteSelectTfOps_framework failed to build
Use --verbose_failures to see the command lines of failed build steps.
INFO: Elapsed time: 38.891s, Critical Path: 38.60s
INFO: 4 processes: 3 internal, 1 local.
FAILED: Build did NOT complete successfully
```
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"From https://pypi.org/project/tensorflow-gpu/:\r\n\r\n> tensorflow-gpu has been removed. Please install tensorflow instead. The tensorflow package supports GPU accelerated operations via Nvidia CUDA.",
"Thanks for clarification.",
"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/59720\">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/59720\">No</a>\n"
] | 2023-02-17T07:47:05 | 2023-02-17T09:40:24 | 2023-02-17T09:40:22 | CONTRIBUTOR | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Build/Install
### Have you reproduced the bug with TF nightly?
No
### Source
binary
### Tensorflow Version
2.12.0rc0
### Custom Code
No
### OS Platform and Distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
```shell
PyPi lists the tensorflow==2.12.0rc0 package, but not the equivalent GPU package tensorflow-gpu==2.12.0rc0:
https://pypi.org/project/tensorflow/#history
https://pypi.org/project/tensorflow-gpu/#history
Is the GPU pre-release package going to be published ad PyPi as well?
```
### Standalone code to reproduce the issue
```shell
pip install tensorflow-gpu==2.12.0rc0
```
### Relevant log output
_No response_</details> | {
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"@benson-guo, I was able to replicate the issue in Ubuntu 20.04 using tensorflow 2.11. Kindly check the output below\r\n@tilakrayal, please look at this issue. Thank you!\r\n```\r\nIteration 0 time: 2445.906400680542\r\nIteration 1 time: 1.2180805206298828\r\nIteration 2 time: 0.8792877197265625\r\nIteration 3 time: 0.7956027984619141\r\nIteration 4 time: 0.8134841918945312\r\nIteration 5 time: 0.8347034454345703\r\nIteration 6 time: 0.7798671722412109\r\nIteration 7 time: 0.7691383361816406\r\nIteration 8 time: 0.79345703125\r\nIteration 9 time: 0.8080005645751953\r\nIteration 10 time: 0.8664131164550781\r\nIteration 11 time: 0.8289813995361328\r\nIteration 12 time: 0.8101463317871094\r\nIteration 13 time: 0.78582763671875\r\nIteration 14 time: 0.7910728454589844\r\nIteration 15 time: 0.8423328399658203\r\nIteration 16 time: 0.8206367492675781\r\nIteration 17 time: 0.8051395416259766\r\nIteration 18 time: 0.7901191711425781\r\nIteration 19 time: 0.8213520050048828\r\nIteration 20 time: 0.8761882781982422\r\nIteration 21 time: 0.8149147033691406\r\nIteration 22 time: 0.858306884765625\r\nIteration 23 time: 0.7889270782470703\r\nIteration 24 time: 0.8196830749511719\r\nIteration 25 time: 0.7832050323486328\r\nIteration 26 time: 0.8084774017333984\r\nIteration 27 time: 0.8132457733154297\r\nIteration 28 time: 0.8044242858886719\r\nIteration 29 time: 0.8461475372314453\r\nIteration 30 time: 0.7991790771484375\r\nIteration 31 time: 0.8070468902587891\r\nIteration 32 time: 0.8008480072021484\r\nIteration 33 time: 0.7684230804443359\r\nIteration 34 time: 0.8289813995361328\r\nIteration 35 time: 0.7951259613037109\r\nIteration 36 time: 0.8077621459960938\r\nIteration 37 time: 0.8130073547363281\r\nIteration 38 time: 2.5610923767089844\r\nIteration 39 time: 6.946086883544922\r\nIteration 40 time: 6.997823715209961\r\nIteration 41 time: 6.977558135986328\r\nIteration 42 time: 6.984710693359375\r\nIteration 43 time: 6.985187530517578\r\nIteration 44 time: 6.985664367675781\r\nIteration 45 time: 6.982564926147461\r\nIteration 46 time: 6.853818893432617\r\nIteration 47 time: 6.2713623046875\r\nIteration 48 time: 6.274938583374023\r\nIteration 49 time: 6.271839141845703\r\n(tf) ynandi@sindhu-ubuntu-20-04:~$ \r\n",
"@synandi @tilakrayal \r\n\r\nThanks for a having a look. For reference, I had also posted this in the TF forums: https://discuss.tensorflow.org/t/inconsistent-runtime-of-compiled-model-inference/14895. \r\n\r\nSomeone had suggested it could be due to thermal throttling, but I was not able to notice a drop in core clock speed.",
"This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n",
"@benson-guo,\r\nI tried to execute the code with **iterations=50 and iterations=100** and observed that the runtime was increased at different points in both cases. It might be due to the computations happening in the middle of the code and also it depends on the GPU where we were trying to execute the code.\r\n\r\n**Iterations:100**\r\n\r\n2023-03-01 09:13:19.755700: I tensorflow/compiler/jit/xla_compilation_cache.cc:477] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\r\nIteration 0 time: 2830.5792808532715\r\nIteration 1 time: 1.2085437774658203\r\nIteration 2 time: 0.9257793426513672\r\nIteration 3 time: 0.8394718170166016\r\nIteration 4 time: 0.8671283721923828\r\nIteration 5 time: 0.804901123046875\r\nIteration 6 time: 0.8020401000976562\r\nIteration 7 time: 0.8029937744140625\r\nIteration 8 time: 0.7991790771484375\r\nIteration 9 time: 0.8249282836914062\r\nIteration 10 time: 0.8022785186767578\r\nIteration 11 time: 0.7832050323486328\r\nIteration 12 time: 0.8487701416015625\r\nIteration 13 time: 0.8215904235839844\r\nIteration 14 time: 0.7920265197753906\r\nIteration 15 time: 0.8149147033691406\r\nIteration 16 time: 0.7905960083007812\r\nIteration 17 time: 0.7901191711425781\r\nIteration 18 time: 0.8051395416259766\r\nIteration 19 time: 0.7903575897216797\r\nIteration 20 time: 0.8058547973632812\r\nIteration 21 time: 0.7767677307128906\r\nIteration 22 time: 0.8132457733154297\r\nIteration 23 time: 0.7922649383544922\r\nIteration 24 time: 0.7941722869873047\r\nIteration 25 time: 0.7932186126708984\r\nIteration 26 time: 0.7929801940917969\r\nIteration 27 time: 0.8213520050048828\r\nIteration 28 time: 0.7803440093994141\r\nIteration 29 time: 0.7965564727783203\r\nIteration 30 time: 0.7948875427246094\r\nIteration 31 time: 0.8120536804199219\r\nIteration 32 time: 0.8111000061035156\r\nIteration 33 time: 0.7951259613037109\r\nIteration 34 time: 0.7946491241455078\r\nIteration 35 time: 0.7963180541992188\r\nIteration 36 time: 0.8103847503662109\r\nIteration 37 time: 0.7822513580322266\r\n**Iteration 38 time: 0.7915496826171875\r\nIteration 39 time: 4.862785339355469\r\nIteration 40 time: 6.767034530639648**\r\nIteration 41 time: 6.778955459594727\r\nIteration 42 time: 6.768226623535156\r\nIteration 43 time: 6.769895553588867\r\nIteration 44 time: 6.781578063964844\r\nIteration 45 time: 6.75511360168457\r\nIteration 46 time: 6.757020950317383\r\nIteration 47 time: 6.769657135009766\r\nIteration 48 time: 6.767749786376953\r\nIteration 49 time: 6.76274299621582\r\nIteration 50 time: 6.770610809326172\r\nIteration 51 time: 6.770133972167969\r\nIteration 52 time: 6.761789321899414\r\nIteration 53 time: 6.768226623535156\r\nIteration 54 time: 6.76727294921875\r\nIteration 55 time: 6.767988204956055\r\nIteration 56 time: 6.771087646484375\r\nIteration 57 time: 6.768465042114258\r\nIteration 58 time: 6.776332855224609\r\nIteration 59 time: 6.762504577636719\r\nIteration 60 time: 6.757020950317383\r\nIteration 61 time: 6.764411926269531\r\nIteration 62 time: 6.769895553588867\r\nIteration 63 time: 6.777286529541016\r\nIteration 64 time: 6.75654411315918\r\nIteration 65 time: 6.771326065063477\r\nIteration 66 time: 6.752252578735352\r\nIteration 67 time: 6.765127182006836\r\nIteration 68 time: 6.781578063964844\r\nIteration 69 time: 6.740093231201172\r\nIteration 70 time: 6.770610809326172\r\nIteration 71 time: 6.651401519775391\r\nIteration 72 time: 6.081104278564453\r\nIteration 73 time: 6.009101867675781\r\nIteration 74 time: 5.947589874267578\r\nIteration 75 time: 5.949735641479492\r\nIteration 76 time: 5.953550338745117\r\nIteration 77 time: 5.947351455688477\r\nIteration 78 time: 5.955934524536133\r\nIteration 79 time: 5.97071647644043\r\nIteration 80 time: 6.098508834838867\r\nIteration 81 time: 6.084680557250977\r\nIteration 82 time: 6.083011627197266\r\nIteration 83 time: 6.061077117919922\r\nIteration 84 time: 6.080389022827148\r\nIteration 85 time: 6.068229675292969\r\nIteration 86 time: 6.074428558349609\r\nIteration 87 time: 6.0787200927734375\r\nIteration 88 time: 6.091594696044922\r\nIteration 89 time: 6.074190139770508\r\nIteration 90 time: 6.08372688293457\r\nIteration 91 time: 6.079673767089844\r\nIteration 92 time: 6.080865859985352\r\nIteration 93 time: 6.078004837036133\r\nIteration 94 time: 6.106138229370117\r\nIteration 95 time: 6.051301956176758\r\nIteration 96 time: 5.996227264404297\r\nIteration 97 time: 5.947113037109375\r\nIteration 98 time: 5.954742431640625\r\nIteration 99 time: 6.06536865234375\r\nIteration 100 time: 6.131410598754883\r\n\r\n`Iterations:100`\r\n\r\n2023-03-01 09:08:55.643995: I tensorflow/compiler/jit/xla_compilation_cache.cc:477] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\r\nIteration 0 time: 3033.7562561035156\r\nIteration 1 time: 2.8045177459716797\r\nIteration 2 time: 0.9644031524658203\r\nIteration 3 time: 0.8635520935058594\r\nIteration 4 time: 0.8149147033691406\r\nIteration 5 time: 0.8897781372070312\r\nIteration 6 time: 0.8149147033691406\r\nIteration 7 time: 0.8230209350585938\r\nIteration 8 time: 0.8220672607421875\r\nIteration 9 time: 0.7984638214111328\r\nIteration 10 time: 0.8337497711181641\r\nIteration 11 time: 0.7987022399902344\r\nIteration 12 time: 0.7767677307128906\r\nIteration 13 time: 0.8323192596435547\r\nIteration 14 time: 0.7944107055664062\r\nIteration 15 time: 0.7920265197753906\r\nIteration 16 time: 0.7965564727783203\r\nIteration 17 time: 0.7848739624023438\r\nIteration 18 time: 0.8008480072021484\r\nIteration 19 time: 0.8194446563720703\r\nIteration 20 time: 0.79345703125\r\nIteration 21 time: 0.7944107055664062\r\nIteration 22 time: 0.7903575897216797\r\nIteration 23 time: 0.8323192596435547\r\nIteration 24 time: 0.7834434509277344\r\nIteration 25 time: 0.7383823394775391\r\nIteration 26 time: 0.7770061492919922\r\nIteration 27 time: 0.7696151733398438\r\nIteration 28 time: 0.8041858673095703\r\nIteration 29 time: 0.7872581481933594\r\nIteration 30 time: 0.8168220520019531\r\nIteration 31 time: 0.7634162902832031\r\nIteration 32 time: 0.8182525634765625\r\nIteration 33 time: 0.7886886596679688\r\nIteration 34 time: 0.8273124694824219\r\nIteration 35 time: 0.7882118225097656\r\n**Iteration 36 time: 0.8006095886230469\r\nIteration 37 time: 7.169246673583984\r\nIteration 38 time: 11.08241081237793**\r\nIteration 39 time: 11.090517044067383\r\nIteration 40 time: 11.073827743530273\r\nIteration 41 time: 11.065483093261719\r\nIteration 42 time: 11.078357696533203\r\nIteration 43 time: 9.224891662597656\r\nIteration 44 time: 9.146690368652344\r\nIteration 45 time: 9.139060974121094\r\nIteration 46 time: 9.15384292602539\r\nIteration 47 time: 9.148120880126953\r\nIteration 48 time: 9.152650833129883\r\nIteration 49 time: 9.154081344604492\r\nIteration 50 time: 9.14454460144043\r\n\r\nAlso we observed that the change in the runtime was not at the same interval point for every execution. Thank you!",
"Hi @tilakrayal \r\n\r\nSo I was reading from this paper (https://hal-lirmm.ccsd.cnrs.fr/lirmm-03775613/document) that in eager async mode, tensorflow operations can return handles that have not been computed yet. To see if that's the case for the compiled function, I tried to force the computation to synchronously execute before the end of the timing block by adding a conditional branch dependent on the result of the inference.\r\n\r\n```\r\nimport tensorflow as tf\r\nimport time\r\n\r\nmodel = tf.keras.Sequential()\r\nlayers = 15\r\nfor _ in range(layers):\r\n model.add(tf.keras.layers.Conv2D(3, 3, data_format=\"channels_first\"))\r\nmodel_input = tf.random.uniform(shape=(12, 3, 256, 256))\r\nxla_fn = tf.function(model, jit_compile=True)\r\niterations = 100\r\nfor i in range(iterations):\r\n start_time = time.time()\r\n model_out = xla_fn(model_input)\r\n # if statement conditional on model output\r\n if tf.reshape(model_out, -1)[0] == 0:\r\n print(\"\")\r\n end_time = time.time()\r\n print(f\"Iteration {i} time: {1000 * (end_time - start_time)}\")\r\n```\r\n\r\nThe resulting profiled runtimes seems consistent now after a few iterations.\r\nIteration 0 time: 5936.74898147583\r\nIteration 1 time: 22.758007049560547\r\nIteration 2 time: 22.4761962890625\r\nIteration 3 time: 22.189855575561523\r\nIteration 4 time: 22.08256721496582\r\nIteration 5 time: 22.035837173461914\r\nIteration 6 time: 22.022485733032227\r\nIteration 7 time: 22.035837173461914\r\nIteration 8 time: 22.036075592041016\r\nIteration 9 time: 22.014141082763672\r\nIteration 10 time: 22.00603485107422\r\nIteration 11 time: 20.064592361450195\r\nIteration 12 time: 20.070552825927734\r\nIteration 13 time: 20.066499710083008\r\nIteration 14 time: 20.09725570678711\r\nIteration 15 time: 20.081281661987305\r\nIteration 16 time: 19.88387107849121\r\nIteration 17 time: 19.904375076293945\r\nIteration 18 time: 19.907712936401367\r\nIteration 19 time: 19.498825073242188\r\nIteration 20 time: 18.95761489868164\r\nIteration 21 time: 18.933773040771484\r\nIteration 22 time: 18.957853317260742\r\nIteration 23 time: 18.938302993774414\r\nIteration 24 time: 18.92995834350586\r\nIteration 25 time: 18.850088119506836\r\nIteration 26 time: 18.827438354492188\r\nIteration 27 time: 18.799543380737305\r\nIteration 28 time: 18.813371658325195\r\nIteration 29 time: 18.778562545776367\r\nIteration 30 time: 18.76211166381836\r\nIteration 31 time: 18.78190040588379\r\nIteration 32 time: 18.78833770751953\r\nIteration 33 time: 18.79286766052246\r\nIteration 34 time: 18.782615661621094\r\nIteration 35 time: 18.767595291137695\r\nIteration 36 time: 18.773317337036133\r\nIteration 37 time: 18.77903938293457\r\nIteration 38 time: 18.7833309173584\r\nIteration 39 time: 18.78976821899414\r\nIteration 40 time: 18.793344497680664\r\nIteration 41 time: 18.777132034301758\r\nIteration 42 time: 18.792390823364258\r\nIteration 43 time: 18.752336502075195\r\nIteration 44 time: 18.66936683654785\r\nIteration 45 time: 18.666505813598633\r\nIteration 46 time: 18.671274185180664\r\nIteration 47 time: 18.65530014038086\r\nIteration 48 time: 18.67055892944336\r\nIteration 49 time: 18.663644790649414\r\nIteration 50 time: 18.662452697753906\r\nIteration 51 time: 18.65530014038086\r\nIteration 52 time: 18.648862838745117\r\nIteration 53 time: 18.680095672607422\r\nIteration 54 time: 18.678665161132812\r\nIteration 55 time: 18.626928329467773\r\nIteration 56 time: 18.750905990600586\r\nIteration 57 time: 19.0277099609375\r\nIteration 58 time: 18.60952377319336\r\nIteration 59 time: 18.702030181884766\r\nIteration 60 time: 18.656492233276367\r\nIteration 61 time: 18.646717071533203\r\nIteration 62 time: 18.682479858398438\r\nIteration 63 time: 18.665552139282227\r\nIteration 64 time: 18.663644790649414\r\nIteration 65 time: 18.66936683654785\r\nIteration 66 time: 18.670082092285156\r\nIteration 67 time: 18.667221069335938\r\nIteration 68 time: 18.694162368774414\r\nIteration 69 time: 18.665313720703125\r\nIteration 70 time: 18.650054931640625\r\nIteration 71 time: 18.680095672607422\r\nIteration 72 time: 18.672943115234375\r\nIteration 73 time: 18.646717071533203\r\nIteration 74 time: 18.663406372070312\r\nIteration 75 time: 18.650531768798828\r\nIteration 76 time: 18.599510192871094\r\nIteration 77 time: 18.951892852783203\r\nIteration 78 time: 18.95451545715332\r\nIteration 79 time: 18.649816513061523\r\nIteration 80 time: 18.6614990234375\r\nIteration 81 time: 18.665313720703125\r\nIteration 82 time: 18.66745948791504\r\nIteration 83 time: 18.651247024536133\r\nIteration 84 time: 18.673419952392578\r\nIteration 85 time: 18.68128776550293\r\nIteration 86 time: 18.65100860595703\r\nIteration 87 time: 18.670082092285156\r\nIteration 88 time: 18.661975860595703\r\nIteration 89 time: 18.643856048583984\r\nIteration 90 time: 18.65553855895996\r\nIteration 91 time: 18.65553855895996\r\nIteration 92 time: 18.65363121032715\r\nIteration 93 time: 18.638134002685547\r\nIteration 94 time: 18.673419952392578\r\nIteration 95 time: 18.662452697753906\r\nIteration 96 time: 18.665313720703125\r\nIteration 97 time: 18.625259399414062\r\nIteration 98 time: 18.651723861694336\r\nIteration 99 time: 18.649816513061523\r\n\r\nSo am I right to conclude that the issue is caused by asynchronous execution? And if so, is there a low overhead method of forcing the execution to synchronize for timing purpopses?\r\n\r\nI had also tried\r\n`tf.config.experimental.set_synchronous_execution(enable=False)`\r\nBut that still leads to weird timing.\r\n",
"Hi, @benson-guo \r\n\r\nApologize for the delayed response and I was able to replicate the same issue with our latest `TF2.12` and `tf-nightly` with `Tesla T4 GPU `allocated by Google Colab and for your reference I have added [gist-file](https://colab.research.google.com/gist/gaikwadrahul8/d171c331bc4e8745574577e4d1565291/-59719.ipynb) and at the moment I'm not sure what is root cause for this inconsistent runtime of XLA compiled model inference so we'll have to dig more into this issue and we'll update soon. Thank you!",
"Ok, thanks for looking into this!",
"@benson-guo I just noticed your issue. And actually you understood alone your problem. Let me completely answer it for you :)\r\n\r\nTensorflow is eager first. When you return from an execution you get \"sort of an eager pointer\" to the result that is not yet populated.\r\n\r\nOne way to force trigger the resync `result = model(data).numpy()`. Unfortunately this triggers a memcpyDtoH which not only introduces a lot of overhead if the output tensor is large but also a lot of jitter.\r\n\r\n`tf.config.experimental.set_synchronous_execution(enable=False)` this API does not work on GPU ;)\r\n\r\nThough, you're in luck :D and the solution is easy. After some long discussion with @reedwm we published this RFC: https://github.com/tensorflow/community/pull/434 which led to this commit: https://github.com/tensorflow/tensorflow/commit/267c63aa0938682ca7b1990dcf09f041495db1fa\r\n\r\nNow allow me to quickly rewrite your code:\r\n\r\n```python\r\nimport tensorflow as tf\r\nimport time\r\n\r\nmodel = tf.keras.Sequential()\r\nlayers = 15\r\nfor _ in range(layers):\r\n model.add(tf.keras.layers.Conv2D(3, 3, data_format=\"channels_first\"))\r\n \r\nmodel_input = tf.random.uniform(shape=(12, 3, 256, 256))\r\nxla_fn = tf.function(model, jit_compile=True)\r\n\r\niterations = 100\r\n\r\nfor i in range(iterations):\r\n start_time = time.perf_counter() # time.time() is not suitable for benchmarking\r\n model_out = xla_fn(model_input)\r\n tf.test.experimental.sync_devices() # Force GPU resync point - Sync about it as \"waiting for the pipeline of async ops to clear out\"\r\n end_time = time.perf_counter()\r\n print(f\"Iteration {i} time: {1000 * (end_time - start_time)}\")\r\n```\r\n\r\nPlease close the issue if this address all your concerns ;) ",
"@DEKHTIARJonathan Thanks, this is exactly what I was looking for! I guess I couldn't find it because it was added so recently :)\r\n\r\nI saw some TF documentation where they time functions without this synchronization, which would not be accurate on the GPU at least (e.g. https://www.tensorflow.org/guide/function#usage). Do you think the documentation should be updated to add this experimental synchronization or have a disclaimer about this issue?)",
"I already mentioned the issue to @reedwm and @yarri-oss . Yes the documentation should be updated accross the board to include `tf.test.experimental.sync_devices()`\r\n\r\nCC: @nluehr @pjannaty",
"> I already mentioned the issue to @reedwm and @yarri-oss . Yes the documentation should be updated accross the board to include `tf.test.experimental.sync_devices()`\r\n> \r\n> CC: @nluehr @pjannaty\r\n\r\nYeah in https://www.tensorflow.org/guide/function#usage, the time printed in the `timeit` part of the tutorial is unreasonably short because a GPU is used and `sync_devices` wasn't called.\r\n\r\nAdding a call to `sync_devices` would make the example very verbose, as it forces the lambdas to be made into top-level functions. Maybe the `numpy` method should be used instead of `sync_devices` for conciseness, e.g. by replacing the third-to-last line in the `timeit` code block with\r\n\r\n```\r\nprint(\"Eager conv:\", timeit.timeit(lambda: conv_layer(image).numpy(), number=10))\r\n```\r\n\r\nCC @MarkDaoust ",
"@reedwm I disagree. We implemented `tf.test.experimental.sync_devices()` precisely for this usecase. Let's not introduce poor/bad practices in the official documentation.\r\n\r\n`.numpy()` is NOT a good idea to measure / assess performance. Hence we had to come up with `sync_devices()`",
"`numpy` also measures the device transfer time, which sometimes you want, but you're right this example does not want to measure this, since it's just comparing the performance of `tf.function`.\r\n\r\nPerhaps the example with `timeit` should be removed. I don't think the example adds much value since it doesn't show `tf.function` being faster, even if `sync_devices` is added.\r\n\r\n"
] | 2023-02-16T23:52:28 | 2023-03-23T22:52:54 | null | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Performance
### Have you reproduced the bug with TF nightly?
No
### Source
binary
### Tensorflow Version
2.11
### Custom Code
Yes
### OS Platform and Distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.8.13
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
11.6
### GPU model and memory
_No response_
### Current Behaviour?
```shell
Hi, I am trying to measure the runtime of a jit compiled model inference using time.time(). Code and output is provided below.
From my understanding, I would expect the first iteration to take longer since the function needs to be traced and compiled. However, I’m confused why the runtime from iteration 36 and onwards increases significantly. Is there an issue with the way I am measuring the runtime? If not, what would explain the increase in runtime?
I am running this on a P40 GPU by the way on tf 2.11. I had tried to run the same code on the nightly version but got the error "*** TypeError: expected bytes, tensorflow.python.client._pywrap_tf_session.StringBuffer found" when calling the compiled function. Thanks in advance.
```
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import time
model = tf.keras.Sequential()
layers = 15
for _ in range(layers):
model.add(tf.keras.layers.Conv2D(3, 3, data_format="channels_first"))
model_input = tf.random.uniform(shape=(12, 3, 256, 256))
xla_fn = tf.function(model, jit_compile=True)
iterations = 50
for i in range(iterations):
start_time = time.time()
xla_fn(model_input)
end_time = time.time()
print(f"Iteration {i} time: {1000 * (end_time - start_time)}")
```
### Relevant log output
```shell
Iteration 0 time: 6884.105682373047
Iteration 1 time: 1.5869140625
Iteration 2 time: 1.2054443359375
Iteration 3 time: 1.1410713195800781
Iteration 4 time: 1.1148452758789062
Iteration 5 time: 1.1096000671386719
Iteration 6 time: 1.1081695556640625
Iteration 7 time: 1.1057853698730469
Iteration 8 time: 1.1026859283447266
Iteration 9 time: 1.0957717895507812
Iteration 10 time: 1.115560531616211
Iteration 11 time: 1.100301742553711
Iteration 12 time: 1.1072158813476562
Iteration 13 time: 1.0993480682373047
Iteration 14 time: 1.102447509765625
Iteration 15 time: 1.100778579711914
Iteration 16 time: 1.0983943939208984
Iteration 17 time: 1.111745834350586
Iteration 18 time: 1.0929107666015625
Iteration 19 time: 1.0929107666015625
Iteration 20 time: 1.10626220703125
Iteration 21 time: 1.1260509490966797
Iteration 22 time: 1.1012554168701172
Iteration 23 time: 1.0957717895507812
Iteration 24 time: 1.1103153228759766
Iteration 25 time: 1.0924339294433594
Iteration 26 time: 1.0981559753417969
Iteration 27 time: 1.0950565338134766
Iteration 28 time: 1.1153221130371094
Iteration 29 time: 1.0952949523925781
Iteration 30 time: 1.100301742553711
Iteration 31 time: 1.1081695556640625
Iteration 32 time: 1.0983943939208984
Iteration 33 time: 1.0955333709716797
Iteration 34 time: 1.0974407196044922
Iteration 35 time: 1.1048316955566406
Iteration 36 time: 13.811588287353516
Iteration 37 time: 18.66292953491211
Iteration 38 time: 18.66936683654785
Iteration 39 time: 18.68462562561035
Iteration 40 time: 18.68152618408203
Iteration 41 time: 19.069910049438477
Iteration 42 time: 17.874717712402344
Iteration 43 time: 18.11075210571289
Iteration 44 time: 18.109560012817383
Iteration 45 time: 18.10741424560547
Iteration 46 time: 18.124818801879883
Iteration 47 time: 18.130064010620117
Iteration 48 time: 17.90904998779297
Iteration 49 time: 17.884492874145508
```
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"@sachinprasadhs @mohantym @arfaian Please have a look into this issue.\r\nThanks in advance \r\n",
"model = \"https://tfhub.dev/google/nnlm-en-dim50/2\"\r\nhub_layer = hub.KerasLayer(model, input_shape=[], dtype=tf.string, trainable=True)\r\nhub_layer(train_examples[:3])",
"@nyadla-sys Thanks for reporting this issue.\r\n\r\nSorry for the delayed response.\r\n\r\nI was able reproduce this issue on TF 2.11. Please find the gist [here](https://colab.research.google.com/gist/pjpratik/461189d8e5729c63a180c46278249798/59716.ipynb).\r\n\r\n@sachinprasadhs Could you please look into this issue? Thanks."
] | 2023-02-16T20:09:58 | 2023-10-04T22:05:02 | null | MEMBER | 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
Refer the below colab to reproduce the issue ..
https://colab.research.google.com/drive/1S_3bVlwRZkMaYvvKwtPfWlyXQLS0Bvxa?usp=sharing
```
(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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"@reedwm FYI ;) ",
"@rjpower seems like your change in 8a216d2c29881745c9851dd3e22b11eca56b96c3 introduced a bug",
"@DEKHTIARJonathan,\r\nThank you for reporting the bug. This bug exists in TF-Nightly **v2.13.0-dev20230216** Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/76773dc7e44d63a28f9fe1b413b4f2d0/2-13-0-dev20230216.ipynb).\r\n\r\nAs a work-around, Could you please give a try with **!pip install tf-nightly==2.13.0-dev20230215** where the type error was not exist. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/1daed70e507d859881747ed53774c4a1/2-13-0-dev20230215.ipynb). Thank you!",
"Testing in https://github.com/horovod/horovod/pull/3854.",
"I can confirm that tf-nightly==2.13.0.dev20230215 works for us:\r\nhttps://github.com/horovod/horovod/actions/runs/4211870049/jobs/7310498369\r\nwhile tf-nightly==2.13.0.dev20230216 fails:\r\nhttps://github.com/horovod/horovod/actions/runs/4201440008/jobs/7290427072\r\n\r\nIt looks like tf-nightly==2.13.0.dev20230218 works again:\r\nhttps://github.com/horovod/horovod/actions/runs/4211705996/jobs/7311034243",
"Is this issue part of tensorflow==2.12.0rc0?\r\n\r\nhttps://buildkite.com/horovod/horovod/builds/8991#01865f8a-8aad-465f-a103-2bc7dc4660b4/6-10595",
"This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n",
"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/59715\">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/59715\">No</a>\n"
] | 2023-02-16T19:26:31 | 2023-03-28T01:57:56 | 2023-03-28T01:57:53 | CONTRIBUTOR | null | null | null | ### Issue Type
Bug
### Have you reproduced the bug with TF nightly?
Yes
### Tensorflow Version
2.13.0-dev20230216
### Standalone code to reproduce the issue
```python
import tensorflow as tf
def generate_model():
return tf.keras.models.Sequential([
tf.keras.layers.Input(shape=(256, 256, 32)),
tf.keras.layers.Conv2D(32, (3, 3), padding='same'),
tf.keras.layers.Activation('relu'),
tf.keras.layers.Conv2D(32, (3, 3)),
tf.keras.layers.Activation('relu'),
tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
tf.keras.layers.Dropout(0.25),
tf.keras.layers.Conv2D(64, (3, 3), padding='same'),
tf.keras.layers.Activation('relu'),
tf.keras.layers.Conv2D(64, (3, 3)),
tf.keras.layers.Activation('relu'),
tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
tf.keras.layers.Dropout(0.25),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512),
tf.keras.layers.Activation('relu'),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(10),
tf.keras.layers.Activation('softmax')
])
model = generate_model()
```
### Relevant log output
```python
Which produces:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 2, in generate_model
File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/trackable/base.py", line 205, in _method_wrapper
result = method(self, *args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3243, in graph_def_versions
return versions_pb2.VersionDef.FromString(self._version_def)
TypeError: expected bytes, tensorflow.python.client._pywrap_tf_session.StringBuffer found
```
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"I've debugged the issue further and the origin of the slow results was due to the usage of lists. The resulting behaviour remains weird however, being the increase in computation time in each new call of the model.call. A quick and easy fix was to replace the lists by tf.constants.\r\n list = [0, 0.8217, 1.03, 5.12, 5.775, 5.938, 14.7207]\r\nbecomes\r\n list = tf.constant([0, 0.8217, 1.03, 5.12, 5.775, 5.938, 14.7207])\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/59713\">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/59713\">No</a>\n"
] | 2023-02-16T18:00:25 | 2023-02-20T08:25:48 | 2023-02-20T08:25:45 | NONE | null | null | null | <details><summary>Click to expand!</summary>
### Issue Type
Performance
### Have you reproduced the bug with TF nightly?
No
### Source
source
### Tensorflow Version
v2.7.0-rc1-69-gc256c071bb2 2.7.0
### Custom Code
Yes
### OS Platform and Distribution
Windows 10
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/Compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current Behaviour?
```shell
(ON CPU) After compilation I expect speed up in speed using @tf.function(jit_compile=True). This is the case, however when used in a loop some of the iterations happen almost instantly whilst others take quite a long time. I feels like the code is recompiling, however using print statements I see that this is not the case. At first I thought there could be easy and difficult input samples to the function, however for further debugging I added an additional loop that just repeats every function evaluation multiple times. Here I saw that it is just one time a very slow one (feels like recompiling) and afterwards on the same input and exact same function it's really quick. No additional print statement is executed, hinting to the fact that the function is not compiled again.
I was wondering if this is a known issue and if so how to fix it.
The last function below is called in a main script and makes use of a tf.keras.layers.Layer object and its call function. The call function is also given below and makes use of the jit_compiled function RK_multistep.
```
### Standalone code to reproduce the issue
```shell
@tf.function(jit_compile=True)
def RK_multistep(hybMod, x_k, u_k, dt):
print("compiling RK")
M = 5
DT = dt/M
...
def call(self, model, x_k, u_k):
print("----- START RK repeats -----")
t1 = time.time()
for k in range(3):
t3 = time.time()
x_kk = self.method(model, x_k, u_k, self.dt)
t4 = time.time()
print(f"RK iteration {k} took {t4 - t3}")
t2 = time.time()
print(f"All RK iterations Took {t2 - t1}")
print("----- END RK repeats -----")
x_kk = self.RK_multistep(model, x_k, u_k, self.dt)
return x_kk
print("----- START model in batches -----")
t1 = time.time()
for k in range(20):
t3 = time.time()
Xbatch = np.expand_dims(X_train[k, :], 0) # add batch dimension: shape (1, state_dims)
Ubatch = np.expand_dims(Utrain[1][k, :, :], 0) # add batch dimension: shape (1, prediction_length, control_dims)
model_input = [Xbatch, Ubatch]
Modelout = model(model_input) # here the model is called that used the call as defined above
t4 = time.time()
print(f"Outer layer iteration {k} took {t4 - t3}")
t2 = time.time()
print(f"Took {t2-t1}")
```
### Relevant log output
```shell
----- START model in batches -----
compiling g
compiling g_inner
compiling PI_big
compiling flow
compiling flow
----- START RK repeats -----
compiling RK
compiling f
compiling forcebalance
compiling pressure_change
RK iteration 0 took 16.773661613464355
RK iteration 1 took 0.0
RK iteration 2 took 0.0
All RK iterations Took 16.773661613464355
----- END RK repeats -----
Outer layer iteration 0 took 17.035797357559204
----- START RK repeats -----
RK iteration 0 took 0.0
RK iteration 1 took 0.0
RK iteration 2 took 0.0
All RK iterations Took 0.0
----- END RK repeats -----
Outer layer iteration 1 took 0.015622138977050781
----- START RK repeats -----
RK iteration 0 took 0.0
RK iteration 1 took 0.0
RK iteration 2 took 0.0
All RK iterations Took 0.0
----- END RK repeats -----
Outer layer iteration 2 took 0.0
----- START RK repeats -----
RK iteration 0 took 0.0
RK iteration 1 took 0.0
RK iteration 2 took 0.0
All RK iterations Took 0.0
----- END RK repeats -----
Outer layer iteration 3 took 0.0
----- START RK repeats -----
RK iteration 0 took 0.0
RK iteration 1 took 0.0
RK iteration 2 took 0.0
All RK iterations Took 0.0
----- END RK repeats -----
Outer layer iteration 4 took 0.015624761581420898
----- START RK repeats -----
RK iteration 0 took 15.472918510437012
RK iteration 1 took 0.01561427116394043
RK iteration 2 took 0.0
All RK iterations Took 15.488532781600952
----- END RK repeats -----
Outer layer iteration 5 took 15.488532781600952
----- START RK repeats -----
RK iteration 0 took 14.715417385101318
RK iteration 1 took 0.0
RK iteration 2 took 0.0
All RK iterations Took 14.715417385101318
----- END RK repeats -----
Outer layer iteration 6 took 14.715417385101318
----- START RK repeats -----
RK iteration 0 took 13.904020547866821
RK iteration 1 took 0.0
RK iteration 2 took 0.0
All RK iterations Took 13.904020547866821
----- END RK repeats -----
Outer layer iteration 7 took 13.921808958053589
----- START RK repeats -----
RK iteration 0 took 0.0
RK iteration 1 took 0.0
RK iteration 2 took 0.0
All RK iterations Took 0.0
----- END RK repeats -----
Outer layer iteration 8 took 0.0
...
```
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