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CategoryEncoding layer lacks consideration of 0-dimension inputs
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[
"Hi @AGFACBNNR ,\r\n\r\nThanks for reporting. I have checked and acknowledge the reported behaviour. The code works fine with Keras2 but has inconsistent behaviour with Keras3. Attached [keras2-gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/432b648914e4828b3b97fa586d60f288/64392_keras2.ipynb#scrollTo=m_Vhm9_MJoQm) and [keras3-gist ](https://colab.sandbox.google.com/gist/SuryanarayanaY/65ffaa8ac77949c929a20dc62d36824f/64392_keras3.ipynb#scrollTo=m_Vhm9_MJoQm)for reference.\r\n\r\nAs per [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/layers/CategoryEncoding#:~:text=A%201D%20or%202D%20tensor%20of%20integer%20inputs.) the inputs should be:\r\n\r\n> inputs:\tA 1D or 2D tensor of integer inputs.\r\n\r\nThe behaviour as follows:\r\n```\r\n# rank-0 Tensor & output_mode='multi_hot' --> Different results wrt Keras2 & 3\r\n# rank-1 Tensor & output_mode='multi_hot' --> Same results wrt Keras2 & 3\r\n# rank-2 Tensor & output_mode='multi_hot' --> Same results wrt Keras2 & 3\r\n\r\n# rank-0 Tensor & output_mode='one_hot' --> Different results wrt Keras2 & 3\r\n# rank-1 Tensor & output_mode='one_hot' --> Different results wrt Keras2 & 3\r\n# rank-2 Tensor & output_mode='one_hot' --> Same results wrt Keras2 & 3\r\n\r\n```\r\n\r\nIMO, This behaviour of Keras3 should be consistent with keras2. Looking for probable fix. \r\n\r\n\r\n\r\n",
"Update: Since Keras3 supports multi backend the Keras3(TF backend) behaviour is different wrt Keras2(TF backend) but inline with Torch and Jax.\r\n\r\nBut there is issue with `compute_output_shape` method with below cases which is not same as actual output shape:\r\n```\r\n# rank-0 Tensor input & output_mode='multi_hot'\r\n# rank-0 Tensor input & output_mode='one_hot'\r\n# rank-1 Tensor input & output_mode='one_hot'\r\n\r\n```\r\n\r\nI am going to propose a fix for same. Thanks!",
"Shall be addressed in Keras PR-[19388](https://github.com/keras-team/keras/pull/19388)",
"Hi @AGFACBNNR ,\r\n\r\nThe proposed PR got merged into master. Could you please test in Keras-nightly and let us know. Thanks!",
" I've tested it in Keras-nightly. Thanks a lot.",
"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/64392\">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/64392\">No</a>\n"
] | 2024-03-25T06:32:59 | 2024-04-02T02:28:55 | 2024-04-02T02:28:52 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf-2.15, tf-2.16.1
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When provided a 0-dim tensor as an input, CategoryEncoding layer will generate an output whose shape is inconsistent with the result of `compute_output_shape()`.
The document does not define the expected behavior of CategoryEncoding layer when the input is a 0-dim tensor.
If it's not an acceptable input, maybe a Raise is needed, otherwise `compute_output_shape()` should have generated a shape matching the layer output.
### Standalone code to reproduce the issue
```shell
You can find a simple example here:
https://colab.research.google.com/drive/1wYYiRlY_ehHz6WCAn0PMUXHycJ6Kfgk3?usp=sharing
```
### Relevant log output
_No response_
|
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Model Loading Error with Bi-directional LSTM layer
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[
"@nilesh-sengupta Please use the appropriate loading function based on the saving format. For example, tf.keras.models.load_model for HDF5 and tf.saved_model.load for SavedModel.\r\nThank you!",
"@sushreebarsa The model is in .keras format, it is saved using model.save('model_name.keras'), loading it using tf.saved_model.load gives the following exception:\r\nTraceback (most recent call last):\r\n File \"/usr/local/bin/mldnn/dnn_1.6/predictor.py\", line 26, in <module>\r\n model = tf.saved_model.load('deep_neural_network.keras')\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/root/miniconda3/envs/model_training/lib/python3.11/site-packages/tensorflow/python/saved_model/load.py\", line 912, in load\r\n result = load_partial(export_dir, None, tags, options)[\"root\"]\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/root/miniconda3/envs/model_training/lib/python3.11/site-packages/tensorflow/python/saved_model/load.py\", line 1016, in load_partial\r\n loader_impl.parse_saved_model_with_debug_info(export_dir))\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/root/miniconda3/envs/model_training/lib/python3.11/site-packages/tensorflow/python/saved_model/loader_impl.py\", line 59, in parse_saved_model_with_debug_info\r\n saved_model = parse_saved_model(export_dir)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/root/miniconda3/envs/model_training/lib/python3.11/site-packages/tensorflow/python/saved_model/loader_impl.py\", line 119, in parse_saved_model\r\n raise IOError(\r\nOSError: SavedModel file does not exist at: deep_neural_network.keras/{saved_model.pbtxt|saved_model.pb}\r\n\r\n\r\nThe previous error is encountered while using tf.keras.models.load_model\r\n",
"@nilesh-sengupta \r\nCould you try to re-save your Keras model as a SavedModel:\r\n```\r\ntf.saved_model.save(model, 'path/to/save/model') # Use a distinct path to avoid overwriting\r\n\r\n```\r\n\r\n\r\nSince your model is in the .keras format, please use tf.keras.models.load_model to load it:\r\n```\r\nmodel = tf.saved_model.load('path/to/save/model')\r\n\r\n\r\n```\r\nThis function is specifically designed to handle models saved in the Keras format.\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64391\">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/64391\">No</a>\n"
] | 2024-03-25T04:01:03 | 2024-04-19T01:47:54 | 2024-04-19T01:47:49 |
NONE
| null | null | null |
I trained a model using tf==2.16.1 and keras 3.0.5.
While loading the same model through the same tf and keras versions (.keras format), the following error is encountered
File "/root/miniconda3/envs/model_training/lib/python3.11/site-packages/keras/src/saving/saving_api.py", line 176, in load_model
return saving_lib.load_model(
^^^^^^^^^^^^^^^^^^^^^^
File "/root/miniconda3/envs/model_training/lib/python3.11/site-packages/keras/src/saving/saving_lib.py", line 192, in load_model
_raise_loading_failure(error_msgs)
File "/root/miniconda3/envs/model_training/lib/python3.11/site-packages/keras/src/saving/saving_lib.py", line 273, in _raise_loading_failure
raise ValueError(msg)
ValueError: A total of 2 objects could not be loaded. Example error message for object <LSTMCell name=lstm_cell, built=True>:
Layer 'lstm_cell' expected 3 variables, but received 0 variables during loading. Expected: ['kernel', 'recurrent_kernel', 'bias']
List of objects that could not be loaded:
[<LSTMCell name=lstm_cell, built=True>, <LSTMCell name=lstm_cell, built=True>]
Investigation mostly suggests it might be a version compatibility issue train vs load, but since both of these are being done in the same conda env I do no think it might be the case.
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Collect quantization results from `ModuleOp`.
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Collect quantization results from `ModuleOp`.
With this change, `QuantizationReport` will have its `quantization_results_` field populated by collecting quantization results from `ModuleOp`.
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Implement `GetQuantizationMethodOrDefault`.
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Implement `GetQuantizationMethodOrDefault`.
This is similar to `GetQuantizationMethod`, but returns a default instance of `Method` when there is no quantization method attribute instead of returning a non-ok `Status`.
This allows to avoid one extra level of indirection from checking the `Status`.
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Expand StableHLO Quantizer presets for the ODML entrypoint.
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Expand StableHLO Quantizer presets for the ODML entrypoint.
The expanded StableHLO Quantizer currently does not have any visible change of behavior as the current expansion is not used downstream, but it will be used by features to be introduced.
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Add a function to determine whether an op is inside a StableHLO op with `Region`.
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Add a function to determine whether an op is inside a StableHLO op with `Region`.
This will be used for filtering target lift patterns in `TableGen` file.
Additionally, this CL does the following:
* Use the `Operation*` than reference-by-value for consistency with MLIR norm.
* Adds additional tests for `IsStablehlOp`.
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[xla:gpu] Support dynamic offset size in AddressComputationThunk
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[xla:gpu] Support dynamic offset size in AddressComputationThunk
Contrary to the documentation, dynamic-slice and DUS ops may have start indices of type different than S64[]. We need to be more precise when loading the offset from device to host at runtime.
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[xla:gpu][NFC] Rename for consistency
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[xla:gpu][NFC] Rename for consistency
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[xla:gpu] Use fusion shape instead of hero shape when emit AddressComputationThunk
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[xla:gpu] Use fusion shape instead of hero shape when emit AddressComputationThunk
Supporting DUS in AddressComputationFusion means the fusion shape might be different from the hero custom call shape. Sometimes not the whole tuple returned by the hero custom call op is used, but only the output (e.g. the workspace is ignored for GEMM custom call). As a result, we should emit AddressComputationThunk based on the shape of the fusion instruction and not the one from hero custom call op
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Automated Code Change
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[
"I adjusted the order of header files as follows: \r\n#include \"tensorflow/lite/array.h\"\r\n#include \"tensorflow/lite/builtin_ops.h\"\r\n#include \"tensorflow/lite/c/c_api_types.h\"\r\n#include \"tensorflow/lite/core/c/builtin_op_data.h\"\r\n#include \"tensorflow/lite/core/c/common.h\"\r\n#include \"tensorflow/lite/core/subgraph.h\"\r\n#include \"tensorflow/lite/kernels/kernel_util.h\"\r\n#include \"tensorflow/lite/util.h\"\r\nAdditionally, I repeatedly executed the Bazel command and made multiple adjustments to the positions of these two functions until the compilation passed successfully.",
"> I adjusted the order of header files as follows: #include \"tensorflow/lite/array.h\" #include \"tensorflow/lite/builtin_ops.h\" #include \"tensorflow/lite/c/c_api_types.h\" #include \"tensorflow/lite/core/c/builtin_op_data.h\" #include \"tensorflow/lite/core/c/common.h\" #include \"tensorflow/lite/core/subgraph.h\" #include \"tensorflow/lite/kernels/kernel_util.h\" #include \"tensorflow/lite/util.h\" Additionally, I repeatedly executed the Bazel command and made multiple adjustments to the positions of these two functions until the compilation passed successfully.\r\n\r\n我也遇到相同的问题，请问你是如何修改这个文件的，能否把这个文件的全部代码发出来？",
"> I adjusted the order of header files as follows: #include \"tensorflow/lite/array.h\" #include \"tensorflow/lite/builtin_ops.h\" #include \"tensorflow/lite/c/c_api_types.h\" #include \"tensorflow/lite/core/c/builtin_op_data.h\" #include \"tensorflow/lite/core/c/common.h\" #include \"tensorflow/lite/core/subgraph.h\" #include \"tensorflow/lite/kernels/kernel_util.h\" #include \"tensorflow/lite/util.h\" Additionally, I repeatedly executed the Bazel command and made multiple adjustments to the positions of these two functions until the compilation passed successfully.\r\n\r\n我也遇到相同的问题，请问你是如何修改这个文件的，能否把这个文件的全部代码发出来？",
"> > I adjusted the order of header files as follows: #include \"tensorflow/lite/array.h\" #include \"tensorflow/lite/builtin_ops.h\" #include \"tensorflow/lite/c/c_api_types.h\" #include \"tensorflow/lite/core/c/builtin_op_data.h\" #include \"tensorflow/lite/core/c/common.h\" #include \"tensorflow/lite/core/subgraph.h\" #include \"tensorflow/lite/kernels/kernel_util.h\" #include \"tensorflow/lite/util.h\" Additionally, I repeatedly executed the Bazel command and made multiple adjustments to the positions of these two functions until the compilation passed successfully.\r\n> \r\n> 我也遇到相同的问题，请问你是如何修改这个文件的，能否把这个文件的全部代码发出来？\r\n调整了这个文件里 [stablehlo_reduce_window.txt](https://github.com/tensorflow/tensorflow/files/14811404/stablehlo_reduce_window.txt)\r\nStridedReduce 和ReduceWindowImpl 两个函数的先后位置\\tensorflow_src\\tensorflow\\lite\\kernels\\stablehlo_reduce_window.cc\r\nReversed the order of the functions StridedReduce and StridedReduce and ReduceWindowImpl in the file tensorflow_src\\tensorflow\\lite\\kernels\\stablehlo_reduce_window.cc.",
"Hi @GuoHuanyu, I'm actually running into a different issue when I compile... I suspect our environments are not matching. Did you follow these steps https://www.tensorflow.org/install/source_windows for your environment? Did you run the command in PowerShell under admin privileges or Through some other interface? Are you in the r2.16 branch or in master/nightly? Are you following any instructions/documentation to do this? Thanks for your help.",
"> Hi @GuoHuanyu, I'm actually running into a different issue when I compile... I suspect our environments are not matching. Did you follow these steps https://www.tensorflow.org/install/source_windows for your environment? Did you run the command in PowerShell under admin privileges or Through some other interface? Are you in the r2.16 branch or in master/nightly? Are you following any instructions/documentation to do this? Thanks for your help.\r\n\r\n1. In above issues, i use the Developer Command Prompt for Visual Studio to execute the command `bazel build --config=monolithic --define=with_select_tf_ops=true -c opt //tensorflow/lite:libtensorflowlite.so`, `bazel build -c opt //tensorflow/lite:tensorflowlite.dll`. its ok for my environment.\r\n2. My environment includes Visual Studio 2019, Bazelisk, and Python 3.11.\r\n3. i follow these steps https://www.tensorflow.org/install/source_windows for my envirmonment to execute the command `python ./configure.py` , `bazel build //tensorflow/tools/pip_package:build_pip_package` using the source code from =https://github.com/tensorflow/tensorflow/releases/tag/v2.16.1. \r\n4. I am still encountering an error in stablehlo_reduce_window.cc. I fix it by reversing the order of the StridedReduce and ReduceWindowImpl functions, but the build process continues to fail with Developer Command Prompt or Developer PowerShell for Visual Studio in administrator mode, `cl.exe D8022 error`\r\n5. Giving up \r\n",
"Hi @GuoHuanyu, apologies for the frustration, while I look more into this -- have you tried building/progressing with WSL instead? Generally speaking that will be a smoother experience.",
"> Hi @GuoHuanyu, apologies for the frustration, while I look more into this -- have you tried building/progressing with WSL instead? Generally speaking that will be a smoother experience.\r\n\r\nI haven't heard of or used WSL before. I would find some time to configure and compile based on WSL. Thank you for providing another good method!",
"Hi @GuoHuanyu, another alternative if you need the tensorflow lite dll is building with cmake instead: https://www.tensorflow.org/lite/guide/build_cmake, let me know if that works for you or unblocks you. Thanks.",
"> Hi @GuoHuanyu, another alternative if you need the tensorflow lite dll is building with cmake instead: https://www.tensorflow.org/lite/guide/build_cmake, let me know if that works for you or unblocks you. Thanks.\r\n \r\n1. Thank you for your timely and professional response. \r\n2. I've experimented with reversing the order of the StridedReduce and ReduceWindowImpl functions and managed to successfully compile TensorFlow Lite using the commands `cmake ../tensorflow_src/tensorflow/lite` and `cmake --build . -j` in the Developer PowerShell for Visual Studio. \r\n3. However, this only produced a tensorflow-lite.lib file of 247MB, and I did not get the corresponding tensorflow-lite.dll.\r\n4. That's why I resorted to using `bazel build -c opt //tensorflow/lite:tensorflowlite.dll`, which yielded both a lib and a dll file. I can obtain a dll with `cmake ../tensorflow_src/tensorflow/lite/c` and` cmake --build . -j`, but I am unsure if it corresponds to the previously generated tensorflow-lite.lib. That's why I used `bazel build -c opt //tensorflow/lite:tensorflowlite.dll.`\r\n5. If there is a way to generate both the lib and dll files using cmake, I believe it would be a better solution.\r\n",
"Hi @GuoHuanyu, if `bazel build -c opt //tensorflow/lite:tensorflowlite.dll` fits your needs, I would say just try it out and see if you run into any other issues. If you required source changes to get that working, and you feel it is something that should be generally fixed (we may need some compile time condition that checks for Windows or something), please create a PR with your fixes so that we may fix it more generally (or at least get started with the review process). Typically, users don't need the static library ... may I understand your use case/context better -- just to ensure you are using best practices, the .lib may not be necessary.",
"> Hi @GuoHuanyu, if `bazel build -c opt //tensorflow/lite:tensorflowlite.dll` fits your needs, I would say just try it out and see if you run into any other issues. If you required source changes to get that working, and you feel it is something that should be generally fixed (we may need some compile time condition that checks for Windows or something), please create a PR with your fixes so that we may fix it more generally (or at least get started with the review process). Typically, users don't need the static library ... may I understand your use case/context better -- just to ensure you are using best practices, the .lib may not be necessary.\r\n\r\nOk，i will create a PR with my fixes, thank your timely response.",
"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/64376\">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/64376\">No</a>\n",
"I think I get into the same issue when building the example from MediaPipe, and the solution above (reordering of the functions `StridedReduce` and `ReduceWindowImpl`) doesn't work for me.\r\n\r\nWhat I found instead is that the problem seems to be the comment before the function `ReduceWindowImpl`, which ends with an extended ASCII character `┘` and followed by the function definition in the next line.\r\n\r\nA simple fix for this is to add a new line between the comment and the function definition. \r\n(Or add any visible ASCII character at the end of the comment could fix the problem, I guess)\r\n```cpp\r\n// Recursively computes strided reductions using a sliding window over the\r\n// given tensor.\r\n//\r\n// The window is defined using a shape and a dilation. The shape defines the\r\n// elements that the window will let the reduction *see*. The dilation defines\r\n// the step between window elements.\r\n//\r\n// For instance: the following window has a [2, 2] shape and [2, 3] dilations.\r\n//\r\n// 3\r\n// ┌────┐\r\n// ┌─┐ ┌─┐\r\n// │X│X X│X│┐\r\n// └─┘ └─┘│2\r\n// X X X X ┘\r\n// ┌─┐ ┌─┐\r\n// │X│X X│X│\r\n// └─┘ └─┘\r\n// <- add new line HERE\r\ntemplate <class Op, class Type>\r\nvoid ReduceWindowImpl(const Type* input, Type* output,\r\n```\r\n\r\nI don't know why the comment would cause the compilation issue, but this solution works for me.\r\nHopefully this will be useful for someone like me if the previous solution doesn't work. (Also found the same [issue](https://stackoverflow.com/questions/78322183/got-trouble-during-on-building-mediapipe-example-hand-tracking-cpu) on stackoverflow)\r\n\r\n---\r\n\r\nFor more details, here is my setup:\r\n- OS: Windows 10\r\n- Bazel: 6.1.1\r\n- GCC/compiler: Visual Studio 2019\r\n- Python: 3.9\r\n- TensorFlow: commit [75bb98b](https://github.com/tensorflow/tensorflow/commit/75bb98bd9d3998431aca05afd4be03e233b14425) (specified in the bazel workspace)\r\n- MediaPipe: commit [72f28f3](https://github.com/google/mediapipe/commit/72f28f37e573c68bccd5e37eea526665ea2303fa)\r\n\r\nWhat I have done:\r\n- Followed the [MediaPipe installation instruction for Windows](https://developers.google.com/mediapipe/framework/getting_started/install#installing_on_windows) and successfully built the `hello_world` example.\r\n- Tried to build the `hand_tracking_cpu` example with the following command but failed.\r\n ```shell\r\n bazel build -c opt --define MEDIAPIPE_DISABLE_GPU=1 --action_env PYTHON_BIN_PATH=\"C://Python39//python.exe\" mediapipe/examples/desktop/hand_tracking:hand_tracking_cpu\r\n ```\r\n\r\nThe error message I got:\r\n```shell\r\nERROR: C:/users/<USER>/_bazel_<USER>/<HASH>/external/org_tensorflow/tensorflow/lite/kernels/BUILD:869:11: Compiling tensorflow/lite/kernels/stablehlo_reduce_window.cc failed: (Exit 2): cl.exe failed: error executing command (from target @org_tensorflow//tensorflow/lite/kernels:builtin_op_kernels) C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\BuildTools\\VC\\Tools\\MSVC\\14.29.30133\\bin\\HostX64\\x64\\cl.exe /nologo /DCOMPILER_MSVC /DNOMINMAX /D_WIN32_WINNT=0x0601 /D_CRT_SECURE_NO_DEPRECATE ... (remaining 126 arguments skipped)\r\ncl : Command line warning D9002 : ignoring unknown option '-O3'\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(361): error C4430: missing type specifier - int assumed. Note: C++ does not support default-int\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(361): error C2143: syntax error: missing ',' before '*'\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(368): error C2065: 'depth': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(368): error C2065: 'rank': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(369): error C2065: 'output_shape': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(369): error C2065: 'depth': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(370): error C2065: 'output': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(370): error C2065: 'init': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(371): error C2065: 'Op': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(371): error C2065: 'input': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(371): error C2065: 'window_shape': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(371): error C2065: 'window_reduce_strides': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(372): error C2065: 'output': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(372): error C2065: 'rank': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(373): error C2065: 'input': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(373): error C2065: 'window_offset_strides': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(373): error C2065: 'depth': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(374): error C2065: 'output': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(374): error C2065: 'output_strides': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(374): error C2065: 'depth': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(377): error C2065: 'output_shape': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(377): error C2065: 'depth': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(378): error C2065: 'Op': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(378): error C2065: 'input': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(378): error C2065: 'output': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(378): error C2065: 'output_shape': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(378): error C2065: 'output_strides': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(379): error C2065: 'window_offset_strides': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(379): error C2065: 'window_shape': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(380): error C2065: 'window_reduce_strides': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(380): error C2065: 'init': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(380): error C2065: 'rank': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(380): error C2065: 'depth': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(381): error C2065: 'input': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(381): error C2065: 'window_offset_strides': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(381): error C2065: 'depth': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(382): error C2065: 'output': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(382): error C2065: 'output_strides': undeclared identifier\r\nexternal/org_tensorflow/tensorflow/lite/kernels/stablehlo_reduce_window.cc(382): error C2065: 'depth': undeclared identifier\r\nTarget //mediapipe/examples/desktop/hand_tracking:hand_tracking_cpu failed to build\r\n```"
] | 2024-03-24T12:41:50 | 2024-04-22T14:58:53 | 2024-04-19T01:47:50 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
window 10
### Mobile device
_No response_
### Python version
3.11
### Bazel version
bazelisk
### GCC/compiler version
visual studio 2019
### CUDA/cuDNN version
no
### GPU model and memory
_No response_
### Current behavior?
I encountered an issue while performing a build with the latest version using the command bazel build -c opt //tensorflow/lite:tensorflowlite. The problem stems from the file tensorflow\lite\kernels\stablehlo_reduce_window.cc, specifically at line 372, which contains the following function:
template <class Op, class Type>
void ReduceWindowImpl(const Type* input, Type* output,
const int64_t* const output_shape,
const int64_t* const output_strides,
const int64_t* const window_offset_strides,
const int64_t* const window_shape,
const int64_t* const window_reduce_strides,
const Type init, const int rank, const int depth)
I managed to resolve this issue by moving the above function above the following function:
template <class Op, class Type>
void StridedReduce(const Type* input, const int64_t* const shape,
const int64_t* const strides, Type& accu, const int rank,
const int depth)
I'm seeking alternative solutions or suggestions to address this issue.
### Standalone code to reproduce the issue
```shell
bazel build -c opt //tensorflow/lite:tensorflowlite
```
### Relevant log output
```shell
ERROR: D:/deepreinforce/tensorflow_src/tensorflow/lite/kernels/BUILD:869:11: Compiling tensorflow/lite/kernels/stablehlo_reduce_window.cc failed: (Exit 2): cl.exe failed: error executing command (from target //tensorflow/lite/kernels:builtin_op_kernels)
cd /d C:/users/guoy6/_bazel_guoy6/amvr52k2/execroot/org_tensorflow
SET INCLUDE=C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\VC\Tools\MSVC\14.29.30133\ATLMFC\include;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\VC\Tools\MSVC\14.29.30133\include;C:\Program Files (x86)\Windows Kits\10\include\10.0.19041.0\ucrt;C:\Program Files (x86)\Windows Kits\10\include\10.0.19041.0\shared;C:\Program Files (x86)\Windows Kits\10\include\10.0.19041.0\um;C:\Program Files (x86)\Windows Kits\10\include\10.0.19041.0\winrt;C:\Program Files (x86)\Windows Kits\10\include\10.0.19041.0\cppwinrt
SET PATH=C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\Common7\IDE\\Extensions\Microsoft\IntelliCode\CLI;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\VC\Tools\MSVC\14.29.30133\bin\HostX64\x64;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\Common7\IDE\VC\VCPackages;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\Common7\IDE\CommonExtensions\Microsoft\TestWindow;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\Common7\IDE\CommonExtensions\Microsoft\TeamFoundation\Team Explorer;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\MSBuild\Current\bin\Roslyn;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\Team Tools\Performance Tools\x64;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\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)\Microsoft Visual Studio\2019\Professional\Common7\Tools\devinit;C:\Program Files (x86)\Windows Kits\10\bin\10.0.19041.0\x64;C:\Program Files (x86)\Windows Kits\10\bin\x64;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\\MSBuild\Current\Bin;C:\Windows\Microsoft.NET\Framework64\v4.0.30319;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\Common7\IDE\;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\Common7\Tools\;;C:\WINDOWS\system32;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\Common7\IDE\CommonExtensions\Microsoft\CMake\CMake\bin;C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\Common7\IDE\CommonExtensions\Microsoft\CMake\Ninja
SET PWD=/proc/self/cwd
SET PYTHON_BIN_PATH=C:/Users/Guoy6/AppData/Local/Programs/Python/Python311/python.exe
SET PYTHON_LIB_PATH=C:/Users/Guoy6/AppData/Local/Programs/Python/Python311/Lib/site-packages
SET TEMP=C:\Users\Guoy6\AppData\Local\Temp
SET TF2_BEHAVIOR=1
SET TMP=C:\Users\Guoy6\AppData\Local\Temp
C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\VC\Tools\MSVC\14.29.30133\bin\HostX64\x64\cl.exe @bazel-out/x64_windows-opt/bin/tensorflow/lite/kernels/_objs/builtin_op_kernels/stablehlo_reduce_window.obj.params
# Configuration: 77cd05648d5203f50dead44fd3ee45a61cf90f8b48918874c5fb79829874f482
# Execution platform: @local_execution_config_platform//:platform
cl: warning D9002 :“-O3”
stablehlo_reduce_window.cc
tensorflow/lite/kernels/stablehlo_reduce_window.cc(361): error C4430:
tensorflow/lite/kernels/stablehlo_reduce_window.cc(361): error C2143:
tensorflow/lite/kernels/stablehlo_reduce_window.cc(368): error C2065: “depth”:
tensorflow/lite/kernels/stablehlo_reduce_window.cc(368): error C2065: “rank”:
```
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Automated Code Change
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Set `SetSingleLineMode` to `true` to embed `Method` textprotos in a single line.
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Set `SetSingleLineMode` to `true` to embed `Method` textprotos in a single line.
Previous implementation manually removed `\n` symbols, which resulted in some verbosity where extra whitespaces are included.
Use `SetSingleLineMode` API to achieve the same, but resulting in a more compact string representation.
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Improper use of PluggableDevice data pointer
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"@jswag180,\r\nI tried to execute the mentioned code with tensorflow v2.15 & v2.16 and observed the code is providing the expected output. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/57baecc027add9bc1f357039841c548d/untitled1930.ipynb).",
"> @jswag180, I tried to execute the mentioned code with tensorflow v2.15 & v2.16 and observed the code is providing the expected output. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/57baecc027add9bc1f357039841c548d/untitled1930.ipynb).\r\n\r\nIt is only broken on PluggableDevices that set opaque in SP_DeviceMemoryBase to something other than virtual address space ptr (host or device). The attached gist is not using a PluggableDevice and will not hit this problem."
] | 2024-03-23T23:27:55 | 2024-06-06T13:53:25 | null |
CONTRIBUTOR
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.15.0
### Custom code
Yes
### OS platform and distribution
Linux Arch
### Mobile device
_No response_
### Python version
3.11.8
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
TensorListFromTensor op assumes that the data pointer from a PluggableDevice tensor is to virtual address space and can add an offset to it.
https://github.com/tensorflow/tensorflow/blob/39cb3719012c249ba40bffc4cebaad66311a4669/tensorflow/core/kernels/list_kernels.h#L805-L821
The assumption that all PluggableDevices set the data pointer of Tensor to be virtual address space is false.
https://github.com/tensorflow/tensorflow/blob/39cb3719012c249ba40bffc4cebaad66311a4669/tensorflow/c/experimental/stream_executor/stream_executor.h#L144-L146
Unfortunately, this is not the only occurrence of a bug like this. I have seen similar issues with distributed training offsetting PluggableDevice data pointers on copy as well but I have not taken the time to look into it yet.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
tf.debugging.set_log_device_placement(True)
tf.config.run_functions_eagerly(False)
a = np.zeros((8, 1), dtype=np.float32)
with tf.device("MY_DEVICE:0"):
b = tf.raw_ops.TensorListFromTensor(tensor=a, element_shape=[1])
print(b)
```
### Relevant log output
_No response_
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Convert absl::string_view to std::string before calling ParseFromString()
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Convert absl::string_view to std::string before calling ParseFromString()
The OSS version of ParseFromString() does not accept absl::string_view yet.
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tflite-support not compatible with python 3.11 aarch64 Raspberry Pi 4
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[
"@Emojibotbot,\r\nHave you got the chance to have a look at this tflite-support .whl file. https://pypi.org/project/tflite-support/#files\r\nAlso for Raspberry Pi, the developer was [commented](https://github.com/tensorflow/tflite-support/issues/959#issuecomment-1914975508) for the similar feature in the tflite-support repo and there are few issues for the same which are still open.\r\n\r\nhttps://github.com/tensorflow/tflite-support/issues/952\r\nhttps://github.com/tensorflow/tflite-support/issues/953\r\n\r\nThank you!",
"Sadly it looks like there is no wheel for aarch64 yet. I updated the title to be more correct since you do indeed have python 3.11 support on x86. Thanks for the reply & I hope an udpate comes soon!",
"Hi @Emojibotbot, you are correct it is not yet supported, please wait for an update.",
"It's now June. How long must we wait?"
] | 2024-03-23T15:53:33 | 2024-06-08T05:01:51 | null |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tflite-support 0.4.4
### Custom code
No
### OS platform and distribution
Raspberry Pi OS Bookworm 64 bit
### Mobile device
Raspberry Pi
### Python version
3.11.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I am unable to install tflite-support into my environment that is using python 3.11.
Install method of pip3 install tflite-support
I also tried to download src and build, but I'm still getting the same errors when I try to install
```
git clone https://github.com/tensorflow/tflite-support.git
cd tflite-support
pip install .
```
### Standalone code to reproduce the issue
```shell
using python 3.11 install via pip
pip3 install tflite-support
```
### Relevant log output
_No response_
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[XLA:GPU][NFC] Move tile_analysis* to symbolic_tile*.
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[XLA:GPU][NFC] Move tile_analysis* to symbolic_tile*.
Related code pertaining to tile propagation/analysis belongs in
symbolic_tile_analysis*.
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XNNPACK is not supported in Linux armv7-a when compile source code
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[
"@candcv You're right that XNNPACK is not officially supported for Linux armv7-a architecture when compiling the source code. This is because armv7-a processors generally lack hardware acceleration features that XNNPACK leverages for performance optimization. You can refer to the XNNPACK documentation [here](https://github.com/google/XNNPACK) for pre-built wheels if available.\r\nThank you!",
"@sushreebarsa Thank you for your help.\r\nThe old Version XNNPACK from https://github.com/google/XNNPACK/tree/test_282285243 can support ARMv7 (with NEON) on Android and **Linux**, but the new version only provide script ARMv7 (with NEON) on Android, **not Linux**. I think the problem is related to this.\r\n\r\n",
"@candcv You are right! It is currently not supported. You can download the specific commit (`test_282285243`) from XNNPACK's GitHub repository. This version should work with ARMv7 NEON on Linux. Please use this with caution because it is not the stable version. It might have unexpected behavior.\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further."
] | 2024-03-23T11:41:36 | 2024-04-19T01:47:52 | 2024-04-19T01:47:52 |
NONE
| null | null | null |
Hi, I followed the instructions in the website:https://tensorflow.google.cn/lite/guide/build_cmake_arm to cross compile and find a question XNNPACK is not supported in Linux armv7-a when compile source code.
```
curl -LO https://storage.googleapis.com/mirror.tensorflow.org/developer.arm.com/media/Files/downloads/gnu-a/8.3-2019.03/binrel/gcc-arm-8.3-2019.03-x86_64-arm-linux-gnueabihf.tar.xz
mkdir -p ${HOME}/toolchains
tar xvf gcc-arm-8.3-2019.03-x86_64-arm-linux-gnueabihf.tar.xz -C ${HOME}/toolchains
```
```
ARMCC_FLAGS="-march=armv7-a -mfpu=neon-vfpv4 -funsafe-math-optimizations -mfp16-format=ieee"
ARMCC_PREFIX=${HOME}/toolchains/gcc-arm-8.3-2019.03-x86_64-arm-linux-gnueabihf/bin/arm-linux-gnueabihf-
cmake -DCMAKE_C_COMPILER=${ARMCC_PREFIX}gcc \
-DCMAKE_CXX_COMPILER=${ARMCC_PREFIX}g++ \
-DCMAKE_C_FLAGS="${ARMCC_FLAGS}" \
-DCMAKE_CXX_FLAGS="${ARMCC_FLAGS}" \
-DCMAKE_VERBOSE_MAKEFILE:BOOL=ON \
-DTFLITE_ENABLE_XNNPACK=ON \
-DCMAKE_SYSTEM_NAME=Linux \
-DCMAKE_SYSTEM_PROCESSOR=armv7 \
../tensorflow/lite/
make -j8
```
**part error log:**
`/tensorflow/build_arm/xnnpack/src/qs8-qc8w-igemm/gen/qs8-qc8w-igemm-2x2c4-minmax-fp32-armsimd32.c:80:15: error: unknown type name ‘int16x2_t’
const int16x2_t va1c02 = __sxtb16(va1);
`
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Optimize XLA Compiler.
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Optimize XLA Compiler.
This results in 2% improvement on some representative large model compilation benchmarks.
1. `MayUseOperandValue(position.index, user)`, `(user->IsRoot() && root_positions.contains(user)` is computed once per <position, user> instead of once per <position, user, operand>. We don't need to pass `i` to `MayUseOperandValue`. We can instead bring the `CHECK` out at the call site.
2. Use `emplace_back` instead of `push_back` and `std::move`. Why: https://godbolt.org/z/sT54Ef88h
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PR #10878: Disable Fusion of FP8 Matrix Bias
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PR #10878: Disable Fusion of FP8 Matrix Bias
Imported from GitHub PR https://github.com/openxla/xla/pull/10878
Disables the fusion of a matrix bias into a GEMM Custom Call when the bias has an FP8 data type.
Copybara import of the project:
--
83d29227b698059b56bed4d84ef44c0967a003fe by Philipp Hack <[email protected]>:
Disable FP8 matrix bias fusion.
Merging this change closes #10878
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10878 from philipphack:u_fp8_disable_bias_xla 83d29227b698059b56bed4d84ef44c0967a003fe
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[XLA:GPU] Remove special pretty printing for symbolic tiles.
Now that the parameters represent only sizes, there is no ambiguity as to
what they represent. Furthermore, this pretty printing is not applied
automatically everywhere, making error messages sometimes inconsistent.
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"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64349\">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/64349\">No</a>\n",
"after installing tf.nightly same issue is occuring.\r\n\r\n```\r\nfrom tensorflow.keras.layers.experimental import einsum\r\nprint(\"Done\")\r\n```\r\n\r\nModuleNotFoundError Traceback (most recent call last)\r\n[<ipython-input-3-fd5e9e3ce5fb>](https://localhost:8080/#) in <cell line: 1>()\r\n----> 1 from tensorflow.keras.layers.experimental import einsum\r\n 2 print(\"Done\")\r\n\r\nModuleNotFoundError: No module named 'tensorflow.keras.layers.experimental'\r\n\r\n> How to reslove this.",
"workaround is to limit the tensorflow version and comment out keras from object detection>packages>tf2>setup.py as:\r\n```\r\n 'tf-models-official >=2.5.1, <2.16.0',\r\n 'tensorflow_io',\r\n #'keras',\r\n```\r\n",
"Hi **@Rajcr2** ,\r\n-Sorry for the delay, Make sure you have the latest versions of TensorFlow and TensorFlow Object Detection API installed.\r\n```\r\npip install --upgrade tensorflow\r\npip install --upgrade tf-models-official\r\n```\r\n-Verify the versions of TensorFlow and Keras you have installed to ensure compatibility.\r\n```\r\nimport tensorflow as tf\r\nprint(tf.__version__)\r\nprint(tf.keras.__version__)\r\n```\r\n-In your script model_builder_tf2_test.py, ensure that you're importing the correct modules and classes. \r\n-Ensure that your environment is correctly set up with the necessary libraries and dependencies. You might need to restart your environment or kernel after making changes.\r\nThank you!",
"hey how do i ensure whether i'm importing the correct modules and classes in the script model_builder_tf2_test.py?",
"Same issue. @DineshNeupane commenting **keras** also not working.",
"Did you solve it?\r\n\r\nOn Fri, 5 Apr 2024, 11:00 am Girraj Sharma, ***@***.***>\r\nwrote:\r\n\r\n> Same issue\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/tensorflow/tensorflow/issues/64349#issuecomment-2038973957>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/A7CL4324T2VJ3VFP7TREMYLY3YZGVAVCNFSM6AAAAABFEPPIFCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDAMZYHE3TGOJVG4>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"try this, helped for me\r\n`pip install tensorflow==2.13.1`",
"Hi @Rajcr2 ,\r\n\r\nThe issue seems to be a compatible issues related to TF/Keras version vs tf_models version. As Keras3 now built to support multiple backends there are some changes in design. Please provide more context and environment details like which TF/keras version,tf-models-official version etc for more details.\r\n\r\nPlease check [setup.py ](https://github.com/tensorflow/models/blob/master/official/pip_package/setup.py) of models repo for TF version compatibility.",
"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.",
"If you are using the latest version of tensorflow, you will likey encounter this error. Tensorflow no longer have 'experimental' module under the 'keras.layers' module. You can access the preprocessing libraries directly under tf.keras.layers instead of tf.keras.layers.experimental.preprocessing. For example, you can do:\r\n\r\ntf.keras.layers.RandomRotation(0.15),\r\ntf.keras.layers.RandomFlip('horizontal'),\r\ntf.keras.layers.RandomZoom(0.12),\r\ntf.keras.layers.RandomContrast(0.12),\r\n\r\ninstead of:\r\n\r\ntf.keras.layers.experimental.preprocessing.RandomRotation(0.15),\r\ntf.keras.layers.experimental.preprocessing.RandomFlip('horizontal'),\r\ntf.keras.layers.experimental.preprocessing.RandomZoom(0.12),\r\ntf.keras.layers.experimental.preprocessing.RandomContrast(0.12),",
"> If you are using the latest version of tensorflow, you will likey encounter this error. Tensorflow no longer have 'experimental' module under the 'keras.layers' module. You can access the preprocessing libraries directly under tf.keras.layers instead of tf.keras.layers.experimental.preprocessing. For example, you can do:\r\n> \r\n> tf.keras.layers.RandomRotation(0.15), tf.keras.layers.RandomFlip('horizontal'), tf.keras.layers.RandomZoom(0.12), tf.keras.layers.RandomContrast(0.12),\r\n> \r\n> instead of:\r\n> \r\n> tf.keras.layers.experimental.preprocessing.RandomRotation(0.15), tf.keras.layers.experimental.preprocessing.RandomFlip('horizontal'), tf.keras.layers.experimental.preprocessing.RandomZoom(0.12), tf.keras.layers.experimental.preprocessing.RandomContrast(0.12),\r\n\r\nHi im still facing this error can you please help me ",
"> > If you are using the latest version of tensorflow, you will likey encounter this error. Tensorflow no longer have 'experimental' module under the 'keras.layers' module. You can access the preprocessing libraries directly under tf.keras.layers instead of tf.keras.layers.experimental.preprocessing. For example, you can do:\r\n> > tf.keras.layers.RandomRotation(0.15), tf.keras.layers.RandomFlip('horizontal'), tf.keras.layers.RandomZoom(0.12), tf.keras.layers.RandomContrast(0.12),\r\n> > instead of:\r\n> > tf.keras.layers.experimental.preprocessing.RandomRotation(0.15), tf.keras.layers.experimental.preprocessing.RandomFlip('horizontal'), tf.keras.layers.experimental.preprocessing.RandomZoom(0.12), tf.keras.layers.experimental.preprocessing.RandomContrast(0.12),\r\n> \r\n> Hi im still facing this error can you please help me\r\n\r\nCan I see your code ?",
"yea can i text you in private ?",
"Also seeing this. This is for automation so would rather the fix come from upstream than any manual patch so I'll be watching - appreciate the effort!\r\n\r\n File \"models/official/modeling/optimization/lr_schedule.py\", line 89, in <module>\r\n tf.keras.experimental.CosineDecay)\r\nAttributeError: module 'keras._tf_keras.keras' has no attribute 'experimental'",
"I am also dealing with this problem. ",
"Ok so I found the solution for this. \r\n**TL;DR: there is more information about migrating to tf 2versions here: https://www.tensorflow.org/guide/migrate**\r\n\r\nI found that tensorflow version 2.16.1 changed keras module such as the current calls using 1.x fail.\r\n\r\nI found the fix by searching similar issues and looking at tf docs.\r\nold version tf 1.15 --> https://typeoverflow.com/developer/docs/tensorflow~1.15/keras/experimental/cosinedecay\r\ntf.keras.experimental.CosineDecay(\r\ninitial_learning_rate, decay_steps, alpha=0.0, name=None\r\n)\r\n\r\nlatest version 2.16.1 --> https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/schedules/CosineDecay\r\ntf.keras.optimizers.schedules.CosineDecay(\r\ninitial_learning_rate,\r\ndecay_steps,\r\nalpha=0.0,\r\nname='CosineDecay',\r\nwarmup_target=None,\r\nwarmup_steps=0\r\n)\r\n\r\n\r\n@Rajcr2 Please, consider trying this and marking it as solved. ",
"So, can we do object detection in latest version of tf?",
"shits not working bro",
"> Ok so I found the solution for this. **TL;DR: there is more information about migrating to tf 2versions here: https://www.tensorflow.org/guide/migrate**\r\n> \r\n> I found that tensorflow version 2.16.1 changed keras module such as the current calls using 1.x fail.\r\n> \r\n> I found the fix by searching similar issues and looking at tf docs. old version tf 1.15 --> https://typeoverflow.com/developer/docs/tensorflow~1.15/keras/experimental/cosinedecay tf.keras.experimental.CosineDecay( initial_learning_rate, decay_steps, alpha=0.0, name=None )\r\n> \r\n> latest version 2.16.1 --> https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/schedules/CosineDecay tf.keras.optimizers.schedules.CosineDecay( initial_learning_rate, decay_steps, alpha=0.0, name='CosineDecay', warmup_target=None, warmup_steps=0 )\r\n> \r\n> @Rajcr2 Please, consider trying this and marking it as solved.\r\n\r\nHi @devVegaAn , thanks for your reply. May I know what did you do in detail? All I can imagine is you might replace \"experimental\" with \"optimizers\" in the python code? Was it what you did? Did it work? Thanks! "
] | 2024-03-23T08:43:08 | 2024-05-28T17:48:34 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.15.0
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Got this Error while installing object detection tensorflow api.
command :
!python object_detection/builders/model_builder_tf2_test.py
### Standalone code to reproduce the issue
```shell
2024-03-23 08:19:24.008197: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Traceback (most recent call last):
File "/content/tf_CrimeDetection/models/research/object_detection/builders/model_builder_tf2_test.py", line 24, in <module>
from object_detection.builders import model_builder
File "/usr/local/lib/python3.10/dist-packages/object_detection/builders/model_builder.py", line 26, in <module>
from object_detection.builders import hyperparams_builder
File "/usr/local/lib/python3.10/dist-packages/object_detection/builders/hyperparams_builder.py", line 27, in <module>
from object_detection.core import freezable_sync_batch_norm
File "/usr/local/lib/python3.10/dist-packages/object_detection/core/freezable_sync_batch_norm.py", line 20, in <module>
class FreezableSyncBatchNorm(tf.keras.layers.experimental.SyncBatchNormalization
AttributeError: module 'keras._tf_keras.keras.layers' has no attribute 'experimental'
```
### Relevant log output
_No response_
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Add support for tiled sharding with replicate_on_last_tile_dim in TpuRewrite pass
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Add support for tiled sharding with replicate_on_last_tile_dim in TpuRewrite pass
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Add debug logging when resharding may be needed.
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MakeArrayFromHostBuffer and AssembleArrayFromSingleDeviceArrays can be shared with all PjRtCompatibleClient subclasses. No sense making them specialized to only work for PjRtClients.
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[xla:gpu][NFC] Add `update` accessor to HloDynamicUpdateSliceInstruction API
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[xla:gpu][NFC] Add `update` accessor to HloDynamicUpdateSliceInstruction API
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[xla:gpu] Support DUS for AddressComputationFusionRewriter
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[xla:gpu] Support DUS for AddressComputationFusionRewriter
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No need to include all of Eigen Tensor to get the threadpool after we moved it to Core.
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No need to include all of Eigen Tensor to get the threadpool after we moved it to Core.
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PR_kwDOArmXAs5qiuDH
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#tf-data Do not skip in the global shuffling mode.
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#tf-data Do not skip in the global shuffling mode.
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Create streams using StreamExecutor::CreateStream rather than creating them and calling Initialize.
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Create streams using StreamExecutor::CreateStream rather than creating them and calling Initialize.
CreateStream does both.
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Internal config test
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Internal config test
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PR_kwDOArmXAs5qih5q
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Add absl::SourceLocation to xla::Cancelled, xla::NotFound, xla::Unavailable, and xla::Unknown.
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Add absl::SourceLocation to xla::Cancelled, xla::NotFound, xla::Unavailable, and xla::Unknown.
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PR_kwDOArmXAs5qihcc
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Fix a bug in the pattern to fuse reshape around batch matmul.
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Fix a bug in the pattern to fuse reshape around batch matmul.
FuseReshapesAroundBatchMatMulLHS1 pattern to apply only when higher shaped LHS is being reshaped. Added a missing check to make sure the LHS shape. is greated than 3.
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Add `tsl::testing::AppendDotExeIfWindows` and use it inside `filecheck.cc` to fix Windows build errors
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[xla:pjrt:cpu] Create owned PjRt buffers when creating zero-copy host buffers
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[xla:pjrt:cpu] Create owned PjRt buffers when creating zero-copy host buffers
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[XLA:Python] Refactor jit argument parsing code.
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[XLA:Python] Refactor jit argument parsing code.
* Put the logic to split static and dynamic arguments under ParseArguments, which returns a ArgumentSignature and a list of dynamic arguments.
* Move the other argument parsing logic for jit and pmap into a separate CallSignature type.
This simplifies the code, and prepares for adding another use case for the argument parsing code without the rest of the jit logic.
Refactoring only, no functional changes intended.
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Migrate to new header
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Migrate to new header
No functional change is intended, just a refactoring.
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Update TFRT dependency to use revision
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Update TFRT dependency to use revision
http://github.com/tensorflow/runtime/commit/5c38721c44d1366b675e38651c4c1cc885054f7b.
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Use the appropriate operand sharding when computing resharding costs for HLO ops when a new ShardingStrategy is generated for n op based on a user annotation.
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Use the appropriate operand sharding when computing resharding costs for HLO ops when a new ShardingStrategy is generated for n op based on a user annotation.
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Fix memory budget lower bound computation for the case of solve_nd_sharding_iteratively=true
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Fix memory budget lower bound computation for the case of solve_nd_sharding_iteratively=true
** Description of the issue
When solve_nd_sharding_iteratively is turned on, say for a 2D mesh [N,M] where N >= M, we would first shard the module across the partial mesh [N,1], and then use these results to prune the search space for sharding the module across the full mesh [N,M]. An artifact of the way we have implemented this is that after sharding the module with the partial mesh, we annotate the module with the computed partial shardings (in the SetHloSharding() function).
Given that we are sharding the devices across a different number of devices each time (N vs N*M), we re-compute the memory budget lower bound (in MemoryBudgetLowerBound()). This function uses pre-existing sharding annotations, if any, on instructions for computing a lower bound on the memory consumed per device. When computing the lower bound for the case of the full mesh, we therefore end up using the partial sharding annotations on the instructions to compute the lower bound, leading to a memory budget that is ~M times larger than it should be.
** Fix
This CL fixes this issue by enforcing that preexisting shardings annotations be used only if they correspond to the same number of devices as the current device mesh.
In some cases, user annotations may not be adjusted for partial meshes. Therefore we skip the above check for instructions with user annotations. If this is found to not affect results, it can be simplified.
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[XLA:GPU][TileAnalysis] Re-think symbolic tiles and their parameter extraction.
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[XLA:GPU][TileAnalysis] Re-think symbolic tiles and their parameter extraction.
Previously, symbolic tiles could take offsets and strides as input parameters.
While there is a use for it at codegen time (e.g. to use the symbolic tile to
directly produce the overall offset for loading a parameter, and similarly for
the strides), the benefit is marginal. However, it turns out to be highly
problematic for more important use cases, such as concrete size propagation;
indeed, a combination of a non-trivial reshape with an unknown stride gives
rise to stride-dependent sizes---thus completely preventing concrete size
propagation until a constant stride is chosen.
If we need to choose a constant stride anyway, then there is no use in carrying
around this parameter. There is likely no such problem with offsets, but we
likewise elide them from the list of input parameters in order to simplify
things.
We take this opportunity to simplify our extraction logic as well, and
implement logic able to handle merge reshapes correctly. Thanks to
the simplification of the structure of symbolic tiles, we can now make stronger
assumptions about our intermediate expressions (e.g. where constants appear),
and as a result the logic is much easier to follow.
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| 2,203,149,995 |
PR_kwDOArmXAs5qhgm6
| 64,323 |
graphcycles: extract NodeIO out of Node
|
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graphcycles: extract NodeIO out of Node
To improve cache locality when doing DFS.
name old time/op new time/op delta
BM_StressTest/2048 492ns ± 2% 470ns ± 1% -4.38% (p=0.008 n=5+5)
BM_StressTest/4096 499ns ± 2% 478ns ± 1% -4.07% (p=0.008 n=5+5)
BM_StressTest/32768 512ns ± 2% 494ns ± 1% -3.52% (p=0.016 n=5+4)
BM_StressTest/262144 558ns ± 2% 537ns ± 1% -3.77% (p=0.016 n=5+4)
BM_StressTest/1048576 621ns ± 2% 692ns ±23% ~ (p=0.690 n=5+5)
BM_ContractEdge/1000 104ns ± 2% 106ns ± 5% ~ (p=0.286 n=5+4)
BM_ContractEdge/10000 119ns ± 2% 119ns ± 1% ~ (p=0.651 n=5+5)
BM_IsReachableNonConst/10 9.76ns ±10% 9.17ns ±12% ~ (p=0.095 n=5+5)
BM_IsReachableNonConst/50 15.4ns ±11% 13.6ns ± 5% -11.41% (p=0.032 n=5+5)
BM_IsReachableNonConst/100 17.5ns ±13% 15.3ns ± 7% -12.66% (p=0.032 n=5+5)
BM_IsReachableNonConst/200 20.1ns ± 7% 18.1ns ± 9% -9.71% (p=0.016 n=5+5)
BM_IsReachableNonConst/1000 25.5ns ± 5% 22.7ns ± 3% -10.98% (p=0.008 n=5+5)
BM_IsReachableNonConst/30000 45.0ns ± 3% 39.2ns ± 3% -12.95% (p=0.008 n=5+5)
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PR_kwDOArmXAs5qhgLV
| 64,322 |
[xla:gpu] multi_output_fusion_test: drop dependency on gunit
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[xla:gpu] multi_output_fusion_test: drop dependency on gunit
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PR_kwDOArmXAs5qhfdE
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Add unbounded dynamism test for ReducePrecisionOp.
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Add unbounded dynamism test for ReducePrecisionOp.
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I_kwDOArmXAs6DUSjN
| 64,320 |
Fail to build from source: API rate limit exceeded for 216.228.127.129
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[
"@DwarKapex,\r\nCould you please confirm whether this issue is happening with only 2.13 or also with the latest stable tensorflow version 2.16.1. Please try and provide the error log if you are facing with 2.16.1 as well. Thank you",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64320\">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/64320\">No</a>\n"
] | 2024-03-22T19:00:45 | 2024-04-10T01:47:43 | 2024-04-10T01:47:39 |
CONTRIBUTOR
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.13.0
### Custom code
No
### OS platform and distribution
linux
### Mobile device
_No response_
### Python version
3.9
### Bazel version
5.3.0
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Complete the build without an issue (now it is intermittent because of the root cause below)
THE ACTUAL PROBLEM:
The problem is the `curl` request [here](https://github.com/tensorflow/text/blob/f0f675cb1e24cfd0e42aa95f3136b7aee3c91bc6/oss_scripts/prepare_tf_dep.sh#L38). The API request response is the following:
```
$> curl -s https://api.github.com/repos/tensorflow/tensorflow/commits/1cb1a030
{"message":"API rate limit exceeded for 216.228.127.129. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.)","documentation_url":"https://docs.github.com/rest/overview/resources-in-the-rest-api#rate-limiting"}
```
Need a more reliable way to get commit_slug, or IMHO it is better to use commit as a reference.
### Standalone code to reproduce the issue
```shell
$> pip install tensorflow_datasets==4.9.2 auditwheel tensorflow==2.13.0
$> git clone https://github.com/tensorflow/text.git /opt/tensorflow-text
$> cd /opt/tensorflow-text
$> git checkout 917a681d7220ebf9b62a08b6f9ce7b7db886ddef
$> git submodule update --init --recursive
$> ./oss_scripts/run_build.sh
```
### Relevant log output
```shell
+ ./oss_scripts/run_build.sh
++ uname -s
++ tr A-Z a-z
+ osname=linux
+ [[ linux == \d\a\r\w\i\n ]]
+ source oss_scripts/configure.sh
+++ uname -s
+++ tr A-Z a-z
++ osname=linux
++ echo linux
linux
++ '[' -e .bazelrc ']'
+++ pip show tensorflow
Using installed tensorflow.
++ [[ Name: tensorflow
Version: 2.13.0
Summary: TensorFlow is an open source machine learning framework for everyone.
Home-page: https://www.tensorflow.org/
Author: Google Inc.
Author-email: [email protected]
License: Apache 2.0
Location: /usr/local/lib/python3.10/dist-packages
Requires: tensorflow-cpu-aws
Required-by: == *tensorflow* ]]
++ echo 'Using installed tensorflow.'
++ is_windows
++ [[ linux =~ msys_nt*|mingw*|cygwin*|uwin* ]]
++ curl https://raw.githubusercontent.com/tensorflow/tensorflow/r2.13/.bazelversion -o .bazelversion
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 85 100 85 0 0 992 0 --:--:-- --:--:-- --:--:-- 1000
++ curl https://raw.githubusercontent.com/tensorflow/tensorflow/r2.13/.bazelrc -o .bazelrc
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 36653 100 36653 0 0 372k 0 --:--:-- --:--:-- --:--:-- 376k
++ sed -i -e 's/build --noincompatible_remove_legacy_whole_archive//' .bazelrc
++ write_to_bazelrc 'build:manylinux2010 --config=release_cpu_linux'
++ echo 'build:manylinux2010 --config=release_cpu_linux'
++ write_to_bazelrc 'build:manylinux2014 --config=release_cpu_linux'
++ echo 'build:manylinux2014 --config=release_cpu_linux'
++ which python3
++ grep -q python3
++ installed_python=python3
++ TF_CFLAGS=($($installed_python -c "import tensorflow as tf; print(' '.join(tf.sysconfig.get_compile_flags()))" | awk '{print $1}'))
+++ python3 -c 'import tensorflow as tf; print('\'' '\''.join(tf.sysconfig.get_compile_flags()))'
+++ awk '{print $1}'
++ TF_LFLAGS=($($installed_python -c "import tensorflow as tf; print(' '.join(tf.sysconfig.get_link_flags()))" | awk '{print $1}'))
+++ python3 -c 'import tensorflow as tf; print('\'' '\''.join(tf.sysconfig.get_link_flags()))'
+++ awk '{print $1}'
++ TF_LFLAGS_2=($($installed_python -c "import tensorflow as tf; print(' '.join(tf.sysconfig.get_link_flags()))" | awk '{print $2}'))
+++ python3 -c 'import tensorflow as tf; print('\'' '\''.join(tf.sysconfig.get_link_flags()))'
+++ awk '{print $2}'
+++ python3 -c 'import tensorflow as tf; print(tf.sysconfig.CXX11_ABI_FLAG)'
++ TF_ABIFLAG=1
++ HEADER_DIR=/usr/local/lib/python3.10/dist-packages/tensorflow/include
++ SHARED_LIBRARY_DIR=/usr/local/lib/python3.10/dist-packages/tensorflow
+++ echo -l:libtensorflow_framework.so.2
+++ rev
+++ cut -d: -f1
+++ rev
++ SHARED_LIBRARY_NAME=libtensorflow_framework.so.2
++ is_macos
++ [[ linux == \d\a\r\w\i\n ]]
++ is_windows
++ [[ linux =~ msys_nt*|mingw*|cygwin*|uwin* ]]
++ export LD_LIBRARY_PATH=/opt/amazon/efa/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/local/lib/python3.10/dist-packages/tensorflow
++ LD_LIBRARY_PATH=/opt/amazon/efa/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/local/lib/python3.10/dist-packages/tensorflow
++ write_action_env_to_bazelrc TF_HEADER_DIR /usr/local/lib/python3.10/dist-packages/tensorflow/include
++ write_to_bazelrc 'build --action_env TF_HEADER_DIR="/usr/local/lib/python3.10/dist-packages/tensorflow/include"'
++ echo 'build --action_env TF_HEADER_DIR="/usr/local/lib/python3.10/dist-packages/tensorflow/include"'
++ write_action_env_to_bazelrc TF_SHARED_LIBRARY_DIR /usr/local/lib/python3.10/dist-packages/tensorflow
++ write_to_bazelrc 'build --action_env TF_SHARED_LIBRARY_DIR="/usr/local/lib/python3.10/dist-packages/tensorflow"'
++ echo 'build --action_env TF_SHARED_LIBRARY_DIR="/usr/local/lib/python3.10/dist-packages/tensorflow"'
++ write_action_env_to_bazelrc TF_SHARED_LIBRARY_NAME libtensorflow_framework.so.2
++ write_to_bazelrc 'build --action_env TF_SHARED_LIBRARY_NAME="libtensorflow_framework.so.2"'
++ echo 'build --action_env TF_SHARED_LIBRARY_NAME="libtensorflow_framework.so.2"'
++ write_action_env_to_bazelrc TF_CXX11_ABI_FLAG 1
++ write_to_bazelrc 'build --action_env TF_CXX11_ABI_FLAG="1"'
++ echo 'build --action_env TF_CXX11_ABI_FLAG="1"'
++ bazel version
++ grep label
++ sed -e 's/.*: //'
2024/03/22 17:33:39 Downloading https://releases.bazel.build/5.3.0/release/bazel-5.3.0-linux-arm64...
Extracting Bazel installation...
Starting local Bazel server and connecting to it...
+ installed_bazel_version=5.3.0
++ head -n 1 .bazelversion
+ tf_bazel_version=5.3.0
+ '[' 5.3.0 '!=' 5.3.0 ']'
+ [[ linux != \d\a\r\w\i\n ]]
+ source oss_scripts/prepare_tf_dep.sh
++ set -ex
++ which python3
++ grep -q python3
++ installed_python=python3
++ ext=
+++ uname -s
+++ tr A-Z a-z
++ osname=linux
++ [[ linux == \d\a\r\w\i\n ]]
+++ python3 -c 'import tensorflow as tf; print(tf.__version__)'
++ tf_version=2.13.0
++ echo 2.13.0
2.13.0
++ sed -i 's/project_version = '\''REPLACE_ME'\''/project_version = '\''2.13.0'\''/' oss_scripts/pip_package/setup.nightly.py
++ sed -i 's/__version__ = .*$/__version__ = "2.13.0"/' tensorflow_text/__init__.py
+++ python3 -c 'import tensorflow as tf; print(tf.__git_version__)'
+++ tail -1
++ short_commit_sha=v2.13.0-0-g1cb1a030
++ [[ linux == \d\a\r\w\i\n ]]
+++ echo v2.13.0-0-g1cb1a030
+++ grep -oP '(?<=-g)[0-9a-f]*$'
++ short_commit_sha=1cb1a030
+++ curl -s https://api.github.com/repos/tensorflow/tensorflow/commits/1cb1a030
+++ grep sha
+++ head -n 1
+++ cut -d '"' -f 4
++ commit_sha=
++ sed -E -i 's/strip_prefix = "tensorflow-2.+",/strip_prefix = "tensorflow-",/' WORKSPACE
++ sed -E -i 's|"https://github.com/tensorflow/tensorflow/archive/v.+\.zip"|"https://github.com/tensorflow/tensorflow/archive/.zip"|' WORKSPACE
+++ grep -A 1 -e 'strip_prefix.*tensorflow-' WORKSPACE
+++ tail -1
+++ awk -F '"' '{print $2}'
++ prev_shasum=447cdb65c80c86d6c6cf1388684f157612392723eaea832e6392d219098b49de
++ sed -i 's/sha256 = "447cdb65c80c86d6c6cf1388684f157612392723eaea832e6392d219098b49de",//' WORKSPACE
+ bazel build --enable_runfiles oss_scripts/pip_package:build_pip_package
INFO: Options provided by the client:
Inherited 'common' options: --isatty=0 --terminal_columns=80
INFO: Reading rc options for 'build' from /opt/tensorflow-text/.bazelrc:
Inherited 'common' options: --experimental_repo_remote_exec
INFO: Reading rc options for 'build' from /opt/tensorflow-text/.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 --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility --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,tensorflow/core/tfrt/utils/debug --action_env TF_HEADER_DIR=/usr/local/lib/python3.10/dist-packages/tensorflow/include --action_env TF_SHARED_LIBRARY_DIR=/usr/local/lib/python3.10/dist-packages/tensorflow --action_env TF_SHARED_LIBRARY_NAME=libtensorflow_framework.so.2 --action_env TF_CXX11_ABI_FLAG=1
INFO: Found applicable config definition build:short_logs in file /opt/tensorflow-text/.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file /opt/tensorflow-text/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:linux in file /opt/tensorflow-text/.bazelrc: --define=build_with_onednn_v2=true --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wno-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-array-bounds --copt=-Wunused-result --copt=-Werror=unused-result --copt=-Wswitch --copt=-Werror=switch --copt=-Wno-error=unused-but-set-variable --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --experimental_guard_against_concurrent_changes
INFO: Found applicable config definition build:dynamic_kernels in file /opt/tensorflow-text/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS
Loading:
Loading: 0 packages loaded
INFO: Repository org_tensorflow instantiated at:
/opt/tensorflow-text/WORKSPACE:71:13: in <toplevel>
Repository rule http_archive defined at:
/root/.cache/bazel/_bazel_root/85e1e75b08f730f1cb0a39e292604ff5/external/bazel_tools/tools/build_defs/repo/http.bzl:355:31: in <toplevel>
WARNING: Download from https://github.com/tensorflow/tensorflow/archive/.zip failed: class java.io.FileNotFoundException GET returned 404 Not Found
ERROR: An error occurred during the fetch of repository 'org_tensorflow':
Traceback (most recent call last):
File "/root/.cache/bazel/_bazel_root/85e1e75b08f730f1cb0a39e292604ff5/external/bazel_tools/tools/build_defs/repo/http.bzl", line 125, column 45, in _http_archive_impl
download_info = ctx.download_and_extract(
Error in download_and_extract: java.io.IOException: Error downloading [https://github.com/tensorflow/tensorflow/archive/.zip] to /root/.cache/bazel/_bazel_root/85e1e75b08f730f1cb0a39e292604ff5/external/org_tensorflow/temp689896470524611324/.zip: GET returned 404 Not Found
ERROR: /opt/tensorflow-text/WORKSPACE:71:13: fetching http_archive rule //external:org_tensorflow: Traceback (most recent call last):
File "/root/.cache/bazel/_bazel_root/85e1e75b08f730f1cb0a39e292604ff5/external/bazel_tools/tools/build_defs/repo/http.bzl", line 125, column 45, in _http_archive_impl
download_info = ctx.download_and_extract(
Error in download_and_extract: java.io.IOException: Error downloading [https://github.com/tensorflow/tensorflow/archive/.zip] to /root/.cache/bazel/_bazel_root/85e1e75b08f730f1cb0a39e292604ff5/external/org_tensorflow/temp689896470524611324/.zip: GET returned 404 Not Found
ERROR: no such package '@org_tensorflow//tensorflow': java.io.IOException: Error downloading [https://github.com/tensorflow/tensorflow/archive/.zip] to /root/.cache/bazel/_bazel_root/85e1e75b08f730f1cb0a39e292604ff5/external/org_tensorflow/temp689896470524611324/.zip: GET returned 404 Not Found
INFO: Elapsed time: 1.254s
INFO: 0 processes.
FAILED: Build did NOT complete successfully (0 packages loaded)
FAILED: Build did NOT complete successfully (0 packages loaded)
```
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Change to MSA to ignore constants while calculating operand distance.
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Change to MSA to ignore constants while calculating operand distance.
This is required for the memory_bound_loop_optimization to properly identify compiler-unrolled loops.
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Add unbounded dynamism test for ShiftRightLogicalOp.
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Add unbounded dynamism test for ShiftRightLogicalOp.
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Add unbounded dynamism test for ShiftRightArithmeticOp.
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[xla:tpu] Allow host offloading ops to pass hlo verification when changing layout.
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[xla:tpu] Allow host offloading ops to pass hlo verification when changing layout.
Host offloading ops (dynamic slice, copy, and dynamic update slice) move tensors between device memory and host memory space. This can cause the hlo verifier to fail as it interprets this layout to be unequal. This change allows for memory space to be ignored when comparing layouts for these ops.
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Add unbounded dynamism test for ShiftLeftOp.
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Add unbounded dynamism test for ShiftLeftOp.
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Add unbounded dynamism test for RemOp.
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Add unbounded dynamism test for RemOp.
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[XLA] Add to ReduceScatterDecomposer target specific hook to allow transforming the reduce-scatter to a supported version
Reverts 5bbc00214f106863808539104be9fdc6e01af02d
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Implement pass to convert custom calls to composites
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Implement pass to convert custom calls to composites
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/62817 from Intel-tensorflow:gaurides/amp_fp16 9780d75ad68275bf30b81c7cf02688fb87e5f1e2
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Use the input's shapes directly instead of getting them from the op
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[XLA] Prioritize instructions that are on mostly a latency path (not a lot of compute on the path) as they can be overlapped with all the compute available.
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[xla][gpu] Make the p2p-schedule-preparation pass to not rely on certain
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[xla][gpu] Make the p2p-schedule-preparation pass to not rely on certain
pattern to find the corresponding Send/Recv for SendDone/RecvDone.
Previously, we relied on certain code patterns to find the Send/Recv for
Send-done/Recv-done for a pipelined loop. Now that we realize copy-insertion
can complicate such code patterns and make such pattern matching fragile.
We modify the p2p-schedule-preparation pass to record Send/Recv in additional
to SendDone/RecvDone for each p2p communication group.
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Update TFRT dependency to use revision
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Update TFRT dependency to use revision
http://github.com/tensorflow/runtime/commit/29bfd29cf353020006fa20ec0a41062ff699678d.
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[XLA:GPU][NFC] Cosmetic and safety fixes to symbolic tile analysis code.
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[XLA:GPU][NFC] Cosmetic and safety fixes to symbolic tile analysis code.
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#shlo_ref Add `or` op.
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#shlo_ref Add `and` op.
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Add a new field `inference` into streamz instrument `UpdateTfMlirBridgeFirstPhaseCounter`
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PR_kwDOArmXAs5qgjY0
| 64,299 |
Replace hard-failing CHECKs in triton emitter with TF_RET_CHECK, TF_RETURN_IF_ERROR, and the like.
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Replace hard-failing CHECKs in triton emitter with TF_RET_CHECK, TF_RETURN_IF_ERROR, and the like.
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PR_kwDOArmXAs5qgihg
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[XLA:GPU][NFC] Remove unused variable
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[XLA:GPU][NFC] Remove unused variable
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PR_kwDOArmXAs5qgcD5
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[xla:gpu] Support dynamic slices for AddressComputationFusionRewriter
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I_kwDOArmXAs6DTIm1
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Segmentation fault (core dumped)
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[
"I made a stupid mistake while doing this experiment. Running this example locally does not give the Segmentation fault (core dumped)...\r\n\r\nIn my other project which I can not upload I got \"Segmentation fault (core dumped)\r\n\" error. By providing the real dataset instead of a dummy random dataset for the converter the error went away...",
"Hi @HajarAva, I have successfully ran that example here: [gist](https://colab.sandbox.google.com/gist/pkgoogle/da76735c7ae506cfa2fb5d546f020011/64296.ipynb), can you check if it looks like your example or if you are maybe missing a step?",
"thanks @pkgoogle for following up on this subject...\r\nI don't remember what I was missing but this code is running on my local env now. \r\n\r\nI have \"Segmentation fault (core dumped)\" error with another project that I can not upload. By replacing the dummy representative dataset with a real representative dataset I could successfully create the full integer tflite model...",
"Ok then... I don't know if I have any other action I can take here, unless you can show me the example with the dummy representative dataset. If you have no more open item, please feel free to close. 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/64296\">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/64296\">No</a>\n"
] | 2024-03-22T16:05:08 | 2024-04-09T10:34:27 | 2024-04-09T10:34:24 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.10.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
RTX 4090
### GPU model and memory
_No response_
### Current behavior?
A similar issue is explained in this [link](https://github.com/tensorflow/tensorflow/issues/61828). I made this new issue as @pkgoogle demanded.
I ran this TensorFlow [example](https://www.tensorflow.org/lite/performance/post_training_integer_quant) locally but I got a Segmentation fault (core dumped) after running this piece of the code:
### Standalone code to reproduce the issue
```shell
The problem is with running the Post-training integer quantization code of the following example locally:
https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/performance/post_training_integer_quant.ipynb#scrollTo=FiwiWU3gHdkW
```
### Relevant log output
```shell
d inputs were expected, but not specified; continuing anyway.
warnings.warn(
2024-03-22 17:02:49.457966: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:364] Ignored output_format.
2024-03-22 17:02:49.457981: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:367] Ignored drop_control_dependency.
2024-03-22 17:02:49.458398: I tensorflow/cc/saved_model/reader.cc:45] Reading SavedModel from: /tmp/tmpc6h9p_cu
2024-03-22 17:02:49.470594: I tensorflow/cc/saved_model/reader.cc:91] Reading meta graph with tags { serve }
2024-03-22 17:02:49.470607: I tensorflow/cc/saved_model/reader.cc:132] Reading SavedModel debug info (if present) from: /tmp/tmpc6h9p_cu
2024-03-22 17:02:49.512533: I tensorflow/cc/saved_model/loader.cc:231] Restoring SavedModel bundle.
2024-03-22 17:02:49.615602: I tensorflow/cc/saved_model/loader.cc:215] Running initialization op on SavedModel bundle at path: /tmp/tmpc6h9p_cu
2024-03-22 17:02:49.679176: I tensorflow/cc/saved_model/loader.cc:314] SavedModel load for tags { serve }; Status: success: OK. Took 220778 microseconds.
2024-03-22 17:02:49.777449: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:255] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2024-03-22 17:02:49.968916: W tensorflow/compiler/mlir/lite/flatbuffer_export.cc:2073] TFLite interpreter needs to link Flex delegate in order to run the model since it contains the following Select TFop(s):
Flex ops: FlexEmptyTensorList, FlexTensorListPushBack, FlexTensorListReserve, FlexTensorListSetItem, FlexTensorListStack
Details:
tf.EmptyTensorList(tensor<0xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<!tf_type.variant>>>) : {device = ""}
tf.EmptyTensorList(tensor<0xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<f32>>>) : {device = ""}
tf.EmptyTensorList(tensor<0xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<i32>>>) : {device = ""}
tf.EmptyTensorList(tensor<1xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<128xf32>>>) : {device = ""}
tf.EmptyTensorList(tensor<1xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<1xi32>>>) : {device = ""}
tf.EmptyTensorList(tensor<1xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<2xi32>>>) : {device = ""}
tf.EmptyTensorList(tensor<1xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<384xf32>>>) : {device = ""}
tf.EmptyTensorList(tensor<2xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<128x128xf32>>>) : {device = ""}
tf.EmptyTensorList(tensor<2xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<128x384xf32>>>) : {device = ""}
tf.EmptyTensorList(tensor<2xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<4x128xf32>>>) : {device = ""}
tf.EmptyTensorList(tensor<2xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<4x64xf32>>>) : {device = ""}
tf.EmptyTensorList(tensor<2xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<64x128xf32>>>) : {device = ""}
tf.EmptyTensorList(tensor<2xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<64x384xf32>>>) : {device = ""}
tf.TensorListPushBack(tensor<!tf_type.variant<tensor<!tf_type.variant>>>, tensor<!tf_type.variant<tensor<4x128xf32>>>) -> (tensor<!tf_type.variant<tensor<!tf_type.variant>>>) : {device = ""}
tf.TensorListPushBack(tensor<!tf_type.variant<tensor<128x128xf32>>>, tensor<128x128xf32>) -> (tensor<!tf_type.variant<tensor<128x128xf32>>>) : {device = ""}
tf.TensorListPushBack(tensor<!tf_type.variant<tensor<128x384xf32>>>, tensor<128x384xf32>) -> (tensor<!tf_type.variant<tensor<128x384xf32>>>) : {device = ""}
tf.TensorListPushBack(tensor<!tf_type.variant<tensor<128xf32>>>, tensor<128xf32>) -> (tensor<!tf_type.variant<tensor<128xf32>>>) : {device = ""}
tf.TensorListPushBack(tensor<!tf_type.variant<tensor<1xi32>>>, tensor<1xi32>) -> (tensor<!tf_type.variant<tensor<1xi32>>>) : {device = ""}
tf.TensorListPushBack(tensor<!tf_type.variant<tensor<2xi32>>>, tensor<2xi32>) -> (tensor<!tf_type.variant<tensor<2xi32>>>) : {device = ""}
tf.TensorListPushBack(tensor<!tf_type.variant<tensor<384xf32>>>, tensor<384xf32>) -> (tensor<!tf_type.variant<tensor<384xf32>>>) : {device = ""}
tf.TensorListPushBack(tensor<!tf_type.variant<tensor<4x128xf32>>>, tensor<4x128xf32>) -> (tensor<!tf_type.variant<tensor<4x128xf32>>>) : {device = ""}
tf.TensorListPushBack(tensor<!tf_type.variant<tensor<4x64xf32>>>, tensor<4x64xf32>) -> (tensor<!tf_type.variant<tensor<4x64xf32>>>) : {device = ""}
tf.TensorListPushBack(tensor<!tf_type.variant<tensor<64x128xf32>>>, tensor<64x128xf32>) -> (tensor<!tf_type.variant<tensor<64x128xf32>>>) : {device = ""}
tf.TensorListPushBack(tensor<!tf_type.variant<tensor<64x384xf32>>>, tensor<64x384xf32>) -> (tensor<!tf_type.variant<tensor<64x384xf32>>>) : {device = ""}
tf.TensorListPushBack(tensor<!tf_type.variant<tensor<f32>>>, tensor<f32>) -> (tensor<!tf_type.variant<tensor<f32>>>) : {device = ""}
tf.TensorListPushBack(tensor<!tf_type.variant<tensor<i32>>>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<i32>>>) : {device = ""}
tf.TensorListReserve(tensor<2xi32>, tensor<i32>) -> (tensor<!tf_type.variant<tensor<4x128xf32>>>) : {device = ""}
tf.TensorListSetItem(tensor<!tf_type.variant<tensor<4x128xf32>>>, tensor<i32>, tensor<4x128xf32>) -> (tensor<!tf_type.variant<tensor<4x128xf32>>>) : {device = "", resize_if_index_out_of_bounds = false}
tf.TensorListStack(tensor<!tf_type.variant<tensor<4x128xf32>>>, tensor<2xi32>) -> (tensor<?x4x128xf32>) : {device = "", num_elements = -1 : i64}
See instructions: https://www.tensorflow.org/lite/guide/ops_select
2024-03-22 17:02:49.968963: I tensorflow/compiler/mlir/lite/flatbuffer_export.cc:2138] Estimated count of arithmetic ops: 1.906 M ops, equivalently 0.953 M MACs
INFO: Created TensorFlow Lite delegate for select TF ops.
2024-03-22 17:02:50.038388: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-22 17:02:50.038506: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-22 17:02:50.038558: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-22 17:02:50.038646: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-22 17:02:50.038691: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-22 17:02:50.038725: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22058 MB memory: -> device: 0, name: NVIDIA GeForce RTX 4090, pci bus id: 0000:01:00.0, compute capability: 8.9
INFO: TfLiteFlexDelegate delegate: 15 nodes delegated out of 227 nodes with 2 partitions.
2024-03-22 17:02:50.043702: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-22 17:02:50.043790: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-22 17:02:50.043838: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-22 17:02:50.043906: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-22 17:02:50.043949: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-03-22 17:02:50.043986: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22058 MB memory: -> device: 0, name: NVIDIA GeForce RTX 4090, pci bus id: 0000:01:00.0, compute capability: 8.9
fully_quantize: 0, inference_type: 6, input_inference_type: FLOAT32, output_inference_type: FLOAT32
Segmentation fault (core dumped)
```
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graphcycles: keep nodes in a vector + move user_data out of it
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[] | 2024-03-22T15:39:13 | 2024-03-22T17:04:24 | 2024-03-22T17:04:23 |
NONE
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graphcycles: keep nodes in a vector + move user_data out of it
This improves cache locality.
Note that user_data currently has no callers, so accessing it
quickly is not something we want to optimize for.
name old time/op new time/op delta
BM_StressTest/2048 474ns ± 1% 492ns ± 1% +3.63% (p=0.008 n=5+5)
BM_StressTest/4096 490ns ± 1% 497ns ± 1% +1.52% (p=0.008 n=5+5)
BM_StressTest/32768 504ns ± 1% 508ns ± 1% +0.86% (p=0.032 n=5+5)
BM_StressTest/262144 553ns ± 1% 557ns ± 3% ~ (p=0.548 n=5+5)
BM_StressTest/1048576 617ns ± 2% 616ns ± 2% ~ (p=0.690 n=5+5)
BM_ContractEdge/1000 121ns ± 2% 104ns ± 6% -13.56% (p=0.008 n=5+5)
BM_ContractEdge/10000 147ns ± 1% 119ns ± 1% -18.86% (p=0.008 n=5+5)
BM_IsReachableNonConst/10 10.4ns ±35% 9.3ns ± 6% ~ (p=0.310 n=5+5)
BM_IsReachableNonConst/50 15.8ns ±12% 15.4ns ±10% ~ (p=0.841 n=5+5)
BM_IsReachableNonConst/100 17.4ns ± 6% 17.5ns ±13% ~ (p=0.841 n=5+5)
BM_IsReachableNonConst/200 19.7ns ± 6% 19.5ns ± 6% ~ (p=0.690 n=5+5)
BM_IsReachableNonConst/1000 25.2ns ± 4% 24.9ns ± 6% ~ (p=0.690 n=5+5)
BM_IsReachableNonConst/30000 49.8ns ± 4% 44.3ns ± 5% -10.98% (p=0.008 n=5+5)
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[XLA:GPU] Remove IndexingContext.
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[XLA:GPU] Remove IndexingContext.
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[XLA:GPU][NFC] Fix style in in_place_dynamic_update_slice_mlir.cc for consistency
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[] | 2024-03-22T15:19:44 | 2024-03-22T17:16:23 | 2024-03-22T17:16:23 |
NONE
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[XLA:GPU][NFC] Fix style in in_place_dynamic_update_slice_mlir.cc for consistency
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