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[ "### Multi class error\r\n\r\nThe issue with this code is that it is using **categorical_crossentropy** loss function for a binary classification problem.**categorical_crossentropy**is meant for multi-class classification problems where each example can belong to one of several classes.\r\n\r\nFor a binary classification problem with only two classes (0 or 1), binary_crossentropy is the appropriate loss function to use.\r\n\r\n```\r\nimport tensorflow as tf\r\n\r\ny_true = tf.constant([[1.]])\r\ny_pred = tf.constant([[1.]])\r\n\r\nloss = tf.keras.losses.binary_crossentropy(y_true, y_pred)\r\nprint(loss) # 0.0\r\n```", "@pat749 i think it because limitations of floating-point arithmetic used by computers.\r\n\r\nObtaining a very small non-zero loss value even when the actual and predicted values are the same is not a bug, it is a normal behavior and expected in most cases.", "@neel-jotaniya That is correct. In many cases, such as in machine learning models, the goal is to minimize the difference between the predicted and actual values, which is usually measured using a loss function. However, it is often impossible to achieve a perfect match between the predicted and actual values, so a very small non-zero loss value is expected and acceptable. This is particularly true for regression problems where the output is a continuous variable.", "This value is due to the epsilon value added here https://github.com/keras-team/keras/blob/bcb0e8bc686df15dbbc905503ec9fc337bbabbb9/keras/backend.py#L7290 \r\nThe small epsilon value is added usually during numerical computation for example when division operation taking place, it's often added to the denominator to prevent a divide by zero error or to avoid overflow/underflow issues.\r\nEpsilon value is very small which is `1e-07 ` and does not create any large impact and can be ignored. Thanks!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "Closing due to lack of recent activity. Please update the issue when new information becomes available, and we will reopen the issue. Thanks!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60113\">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/60113\">No</a>\n" ]

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[ "@MaheshwariAnkit,\r\nI checked the error log and observed that the **Python** library was not being detected which means the issue is with the path setting. \r\nAnd also please make sure that all the required packages which was mentioned in the [setup.py](https://github.com/tensorflow/tensorflow/blob/r2.11/tensorflow/tools/pip_package/setup.py) were installed before the bazel build.\r\n\r\nAnd while installing the [bazel](https://bazel.build/install/windows), please try to follow the mentioned steps. Could you please confirm whether you have followed the instructions mentioned [here](https://www.tensorflow.org/install/source_windows#setup_for_windows) \r\n\r\nBelow are the options from `.bazelrc` file for **AVX** instructions\r\n```\r\n\r\nbazel build --config=opt --copt=-march=native\r\nbuild:avx_win --copt=/arch=AVX\r\nbuild:avx2_win --copt=/arch=AVX2\r\n\r\nbuild:native_arch_linux --copt=-march=native\r\n```", "Python path configuration -\r\n\r\n\r\n\r\n\r\nconfiguration.py \r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nError after bazel run- \r\n\r\n\r\nC:\\Users\\Anmaheshwari\\TF_2\\tensorflow>bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=172\r\nINFO: Reading rc options for 'build' from c:\\users\\anmaheshwari\\tf_2\\tensorflow\\.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Options provided by the client:\r\n 'build' options: --python_path=C:/Users/Anmaheshwari/python/python.exe\r\nINFO: Reading rc options for 'build' from c:\\users\\anmaheshwari\\tf_2\\tensorflow\\.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility\r\nINFO: Reading rc options for 'build' from c:\\users\\anmaheshwari\\tf_2\\tensorflow\\.tf_configure.bazelrc:\r\n 'build' options: --action_env PYTHON_BIN_PATH=C:/Users/Anmaheshwari/python/python.exe --action_env PYTHON_LIB_PATH=C:/Users/Anmaheshwari/python/lib/site-packages --python_path=C:/Users/Anmaheshwari/python/python.exe --copt=/d2ReducedOptimizeHugeFunctions --host_copt=/d2ReducedOptimizeHugeFunctions --define=override_eigen_strong_inline=true\r\nINFO: Reading rc options for 'build' from c:\\users\\anmaheshwari\\tf_2\\tensorflow\\.bazelrc:\r\n 'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils\r\nINFO: Found applicable config definition build:short_logs in file c:\\users\\anmaheshwari\\tf_2\\tensorflow\\.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file c:\\users\\anmaheshwari\\tf_2\\tensorflow\\.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:opt in file c:\\users\\anmaheshwari\\tf_2\\tensorflow\\.tf_configure.bazelrc: --copt=/arch:AVX --host_copt=/arch:AVX\r\nINFO: Found applicable config definition build:windows in file c:\\users\\anmaheshwari\\tf_2\\tensorflow\\.bazelrc: --copt=/W0 --host_copt=/W0 --copt=/Zc:__cplusplus --host_copt=/Zc:__cplusplus --copt=/D_USE_MATH_DEFINES --host_copt=/D_USE_MATH_DEFINES --features=compiler_param_file --copt=/d2ReducedOptimizeHugeFunctions --host_copt=/d2ReducedOptimizeHugeFunctions --cxxopt=/std:c++17 --host_cxxopt=/std:c++17 --config=monolithic --copt=-DWIN32_LEAN_AND_MEAN --host_copt=-DWIN32_LEAN_AND_MEAN --copt=-DNOGDI --host_copt=-DNOGDI --copt=/Zc:preprocessor --host_copt=/Zc:preprocessor --linkopt=/DEBUG --host_linkopt=/DEBUG --linkopt=/OPT:REF --host_linkopt=/OPT:REF --linkopt=/OPT:ICF --host_linkopt=/OPT:ICF --verbose_failures --features=compiler_param_file\r\nINFO: Found applicable config definition build:monolithic in file c:\\users\\anmaheshwari\\tf_2\\tensorflow\\.bazelrc: --define framework_shared_object=false --define tsl_protobuf_header_only=false --experimental_link_static_libraries_once=false\r\nINFO: Repository local_execution_config_python instantiated at:\r\n C:/users/anmaheshwari/tf_2/tensorflow/WORKSPACE:15:14: in <toplevel>\r\n C:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:962:19: in workspace\r\n C:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:96:27: in _tf_toolchains\r\n C:/users/anmaheshwari/tf_2/tensorflow/tensorflow/tools/toolchains/remote_config/configs.bzl:6:28: in initialize_rbe_configs\r\n C:/users/anmaheshwari/tf_2/tensorflow/tensorflow/tools/toolchains/remote_config/rbe_config.bzl:158:27: in _tensorflow_local_config\r\nRepository rule local_python_configure defined at:\r\n C:/users/anmaheshwari/tf_2/tensorflow/third_party/py/python_configure.bzl:279:41: in <toplevel>\r\nINFO: Repository local_config_python instantiated at:\r\n C:/users/anmaheshwari/tf_2/tensorflow/WORKSPACE:15:14: in <toplevel>\r\n C:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:962:19: in workspace\r\n C:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:106:21: in _tf_toolchains\r\nRepository rule python_configure defined at:\r\n C:/users/anmaheshwari/tf_2/tensorflow/third_party/py/python_configure.bzl:298:35: in <toplevel>\r\nERROR: An error occurred during the fetch of repository 'local_execution_config_python':\r\n Traceback (most recent call last):\r\n File \"C:/users/anmaheshwari/tf_2/tensorflow/third_party/py/python_configure.bzl\", line 212, column 22, in _create_local_python_repository\r\n _check_python_bin(repository_ctx, python_bin)\r\n File \"C:/users/anmaheshwari/tf_2/tensorflow/third_party/py/python_configure.bzl\", line 145, column 25, in _check_python_bin\r\n auto_config_fail(\"--define %s='%s' is not executable. Is it the python binary?\" % (\r\n File \"C:/users/anmaheshwari/tf_2/tensorflow/third_party/remote_config/common.bzl\", line 12, column 9, in auto_config_fail\r\n fail(\"%sConfiguration Error:%s %s\\n\" % (red, no_color, msg))\r\nError in fail: Configuration Error: --define PYTHON_BIN_PATH='C:/Users/Anmaheshwari/python/python.exe' is not executable. Is it the python binary?\r\nERROR: C:/users/anmaheshwari/tf_2/tensorflow/WORKSPACE:15:14: fetching local_python_configure rule //external:local_execution_config_python: Traceback (most recent call last):\r\n File \"C:/users/anmaheshwari/tf_2/tensorflow/third_party/py/python_configure.bzl\", line 212, column 22, in _create_local_python_repository\r\n _check_python_bin(repository_ctx, python_bin)\r\n File \"C:/users/anmaheshwari/tf_2/tensorflow/third_party/py/python_configure.bzl\", line 145, column 25, in _check_python_bin\r\n auto_config_fail(\"--define %s='%s' is not executable. Is it the python binary?\" % (\r\n File \"C:/users/anmaheshwari/tf_2/tensorflow/third_party/remote_config/common.bzl\", line 12, column 9, in auto_config_fail\r\n fail(\"%sConfiguration Error:%s %s\\n\" % (red, no_color, msg))\r\nError in fail: Configuration Error: --define PYTHON_BIN_PATH='C:/Users/Anmaheshwari/python/python.exe' is not executable. Is it the python binary?\r\nINFO: Repository stablehlo instantiated at:\r\n C:/users/anmaheshwari/tf_2/tensorflow/WORKSPACE:15:14: in <toplevel>\r\n C:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:965:28: in workspace\r\n C:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:83:14: in _initialize_third_party\r\n C:/users/anmaheshwari/tf_2/tensorflow/third_party/stablehlo/workspace.bzl:11:20: in repo\r\n C:/users/anmaheshwari/tf_2/tensorflow/third_party/repo.bzl:136:21: in tf_http_archive\r\nRepository rule _tf_http_archive defined at:\r\n C:/users/anmaheshwari/tf_2/tensorflow/third_party/repo.bzl:89:35: in <toplevel>\r\nINFO: Repository termcolor_archive instantiated at:\r\n C:/users/anmaheshwari/tf_2/tensorflow/WORKSPACE:15:14: in <toplevel>\r\n C:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:972:21: in workspace\r\n C:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:388:20: in _tf_repositories\r\n C:/users/anmaheshwari/tf_2/tensorflow/third_party/repo.bzl:136:21: in tf_http_archive\r\nRepository rule _tf_http_archive defined at:\r\n C:/users/anmaheshwari/tf_2/tensorflow/third_party/repo.bzl:89:35: in <toplevel>\r\nINFO: Repository eigen_archive instantiated at:\r\n C:/users/anmaheshwari/tf_2/tensorflow/WORKSPACE:15:14: in <toplevel>\r\n C:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:965:28: in workspace\r\n C:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:65:11: in _initialize_third_party\r\n C:/users/anmaheshwari/tf_2/tensorflow/third_party/eigen3/workspace.bzl:14:20: in repo\r\n C:/users/anmaheshwari/tf_2/tensorflow/third_party/repo.bzl:136:21: in tf_http_archive\r\nRepository rule _tf_http_archive defined at:\r\n C:/users/anmaheshwari/tf_2/tensorflow/third_party/repo.bzl:89:35: in <toplevel>\r\nINFO: Repository cpuinfo instantiated at:\r\n C:/users/anmaheshwari/tf_2/tensorflow/WORKSPACE:15:14: in <toplevel>\r\n C:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:972:21: in workspace\r\n C:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:166:20: in _tf_repositories\r\n C:/users/anmaheshwari/tf_2/tensorflow/third_party/repo.bzl:136:21: in tf_http_archive\r\nRepository rule _tf_http_archive defined at:\r\n C:/users/anmaheshwari/tf_2/tensorflow/third_party/repo.bzl:89:35: in <toplevel>\r\nINFO: Repository sobol_data instantiated at:\r\n C:/users/anmaheshwari/tf_2/tensorflow/WORKSPACE:15:14: in <toplevel>\r\n C:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:965:28: in workspace\r\n C:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:82:15: in _initialize_third_party\r\n C:/users/anmaheshwari/tf_2/tensorflow/third_party/sobol_data/workspace.bzl:6:20: in repo\r\n C:/users/anmaheshwari/tf_2/tensorflow/third_party/repo.bzl:136:21: in tf_http_archive\r\nRepository rule _tf_http_archive defined at:\r\n C:/users/anmaheshwari/tf_2/tensorflow/third_party/repo.bzl:89:35: in <toplevel>\r\nINFO: Repository go_sdk instantiated at:\r\n C:/users/anmaheshwari/tf_2/tensorflow/WORKSPACE:23:14: in <toplevel>\r\n C:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace0.bzl:135:20: in workspace\r\n C:/users/anmaheshwari/_bazel_anmaheshwari/unnl57ya/external/com_github_grpc_grpc/bazel/grpc_extra_deps.bzl:36:27: in grpc_extra_deps\r\n C:/users/anmaheshwari/_bazel_anmaheshwari/unnl57ya/external/io_bazel_rules_go/go/private/sdk.bzl:431:28: in go_register_toolchains\r\n C:/users/anmaheshwari/_bazel_anmaheshwari/unnl57ya/external/io_bazel_rules_go/go/private/sdk.bzl:130:21: in go_download_sdk\r\nRepository rule _go_download_sdk defined at:\r\n C:/users/anmaheshwari/_bazel_anmaheshwari/unnl57ya/external/io_bazel_rules_go/go/private/sdk.bzl:117:35: in <toplevel>\r\nERROR: Analysis of target '//tensorflow/tools/pip_package:build_pip_package' failed; build aborted: Configuration Error: --define PYTHON_BIN_PATH='C:/Users/Anmaheshwari/python/python.exe' is not executable. Is it the python binary?\r\nINFO: Elapsed time: 1.792s\r\nINFO: 0 processes.\r\nFAILED: Build did NOT complete successfully (34 packages loaded, 194 targets configured)\r\n currently loading: @llvm-project//llvm ... (11 packages)\r\n Fetching @boringssl; fetching\r\n Fetching @com_google_absl; fetching\r\n Fetching @libjpeg_turbo; fetching\r\n Fetching @png; fetching\r\n Fetching ...ingssl; Extracting C:/users/anmaheshwari/_bazel_anmaheshwari/unnl57ya/external/boringssl/temp13612522096612888484/c00d7ca810e93780bd0c8ee4eea28f4f2ea4bcdc\\\r\n.tar.gz\r\n\r\nC:\\Users\\Anmaheshwari\\TF_2\\tensorflow>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "Any update?", "Any update?", "any update?\r\n", "Hi @AIML-ankit, the error is due to incorrect Python path set up. Please try following commands, also set Python path in Environmental Variable\r\n\r\nSet your PATH \r\n\r\nset PATH=C:/Tools/bazel\r\nset PATH=path/to/python_virtualenv/Scripts\r\nset PATH=C:/Python310/Scripts\r\nset PATH=C:/Python310\r\n\r\nPython \r\nset PYTHON_BIN_PATH=path/to/python_virtualenv/Scripts/python.exe\r\nset PYTHON_LIB_PATH=path/to/python virtualenv/lib/site-packages\r\nset PYTHON_DIRECTORY=path/to/python_virtualenv/Scripts\r\n", "Hi @MaheshwariAnkit ,\r\n\r\nLooking at the log it seems the error is due to python path. Please refer to the above [comment](https://github.com/tensorflow/tensorflow/issues/60112#issuecomment-1498180351) in setting path and let us know if it helps.\r\n\r\nThank you!\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60112\">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/60112\">No</a>\n" ]

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[ "> In github repository u can find CI(continious integrity). It contains pipelines in workflows directory used for build, run, test and deploy of tensorflow code for projects. For windows you have '**windows-ci.yml**' pipelines which uses visual studio to build and run. For mac you have there is '**macos-ci.yml**' For linu7x it is '**linux-ci.yml**'\r\n> \r\n> Now to build and test pipeline used is '**nightly-pip-package.yml**' For tensorflow doc we use '**docs-presubmit.yml**' To buld, run and release the packages we use '**release.yml**' You can look over many pipelines in workflows directory.\r\n\r\nThank you very much!", "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/60111\">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/60111\">No</a>\n", "> In github repository u can find CI(continious integrity). It contains pipelines in workflows directory used for build, run, test and deploy of tensorflow code for projects. For windows you have '**windows-ci.yml**' pipelines which uses visual studio to build and run. For mac you have there is '**macos-ci.yml**' For linu7x it is '**linux-ci.yml**'\r\n> \r\n> Now to build and test pipeline used is '**nightly-pip-package.yml**' For tensorflow doc we use '**docs-presubmit.yml**' To buld, run and release the packages we use '**release.yml**' You can look over many pipelines in workflows directory.\r\n\r\n\r\nHi! @avinashrajavarapu , Can you tell me the difference between CI that runs on GitHub Actions and CI that runs on Jenkins? Or, when is a full CI run, and when is a partial CI run? What determines this? Thank you very much.\r\n" ]

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[ "CC @reedwm." ]

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[ "The error in the given code is that the PyTorch version specified in the pip install command does not exist. The correct version for CUDA 11.1 (cu111) is **torch==1.9.1+cu111** , not **torch==2.0.0+cu118**. The correct pip install command should be:\r\n```\r\npip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/cu111/torch_stable.html\r\n```\r\nAdditionally, if running TensorFlow in a Docker container, you will need to ensure that both TensorFlow and PyTorch are installed inside the container. You can do this by creating a Dockerfile with the following contents:\r\n\r\n```\r\nFROM tensorflow/tensorflow:2.12.0-gpu\r\nRUN pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/cu111/torch_stable.html\r\n```\r\nThen, build the Docker image using the command **docker build -t my_image .** and run it with **docker run -it my_image**.", "Hi @pat749, when I run nvidia-smi from within the tensorflow/tensorflow:2.12.0-gpu docker image, the docker image is using CUDA version 11.8, not 11.1. Therefore I think torch 2.0+cu118 is correct. That package does indeed exist as it raises no complaints when pip installing, nor when importing torch if TF is not already imported.", "Hi @TortoiseHam, Thanks for reporting the issue!\r\nI was able to replicate the issue in Ubuntu 20.04. Kindly find the screenshot below.\r\n\r\n\r\nThank you!", "@SuryanarayanaY Could you please look into this issue? ", "@TortoiseHam , Tested build configuration for Tensorflow 2.12 is cuDNN 8.6 and CUDA 11.8. Make sure while installing torch also this should not conflict.\r\nAlso. could you please do one more check by creating two different environments and installing `torch` and `Tensorflow` separately and check the internal package dependency versions using `pip list`.\r\nMy hunch is that it might be because of version conflicts on package dependencies.", "Sure @sachinprasadhs, so starting with the tensorflow docker image to get Cuda 11.8, I then run `pip freeze | xargs pip uninstall -y` to clear all the existing dependencies, then `pip install tensorflow`. This gives the following dependency list:\r\n\r\n```\r\nPackage Version\r\n---------------------------- ---------\r\nabsl-py 1.4.0\r\nastunparse 1.6.3\r\ncachetools 5.3.0\r\ncertifi 2022.12.7\r\ncharset-normalizer 3.1.0\r\nflatbuffers 23.3.3\r\ngast 0.4.0\r\ngoogle-auth 2.17.1\r\ngoogle-auth-oauthlib 1.0.0\r\ngoogle-pasta 0.2.0\r\ngrpcio 1.53.0\r\nh5py 3.8.0\r\nidna 3.4\r\nimportlib-metadata 6.1.0\r\njax 0.4.8\r\nkeras 2.12.0\r\nlibclang 16.0.0\r\nMarkdown 3.4.3\r\nMarkupSafe 2.1.2\r\nml-dtypes 0.0.4\r\nnumpy 1.23.5\r\noauthlib 3.2.2\r\nopt-einsum 3.3.0\r\npackaging 23.0\r\npip 23.0.1\r\nprotobuf 4.22.1\r\npyasn1 0.4.8\r\npyasn1-modules 0.2.8\r\nrequests 2.28.2\r\nrequests-oauthlib 1.3.1\r\nrsa 4.9\r\nscipy 1.10.1\r\nsetuptools 67.6.0\r\nsix 1.16.0\r\ntensorboard 2.12.1\r\ntensorboard-data-server 0.7.0\r\ntensorboard-plugin-wit 1.8.1\r\ntensorflow 2.12.0\r\ntensorflow-estimator 2.12.0\r\ntensorflow-io-gcs-filesystem 0.32.0\r\ntermcolor 2.2.0\r\ntyping_extensions 4.5.0\r\nurllib3 1.26.15\r\nWerkzeug 2.2.3\r\nwheel 0.40.0\r\nwrapt 1.14.1\r\nzipp 3.15.0\r\n```\r\n\r\nIf I then delete everything and install torch I get:\r\n```\r\nPackage Version\r\n------------------ ------------\r\ncertifi 2022.12.7\r\ncharset-normalizer 3.1.0\r\ncmake 3.26.1\r\nfilelock 3.10.7\r\nidna 3.4\r\nJinja2 3.1.2\r\nlit 16.0.0\r\nMarkupSafe 2.1.2\r\nmpmath 1.3.0\r\nnetworkx 3.0\r\nnumpy 1.24.2\r\nPillow 9.4.0\r\npip 23.0.1\r\nrequests 2.28.2\r\nsetuptools 67.6.0\r\nsympy 1.11.1\r\ntorch 2.0.0+cu118\r\ntorchaudio 2.0.1+cu118\r\ntorchvision 0.15.1+cu118\r\ntriton 2.0.0\r\ntyping_extensions 4.5.0\r\nurllib3 1.26.15\r\nwheel 0.40.0\r\n```\r\n\r\nIf I delete everything, then install TF followed by Torch I get:\r\n```\r\nPackage Version\r\n---------------------------- ------------\r\nabsl-py 1.4.0\r\nastunparse 1.6.3\r\ncachetools 5.3.0\r\ncertifi 2022.12.7\r\ncharset-normalizer 3.1.0\r\ncmake 3.26.1\r\nfilelock 3.10.7\r\nflatbuffers 23.3.3\r\ngast 0.4.0\r\ngoogle-auth 2.17.1\r\ngoogle-auth-oauthlib 1.0.0\r\ngoogle-pasta 0.2.0\r\ngrpcio 1.53.0\r\nh5py 3.8.0\r\nidna 3.4\r\nimportlib-metadata 6.1.0\r\njax 0.4.8\r\nJinja2 3.1.2\r\nkeras 2.12.0\r\nlibclang 16.0.0\r\nlit 16.0.0\r\nMarkdown 3.4.3\r\nMarkupSafe 2.1.2\r\nml-dtypes 0.0.4\r\nmpmath 1.3.0\r\nnetworkx 3.0\r\nnumpy 1.23.5\r\noauthlib 3.2.2\r\nopt-einsum 3.3.0\r\npackaging 23.0\r\nPillow 9.4.0\r\npip 23.0.1\r\nprotobuf 4.22.1\r\npyasn1 0.4.8\r\npyasn1-modules 0.2.8\r\nrequests 2.28.2\r\nrequests-oauthlib 1.3.1\r\nrsa 4.9\r\nscipy 1.10.1\r\nsetuptools 67.6.0\r\nsix 1.16.0\r\nsympy 1.11.1\r\ntensorboard 2.12.1\r\ntensorboard-data-server 0.7.0\r\ntensorboard-plugin-wit 1.8.1\r\ntensorflow 2.12.0\r\ntensorflow-estimator 2.12.0\r\ntensorflow-io-gcs-filesystem 0.32.0\r\ntermcolor 2.2.0\r\ntorch 2.0.0+cu118\r\ntorchaudio 2.0.1+cu118\r\ntorchvision 0.15.1+cu118\r\ntriton 2.0.0\r\ntyping_extensions 4.5.0\r\nurllib3 1.26.15\r\nWerkzeug 2.2.3\r\nwheel 0.40.0\r\nwrapt 1.14.1\r\nzipp 3.15.0\r\n```\r\n\r\nComparing this list to the first one, the process of installing torch did not change any of the existing tensorflow installed dependencies, but did add cmake, filelock, Jinja2, lit, mpmath, networkx, Pillow, sympy, triton, and torch/torchvision/torchaudio. Given that there doesn't seem to be any dependency version conflict, and that the two frameworks do both work simultaneously if torch is imported before tensorflow, I think this is more likely related to something TF 2.12 is doing the python or cuda environment on import.", "Thanks for your time and effort on detailed investigation. \r\n@angerson, Could you please take a look into this. Thanks!", "PyTorch 1.9 is quite old and may not work along with TF 2.12 that uses Cuda 11.8. When I imported TF 1.12 and then Torch 2.0, which are both compatible with Cuda 11.8 on Colab, I don't see any issue.\r\nhttps://colab.research.google.com/drive/1nsnraqpXqZuM5coBm_Gj8zrn96f9Os4D?usp=sharing\r\n\r\nIf you want to use Pytorch 1.9 with TF, please use TF 2.11 which is compatible with Cuda 11.2 similar to PyTorch 1.9", "Hi @sampathweb, this issue is not related to torch 1.9 - that was an extraneous comment. The issue is related to TF 1.12 + Torch 2.0. It's very interesting that it seems to work inside your colab notebook since it doesn't work within the TF docker container. Any idea what the notebook might be doing that running raw within docker doesn't?", "Thanks for confirming. I am able to see the issue in Docker Container and reproduced the hanging issue with TF 2.12 and Torch 2.0. \r\n\r\n```\r\n# docker pull tensorflow/tensorflow:2.12.0 also has same issue\r\ndocker pull tensorflow/tensorflow:2.12.0-gpu\r\ndocker run -it tensorflow/tensorflow:2.12.0-gpu\r\n\r\n# Install Torch 2.0\r\n# GPU wheel URL also has same issue - https://download.pytorch.org/whl/cu118\r\npip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu\r\n\r\n# As stated by the user, importing torch before TF works fine.\r\npython\r\nimport torch # works\r\nimport tensorflow # works\r\n\r\npython\r\nimport tensorflow # Works\r\nimport torch # hangs\r\n\r\n# Hangs here -\r\n File \"/usr/local/lib/python3.8/dist-packages/torch/__init__.py\", line 229, in <module>\r\n from torch._C import * # noqa: F403\r\n```\r\nThis problem is not there in Colab which has both TF 2.12 and Torch 2.0\r\n@angerson - FYI", "The hang comes from ABI incompatibility between TF and Torch. Pytorch has compiled their wheels with `_GLIBCXX_USE_CXX11_ABI=0` (which is out-of-date). TensorFlow upgraded to `_GLIBCXX_USE_CXX11_ABI=1` with the release of [TensorFlow 2.9](https://github.com/tensorflow/tensorflow/releases/tag/v2.9.0). TF 2.12 + Torch 2.0 is the first pairing where this happens---I am not sure why. TF 2.11 + Torch 2.0, and TF 2.12 + Torch 1.13, are both fine.\r\n\r\nAccording to `auditwheel inspect`, torch 1.13 -> 2.0 adds a dependency on libgomp.so.1, which seems innocuous. TF 2.11 -> 2.12 added a new shared object dependency on libtensorflow_cc.so.2 with a version called \"tensorflow.\" It is possible that this was unintended and ultimately exposed this particular breakage; I am not sure. @vam-google and @learning-to-play may be interested in this.\r\n\r\nYou can verify that CXX11 ABI compatibility is (a) problem by checking a wheel compiled explicitly with the CXX11 ABI enabled. I'm exceedingly grateful for https://github.com/pytorch/builder/pull/990, which added CXX11-compatible wheels to Pytorch's CI system, and you can install them both and compare:\r\n\r\n```\r\ndocker run -it --rm python:3.10 bash\r\nwget https://download.pytorch.org/whl/cpu/torch-2.0.0%2Bcpu-cp310-cp310-linux_x86_64.whl\r\nwget https://download.pytorch.org/whl/cpu-cxx11-abi/torch-2.0.0%2Bcpu.cxx11.abi-cp310-cp310-linux_x86_64.whl\r\npip install tensorflow-cpu\r\npip install torch-2.0.0+cpu.cxx11.abi-cp310-cp310-linux_x86_64.whl\r\npython -c \"import tensorflow; import torch\"\r\npip uninstall torch\r\npip install torch-2.0.0+cpu-cp310-cp310-linux_x86_64.whl\r\npython -c \"import tensorflow; import torch\"\r\n```\r\n\r\nThe final command hangs, as expected (you can kill it with Ctrl-D).\r\n\r\n@MichaelHudgins FYI\r\n\r\nJust to be clear, the TF team's stance on this is that it's an issue that needs to be resolved in PyTorch's builds.", "Also FYI @perfinion ", "> The hang comes from ABI incompatibility between TF and Torch. Pytorch has compiled their wheels with `_GLIBCXX_USE_CXX11_ABI=0` (which is out-of-date). TensorFlow upgraded to `_GLIBCXX_USE_CXX11_ABI=1` with the release of [TensorFlow 2.9](https://github.com/tensorflow/tensorflow/releases/tag/v2.9.0). TF 2.12 + Torch 2.0 is the first pairing where this happens---I am not sure why. TF 2.11 + Torch 2.0, and TF 2.12 + Torch 1.13, are both fine.\r\n\r\nAlso, do you have any idea why it works well on colab?\r\n", "> Also, do you have any idea why it works well on colab?\r\n\r\nNo, I don't know.", "Colab used to build their own version of the wheels. Unsure if that still happens or not" ]

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[ "@reedwm @kaixih Could you please review? Thanks!\r\n\r\n@kaixih Do you know if CudnnRnn has unit tests?", "Yes, the `cudnn_rnn_ops_test.cc` is one C++ test which needs bazel build. And `cudnn_rnn_grad.py` is one python test." ]

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[ "Hey! I wish to confirm if it occurred to you multiple times. I have faced that error, but it resolved when I restarted the colab instance. Please check whether your resources were caped when running this. In case of low disk space, colab does not allow further imports sometimes. Just reopening the instance solves the issue most of the times.", "In case it is still persistent, please instruct me on how to reproduce the error.", "\r\nMore technical log. Note: SetupTools has been updated.\r\n\r\n```\r\n**UserWarning**: The version specified ('729681c50ce87d0dea774ea8b1f893eacdfccc52\\n') is an invalid version, \r\nthis may not work as expected with newer versions of setuptools, pip, and PyPI. Please see PEP 440 for more details.\r\n\r\n python setup.py egg_info did not run successfully.\r\n exit code: 1\r\n\r\n\r\n###################\r\n# Invalid version #\r\n###################\r\n'729681c50ce87d0dea774ea8b1f893eacdfccc52\\n' is not valid according to PEP 440.\r\n\r\nPlease make sure specify a valid version for your package.\r\nAlso note that future releases of setuptools may halt the build process\r\nif an invalid version is given.\r\n\r\n```", "@18mdemark Could you please confirm if the issue has been resolved after restarting the colab?\r\nThank you!", "Hi, i'm having the same issue, i've already restarted colab, also i tried to install it in a local environment and the same happened", "Oh, if it is a general issue then I can work on it and try to see what's causing the problem.", "@sushreebarsa if you are not working on it then can you please assign the issue to me?", "Good evening,\r\n\r\nSince yesterday I am having the same issue when executing the first cell of the TensorFlow Segmentation tutorial: https://www.tensorflow.org/tutorials/images/segmentation. No issues before yesterday. \r\n\r\nRestarting Colab does not solve the issue for me.\r\n\r\nRegards,\r\nFederico", "Hey, @fedezocco @alecamacho @18mdemark, I guess it is a problem with pip installing git.\r\n\r\nPlease try these commands in order once:\r\n1. `!apt remove git -y` - remove any degraded git module\r\n2. `!apt-get install git -y && git clone https://github.com/tensorflow/examples.git` - Install git first, then clone.\r\n\r\nMeanwhile, I will look at possible problems and create a PR if I am able to solve it.", "@Shreyas-SAS, thanks for the prompt reply. Should I execute 1 and 2 **before**\r\n\r\n!pip install git+https://github.com/tensorflow/examples.git ?\r\n\r\nIf that is what you meant, then I tried and it does not solve the problem. I get the same error message.\r\n\r\nFederico", "> @Shreyas-SAS, thanks for the prompt reply. Should I execute 1 and 2 **before**\n> \n> !pip install git+https://github.com/tensorflow/examples.git ?\n> \n> If that is what you meant, then I tried and it does not solve the problem. I get the same error message.\n> \n> Federico\n\nNo actually there seems to be a problem with installing git using pip. \n\nThe 1st code line is to remove any instance of git if it was not completely installed. \n\n2nd is to first install git using apt and then clone the repo. ", "The second is an alternative to the pip command that you mentioned. ", "I see. So I am executing 1 and 2 instead of the pip command I mentioned. Then, I proceed with the Segmentation tutorial as usual. Now I get the error below\r\n\r\n", "> I see. So I am executing 1 and 2 instead of the pip command I mentioned. Then, I proceed with the Segmentation tutorial as usual. Now I get the error below\n> \n> \n\nOh. It actually worked fine for me yesterday. Please give me some time, I will have to check again if there wasn't any error due to pre-imported tensorflow module in my system, I will revert back as soon as possible after finishing my classes. ", "Hi, if you're cloning https://github.com/tensorflow/examples.git in colab and want to import pix2pix model from the tensorflow_examples' model package, then make sure that you're using the import command from the right path.\r\n\r\nIt seems that the command `from examples.tensorflow_examples.models.pix2pix import pix2pix` doesn't work because the module tensorflow_examples is not in your working directory. You can either change the working directory or can change the import statement.\r\n\r\nFor example, if the examples repository folder is in your working directory, use `from examples.tensorflow_examples.models.pix2pix import pix2pix`. \r\n\r\nI hope this helps!\r\n\r\n<img width=\"1344\" alt=\"image\" src=\"https://user-images.githubusercontent.com/72040194/228700520-956952a9-a976-43b4-95c8-88d74580fc69.png\">\r\n", "> Hi, if you're cloning https://github.com/tensorflow/examples.git in colab and want to import pix2pix model from the tensorflow_examples' model package, then make sure that you're using the import command from the right path.\r\n> \r\n> It seems that the command `from examples.tensorflow_examples.models.pix2pix import pix2pix` doesn't work because the module tensorflow_examples is not in your working directory. You can either change the working directory or can change the import statement.\r\n> \r\n> For example, if the examples repository folder is in your working directory, use `from examples.tensorflow_examples.models.pix2pix import pix2pix`.\r\n> \r\n> I hope this helps!\r\n> \r\n> <img alt=\"image\" width=\"1344\" src=\"https://user-images.githubusercontent.com/72040194/228700520-956952a9-a976-43b4-95c8-88d74580fc69.png\">\r\n\r\nThank you @yuktathapliyal and @Shreyas-SAS, you solved my issues.", "Glad that I could help. Still, I am trying to understand why the pip command isn't working. Is there an OpenSource repo for pip? Someone can tell them about this problem. If it's working fine with other package managers, then there is no problem on TensorFlows side I guess.", "Sorry for responding to the thread late, I've been running into identical issues as Feezocco. That workaround from yuktathapliyal after cloning the repository into my colab instance is working.\r\n\r\nTried to use pip and still nothing, but this will work for my project.\r\n\r\nThanks!", "@18mdemark Thank you for your response!\r\nCould you please confirm if the issue has been fixed and close the ticket?\r\nThank you!", "> \r\n\r\nI'm facing the same problem, after running:\r\n\r\n!apt remove git -y\r\n!apt-get install git -y && git clone https://github.com/tensorflow/examples.git\r\n\r\n**I still get error:**\r\n\r\n```\r\n1 from tensorflow_examples.models.pix2pix import pix2pix\r\n 2 from tensorflow.keras.layers import Input, Conv2DTranspose, Concatenate\r\n 3 \r\n 4 up_stack = [\r\n 5 pix2pix.upsample(512, 3), # 4x4 -> 8x8\r\n\r\nModuleNotFoundError: No module named 'tensorflow_examples'\r\n```\r\n\r\nWhat are we missing?", "> from examples.tensorflow_examples.models.pix2pix import pix2pix\r\n\r\ncan you try once using `from examples.tensorflow_examples.models.pix2pix import pix2pix` and check. \r\nReally sorry for the late reply though.", "`!git clone https://github.com/tensorflow/examples.git`\r\n`import sys\r\nsys.path.append('/content/examples')\r\n`\r\n`import tensorflow_examples as tf_ex\r\n`\r\ni hope it could work!", "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.", "> when running\r\n> \r\n> > _!pip install -q git+https://github.com/tensorflow/examples.git_\r\n> \r\n> I get kicked this error\r\n> \r\n> > error: subprocess-exited-with-error\r\n> \r\n> × python setup.py egg_info did not run successfully. │ exit code: 1 ╰─> See above for output.\r\n> \r\n> note: This error originates from a subprocess, and is likely not a problem with pip. Preparing metadata (setup.py) ... error error: metadata-generation-failed\r\n> \r\n> × Encountered error while generating package metadata. ╰─> See above for output.\r\n> \r\n> note: This is an issue with the package mentioned above, not pip. hint: See above for details.\r\n> \r\n> Anyone know any fixes for this problem?\r\n\r\n\r\n\r\n\r\nhi . How did you fix it\r\n", "> Hey, @fedezocco @alecamacho @18mdemark, I guess it is a problem with pip installing git.\r\n> \r\n> Please try these commands in order once:\r\n> \r\n> 1. `!apt remove git -y` - remove any degraded git module\r\n> 2. `!apt-get install git -y && git clone https://github.com/tensorflow/examples.git` - Install git first, then clone.\r\n> \r\n> Meanwhile, I will look at possible problems and create a PR if I am able to solve it.\r\n\r\nI solved this problem by adding this before installing.", "Same problem here, I can install other packages using pip, the problem is with this package. Could be something about upgrading the python version from 3.7 to 3.10 in colab?", "Hello, @18mdemark! Could you please let us know if this is still an issue for you ? I am able to run the code successfully on colab using TF v2.12. Please find the gist [here](https://colab.research.google.com/gist/sushreebarsa/7477799a916fe51c36cc874744f152c7/60106.ipynb) and confirm the same?\r\n\r\nThank you!\r\n", "> Hello, @18mdemark! Could you please let us know if this is still an issue for you ? I am able to run the code successfully on colab using TF v2.12. Please find the gist [here](https://colab.research.google.com/gist/sushreebarsa/7477799a916fe51c36cc874744f152c7/60106.ipynb) and confirm the same?\r\n> \r\n> Thank you!\r\n\r\nIt works! at least for me.\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further." ]

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[ "> Any numerical differences or conformance tests enabled by this?\r\n\r\nBefore/After patch have numerically identical output for small inputs. The patch fixes a bug where the intermediate values in the softmax calculation exceed std::numeric_limits<float>::max();.\r\nIn short, the patch improves numerical stability in cases of larger input values." ]

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[ "Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/60104/checks?check_run_id=12260883987) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.", "Please always makes PRs from master branch against master branch.\r\n\r\nThe release branches are only updated when we do patch releases and those are limited. Currently, only the latest release is considered for patch release. This means that r2.8 won't be updated anymore." ]

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[ "The error message suggests that there is an issue with converting **None** values to a tensor. Specifically, the error occurs when trying to convert elements of **[1, 1, 1, None, 1]** to a **tensor**. The error message also points to a layer called **conv1_bn** of type **BatchNormalization** that encountered the error.", "@dstansby,\r\n In order to expedite the trouble-shooting process, could you please provide a complete code snippet you are using which helps us to analyse the issue in an effective way. Thank you!\r\n", "The error message suggests that there is an issue with converting elements of [1, 1, 1, None, 1] to a tensor. The presence of None in the list might be causing the problem.\r\n\r\nWithout a complete code example, it's difficult to provide a specific solution. However, based on the information provided, you can try the following general suggestions:\r\n\r\nEnsure that the dimensions of your input data are correctly specified. It seems that there is a None value in the dimensions of the input tensor, which might be causing the issue. If you're using a custom input shape, double check that it's specified correctly.\r\n\r\nIf the None value is expected (for instance, to represent a dynamic batch size), you can try specifying the input shape of your model explicitly to include the None value. For example:\r\n\r\ninput_shape = (None, height, width, channels)\r\ninput_layer = keras.Input(shape=input_shape)\r\n\r\n\r\nIf you're using a custom layer or modifying the behavior of an existing layer, ensure that it properly handles None values in the input shape. You may need to add conditional logic to handle cases when the shape contains None.\r\n\r\nCheck your data preprocessing pipeline to ensure it generates data with the correct shape and type. Ensure that the pipeline doesn't introduce any unexpected None values in the data.\r\n\r\nIf these suggestions don't help, please provide a minimal reproducible example that showcases the issue. This will make it easier to diagnose the problem and offer a specific solution.", "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/60102\">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/60102\">No</a>\n" ]

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[ "@tg2k \r\nSorry for the late reply, As per the documentation, TF v2.12.0 requires 8.6 cuDNN and 11.8 CUDA version.Could you please make sure to follow the instructions mentioned [here](https://www.tensorflow.org/install/pip#windows-wsl2) and check the [tested build configuration](https://www.tensorflow.org/install/source#gpu) as well. Please let us know if it helps?\r\nThank you!", "@tiruk007 Having the same issue. CUDA 11.8 and cuDNN 8.6 ", "The issue you are experiencing could be due to TensorFlow 2.12 not being properly configured for GPU support in your new environment. When you install TensorFlow with pip, it doesn't automatically install the GPU version, which is tensorflow-gpu. Here's how to set up your environment to use TensorFlow with GPU support:\r\npip uninstall tensorflow\r\npip install tensorflow-gpu\r\nbut note that the latest version of tensorflow comes with the gpu installed already in the installation command pip install tensorflow and not pip install tensorflow-gpu. pip install tensorflow-gpu==2.10 and below could be installed like this\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "The issue is still unresolved; when doing `pip install tensorFlow` => it installs tensorflow_intel instead of the GPU, and `tf.config.list_physical_devices('GPU')` returns empty.", "@Monah84 i disagree with that, i have the lastest tensorflow with gpu by installing using pip install tensorflow as seen [here](https://pypi.org/project/tensorflow/) . \r\n\r\nProject description [here](https://pypi.org/project/tensorflow-gpu/)\r\ntensorflow-gpu has been removed. Please install tensorflow instead. The tensorflow package supports GPU accelerated operations via Nvidia CUDA. furthermore, i made this link to show you how to install [cuda](https://medium.com/@soji4u2c/how-to-install-the-nvidia-cuda-driver-12-0-toolkit-cudnn-8-8-1-3-on-wsl2-in-year-2023-23165024dc16) on WSL . what OS are you on? ", "@Adesoji1 \r\n\r\nSo I have to use` WSL`? I am using `Windows 11 + Nvidia RTX 5000`. I installed `cuda 11.8 `+ `cudnn 8.6` + `tensorflow 2.12` as stated on the TensorFlow website. But it is not working. \r\n\r\nNote: it was working before using `cuda 11.2` and ` cudnn 8.1` and `tensorflow_gpu 2.10` normally. When I upgraded the version. tensorflow not seeing the GPU device. ", "@tiruk007 The short answer is this worked.\r\n\r\nLong answer:\r\n\r\nI had been running unintentionally with cudatoolkit>=11.2 and cudnn>=8.1.0. I've swapped to the cudatoolkit>=11.8.0 conda-forge package and the nvidia-cudnn-cu11>=8.6.0.163 pip package.\r\n\r\nI noted that after running this, my /usr/lib/wsl/lib/libcuda* files got overwritten, which is a warning mentioned at [NVidia's WSL User Guide](https://docs.nvidia.com/cuda/wsl-user-guide/index.html) . I re-ran their install instructions, to no apparent effect (same files present afterward). I also applied a Windows-side post-fix that is documented [here](https://github.com/microsoft/WSL/issues/5663#issuecomment-1068499676).\r\n\r\nAt this point I restarted the WSL instance and went back into Jupyter notebooks and swapped between my TF 2.11 and TF 2.12 environments, and the behavior remained the same: TF 2.11 saw the GPUS, while TF 2.12 did not.\r\n\r\nIt turned out I missed the part in the [TF WSL install guide](https://www.tensorflow.org/install/pip#windows-wsl2) where CUDNN_PATH and LD_LIBRARY_PATH get set. I had something alternative to this, which worked for TF 2.11, but does not work for TF 2.12. Once I got those lines into my env_vars.sh, it worked.", "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/60101\">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/60101\">No</a>\n", "@cfields5 @Monah84 \r\nSince the issue was closed, Could you please raise another request from [here](https://github.com/tensorflow/tensorflow/issues/new/choose). It helps us to track and debug the issue in an effective way.\r\n\r\nThank you !", "@Adesoji1 there is no longer a separate tensorflow-gpu package. It's all packaged in tensorflow.\r\n\r\n@Monah84 I just ended up using WSL and it worked like a charm. TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Since I have windows 11, I couldn't use archived versions of NVIDIA's software. Had to go with WSL. Honestly much easier than dealing with windows anyways.", "@cfields5 but note that the latest version of tensorflow comes with the gpu installed already in the installation command pip install tensorflow and not pip install tensorflow-gpu. did you see this earlier?", "> @Adesoji1 there is no longer a separate tensorflow-gpu package. It's all packaged in tensorflow.\n> \n> \n> \n> @Monah84 I just ended up using WSL and it worked like a charm. TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Since I have windows 11, I couldn't use archived versions of NVIDIA's software. Had to go with WSL. Honestly much easier than dealing with windows anyways.\n\nI do agree with you. Actually, I am using now the WSL and it is working perfectly as well, as you said. Even I got more benefits in this way. Some libraries are not available for windows and work with Linux systems now are available for me to use them.\n\nThank you for sharing your feedback", "Is the support upward compatible. I installed tensorflow 2.13.0 on wsl2-ubuntu but gpu was not found. Do i have to downgrade to 2.10", "@ogahozy senior man, I am not sure yet about 2.13 but you can downgrade. Follow this tutorial to help you with the installation https://medium.com/@soji4u2c/how-to-install-the-nvidia-cuda-driver-12-0-toolkit-cudnn-8-8-1-3-on-wsl2-in-year-2023-23165024dc16 ", "Thanks sir\r\n\r\nOn Tue, 8 Aug 2023, 5:50 pm Adesoji Alu, ***@***.***> wrote:\r\n\r\n> @ogahozy <https://github.com/ogahozy> senior man, I am not sure yet about\r\n> 2.13 but you can downgrade. Follow this tutorial to help you with the\r\n> installation\r\n> ***@***.***/how-to-install-the-nvidia-cuda-driver-12-0-toolkit-cudnn-8-8-1-3-on-wsl2-in-year-2023-23165024dc16\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/tensorflow/tensorflow/issues/60101#issuecomment-1669973418>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AFJ7EKUPJPVBNG7VMRLZDOTXUJU5VANCNFSM6AAAAAAWGZ365Y>\r\n> .\r\n> You are receiving this because you were mentioned.Message ID:\r\n> ***@***.***>\r\n>\r\n" ]

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[]

[]

[ "Closing since there won't be any more patches to r2.11 branch" ]

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[ "Hi @nickkchenn ,\r\n\r\nCould you please confirm the command used for build. I am getting the error below because of the {{ extra_options }}. Is it related to the the java environment? If I exclude {{ extra_options }} this then build starts compiling.\r\n\r\nFor me the command `bazel --host_jvm_args=-Djavax.net.ssl.trustStore='/usr/local/java/jre/lib/security/cacerts' --host_jvm_args=-Djavax.net.ssl.trustStorePassword='changeit' build --jobs=16 --copt=\"-mtune=generic\" --copt=\"-march=armv8-a\" --copt=\"-O3\" //tensorflow/tools/pip_package:build_pip_package --verbose_failures` seems working. \r\n\r\nI see from your log the build failed within 76.7 seconds. For me the compiling happened more than 1 hour and then my VM got disconnected and hence can't share the logs.Will try again and share the complete logs.\r\n\r\nIf you can confirm the argument `{{ extra_options }} ` seems stopping me from build and getting build fail error immediately as below.\r\n\r\n```\r\n(tf-bazel) suryanarayanay@ubuntu22-arm:~/tensorflow$ bazel --host_jvm_args=-Djavax.net.ssl.trustStore='/usr/local/java/jre/lib/security/cacerts' --host_jvm_args=-Djavax.net.ssl.trustStorePassword='changeit' build --jobs=16 {{ extra_options }} --copt=\"-mtune=generic\" --copt=\"-march=armv8-a\" --copt=\"-O3\" //tensorflow/tools/pip_package:build_pip_package --verbose_failures\r\nAnother command (pid=1450) is running. Waiting for it to complete on the server (server_pid=1454)...\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=91\r\nINFO: Reading rc options for 'build' from /home/suryanarayanay/tensorflow/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /home/suryanarayanay/tensorflow/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/common,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils\r\nINFO: Found applicable config definition build:short_logs in file /home/suryanarayanay/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /home/suryanarayanay/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:linux in file /home/suryanarayanay/tensorflow/.bazelrc: --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wno-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-unknown-warning --copt=-Wno-array-parameter --copt=-Wno-stringop-overflow --copt=-Wno-array-bounds --copt=-Wunused-result --copt=-Werror=unused-result --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --distinct_host_configuration=false --experimental_guard_against_concurrent_changes\r\nINFO: Found applicable config definition build:dynamic_kernels in file /home/suryanarayanay/tensorflow/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/tensorflow/runtime/archive/4ce3e4da2e21ae4dfcee9366415e55f408c884ec.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nERROR: Skipping '}}': no such target '//:}}': target '}}' not declared in package '' defined by /home/suryanarayanay/tensorflow/BUILD\r\nERROR: no such target '//:}}': target '}}' not declared in package '' defined by /home/suryanarayanay/tensorflow/BUILD\r\nINFO: Elapsed time: 1009.651s\r\nINFO: 0 processes.\r\nFAILED: Build did NOT complete successfully (0 packages loaded)\r\n```\r\n\r\n\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60099\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60099\">No</a>\n", "Attaching the logs below which overrides the error reported and starts compiling.\r\n\r\n```\r\n(tf-bazel) suryanarayanay@ubuntu22-arm:~/tensorflow$ bazel --host_jvm_args=-Djavax.net.ssl.trustStore='/usr/local/java/jre/lib/security/cacerts' --host_jvm_args=-Djavax.net.ssl.trustStorePassword='changeit' build --jobs=16 --copt=\"-mtune=generic\" --copt=\"-march=armv8-a\" --copt=\"-O3\" //tensorflow/tools/pip_package:build_pip_package --verbose_failures\r\nStarting local Bazel server and connecting to it...\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=100\r\nINFO: Reading rc options for 'build' from /home/suryanarayanay/tensorflow/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /home/suryanarayanay/tensorflow/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/common,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils\r\nINFO: Found applicable config definition build:short_logs in file /home/suryanarayanay/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /home/suryanarayanay/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:linux in file /home/suryanarayanay/tensorflow/.bazelrc: --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wno-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-unknown-warning --copt=-Wno-array-parameter --copt=-Wno-stringop-overflow --copt=-Wno-array-bounds --copt=-Wunused-result --copt=-Werror=unused-result --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --distinct_host_configuration=false --experimental_guard_against_concurrent_changes\r\nINFO: Found applicable config definition build:dynamic_kernels in file /home/suryanarayanay/tensorflow/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS\r\nINFO: Analyzed target //tensorflow/tools/pip_package:build_pip_package (542 packages loaded, 30547 targets configured).\r\nINFO: Found 1 target...\r\nINFO: Deleting stale sandbox base /home/suryanarayanay/.cache/bazel/_bazel_suryanarayanay/e93ef42332b258ebf8397106d59e39e3/sandbox\r\n[11,362 / 11,465] 16 actions, 4 running\r\n Compiling tensorflow/compiler/xla/service/hlo_instruction.cc; 13s local\r\n Compiling tensorflow/compiler/xla/literal.cc; 12s local\r\n Compiling tensorflow/core/protobuf/tpu/optimization_parameters.pb.cc; 3s local\r\n Compiling tensorflow/compiler/xla/comparison_util.cc; 1s local\r\n [Sched] Compiling tensorflow/compiler/xla/service/hlo_module.cc; 18s\r\n [Sched] Compiling tensorflow/core/ops/tpu_cross_replica_ops.cc; 16s\r\n```\r\n" ]

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[ "Is there any more for me to do here?" ]

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[]

[ "Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/60096/checks?check_run_id=12243040322) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.", "You have successfully added a new Scorecard configuration `supply-chain/branch-protection`. As part of the setup process, we have scanned this repository and found no existing alerts. In the future, you will see all code scanning alerts on the repository [Security tab](/tensorflow/tensorflow/security/code-scanning?query=pr%3A60096+tool%3AScorecard+is%3Aopen).", "You have successfully added a new Scorecard configuration `supply-chain/local`. As part of the setup process, we have scanned this repository and found 92 existing alerts. Please check the repository [Security tab](/tensorflow/tensorflow/security/code-scanning?query=pr%3A60096+tool%3AScorecard+is%3Aopen) to see all alerts.", "You have successfully added a new Scorecard configuration `supply-chain/online-scm`. As part of the setup process, we have scanned this repository and found 2 existing alerts. Please check the repository [Security tab](/tensorflow/tensorflow/security/code-scanning?query=pr%3A60096+tool%3AScorecard+is%3Aopen) to see all alerts.", "It is also plagiarizing https://github.com/tensorflow/tensorflow/pull/57019" ]

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[ "Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/60094/checks?check_run_id=12242346983) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request." ]

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[ "Hey @trickiwoo , \r\n\r\nYes, rounding functions have a finite range and hence gradient zero. Although there are approximations available (refer [here](https://stackoverflow.com/questions/46596636/differentiable-round-function-in-tensorflow)), I don't think that's what documentation would be referring to. \r\n\r\n@sushreebarsa If you think this analysis is correct, please let me know. I will raise a PR correcting it", "@sushreebarsa the problem is we are registering gradient for round function in registry https://github.com/tensorflow/tensorflow/blob/f46ff5e9cfff71a4de98a8aac79d44df27df48f4/tensorflow/python/ops/math_grad.py#L1812 \r\nAnd the logic to add documentation is whether a op is present in registry or not\r\nhttps://github.com/tensorflow/tensorflow/blob/f46ff5e9cfff71a4de98a8aac79d44df27df48f4/tensorflow/tools/docs/generate2.py#L143 \r\n\r\nJust need to be sure this is the only use case of registering gradients", "@trickiwoo Thank you for raising an issue!\r\nCould you please refer to the comments above and let us know if you want these changes in the documentation?\r\n@mayankagarwals Thank you for your response here. \r\n\r\nAs per my understanding, some tf.Operations are registered as being non-differentiable and will return None. Others have no gradient registered.\r\n\r\nThe tf.raw_ops page shows which low-level ops have gradients registered.\r\n\r\nIf you attempt to take a gradient through a float op that has no gradient registered the tape will throw an error instead of silently returning None. This way you know something has gone wrong.\r\n\r\nFor example, the tf.image.adjust_contrast function wraps raw_ops.AdjustContrastv2, which could have a gradient but the gradient is not implemented.\r\n\r\nThank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "@sushreebarsa @mayankagarwals Thank you for the comments! I didn't know that \"has gradient\" means \"has gradient registered\" but it can still be non-differentiable. I think it would be helpful to make it clear in the document, or even better, only mark the differentiable ops as \"has gradient\" (not sure if that can be done easily though).\r\n\r\nFurther, I wonder why register a `None` gradient (say for `round`) instead of don't register at all? In my understanding, it is better to throw an error for the non-differentiable operator, instead of silently returning None for the whole computation.", "Hi registering gradient to return None for `Round` to silently return None instead of throwing error when doing gradient computation.\r\n```\r\n@ops.RegisterGradient(\"Round\") \r\ndef _RoundGrad(_, unused_grad): \r\n return [None]\r\n```", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60093\">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/60093\">No</a>\n" ]

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[ "@sachinprasadhs,\r\nI tried to execute the mentioned code on tensoflow v2.9 and nightly & it was failing due to `No OpKernel was registered` error. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/22ac50ef65e150184ddce505c2bccaba/invalidargumenterror_unsupported_data_type_for_tpu_string.ipynb).", "@tilakrayal \r\nThat is because, you're trying to initiate tpu-vm from colab. Colab only provides tpu-node, which initialization process is bit different. And as it's mentioned, I've used tpu-vm (on kaggle) and not tpu-node. Node requires to have data on GCP bucket or something and doesn't work on local file system but tpu-vm does. \r\n\r\nHowever, the actual issue can be reproduced in any system.", "cc @mattdangerw any thoughts?", "From TPU [doc](https://cloud.google.com/tpu/docs/troubleshooting/trouble-tf)\r\n\r\n> Currently, only the tf.float32, tf.int32, tf.bfloat16, and tf.bool data types are supported on the TPU. Other common data types, such as tf.uint8, tf.string, and tf.int64, must be converted to one of the supported data types during data pre-processing (that is, in the tf.data.Dataset pipeline).\r\n\r\nMaybe excluding tokenizer from model (`tfst_model`) and add that to data loader with uniform padding, so that TPU can digest! \r\n\r\n---\r\n\r\n**update**\r\n\r\nSolved with above approach.", "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/60092\">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/60092\">No</a>\n" ]

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[ "To fix this, the expected output of the **_update** function should be printed before and after the interpreter is invoked, and the output of the interpreter should be captured and printed. This can be done using the following code:\r\n\r\n```\r\nargs = jnp.ones(2)\r\n\r\nexpected = _update(args)\r\nprint(\"Expected output:\", expected)\r\n\r\ninterpreter.set_tensor(input_details[0]['index'], args)\r\ninterpreter.invoke()\r\n\r\noutput = interpreter.get_tensor(output_details[0]['index'])\r\nprint(\"Output:\", output)\r\n```", "> To fix this, the expected output of the **_update** function should be printed before and after the interpreter is invoked, and the output of the interpreter should be captured and printed. This can be done using the following code:\r\n> \r\n> ```\r\n> args = jnp.ones(2)\r\n> \r\n> expected = _update(args)\r\n> print(\"Expected output:\", expected)\r\n> \r\n> interpreter.set_tensor(input_details[0]['index'], args)\r\n> interpreter.invoke()\r\n> \r\n> output = interpreter.get_tensor(output_details[0]['index'])\r\n> print(\"Output:\", output)\r\n> ```\r\n\r\nNope, still got the same error (the same happens for tf 2.12)\r\n\r\nhttps://colab.research.google.com/drive/1VGVrdBBqN_0qrvZC_w99ZxVTJqp6_9Nt?usp=sharing\r\n", "PS\r\n\r\nThe same functionality can one implemented with `jax.nn.one_hot` and this works. So clearly the issue is with at.\r\n\r\n@tiruk007 does it make sense to try the path Jax->jax2tf->tf.lite or is this under the hood of `tf.lite.TFLiteConverter.experimental_from_jax` ?\r\n", "@krzysztofrusek \r\nSorry for the late reply.\r\n@pjpratik \r\nCould you please look into this? I was able to reproduce the issue on Colab using TFv2.12. Please find the gist [here](https://colab.research.google.com/gist/tiruk007/09d4f2106e8658d7ad5cc23314706a7c/untitled169.ipynb) for reference.\r\n\r\nThank you !", "Hi @krzysztofrusek\r\n\r\nThe error occurs while we are updating a scalar value and as the error suggests when we use `jp.ones((1,2))` and try to update `x.at[0].set[4]`, it runs without any error. Please find the [gist](https://colab.research.google.com/gist/pjpratik/0ef750b5a7b394c700ad42770ebac4a3/60090.ipynb) here. \r\n\r\n@sachinprasadhs Could you please check if this is intended behaviour? \r\n\r\nThanks.", "Hi @pjpratik thanks for the hint. This is some workaround.", "Hi @krzysztofrusek \r\n\r\nThe latest nightly TF 2.14.0-dev20230716 says\r\n```\r\nTFLiteConverterV2.experimental_from_jax (from tensorflow.lite.python.lite) is deprecated and will be removed in a future version.\r\nInstructions for updating:\r\nUse `jax2tf.convert` and (`lite.TFLiteConverter.from_saved_model` or `lite.TFLiteConverter.from_concrete_functions`) instead.\r\n```\r\nSo the right way is to jax->jax2tf->lite moving forward.\r\n\r\nPlease find the resolved [gist](https://colab.research.google.com/gist/pjpratik/b9c36fbf4630d220768454e6262cc249/60090.ipynb) for the same.\r\n\r\nThanks. ", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60090\">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/60090\">No</a>\n" ]

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[ "I debugged this and the issue seems to be here:\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/10b6eaaf2c97ed0824002866d3af672679760e14/tensorflow/compiler/mlir/lite/flatbuffer_import.cc#L557-L567\r\n\r\nNotice that the static variable `stateful_variable_idx` gets incremented for every call to `GetSplat`. This is causing the `InputCellState` for the `tfl.unidirectional_sequence_lstm` op to be a tensor of ones instead of zeros. Removing the `++` resolves the issue without failing any tests. However, I'm not sure why this was being done. Does anyone know?\r\n\r\nIt looks like the relevant code was added via https://github.com/tensorflow/tensorflow/commit/b6a59d53d359c027844fd27f7e4494a1667c0994.\r\n" ]

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[ "Attached the toy model that you can use to reproduce this issue.\r\n\r\n[toy_tflite_model_20230308.tflite.zip](https://github.com/tensorflow/tensorflow/files/11054748/toy_tflite_model_20230308.tflite.zip)\r\n", "\r\nHi @heguanyu I was successfully able to build the TFLite framework. Please refer the screenshot here. \r\n\r\n<img width=\"744\" alt=\"Screenshot 2023-03-24 at 9 48 04 PM\" src=\"https://user-images.githubusercontent.com/118897289/227583969-4a39c87e-58c8-424e-b88a-5980cdde7e33.png\">\r\n\r\nI have used `toy_tflite_model_20230308.tflite` which is provided as a toy model rather than `tflite_model_vocab_changes_run_18026681_full_dataset.tflite` which is mentioned in the command which I don't think is an issue.\r\n\r\nCan you try at your end again freshly and let us know if the issue still persists?\r\n\r\nThanks.", "Yes it still persist with a brand new git cloned repo\r\n\r\n\r\n", "@heguanyu Thanks for the information.\r\n\r\n@sachinprasadhs Could you please look into this issue. Thanks.\r\n", "Hi @sachinprasadhs after updating to the latest tensorflow code base, I'm now seeing another different error.. can you please take a look? Thanks\r\n\r\n```\r\nERROR: /private/var/tmp/_bazel_ghe/a40be6b67c18b5da908566b099777def/external/llvm-project/mlir/BUILD.bazel:5691:11: Compiling mlir/lib/Transforms/Utils/OneToNTypeConversion.cpp [for host] failed: (Aborted): wrapped_clang_pp failed: error executing command external/local_config_cc/wrapped_clang_pp '-D_FORTIFY_SOURCE=1' -fstack-protector -fcolor-diagnostics -Wall -Wthread-safety -Wself-assign -fno-omit-frame-pointer -g0 -O2 -DNDEBUG ... (remaining 122 arguments skipped)\r\nexternal/llvm-project/mlir/lib/Transforms/Utils/OneToNTypeConversion.cpp:98:21: error: no template named 'unordered_map' in namespace 'std'\r\n static const std::unordered_map<CastKind, StringRef> castKindNames = {\r\n ~~~~~^\r\n1 error generated.\r\nError in child process '/usr/bin/xcrun'. 1\r\nTarget //tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nINFO: Elapsed time: 275.224s, Critical Path: 36.37s\r\nINFO: 1413 processes: 12 internal, 1401 local.\r\nFAILED: Build did NOT complete successfully\r\n```", "@yishuangP , As per your comment here https://github.com/tensorflow/tensorflow/issues/59853#issuecomment-1481642687, Could you pleas let me know what part of Tensorflow has to be looked into, so that I can assign it to the specific team. Thanks!", "I did a git pull again this morning and the error is back to the `declared output 'tensorflow/libtensorflow_framework.2.dylib' was not created by genrule.`\r\n\r\nI'd updated the python to python3.10 but error persist.\r\n\r\ncan someone help? Thanks\r\n\r\n```\r\n$ bash tensorflow/lite/ios/build_frameworks.sh --input_models=toy_tflite_model_20230308.tflite --target_archs=x86_64,armv7,arm64\r\nStarting local Bazel server and connecting to it...\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=316\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.tf_configure.bazelrc:\r\n 'build' options: --action_env PYTHON_BIN_PATH=/Library/Frameworks/Python.framework/Versions/3.10/bin/python3 --action_env PYTHON_LIB_PATH=/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages --python_path=/Library/Frameworks/Python.framework/Versions/3.10/bin/python3\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.bazelrc:\r\n 'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils\r\nINFO: Found applicable config definition build:short_logs in file /Users/ghe/projects/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /Users/ghe/projects/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:ios in file /Users/ghe/projects/tensorflow/.bazelrc: --apple_platform_type=ios --apple_bitcode=embedded --copt=-fembed-bitcode --copt=-Wno-c++11-narrowing --noenable_platform_specific_config --copt=-w --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --define=with_xla_support=false\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/llvm/llvm-project/archive/c6a132a8023db649a658aeceb2edaf046097dbff.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nINFO: Analyzed target //tensorflow/lite/ios/tmp:TensorFlowLiteC_framework (144 packages loaded, 7613 targets configured).\r\nINFO: Found 1 target...\r\nTarget //tensorflow/lite/ios/tmp:TensorFlowLiteC_framework up-to-date:\r\n bazel-out/applebin_ios-ios_x86_64-opt-ST-74bbdaf492e7/bin/tensorflow/lite/ios/tmp/TensorFlowLiteC_framework.zip\r\nINFO: Elapsed time: 94.481s, Critical Path: 13.92s\r\nINFO: 389 processes: 2 internal, 387 local.\r\nINFO: Build completed successfully, 389 total actions\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=316\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.tf_configure.bazelrc:\r\n 'build' options: --action_env PYTHON_BIN_PATH=/Library/Frameworks/Python.framework/Versions/3.10/bin/python3 --action_env PYTHON_LIB_PATH=/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages --python_path=/Library/Frameworks/Python.framework/Versions/3.10/bin/python3\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.bazelrc:\r\n 'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils\r\nINFO: Found applicable config definition build:short_logs in file /Users/ghe/projects/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /Users/ghe/projects/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:monolithic in file /Users/ghe/projects/tensorflow/.bazelrc: --define framework_shared_object=false --define tsl_protobuf_header_only=false --experimental_link_static_libraries_once=false\r\nINFO: Found applicable config definition build:macos in file /Users/ghe/projects/tensorflow/.bazelrc: --apple_platform_type=macos --copt=-DGRPC_BAZEL_BUILD --copt=-w --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/llvm/llvm-project/archive/c6a132a8023db649a658aeceb2edaf046097dbff.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nINFO: Build options --apple_bitcode, --apple_platform_type, --copt, and 1 more have changed, discarding analysis cache.\r\nINFO: Analyzed target //tensorflow/lite/tools:list_flex_ops_no_kernel_main (7 packages loaded, 1243 targets configured).\r\nINFO: Found 1 target...\r\nTarget //tensorflow/lite/tools:list_flex_ops_no_kernel_main up-to-date:\r\n bazel-bin/tensorflow/lite/tools/list_flex_ops_no_kernel_main\r\nINFO: Elapsed time: 3.091s, Critical Path: 1.62s\r\nINFO: 37 processes: 1 internal, 36 local.\r\nINFO: Build completed successfully, 37 total actions\r\n~/projects/tensorflow/tensorflow/lite/ios/tmp ~/projects/tensorflow\r\n~/projects/tensorflow\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=316\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.tf_configure.bazelrc:\r\n 'build' options: --action_env PYTHON_BIN_PATH=/Library/Frameworks/Python.framework/Versions/3.10/bin/python3 --action_env PYTHON_LIB_PATH=/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages --python_path=/Library/Frameworks/Python.framework/Versions/3.10/bin/python3\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.bazelrc:\r\n 'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils\r\nINFO: Found applicable config definition build:short_logs in file /Users/ghe/projects/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /Users/ghe/projects/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:ios in file /Users/ghe/projects/tensorflow/.bazelrc: --apple_platform_type=ios --apple_bitcode=embedded --copt=-fembed-bitcode --copt=-Wno-c++11-narrowing --noenable_platform_specific_config --copt=-w --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --define=with_xla_support=false\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/llvm/llvm-project/archive/c6a132a8023db649a658aeceb2edaf046097dbff.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nINFO: Build options --apple_bitcode, --apple_platform_type, --copt, and 2 more have changed, discarding analysis cache.\r\nINFO: Analyzed target //tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework (436 packages loaded, 36598 targets configured).\r\nINFO: Found 1 target...\r\nERROR: /Users/ghe/projects/tensorflow/tensorflow/BUILD:1128:21: declared output 'tensorflow/libtensorflow_framework.2.dylib' was not created by genrule. This is probably because the genrule actually didn't create this output, or because the output was a directory and the genrule was run remotely (note that only the contents of declared file outputs are copied from genrules run remotely)\r\nERROR: /Users/ghe/projects/tensorflow/tensorflow/BUILD:1128:21: Executing genrule //tensorflow:libtensorflow_framework.2.dylib_sym [for host] failed: not all outputs were created or valid\r\nrealpath: illegal option -- -\r\nusage: realpath [-q] [path ...]\r\nTarget //tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nINFO: Elapsed time: 845.682s, Critical Path: 65.05s\r\nINFO: 7414 processes: 158 internal, 7256 local.\r\nFAILED: Build did NOT complete successfully\r\n```", "Hey @sachinprasadhs, the error we are getting are from tensorflow/BUILD file. looks like the error occurred when we generate the tensorflow shared library `tensorflow/libtensorflow_framework.2.dylib`. Could you help triage to someone from the tensorflow side? I don't know if this is a build config issue or something changed. Thanks\r\n```\r\ntensorflow/tensorflow/BUILD:1128:21: declared output 'tensorflow/libtensorflow_framework.2.dylib' was not created by genrule. This is probably because the genrule actually didn't create this output, or because the output was a directory and the genrule was run remotely (note that only the contents of declared file outputs are copied from genrules run remotely)\r\n```\r\nAlso @heguanyu, can you try if tesorflow 2.11.0 has this issue? Thanks!", "@nitins17 , Could you please look into this.\r\ncc: @learning-to-play \r\n\r\n> Hey @sachinprasadhs, the error we are getting are from tensorflow/BUILD file. looks like the error occurred when we generate the tensorflow shared library `tensorflow/libtensorflow_framework.2.dylib`. Could you help triage to someone from the tensorflow side? I don't know if this is a build config issue or something changed. Thanks\r\n> \r\n> ```\r\n> tensorflow/tensorflow/BUILD:1128:21: declared output 'tensorflow/libtensorflow_framework.2.dylib' was not created by genrule. This is probably because the genrule actually didn't create this output, or because the output was a directory and the genrule was run remotely (note that only the contents of declared file outputs are copied from genrules run remotely)\r\n> ```\r\n> \r\n> Also @heguanyu, can you try if tesorflow 2.11.0 has this issue? Thanks!\r\n\r\n", "From the logs, it might possibly be a realpath issue I have seen in one of our other builds. \r\n```\r\nrealpath: illegal option -- -\r\nusage: realpath [-q] [path ...]\r\n```\r\n\r\nAre you using the GNU version of realpath? If not, could you [install it](https://formulae.brew.sh/formula/coreutils) and try building again?", "Hi @nitins17 I tried installing coreutils as your suggestion, but not getting luck. Is version 9.2 correct? \r\n\r\n```\r\nINFO: Build options --apple_bitcode, --apple_platform_type, --copt, and 2 more have changed, discarding analysis cache.\r\nINFO: Analyzed target //tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework (437 packages loaded, 36613 targets configured).\r\nINFO: Found 1 target...\r\nERROR: /Users/ghe/projects/tensorflow/tensorflow/BUILD:1128:21: declared output 'tensorflow/libtensorflow_framework.2.dylib' was not created by genrule. This is probably because the genrule actually didn't create this output, or because the output was a directory and the genrule was run remotely (note that only the contents of declared file outputs are copied from genrules run remotely)\r\nERROR: /Users/ghe/projects/tensorflow/tensorflow/BUILD:1128:21: Executing genrule //tensorflow:libtensorflow_framework.2.dylib_sym [for host] failed: not all outputs were created or valid\r\nrealpath: illegal option -- -\r\nusage: realpath [-q] [path ...]\r\nTarget //tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nINFO: Elapsed time: 1345.515s, Critical Path: 91.51s\r\nINFO: 9646 processes: 191 internal, 9455 local.\r\nFAILED: Build did NOT complete successfully\r\nghe-mn2:tensorflow ghe:master]$ brew install coreutils\r\n==> Downloading https://formulae.brew.sh/api/formula.jws.json\r\n##O#-# \r\n==> Downloading https://formulae.brew.sh/api/cask.jws.json\r\n#=#=# \r\nWarning: coreutils 9.2 is already installed and up-to-date.\r\nTo reinstall 9.2, run:\r\n brew reinstall coreutils\r\nghe-mn2:tensorflow ghe:master]$ \r\n```", "Yeah, I believe the latest version should work. Note that the GNU utilities are installed with a 'g' prefix. That is, the gnu version is `grealpath` where as Bazel runs using just `realpath` so it is possible that the macOS version is still getting picked up. Can you try [replacing the macOS utilities with the GNU utilities](https://apple.stackexchange.com/questions/69223/how-to-replace-mac-os-x-utilities-with-gnu-core-utilities)?\r\n\r\nOnce you have replaced the defaults with GNU utilities, run `realpath --help`. If it points to the GNU version, it should print out its usage and other info. The default macOS version just throws an error like the one above in https://github.com/tensorflow/tensorflow/issues/60088#issuecomment-1499766349. ", "> Hey @sachinprasadhs, the error we are getting are from tensorflow/BUILD file. looks like the error occurred when we generate the tensorflow shared library `tensorflow/libtensorflow_framework.2.dylib`. Could you help triage to someone from the tensorflow side? I don't know if this is a build config issue or something changed. Thanks\r\n> \r\n> ```\r\n> tensorflow/tensorflow/BUILD:1128:21: declared output 'tensorflow/libtensorflow_framework.2.dylib' was not created by genrule. This is probably because the genrule actually didn't create this output, or because the output was a directory and the genrule was run remotely (note that only the contents of declared file outputs are copied from genrules run remotely)\r\n> ```\r\n> \r\n> Also @heguanyu, can you try if tesorflow 2.11.0 has this issue? Thanks!\r\n\r\nSorry but no luck when I tried the same command after `git checkout v2.11.0` ", "@nitins17 Thanks for the guide. After installing realpath according to your instruction, previous error was gone, however it now shows another error.. `ModuleNotFoundError: No module named 'packaging'\r\nTarget //tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework failed to build`\r\n\r\n@yishuangP is this specific to SelectTfOps? I'm using v2.11.0. \r\n\r\n```\r\nINFO: Build options --apple_bitcode, --apple_platform_type, --copt, and 2 more have changed, discarding analysis cache.\r\nINFO: Analyzed target //tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework (395 packages loaded, 34048 targets configured).\r\nINFO: Found 1 target...\r\nERROR: /Users/ghe/projects/tensorflow/tensorflow/BUILD:1426:19: Executing genrule //tensorflow:tf_python_api_gen_v2 [for host] failed: (Exit 1): bash failed: error executing command /bin/bash -c ... (remaining 1 argument skipped)\r\nTraceback (most recent call last):\r\n File \"/private/var/tmp/_bazel_ghe/a40be6b67c18b5da908566b099777def/execroot/org_tensorflow/bazel-out/host/bin/tensorflow/create_tensorflow.python_api_tf_python_api_gen_v2.runfiles/org_tensorflow/tensorflow/python/tools/api/generator/create_python_api.py\", line 22, in <module>\r\n from tensorflow.python.tools.api.generator import doc_srcs\r\n File \"/private/var/tmp/_bazel_ghe/a40be6b67c18b5da908566b099777def/execroot/org_tensorflow/bazel-out/host/bin/tensorflow/create_tensorflow.python_api_tf_python_api_gen_v2.runfiles/org_tensorflow/tensorflow/python/__init__.py\", line 42, in <module>\r\n from tensorflow.python import data\r\n File \"/private/var/tmp/_bazel_ghe/a40be6b67c18b5da908566b099777def/execroot/org_tensorflow/bazel-out/host/bin/tensorflow/create_tensorflow.python_api_tf_python_api_gen_v2.runfiles/org_tensorflow/tensorflow/python/data/__init__.py\", line 21, in <module>\r\n from tensorflow.python.data import experimental\r\n File \"/private/var/tmp/_bazel_ghe/a40be6b67c18b5da908566b099777def/execroot/org_tensorflow/bazel-out/host/bin/tensorflow/create_tensorflow.python_api_tf_python_api_gen_v2.runfiles/org_tensorflow/tensorflow/python/data/experimental/__init__.py\", line 96, in <module>\r\n from tensorflow.python.data.experimental import service\r\n File \"/private/var/tmp/_bazel_ghe/a40be6b67c18b5da908566b099777def/execroot/org_tensorflow/bazel-out/host/bin/tensorflow/create_tensorflow.python_api_tf_python_api_gen_v2.runfiles/org_tensorflow/tensorflow/python/data/experimental/service/__init__.py\", line 419, in <module>\r\n from tensorflow.python.data.experimental.ops.data_service_ops import distribute\r\n File \"/private/var/tmp/_bazel_ghe/a40be6b67c18b5da908566b099777def/execroot/org_tensorflow/bazel-out/host/bin/tensorflow/create_tensorflow.python_api_tf_python_api_gen_v2.runfiles/org_tensorflow/tensorflow/python/data/experimental/ops/data_service_ops.py\", line 22, in <module>\r\n from tensorflow.python.data.experimental.ops import compression_ops\r\n File \"/private/var/tmp/_bazel_ghe/a40be6b67c18b5da908566b099777def/execroot/org_tensorflow/bazel-out/host/bin/tensorflow/create_tensorflow.python_api_tf_python_api_gen_v2.runfiles/org_tensorflow/tensorflow/python/data/experimental/ops/compression_ops.py\", line 16, in <module>\r\n from tensorflow.python.data.util import structure\r\n File \"/private/var/tmp/_bazel_ghe/a40be6b67c18b5da908566b099777def/execroot/org_tensorflow/bazel-out/host/bin/tensorflow/create_tensorflow.python_api_tf_python_api_gen_v2.runfiles/org_tensorflow/tensorflow/python/data/util/structure.py\", line 29, in <module>\r\n from tensorflow.python.ops import resource_variable_ops\r\n File \"/private/var/tmp/_bazel_ghe/a40be6b67c18b5da908566b099777def/execroot/org_tensorflow/bazel-out/host/bin/tensorflow/create_tensorflow.python_api_tf_python_api_gen_v2.runfiles/org_tensorflow/tensorflow/python/ops/resource_variable_ops.py\", line 38, in <module>\r\n from tensorflow.python.framework import meta_graph\r\n File \"/private/var/tmp/_bazel_ghe/a40be6b67c18b5da908566b099777def/execroot/org_tensorflow/bazel-out/host/bin/tensorflow/create_tensorflow.python_api_tf_python_api_gen_v2.runfiles/org_tensorflow/tensorflow/python/framework/meta_graph.py\", line 18, in <module>\r\n from packaging import version as packaging_version # pylint: disable=g-bad-import-order\r\nModuleNotFoundError: No module named 'packaging'\r\nTarget //tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nERROR: /Users/ghe/projects/tensorflow/tensorflow/python/tools/BUILD:281:10 Middleman _middlemen/tensorflow_Spython_Stools_Sprint_Uselective_Uregistration_Uheader-runfiles failed: (Exit 1): bash failed: error executing command /bin/bash -c ... (remaining 1 argument skipped)\r\nINFO: Elapsed time: 1781.743s, Critical Path: 157.11s\r\nINFO: 10487 processes: 243 internal, 10244 local.\r\nFAILED: Build did NOT complete successfully\r\n```", "Yeah this new error is specific to SelectTfOps. This seems like a python issue? The error is in `tensorflow/python/tools/BUILD`. @nitins17 do you have any idea what might be the issue here?", "```\r\nModuleNotFoundError: No module named 'packaging'\r\n```\r\n\r\nIs the [packaging](https://pypi.org/project/packaging/) module installed? ", "@nitins17 \r\n\r\nI haven't installed `packaging`. As an iOS engineer, python is not often used, so the python is pretty much in vanilla state. \r\n\r\nAfter installing it and rerunning the script, now it asks me to install `requests` module...\r\n```\r\nModuleNotFoundError: No module named 'requests'\r\n```\r\nWith both modules pip installed, I'm finally build successfully! However still seeing another issue\r\n\r\n```\r\nINFO: Elapsed time: 711.042s, Critical Path: 174.60s\r\nINFO: 2046 processes: 3 internal, 2043 local.\r\nINFO: Build completed successfully, 2046 total actions\r\nOutput can be found here:\r\nls: cannot access '/Users/ghe/projects/tensorflow/bazel-out/applebin_ios-ios_sim_arm64-opt-ST-f882807c96e5/bin/tensorflow/lite/ios/tmp/TensorFlowLiteC_framework.zip': No such file or directory\r\n```\r\n\r\nI tried looking for the file, according to the instruction, at `bazel-bin/tensorflow/lite/ios/tmp/TensorFlowLiteC_framework.zip` and it was also missing..(there is no tmp directory). Any idea what's the reason and where should look for this file? \r\n\r\n```\r\nghe-mn2:ios ghe]$ pwd\r\n/Users/ghe/projects/tensorflow/bazel-bin/tensorflow/lite/ios\r\nghe-mn2:ios ghe]$ ls -al\r\ntotal 0\r\ndrwxrwxrwx 2 ghe wheel 64 Apr 7 13:36 .\r\ndrwxrwxrwx 45 ghe wheel 1440 Apr 6 18:43 ..\r\n```\r\n\r\n\r\n\r\n\r\nAlso, can your team please add these dependencies, along with the above gnu utility tricks, to either Pre-requisites in the [official instruction](https://www.tensorflow.org/lite/guide/build_ios#selectively_build_tflite_frameworks), or just put them into the build scripts? I asked my colleagues and they are seeing same errors as myself. I believe this would be beneficial to a lot more people in ios-TF community. ", "> With both modules pip installed, I'm finally build successfully! However still seeing another issue\r\n> \r\n> ```\r\n> INFO: Elapsed time: 711.042s, Critical Path: 174.60s\r\n> INFO: 2046 processes: 3 internal, 2043 local.\r\n> INFO: Build completed successfully, 2046 total actions\r\n> Output can be found here:\r\n> ls: cannot access '/Users/ghe/projects/tensorflow/bazel-out/applebin_ios-ios_sim_arm64-opt-ST-f882807c96e5/bin/tensorflow/lite/ios/tmp/TensorFlowLiteC_framework.zip': No such file or directory\r\n> ```\r\n> \r\n> I tried looking for the file, according to the instruction, at `bazel-bin/tensorflow/lite/ios/tmp/TensorFlowLiteC_framework.zip` and it was also missing..(there is no tmp directory). Any idea what's the reason and where should look for this file?\r\n> \r\n\r\nSorry but I don't know whats happening here. @yishuangP do you know?\r\n\r\n> Also, can your team please add these dependencies, along with the above gnu utility tricks, to either Pre-requisites in the [official instruction](https://www.tensorflow.org/lite/guide/build_ios#selectively_build_tflite_frameworks), or just put them into the build scripts? I asked my colleagues and they are seeing same errors as myself. I believe this would be beneficial to a lot more people in ios-TF community.\r\n\r\nThanks for letting us know! Maybe it would be useful to put these in a FAQs or as comments in the build scripts but I'll leave the decision up to @yishuangP and the TF Lite team.", "Thanks [nitins17](https://github.com/nitins17) for the help!\r\n\r\nHi [heguanyu](https://github.com/heguanyu), sorry for the inconvenience. Glad to hear that it finally build successfully! Since you already have the build file under `tensorflow/lite/ios/tmp/`, can you just try running this command `bazel build -c opt --config=ios --ios_multi_cpus=arm64 --use_top_level_targets_for_symlinks --define=tflite_with_xnnpack=false //tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework`?\r\n\r\n\r\nI think all these problems might come from different MacOs versions. The build command works fine for an older OS version.\r\n\r\n> Also, can your team please add these dependencies, along with the above gnu utility tricks, to either Pre-requisites in the [official instruction](https://www.tensorflow.org/lite/guide/build_ios#selectively_build_tflite_frameworks), or just put them into the build scripts? I asked my colleagues and they are seeing same errors as myself. I believe this would be beneficial to a lot more people in ios-TF community.\r\n\r\nThanks for the suggestion! We'll update our public website.", "@yishuangP I think the BUILD file in tmp was deleted after the build is successful, seeing\r\n\r\n```\r\nghe-mn2:tensorflow ghe:(v2.11.0)]$ bazel build -c opt --config=ios --ios_multi_cpus=arm64 --use_top_level_targets_for_symlinks --define=tflite_with_xnnpack=false //tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework\r\nStarting local Bazel server and connecting to it...\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=312\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.tf_configure.bazelrc:\r\n 'build' options: --action_env PYTHON_BIN_PATH=/Library/Frameworks/Python.framework/Versions/3.10/bin/python3 --action_env PYTHON_LIB_PATH=/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages --python_path=/Library/Frameworks/Python.framework/Versions/3.10/bin/python3\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.bazelrc:\r\n 'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/common,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils\r\nINFO: Found applicable config definition build:short_logs in file /Users/ghe/projects/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /Users/ghe/projects/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:ios in file /Users/ghe/projects/tensorflow/.bazelrc: --apple_platform_type=ios --apple_bitcode=embedded --copt=-fembed-bitcode --copt=-Wno-c++11-narrowing --noenable_platform_specific_config --copt=-w --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --define=with_xla_support=false\r\nERROR: Skipping '//tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework': no such package 'tensorflow/lite/ios/tmp': BUILD file not found in any of the following directories. Add a BUILD file to a directory to mark it as a package.\r\n - /Users/ghe/projects/tensorflow/tensorflow/lite/ios/tmp\r\nWARNING: Target pattern parsing failed.\r\nERROR: no such package 'tensorflow/lite/ios/tmp': BUILD file not found in any of the following directories. Add a BUILD file to a directory to mark it as a package.\r\n - /Users/ghe/projects/tensorflow/tensorflow/lite/ios/tmp\r\nINFO: Elapsed time: 2.316s\r\nINFO: 0 processes.\r\nFAILED: Build did NOT complete successfully (0 packages loaded)\r\n```", "Just pulled from master branch and still seeing the file not found error. \r\n\r\n```\r\nINFO: Found applicable config definition build:short_logs in file /Users/ghe/projects/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /Users/ghe/projects/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:ios in file /Users/ghe/projects/tensorflow/.bazelrc: --apple_platform_type=ios --apple_bitcode=embedded --copt=-fembed-bitcode --copt=-Wno-c++11-narrowing --noenable_platform_specific_config --copt=-w --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --define=with_xla_support=false\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/llvm/llvm-project/archive/e2b15ec235fed7e4ff1f99193c7bbffe830d4934.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nINFO: Build options --apple_bitcode, --apple_platform_type, --copt, and 2 more have changed, discarding analysis cache.\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/XNNPACK/archive/b9d4073a6913891ce9cbd8965c8d506075d2a45a.zip failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/openxla/stablehlo/archive/1d6a8587ea959db3574c1311905a8bf1ef509db0.zip failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nINFO: Analyzed target //tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework (437 packages loaded, 36804 targets configured).\r\nINFO: Found 1 target...\r\nTarget //tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework up-to-date:\r\n bazel-out/applebin_ios-ios_x86_64-opt-ST-74bbdaf492e7/bin/tensorflow/lite/ios/tmp/TensorFlowLiteSelectTfOps_framework.zip\r\nINFO: Elapsed time: 2668.454s, Critical Path: 299.53s\r\nINFO: 13599 processes: 944 internal, 12655 local.\r\nINFO: Build completed successfully, 13599 total actions\r\nOutput can be found here:\r\nls: cannot access '/Users/ghe/projects/tensorflow/bazel-out/applebin_ios-ios_sim_arm64-opt-ST-f882807c96e5/bin/tensorflow/lite/ios/tmp/TensorFlowLiteC_framework.zip': No such file or directory\r\n\r\nghe-mn2:tensorflow ghe:master]$ bazel build -c opt --config=ios --ios_multi_cpus=arm64 --use_top_level_targets_for_symlinks --define=tflite_with_xnnpack=false //tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=312\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.tf_configure.bazelrc:\r\n 'build' options: --action_env PYTHON_BIN_PATH=/Library/Frameworks/Python.framework/Versions/3.10/bin/python3 --action_env PYTHON_LIB_PATH=/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages --python_path=/Library/Frameworks/Python.framework/Versions/3.10/bin/python3\r\nINFO: Reading rc options for 'build' from /Users/ghe/projects/tensorflow/.bazelrc:\r\n 'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils\r\nINFO: Found applicable config definition build:short_logs in file /Users/ghe/projects/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /Users/ghe/projects/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:ios in file /Users/ghe/projects/tensorflow/.bazelrc: --apple_platform_type=ios --apple_bitcode=embedded --copt=-fembed-bitcode --copt=-Wno-c++11-narrowing --noenable_platform_specific_config --copt=-w --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --define=with_xla_support=false\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/llvm/llvm-project/archive/e2b15ec235fed7e4ff1f99193c7bbffe830d4934.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nERROR: Skipping '//tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework': no such package 'tensorflow/lite/ios/tmp': BUILD file not found in any of the following directories. Add a BUILD file to a directory to mark it as a package.\r\n - /Users/ghe/projects/tensorflow/tensorflow/lite/ios/tmp\r\nWARNING: Target pattern parsing failed.\r\nERROR: no such package 'tensorflow/lite/ios/tmp': BUILD file not found in any of the following directories. Add a BUILD file to a directory to mark it as a package.\r\n - /Users/ghe/projects/tensorflow/tensorflow/lite/ios/tmp\r\nINFO: Elapsed time: 0.135s\r\nINFO: 0 processes.\r\nFAILED: Build did NOT complete successfully (0 packages loaded)\r\n```", "Hi @nitins17 @yishuangP Bumping up this thread again - can you help checking what might be the issue that file get missing?\r\n\r\nThank you!", "I am able to replicate on r2.13 branch:\r\n\r\n```\r\nbazel build -c opt --config=ios --ios_multi_cpus=arm64 --use_top_level_targets_for_symlinks --define=tflite_with_xnnpack=false //tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework\r\nStarting local Bazel server and connecting to it...\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=178\r\nINFO: Reading rc options for 'build' from /Users/pisethk/git/tensorflow/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /Users/pisethk/git/tensorflow/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility\r\nINFO: Reading rc options for 'build' from /Users/pisethk/git/tensorflow/.tf_configure.bazelrc:\r\n 'build' options: --action_env PYTHON_BIN_PATH=/Users/pisethk/miniforge3/bin/python3 --action_env PYTHON_LIB_PATH=/Users/pisethk/miniforge3/lib/python3.10/site-packages --python_path=/Users/pisethk/miniforge3/bin/python3\r\nINFO: Reading rc options for 'build' from /Users/pisethk/git/tensorflow/.bazelrc:\r\n 'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils,tensorflow/core/tfrt/utils/debug\r\nINFO: Found applicable config definition build:short_logs in file /Users/pisethk/git/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /Users/pisethk/git/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:ios in file /Users/pisethk/git/tensorflow/.bazelrc: --apple_platform_type=ios --apple_bitcode=embedded --copt=-fembed-bitcode --copt=-Wno-c++11-narrowing --noenable_platform_specific_config --copt=-w --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --define=with_xla_support=false\r\nERROR: Skipping '//tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework': no such package 'tensorflow/lite/ios/tmp': BUILD file not found in any of the following directories. Add a BUILD file to a directory to mark it as a package.\r\n - /Users/pisethk/git/tensorflow/tensorflow/lite/ios/tmp\r\nWARNING: Target pattern parsing failed.\r\nERROR: no such package 'tensorflow/lite/ios/tmp': BUILD file not found in any of the following directories. Add a BUILD file to a directory to mark it as a package.\r\n - /Users/pisethk/git/tensorflow/tensorflow/lite/ios/tmp\r\nINFO: Elapsed time: 2.798s\r\nINFO: 0 processes.\r\nFAILED: Build did NOT complete successfully (0 packages loaded)\r\n```", "Hi, just to clarify, how are you running the script? The following command works for me. Are you running the command under `/Users/pisethk/git/tensorflow`? \r\n```\r\nbash tensorflow/lite/ios/build_frameworks.sh --input_models=\"${PWD}/tensorflow/lite/testdata/softplus_flex.bin\" --target_archs=x86_64,arm64\r\n```\r\n\r\nLooking at the error you got, it seems that the tmp directly is not successfully created.\r\n```\r\nERROR: Skipping '//tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework': no such package 'tensorflow/lite/ios/tmp': BUILD file not found in any of the following directories. Add a BUILD file to a directory to mark it as a package.\r\n```", "> Target //tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework up-to-date:\r\n> bazel-out/applebin_ios-ios_x86_64-opt-ST-74bbdaf492e7/bin/tensorflow/lite/ios/tmp/TensorFlowLiteSelectTfOps_framework.zip\r\n\r\nHi really sorry for the late reply. If you look at the log, the framework is already successfully built and the artifact can be found at `bazel-out/applebin_ios-ios_x86_64-opt-ST-74bbdaf492e7/bin/tensorflow/lite/ios/tmp/TensorFlowLiteSelectTfOps_framework.zip`. The script tensorflow/lite/ios/build_frameworks.sh will always delete the temporary directory `tensorflow/lite/ios/tmp/` at the end of the script, so your next command failed.\r\n```\r\nTarget //tensorflow/lite/ios/tmp:TensorFlowLiteSelectTfOps_framework up-to-date:\r\n bazel-out/applebin_ios-ios_x86_64-opt-ST-74bbdaf492e7/bin/tensorflow/lite/ios/tmp/TensorFlowLiteSelectTfOps_framework.zip\r\n```", "Hi @yishuangP Thanks! I do find it in that folder! 2 more question tho:\r\n\r\n1. I noticed the size is huge(180+MB for zip file, and 738MB after decompress), is this expected at all? The original framework size is only around 4MB... \r\n\r\n```\r\n➜ tmp ls -al\r\ntotal 200872\r\ndrwxrwxrwx 9 ghe 288 Jun 12 10:26 .\r\ndrwxrwxrwx 6 ghe 192 Jun 6 16:25 ..\r\ndrwxrwxrwx 7 ghe 224 Jun 6 16:25 TensorFlowLiteC_framework-intermediates\r\ndrwxr-xr-x 3 ghe 96 Jun 6 16:22 TensorFlowLiteC_framework.modulemaps-intermediates\r\n-r-xr-xr-x 1 ghe 20355185 Jun 6 16:25 TensorFlowLiteC_framework.zip\r\ndrwxr-xr-x 3 ghe 96 Jun 6 16:25 TensorFlowLiteC_framework_archive-root\r\ndrwxrwxrwx 6 ghe 192 Jun 12 10:26 TensorFlowLiteSelectTfOps_framework-intermediates\r\n-r-xr-xr-x 1 ghe 182243080 Jun 12 10:26 TensorFlowLiteSelectTfOps_framework.zip\r\ndrwxr-xr-x 3 ghe 96 Jun 12 10:26 TensorFlowLiteSelectTfOps_framework_archive-root\r\n```\r\n\r\n3. Is it possible to fix the script so that the zip file will be copied to some fixed location, instead of a temporary path that I have to look up from the stdout everytime? That will help automating some processes in the CD pipeline. \r\n\r\n\r\nThank you!", "> I noticed the size is huge(180+MB for zip file, and 738MB after decompress), is this expected at all? The original framework size is only around 4MB...\r\n\r\nHi @heguanyu, yes unfortunately the binary size with selected ops will be larger because it links in TF kernels. What are the targeted archs you built for? You probably only need `arm64`?\r\n", "@yishuangP We noticed that the bazel script logic is that - if it detects any select operators in the model, it will add ALL select operators into the binary, whether or not it is being used in the model. Is there any way to ONLY include the operators that being used by the tflite model?\r\n\r\nAs per your 2nd suggestion, limiting the architect to only arm64 will just cut the size in half - down to 364MB - but it is still too huge for a iOS device...", "Hi @heguanyu this is what the script is supposed to do. Here we only generate targets for your models https://github.com/tensorflow/tensorflow/blob/54751245dfb6c551e5118c6bd279baf806f784cb/tensorflow/lite/ios/build_frameworks.sh#L92 . Could you run the following command and check if the ops_list.txt contains exactly the ops you need?\r\n```\r\nFLAG_MODELS= <your model>\r\nTMP_DIR=<your temporary directory>\r\nbazel build -c opt --config=monolithic //tensorflow/lite/tools:list_flex_ops_no_kernel_main\r\nbazel-bin/tensorflow/lite/tools/list_flex_ops_no_kernel_main --graphs=${FLAG_MODELS} > ${TMP_DIR}/ops_list.txt\r\n```", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you." ]

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[ "@HripsimeS,\r\nCould you please try to check the below sample workaround where we can plot training and the validation loss. Also Kindly find the [gist](https://colab.research.google.com/gist/tilakrayal/fd6ccd5a44fa5142b5e2854780fc2272/tflite-training-sinewave.ipynb) for the reference.\r\n```\r\n# Draw a graph of the loss, which is the distance between\r\n# the predicted and actual values during training and validation.\r\nloss = training_info.history['loss']\r\nvalidation_loss = training_info.history['val_loss']\r\n\r\nepochs = range(1, len(loss) + 1)\r\n\r\nplt.plot(epochs, loss, 'g.', label='Training loss')\r\nplt.plot(epochs, validation_loss, 'b', label='Validation loss')\r\nplt.title('Training and validation loss')\r\nplt.xlabel('Epochs')\r\nplt.ylabel('Loss')\r\nplt.legend()\r\nplt.show()\r\n```\r\nThank you!", "@tilakrayal thank you very much for your reply. The notebook you shared they created the model **model = model.fit(...)**\r\n\r\nIn my case I use the following notebook https://www.tensorflow.org/lite/models/modify/model_maker/object_detection\r\nAnd they create the model with **model = object_detector.create(....)**\r\n\r\nThat's why when I use the history to get the information about losses, it gives me this error \r\n**AttributeError: 'ObjectDetector' object has no attribute 'history'**\r\n\r\nMy question is how to get information about losses if you using **object_detector.create** to train the mobile models?", "Hi @HripsimeS \r\n\r\nThe object detector class returns Keras model which has callbacks history enabled. You can obtain the information about losses by calling the history function on the model returned by the object detector's Keras model. Please try the following snippet to get the train loss vs val loss trained using object detector.\r\n\r\n```\r\nhistory = model.model.history\r\nloss = history.history['loss']\r\nval_loss = history.history['val_loss']\r\nplt.plot(loss, label='Training Loss')\r\nplt.plot(val_loss, label='Validation Loss')\r\nplt.legend(loc='upper right')\r\nplt.ylabel('Cross Entropy')\r\nplt.title('Training and Validation Loss')\r\nplt.xlabel('epoch')\r\nplt.show() \r\n```\r\n Please find the gist [here](https://colab.research.google.com/gist/pjpratik/297650bb92325965a45431bf01827fec/model-maker-object-detection-tutorial.ipynb#scrollTo=6JfXtC4UTiTP) in which the train loss and val loss are plotted using object detection example.\r\n\r\nThanks.", "@pjpratik thank you very much, it was very useful 🥇 So the only part was missing was **model.model.history** :) ", "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/60087\">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/60087\">No</a>\n" ]

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[ "Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/60086/checks?check_run_id=12228598133) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.", "Please don't use \"update <file>\" commit messages. These make it harder to understand at a glance at commit history what the changes are.\r\n\r\nhttps://cbea.ms/git-commit/", "Is this fixing a bug or something?" ]

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[ "@trickiwoo \r\nI was able to replicate the issue in Colab using TF v2.12. Please find the gists [here](https://colab.research.google.com/gist/tiruk007/fdc546be405bbbdd29c0f220e1cd6026/untitled164.ipynb) for reference. It seems like we have to dig more into this issue, we will update soon here.\r\n\r\nThank you !", "There seems to problem with `atan2` function with broadcastable shapes under Gradient Tape.When target is `atan2` function then `tape.jacobian` expecting both target and sources to be of same shape. With both same shape there is no error and if shape changes then there is compatibility error. `tape.gradient `works fine for both cases.\r\n\r\nHowever if target is some other function like `tf.nn.sigmoid()` then with broadcastable shapes `GradientTape().jacobian` works fine without error.\r\n\r\nAll of my observations replicated in the attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/44d64dbe14affc55ff1d170ba0836cb0/60085.ipynb).\r\n\r\nThis seems to be valid bug for me. \r\n\r\n@trickiwoo , do you have any Idea/plan to fix this ?\r\n", "@trickiwoo, The error you are encountering is due to the mismatch in the shape of the gradient. In this case, since you want to compute the Jacobian of value2 with respect to b, you need to reshape the tensors a and b to have compatible shapes. \r\nDo this below \r\n\r\nimport tensorflow as tf\r\nimport numpy as np\r\n\r\na = tf.ones([1, 1, 3, 3])\r\nb = tf.ones([1, 1, 3, 3]) # Reshape b to have the same shape as a\r\n\r\ndef atan2(a, b):\r\n a_b = tf.math.atan2(a, b)\r\n return a_b\r\n\r\nvalue = atan2(a, b)\r\nprint(value.shape)\r\n\r\nwith tf.GradientTape() as tape:\r\n tape.watch(b)\r\n value2 = atan2(a, b)\r\n\r\ngradient = tape.jacobian(value2, b)\r\nprint(gradient) . \r\n\r\nExpected output is \r\n\r\n(1, 1, 3, 3)\r\ntf.Tensor(\r\n[[[[[[[[-0.5 -0. -0. ]\r\n [-0. -0. -0. ]\r\n [-0. -0. -0. ]]]]\r\n\r\n\r\n\r\n [[[[-0. -0.5 -0. ]\r\n [-0. -0. -0. ]\r\n [-0. -0. -0. ]]]]\r\n\r\n\r\n\r\n [[[[-0. -0. -0.5]\r\n [-0. -0. -0. ]\r\n [-0. -0. -0. ]]]]]\r\n\r\n\r\n\r\n\r\n [[[[[-0. -0. -0. ]\r\n [-0.5 -0. -0. ]\r\n [-0. -0. -0. ]]]]\r\n\r\n\r\n\r\n [[[[-0. -0. -0. ]\r\n [-0. -0.5 -0. ]\r\n [-0. -0. -0. ]]]]\r\n\r\n\r\n\r\n [[[[-0. -0. -0. ]\r\n [-0. -0. -0.5]\r\n [-0. -0. -0. ]]]]]\r\n\r\n\r\n\r\n\r\n [[[[[-0. -0. -0. ]\r\n [-0. -0. -0. ]\r\n [-0.5 -0. -0. ]]]]\r\n\r\n\r\n\r\n [[[[-0. -0. -0. ]\r\n [-0. -0. -0. ]\r\n [-0. -0.5 -0. ]]]]\r\n\r\n\r\n\r\n [[[[-0. -0. -0. ]\r\n [-0. -0. -0. ]\r\n [-0. -0. -0.5]]]]]]]], shape=(1, 1, 3, 3, 1, 1, 3, 3), dtype=float32)", "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/60085\">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/60085\">No</a>\n", "@trickiwoo ,\r\n\r\nI have verified the issue with nightly(2.14.0.dev20230516) and the issue is resolved with above commit. Please refer to attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/9c550b6120e21d8cf8f22405a78e4600/60085_nightly-2-14.ipynb).", "don't i get an accolade for my input @SuryanarayanaY i resolved the issue earlier" ]

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[ "Hi @trickiwoo, Apologies for the delay. We were able to replicate the issue in Colab using TF v2.11. Please find the gist [here](https://colab.sandbox.google.com/gist/synandi/40e7a6bb108d9a9b97bcbe76ab0c67da/60084.ipynb). It seems like we have to dig deep into the issue, we'll update here soon. Thank you!" ]

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[ "@AIML-ankit Could you please confirm if you are using TF v2.12 which is the latest version?\r\nAs per the tested build configuration listed [here](https://www.tensorflow.org/install/source_windows#tested_build_configurations) please let us know if the build from source on windows is not failing now!\r\nFor installing TF lite with CMake, please refer to [this](https://www.tensorflow.org/lite/guide/build_cmake). \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.", "C:\\Users\\Anmaheshwari\\TF_2\\tensorflow>bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package\r\nINFO: Options provided by the client:\r\nInherited 'common' options: --isatty=1 --terminal_columns=172\r\nINFO: Reading rc options for 'build' from c:\\users\\anmaheshwari\\tf_2\\tensorflow.bazelrc:\r\nInherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Options provided by the client:\r\n'build' options: --python_path=C:/Users/Anmaheshwari/python/python.exe\r\nINFO: Reading rc options for 'build' from c:\\users\\anmaheshwari\\tf_2\\tensorflow.bazelrc:\r\n'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility\r\nINFO: Reading rc options for 'build' from c:\\users\\anmaheshwari\\tf_2\\tensorflow.tf_configure.bazelrc:\r\n'build' options: --action_env PYTHON_BIN_PATH=C:/Users/Anmaheshwari/python/python.exe --action_env PYTHON_LIB_PATH=C:/Users/Anmaheshwari/python/lib/site-packages --python_path=C:/Users/Anmaheshwari/python/python.exe --copt=/d2ReducedOptimizeHugeFunctions --host_copt=/d2ReducedOptimizeHugeFunctions --define=override_eigen_strong_inline=true\r\nINFO: Reading rc options for 'build' from c:\\users\\anmaheshwari\\tf_2\\tensorflow.bazelrc:\r\n'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils\r\nINFO: Found applicable config definition build:short_logs in file c:\\users\\anmaheshwari\\tf_2\\tensorflow.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file c:\\users\\anmaheshwari\\tf_2\\tensorflow.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:opt in file c:\\users\\anmaheshwari\\tf_2\\tensorflow.tf_configure.bazelrc: --copt=/arch:AVX --host_copt=/arch:AVX\r\nINFO: Found applicable config definition build:windows in file c:\\users\\anmaheshwari\\tf_2\\tensorflow.bazelrc: --copt=/W0 --host_copt=/W0 --copt=/Zc:__cplusplus --host_copt=/Zc:__cplusplus --copt=/D_USE_MATH_DEFINES --host_copt=/D_USE_MATH_DEFINES --features=compiler_param_file --copt=/d2ReducedOptimizeHugeFunctions --host_copt=/d2ReducedOptimizeHugeFunctions --cxxopt=/std:c++17 --host_cxxopt=/std:c++17 --config=monolithic --copt=-DWIN32_LEAN_AND_MEAN --host_copt=-DWIN32_LEAN_AND_MEAN --copt=-DNOGDI --host_copt=-DNOGDI --copt=/Zc:preprocessor --host_copt=/Zc:preprocessor --linkopt=/DEBUG --host_linkopt=/DEBUG --linkopt=/OPT:REF --host_linkopt=/OPT:REF --linkopt=/OPT:ICF --host_linkopt=/OPT:ICF --verbose_failures --features=compiler_param_file\r\nINFO: Found applicable config definition build:monolithic in file c:\\users\\anmaheshwari\\tf_2\\tensorflow.bazelrc: --define framework_shared_object=false --define tsl_protobuf_header_only=false --experimental_link_static_libraries_once=false\r\nINFO: Repository local_execution_config_python instantiated at:\r\nC:/users/anmaheshwari/tf_2/tensorflow/WORKSPACE:15:14: in\r\nC:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:962:19: in workspace\r\nC:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:96:27: in _tf_toolchains\r\nC:/users/anmaheshwari/tf_2/tensorflow/tensorflow/tools/toolchains/remote_config/configs.bzl:6:28: in initialize_rbe_configs\r\nC:/users/anmaheshwari/tf_2/tensorflow/tensorflow/tools/toolchains/remote_config/rbe_config.bzl:158:27: in _tensorflow_local_config\r\nRepository rule local_python_configure defined at:\r\nC:/users/anmaheshwari/tf_2/tensorflow/third_party/py/python_configure.bzl:279:41: in\r\nINFO: Repository local_config_python instantiated at:\r\nC:/users/anmaheshwari/tf_2/tensorflow/WORKSPACE:15:14: in\r\nC:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:962:19: in workspace\r\nC:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:106:21: in _tf_toolchains\r\nRepository rule python_configure defined at:\r\nC:/users/anmaheshwari/tf_2/tensorflow/third_party/py/python_configure.bzl:298:35: in\r\nERROR: An error occurred during the fetch of repository 'local_execution_config_python':\r\nTraceback (most recent call last):\r\nFile \"C:/users/anmaheshwari/tf_2/tensorflow/third_party/py/python_configure.bzl\", line 212, column 22, in _create_local_python_repository\r\n_check_python_bin(repository_ctx, python_bin)\r\nFile \"C:/users/anmaheshwari/tf_2/tensorflow/third_party/py/python_configure.bzl\", line 145, column 25, in _check_python_bin\r\nauto_config_fail(\"--define %s='%s' is not executable. Is it the python binary?\" % (\r\nFile \"C:/users/anmaheshwari/tf_2/tensorflow/third_party/remote_config/common.bzl\", line 12, column 9, in auto_config_fail\r\nfail(\"%sConfiguration Error:%s %s\\n\" % (red, no_color, msg))\r\nError in fail: Configuration Error: --define PYTHON_BIN_PATH='C:/Users/Anmaheshwari/python/python.exe' is not executable. Is it the python binary?\r\nERROR: C:/users/anmaheshwari/tf_2/tensorflow/WORKSPACE:15:14: fetching local_python_configure rule //external:local_execution_config_python: Traceback (most recent call last):\r\nFile \"C:/users/anmaheshwari/tf_2/tensorflow/third_party/py/python_configure.bzl\", line 212, column 22, in _create_local_python_repository\r\n_check_python_bin(repository_ctx, python_bin)\r\nFile \"C:/users/anmaheshwari/tf_2/tensorflow/third_party/py/python_configure.bzl\", line 145, column 25, in _check_python_bin\r\nauto_config_fail(\"--define %s='%s' is not executable. Is it the python binary?\" % (\r\nFile \"C:/users/anmaheshwari/tf_2/tensorflow/third_party/remote_config/common.bzl\", line 12, column 9, in auto_config_fail\r\nfail(\"%sConfiguration Error:%s %s\\n\" % (red, no_color, msg))\r\nError in fail: Configuration Error: --define PYTHON_BIN_PATH='C:/Users/Anmaheshwari/python/python.exe' is not executable. Is it the python binary?\r\nINFO: Repository stablehlo instantiated at:\r\nC:/users/anmaheshwari/tf_2/tensorflow/WORKSPACE:15:14: in\r\nC:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:965:28: in workspace\r\nC:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:83:14: in _initialize_third_party\r\nC:/users/anmaheshwari/tf_2/tensorflow/third_party/stablehlo/workspace.bzl:11:20: in repo\r\nC:/users/anmaheshwari/tf_2/tensorflow/third_party/repo.bzl:136:21: in tf_http_archive\r\nRepository rule _tf_http_archive defined at:\r\nC:/users/anmaheshwari/tf_2/tensorflow/third_party/repo.bzl:89:35: in\r\nINFO: Repository termcolor_archive instantiated at:\r\nC:/users/anmaheshwari/tf_2/tensorflow/WORKSPACE:15:14: in\r\nC:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:972:21: in workspace\r\nC:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:388:20: in _tf_repositories\r\nC:/users/anmaheshwari/tf_2/tensorflow/third_party/repo.bzl:136:21: in tf_http_archive\r\nRepository rule _tf_http_archive defined at:\r\nC:/users/anmaheshwari/tf_2/tensorflow/third_party/repo.bzl:89:35: in\r\nINFO: Repository eigen_archive instantiated at:\r\nC:/users/anmaheshwari/tf_2/tensorflow/WORKSPACE:15:14: in\r\nC:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:965:28: in workspace\r\nC:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:65:11: in _initialize_third_party\r\nC:/users/anmaheshwari/tf_2/tensorflow/third_party/eigen3/workspace.bzl:14:20: in repo\r\nC:/users/anmaheshwari/tf_2/tensorflow/third_party/repo.bzl:136:21: in tf_http_archive\r\nRepository rule _tf_http_archive defined at:\r\nC:/users/anmaheshwari/tf_2/tensorflow/third_party/repo.bzl:89:35: in\r\nINFO: Repository cpuinfo instantiated at:\r\nC:/users/anmaheshwari/tf_2/tensorflow/WORKSPACE:15:14: in\r\nC:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:972:21: in workspace\r\nC:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:166:20: in _tf_repositories\r\nC:/users/anmaheshwari/tf_2/tensorflow/third_party/repo.bzl:136:21: in tf_http_archive\r\nRepository rule _tf_http_archive defined at:\r\nC:/users/anmaheshwari/tf_2/tensorflow/third_party/repo.bzl:89:35: in\r\nINFO: Repository sobol_data instantiated at:\r\nC:/users/anmaheshwari/tf_2/tensorflow/WORKSPACE:15:14: in\r\nC:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:965:28: in workspace\r\nC:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace2.bzl:82:15: in _initialize_third_party\r\nC:/users/anmaheshwari/tf_2/tensorflow/third_party/sobol_data/workspace.bzl:6:20: in repo\r\nC:/users/anmaheshwari/tf_2/tensorflow/third_party/repo.bzl:136:21: in tf_http_archive\r\nRepository rule _tf_http_archive defined at:\r\nC:/users/anmaheshwari/tf_2/tensorflow/third_party/repo.bzl:89:35: in\r\nINFO: Repository go_sdk instantiated at:\r\nC:/users/anmaheshwari/tf_2/tensorflow/WORKSPACE:23:14: in\r\nC:/users/anmaheshwari/tf_2/tensorflow/tensorflow/workspace0.bzl:135:20: in workspace\r\nC:/users/anmaheshwari/_bazel_anmaheshwari/unnl57ya/external/com_github_grpc_grpc/bazel/grpc_extra_deps.bzl:36:27: in grpc_extra_deps\r\nC:/users/anmaheshwari/_bazel_anmaheshwari/unnl57ya/external/io_bazel_rules_go/go/private/sdk.bzl:431:28: in go_register_toolchains\r\nC:/users/anmaheshwari/_bazel_anmaheshwari/unnl57ya/external/io_bazel_rules_go/go/private/sdk.bzl:130:21: in go_download_sdk\r\nRepository rule _go_download_sdk defined at:\r\nC:/users/anmaheshwari/_bazel_anmaheshwari/unnl57ya/external/io_bazel_rules_go/go/private/sdk.bzl:117:35: in\r\nERROR: Analysis of target '//tensorflow/tools/pip_package:build_pip_package' failed; build aborted: Configuration Error: --define PYTHON_BIN_PATH='C:/Users/Anmaheshwari/python/python.exe' is not executable. Is it the python binary?\r\nINFO: Elapsed time: 1.792s\r\nINFO: 0 processes.\r\nFAILED: Build did NOT complete successfully (34 packages loaded, 194 targets configured)\r\ncurrently loading: @llvm-project//llvm ... (11 packages)\r\nFetching https://github.com/boringssl; fetching\r\nFetching @com_google_absl; fetching\r\nFetching @libjpeg_turbo; fetching\r\nFetching @png; fetching\r\nFetching ...ingssl; Extracting C:/users/anmaheshwari/_bazel_anmaheshwari/unnl57ya/external/boringssl/temp13612522096612888484/c00d7ca810e93780bd0c8ee4eea28f4f2ea4bcdc\r\n.tar.gz\r\n\r\nC:\\Users\\Anmaheshwari\\TF_2\\tensorflow>", "Hi @AIML-ankit \r\n\r\nLooks like this is duplicate of issue #60112 . Can you please close this issue, since it is already being tracked there?\r\n\r\nThanks.", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60083\">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/60083\">No</a>\n" ]

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[ "Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/60082/checks?check_run_id=12219818582) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request." ]

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[ "@shkarupa-alex ~My guess is that you're on an arm64 device (M1, M2) and you're trying to use TF 2.12 under Rosetta using the main `tensorflow` package. The main `tensorflow` package only distributes binary wheels that require AVX2 instructions, which Rosetta does not support. In my experience, trying to run TensorFlow under Rosetta results in a segmentation fault like the one you experienced.~ (EDIT: Reply below indicates the environment is a core i7 (x86_64) machine. Using `tensorflow-macos` once it's released might still help 🤷‍♂️ )\r\n\r\nThis is at least in part a symptom of TensorFlow's unusual and confusing packaging practices, which results in new TensorFlow versions being released at different times for different architectures and operating systems. Instead of having a single package named `tensorflow` with binary distributions for the full set of supported architectures and operating systems, TensorFlow has three different packages, intended for use on different architectures and operating systems:\r\n* `tensorflow`: For x86_64 processors on any operating system\r\n* `tensorflow-macos`: For macOS systems, using either x86_64 or arm64 processors\r\n* `tensorflow-cpu-aws`: For Linux aarch64 systems\r\n\r\nIf you want to use TensorFlow on an arm64 macOS system, you need to install `tensorflow-macos` in a native arm64 Python environment. No other combination of Python environment and TensorFlow package will work.\r\n\r\nUnfortunately, TF 2.12 wheels have not yet been published for `tensorflow-macos`, so you'll have to wait if you want to use TF 2.12.\r\n\r\nI'd love to someday see TF packaging return to a more conventional approach of using a single package and distributing binary wheels for each architecture and operating system as part of that single package. My guess is that there are either some challenges with TF's extremely complex build system (Bazel) or issues with TF's organizational structure that have resulted in the current package distribution approach that are unlikely to change.\r\n\r\nIf you're not particularly attached to TensorFlow, PyTorch distributes binary wheels for the most common architectures and operating systems in a single package on PyPI, and they just made their 2.0 release. I haven't tried PyTorch under Rosetta, but if that's a requirement for you it's more likely to work than TensorFlow", "I'm on MacOS with Core i7\r\n\r\n>> tensorflow-macos: For macOS systems, using either x86_64 or arm64 processors\r\nThanks, will wait for it", "@shkarupa-alex,\r\nI was able to reproduce the issue and also getting the same error which was mentioned above.\r\nAlso please have a look at the official document, currently there is no official GPU support for MacOS. \r\n**https://www.tensorflow.org/install/pip#macos**\r\n\r\nIt is better to post this issue here for the quick resolution and the reference.\r\n**https://developer.apple.com/forums/thread/725592\r\nhttps://developer.apple.com/forums/tags/tensorflow-metal**\r\n\r\n\r\n\r\n```\r\ninstallLibSigSegfault exception: libSegFault not found\r\ntests/test_TFUtil.py:12: DeprecationWarning: Importing from numpy.testing.utils has been deprecated since 1.15.0, import from numpy.testing instead.\r\n from numpy.testing.utils import assert_almost_equal, assert_allclose\r\nTF version: 2.11.0\r\nMetal device set to: Apple M1 Pro\r\n\r\n\r\n2023-03-23 17:29:40.4: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\r\n2023-03-23 17:29:40.4: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:272] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\r\nExecuting: test_get_variable_grad_from_update_ops\r\nOptimizer: <tensorflow.python.training.adam.AdamOptimizer object at 0x14e550670>\r\nupdate ops: [<tf.Operation 'test_get_variable_grad_from_update_ops/Adam/update_test_get_variable_grad_from_update_ops/var/ResourceApplyAdam' type=ResourceApplyAdam>]\r\nupdate op keys: ['_has_manual_control_dependencies', 'use_locking', 'T', '_class', 'use_nesterov']\r\nupdate op inputs by name: ['var', 'm', 'v', 'beta1_power', 'beta2_power', 'lr', 'beta1', 'beta2', 'epsilon', 'grad']\r\n2023-03-23 17:29:40.5: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:357] MLIR V1 optimization pass is not enabled\r\n2023-03-23 17:29:41.5: W tensorflow/c/c_api.cc:291] Operation '{name:'test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Adam_1/Assign' id:60 op device:{requested: '', assigned: ''} def:{{{node test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Adam_1/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Adam_1, test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Adam_1/Initializer/zeros)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\r\ngrad: Tensor(\"test_get_variable_grad_from_update_ops/gradients/test_get_variable_grad_from_update_ops/sub_grad/tuple/control_dependency:0\", shape=(), dtype=float32)\r\nOptimizer: <tensorflow.python.training.gradient_descent.GradientDescentOptimizer object at 0x14e5506a0>\r\nupdate ops: [<tf.Operation 'test_get_variable_grad_from_update_ops/GradientDescent/update_test_get_variable_grad_from_update_ops/var/ResourceApplyGradientDescent' type=ResourceApplyGradientDescent>]\r\nupdate op keys: ['_has_manual_control_dependencies', 'use_locking', 'T', '_class']\r\nupdate op inputs by name: ['var', 'alpha', 'delta']\r\n2023-03-23 15:29:40.680: W tensorflow/c/c_api.cc:291] Operation '{name:'test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Adam_1/Assign' id:60 op device:{requested: '', assigned: ''} def:{{{node test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Adam_1/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Adam_1, test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Adam_1/Initializer/zeros)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\r\ngrad: Tensor(\"test_get_variable_grad_from_update_ops/gradients_1/test_get_variable_grad_from_update_ops/sub_grad/tuple/control_dependency:0\", shape=(), dtype=float32)\r\nOptimizer: <tensorflow.python.training.momentum.MomentumOptimizer object at 0x14e550730>\r\nupdate ops: [<tf.Operation 'test_get_variable_grad_from_update_ops/Momentum/update_test_get_variable_grad_from_update_ops/var/ResourceApplyMomentum' type=ResourceApplyMomentum>]\r\nupdate op keys: ['_has_manual_control_dependencies', 'use_locking', 'T', '_class', 'use_nesterov']\r\nupdate op inputs by name: ['var', 'accum', 'lr', 'grad', 'momentum']\r\n2023-03-23 19:29:40.65: W tensorflow/c/c_api.cc:291] Operation '{name:'test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Momentum/Assign' id:138 op device:{requested: '', assigned: ''} def:{{{node test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Momentum/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Momentum, test_get_variable_grad_from_update_ops/test_get_variable_grad_from_update_ops/var/Momentum/Initializer/zeros)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\r\ngrad: Tensor(\"test_get_variable_grad_from_update_ops/gradients_2/test_get_variable_grad_from_update_ops/sub_grad/tuple/control_dependency:0\", shape=(), dtype=float32)\r\nFatal Python error: Segmentation fault\r\n```\r\n", "@tilakrayal Your logs seem to indicate that you're using TensorFlow 2.11:\r\n\r\n> TF version: 2.11.0\r\n\r\nSince this issue is specifically for TF 2.12, it might be worth opening a new issue for your problem", "I've installed `tensorflow-macos 2.12.0` and got segfault when importing tensorflow", "Here is lldb stack:\r\n```bash\r\nalex@MacBook-Pro Downloads % lldb /Users/alex/.pyenv/versions/3.8.6/bin/python x.py\r\n(lldb) target create \"/Users/alex/.pyenv/versions/3.8.6/bin/python\"\r\nCurrent executable set to '/Users/alex/.pyenv/versions/3.8.6/bin/python' (x86_64).\r\n(lldb) settings set -- target.run-args \"x.py\"\r\n(lldb) run\r\nProcess 38275 launched: '/Users/alex/.pyenv/versions/3.8.6/bin/python' (x86_64)\r\n2023-04-10 16:44:06.224255: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\nProcess 38275 stopped\r\n* thread #1, queue = 'com.apple.main-thread', stop reason = EXC_BAD_ACCESS (code=1, address=0x390)\r\n frame #0: 0x00000001587f028c _multiarray_umath.cpython-38-darwin.so`_warn_if_cast_exists_already + 76\r\n_multiarray_umath.cpython-38-darwin.so`:\r\n-> 0x1587f028c <+76>: movq 0x390(%rax), %rax\r\n 0x1587f0293 <+83>: movq 0x48(%rax), %rdi\r\n 0x1587f0297 <+87>: movq %rbx, %rsi\r\n 0x1587f029a <+90>: callq 0x1589f5152 ; symbol stub for: PyDict_GetItemWithError\r\nTarget 0: (python) stopped.\r\n\r\n```", "@shkarupa-alex , tensorflow-macos is a community build package and it is not officially released by google.\r\nYou can build the packahe from source for MacOs by following the instructions here https://www.tensorflow.org/install/source#macos\r\n\r\nFor Intel based MacOs, you can install tensorflow package using pip. \r\n\r\nBelow are the screenshots for MacOS i5 using `pip install tensorflow`.\r\n\r\n\r\n\r\n\r\n", "@sachinprasadhs , i've tried to install tensorflow, tensorflow-macos and build tensorflow from source. But always get segfault (starting from r2.12, 2.11 works well from pip and source).\r\n\r\nHere is what i got when building from source:\r\n```bash\r\nalex@MacBook-Pro tensorflow-r2.12 % bazel build --action_env=CUDA_VISIBLE_DEVICES=-1 --config=release_cpu_macos //tensorflow/tools/pip_package:build_pip_package \r\nWARNING: The following configs were expanded more than once: [v2]. For repeatable flags, repeats are counted twice and may lead to unexpected behavior.\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=241\r\nINFO: Reading rc options for 'build' from /Users/alex/Downloads/tensorflow-r2.12/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /Users/alex/Downloads/tensorflow-r2.12/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility\r\nINFO: Reading rc options for 'build' from /Users/alex/Downloads/tensorflow-r2.12/.tf_configure.bazelrc:\r\n 'build' options: --action_env PYTHON_BIN_PATH=/Users/alex/.pyenv/shims/python --action_env PYTHON_LIB_PATH=/Users/alex/.pyenv/versions/3.8.6/lib/python3.8/site-packages --python_path=/Users/alex/.pyenv/shims/python\r\nINFO: Reading rc options for 'build' from /Users/alex/Downloads/tensorflow-r2.12/.bazelrc:\r\n 'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils\r\nINFO: Found applicable config definition build:short_logs in file /Users/alex/Downloads/tensorflow-r2.12/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /Users/alex/Downloads/tensorflow-r2.12/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:release_cpu_macos in file /Users/alex/Downloads/tensorflow-r2.12/.bazelrc: --config=release_base --config=avx_linux\r\nINFO: Found applicable config definition build:release_base in file /Users/alex/Downloads/tensorflow-r2.12/.bazelrc: --config=v2 --distinct_host_configuration=false\r\nINFO: Found applicable config definition build:v2 in file /Users/alex/Downloads/tensorflow-r2.12/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:avx_linux in file /Users/alex/Downloads/tensorflow-r2.12/.bazelrc: --copt=-mavx --host_copt=-mavx\r\nINFO: Found applicable config definition build:macos in file /Users/alex/Downloads/tensorflow-r2.12/.bazelrc: --apple_platform_type=macos --copt=-DGRPC_BAZEL_BUILD --copt=-w --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17\r\nINFO: Build options --action_env and --python_path have changed, discarding analysis cache.\r\nINFO: Analyzed target //tensorflow/tools/pip_package:build_pip_package (584 packages loaded, 34021 targets configured).\r\nINFO: Found 1 target...\r\nERROR: /Users/alex/Downloads/tensorflow-r2.12/tensorflow/BUILD:1591:19: Executing genrule //tensorflow:tf_python_api_gen_v2 failed: (Segmentation fault): bash failed: error executing command /bin/bash -c ... (remaining 1 argument skipped)\r\n2023-04-11 13:22:35.852895: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n/bin/bash: line 1: 53085 Segmentation fault: 11 bazel-out/darwin-opt/bin/tensorflow/create_tensorflow.python_api_tf_python_api_gen_v2 --root_init_template=tensorflow/api_template.__init__.py --apidir=bazel-out/darwin-opt/bin/tensorflow_api/v2/ --apiname=tensorflow --apiversion=2 --compat_apiversion=1 --compat_apiversion=2 --compat_init_template=tensorflow/compat_template_v1.__init__.py --compat_init_template=tensorflow/compat_template.__init__.py --packages=tensorflow.python,tensorflow.dtensor.python.accelerator_util,tensorflow.dtensor.python.api,tensorflow.dtensor.python.config,tensorflow.dtensor.python.d_checkpoint,tensorflow.dtensor.python.d_variable,tensorflow.dtensor.python.input_util,tensorflow.dtensor.python.layout,tensorflow.dtensor.python.mesh_util,tensorflow.dtensor.python.tpu_util,tensorflow.dtensor.python.save_restore,tensorflow.lite.python.analyzer,tensorflow.lite.python.lite,tensorflow.lite.python.authoring.authoring,tensorflow.python.modules_with_exports --output_package=tensorflow._api.v2 --use_relative_imports=True --loading=static --loading=default bazel-out/darwin-opt/bin/tensorflow/_api/v2/v2.py bazel-out/darwin-opt/bin/tensorflow/_api/v2/__internal__/__init__.py bazel-out/darwin-opt/bin/tensorflow/_api/v2/__internal__/autograph/__init__.py bazel-out/darwin-opt/bin/tensorflow/_api/v2/__internal__/decorator/__init__.py bazel-out/darwin-opt/bin/tensorflow/_api/v2/__internal__/dispatch/__init__.py 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bazel-out/darwin-opt/bin/tensorflow/_api/v2/compat/v1/train/experimental/__init__.py bazel-out/darwin-opt/bin/tensorflow/_api/v2/compat/v1/train/queue_runner/__init__.py bazel-out/darwin-opt/bin/tensorflow/_api/v2/compat/v1/types/__init__.py bazel-out/darwin-opt/bin/tensorflow/_api/v2/compat/v1/types/experimental/__init__.py bazel-out/darwin-opt/bin/tensorflow/_api/v2/compat/v1/user_ops/__init__.py bazel-out/darwin-opt/bin/tensorflow/_api/v2/compat/v1/version/__init__.py bazel-out/darwin-opt/bin/tensorflow/_api/v2/compat/v1/xla/__init__.py bazel-out/darwin-opt/bin/tensorflow/_api/v2/compat/v1/xla/experimental/__init__.py bazel-out/darwin-opt/bin/tensorflow/_api/v2/compat/v1/compat/v1/__init__.py bazel-out/darwin-opt/bin/tensorflow/_api/v2/compat/v1/compat/v2/__init__.py bazel-out/darwin-opt/bin/tensorflow/_api/v2/compat/v1/compat/v1/compat/__init__.py bazel-out/darwin-opt/bin/tensorflow/_api/v2/compat/v1/compat/v2/compat/__init__.py bazel-out/darwin-opt/bin/tensorflow/_api/v2/compat/v2/compat/v1/__init__.py bazel-out/darwin-opt/bin/tensorflow/_api/v2/compat/v2/compat/v2/__init__.py bazel-out/darwin-opt/bin/tensorflow/_api/v2/compat/v2/compat/v1/compat/__init__.py bazel-out/darwin-opt/bin/tensorflow/_api/v2/compat/v2/compat/v2/compat/__init__.py\r\nTarget //tensorflow/tools/pip_package:build_pip_package failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nERROR: /Users/alex/Downloads/tensorflow-r2.12/tensorflow/python/tools/BUILD:82:10 Middleman _middlemen/tensorflow_Spython_Stools_Sfreeze_Ugraph-runfiles failed: (Segmentation fault): bash failed: error executing command /bin/bash -c ... (remaining 1 argument skipped)\r\nINFO: Elapsed time: 8442.628s, Critical Path: 700.25s\r\nINFO: 15803 processes: 817 internal, 14986 local.\r\nFAILED: Build did NOT complete successfully\r\n```", "Couldn't solve this issue with different hacks.\r\nInstalled clean Python 3.11 and it start works out of the box.", "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/60081\">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/60081\">No</a>\n" ]

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[ "Looks like TF 2.12.0 tag is created from r2.11 branch instead of r2.12. Because r2.12 branch shows correct version in setup.py.", "@npanpaliya \r\nThe version of TF in v2.12.0 tag is updated to 2.12.0. Please check this [link](https://github.com/tensorflow/tensorflow/blob/v2.12.0/tensorflow/tools/pip_package/setup.py#L50) and confirm the same.\r\n\r\nThank you!", "> @npanpaliya The version of TF in v2.12.0 tag is updated to 2.12.0. Please check this [link](https://github.com/tensorflow/tensorflow/blob/v2.12.0/tensorflow/tools/pip_package/setup.py#L50) and confirm the same.\r\n> \r\n> Thank you!\r\n\r\nYes, I did notice this yesterday night that it is fixed. Thank you for fixing this. ", "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/60080\">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/60080\">No</a>\n" ]

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[ "@petered Thank you for reporting an issue!\r\nCould you please provide a complete standalone code to replicate this issue?\r\nThank you!", "Out of curiosity. Do you know if it changes anything with/without GPU? \r\n\r\n@sushreebarsa this should be enough for you to reproduce ...\r\n\r\n```python\r\nimport tensorflow as tf\r\nassert int(tf.image.convert_image_dtype(1., tf.uint32))==2**32-1\r\n```", "Thanks for looking into it @sushreebarsa and thanks @DEKHTIARJonathan for responding. I never tried with GPU. Other possibly relevant info - the Mac in question (which provides the correct, continuous version `convert_image_dtype`) is an M1 Mac. ", "@petered Could you please let us know the exact command you have used to install tensorflow here, it would help us to analyze the issue. Thank you!", "I believe it was just `pip install tensorflow` though I don't have the command-history available at the moment. Are you not able to reproduce it?", "@petered , @DEKHTIARJonathan \r\n\r\nThe behaviour is due to the device the operation being executed.Please check the results on Colab(Linux environment) in the attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/5171e789dd75342c0711d9f6405fc4dd/60078.ipynb) with CPU and GPU respectively. The issue is due to casting behaviour on CPU vs GPU with undefined/overflow values.\r\n\r\nPlease refer to the developer comment for similar issue [here](https://github.com/tensorflow/tensorflow/issues/58749#issuecomment-1467086661).\r\n\r\nOn MacOS by default the operation seems executing on GPU and on WIndows it is executing on CPU.Hence you are getting different results here.\r\n\r\nThankyou!", "Thanks @SuryanarayanaY for looking into that. But it seems MacOS is getting the correct answer on CPU, not just GPU. (I have an M1 2020 Macbook air). Running\r\n\r\n```\r\nimport tensorflow as tf\r\nprint(tf.__version__)\r\nwith tf.device('CPU'):\r\n print('With CPU:')\r\n print(tf.image.convert_image_dtype(1., tf.uint32))\r\n```\r\nPrints:\r\n```\r\n2.9.1\r\nWith CPU:\r\ntf.Tensor(4294967295, shape=(), dtype=uint32)\r\n```\r\n\r\n... Which is the \"correct\" answer.", "This is due to undefined behaviour with cast overflow values. Please refer to this [comment](https://github.com/tensorflow/tensorflow/issues/58749#issuecomment-1467086661) for more context. ", "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.", "Activity!\n\nOn Tue, Apr 25, 2023, 6:54 PM github-actions[bot] ***@***.***>\nwrote:\n\n> This issue is stale because it has been open for 7 days with no activity.\n> It will be closed if no further activity occurs. Thank you.\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/tensorflow/tensorflow/issues/60078#issuecomment-1522648481>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AAIYO7ZJ2FOZIQNVWZZLY5DXDB55VANCNFSM6AAAAAAWESRCCY>\n> .\n> You are receiving this because you were mentioned.Message ID:\n> ***@***.***>\n>\n", "Hi @petered ,\r\n\r\nThe issue has been fixed in tf-nightly(2.14.0-dev20230503).Please refer to attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/d168fa4aa643a64d269771ad75bfaa55/60078_r1.ipynb).\r\n\r\nBut there is one point to note here. Images that are represented using floating point values are expected to have values in the range [0,1).If you are using floats outside this range you need to set `saturate=True` in the API. \r\n\r\nSince floating point inputs to integer types may lead to over/underflow problems. Set saturate to True to avoid such problem in problematic conversions. If enabled, saturation will clip the output into the allowed range before performing a potentially dangerous cast. The same is documented now and you can refer the API Doc [source](https://www.tensorflow.org/api_docs/python/tf/image/convert_image_dtype) for more explanation.\r\n\r\nThank you!\r\n\r\n\r\n", "That's great, thank you for the fix.\n\nOn Wed, May 3, 2023, 10:52 PM SuryanarayanaY ***@***.***>\nwrote:\n\n> Hi @petered <https://github.com/petered> ,\n>\n> The issue has been fixed in tf-nightly(2.14.0-dev20230503).Please refer to\n> attached gist\n> <https://colab.research.google.com/gist/SuryanarayanaY/d168fa4aa643a64d269771ad75bfaa55/60078_r1.ipynb>\n> .\n>\n> But there is one point to note here. Images that are represented using\n> floating point values are expected to have values in the range [0,1).If you\n> are using floats outside this range you need to set saturate=True in the\n> API.\n>\n> Since floating point inputs to integer types may lead to over/underflow\n> problems. Set saturate to True to avoid such problem in problematic\n> conversions. If enabled, saturation will clip the output into the allowed\n> range before performing a potentially dangerous cast. The same is\n> documented now and you can refer the API Doc source\n> <https://www.tensorflow.org/api_docs/python/tf/image/convert_image_dtype>\n> for more explanation.\n>\n> Thank you!\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/tensorflow/tensorflow/issues/60078#issuecomment-1534128123>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AAIYO73POEXMX5TFVEZ5MJDXEM7YHANCNFSM6AAAAAAWESRCCY>\n> .\n> You are receiving this because you were mentioned.Message ID:\n> ***@***.***>\n>\n", "Hi @petered ,\r\n\r\nCould you please spare some time to close the issue as it resolved already. 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/60078\">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/60078\">No</a>\n" ]

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[ "Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/60077/checks?check_run_id=12210034234) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.", "Hi @jongkweh Can you please sign CLA. Thank you!" ]

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[ "Fixed issues found by PyLint", "Hi @pjannaty Can you please review this PR ? Thank you!", "Hi @pjannaty Can you please review this PR ? Thank you!", "Hi @pjannaty Can you please review this PR ? Thank you!", "Hi @pjannaty Can you please review this PR ? Thank you!", "Hi @drivanov, @pjannaty I'm going to go ahead and close this PR, because it seems to have stalled. If you're still interested in pursing this (and responding to my comments), please feel free to reopen! Thank you!" ]

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[ "@bfontain @rainwoodman @pjannaty ", "FYI: @RuijiaoSun ", "Thanks for the PR. The changes looked good to me. \r\n\r\nI'll pull it internally to give the XLA part of the logic a try on TPUs.", "Good news! The tests for the XLA code path passed on our V3 TPU machines. \r\n" ]

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[ "@SamuelMarks,\r\nFrom tensorflow v2.12, removed redundant packages **tensorflow-gpu and tf-nightly-gpu**. These packages were removed and replaced with packages that direct users to switch to **tensorflow or tf-nightly respectively**. \r\n\r\nPlease take a look at the official release doc for the reference.\r\nhttps://github.com/tensorflow/tensorflow/releases\r\n\r\nSince TensorFlow 2.1, the only difference between these two sets of packages was their names, so there is no loss of functionality or GPU support. See https://pypi.org/project/tensorflow-gpu for more details.\r\nhttps://pypi.org/project/tensorflow-gpu/\r\n\r\n`Tensorflow-gpu` has been removed. Please install tensorflow instead. The tensorflow package supports GPU accelerated operations via Nvidia CUDA. Thank you!", "Ah, ok.\r\n\r\nJust to confirm, TensorFlow doesn't support my Navi 22 [Radeon RX 6700/6700 XT/6750 XT / 6800M]?\r\n\r\n(I might need to look closer into ROCm)", "@SamuelMarks,\r\nThe **!pip install tensorflow-gpu and tf-nightly-gpu** were removed from tensorflow v2.12. We can directly use **pip install tensorflow** for the gpu.\r\nhttps://pypi.org/project/tensorflow-gpu/\r\n Thank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60074\">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/60074\">No</a>\n" ]

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[ "CC @fcoUnda @learning-to-play ", "Closing this -- it's not needed." ]

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[ "@suyash-narain \r\nCould you please provide detailed steps of the tflite imagenet evaluation tool to replicate the issue reported here ?\r\n\r\nThank you!", "> @suyash-narain Could you please provide detailed steps of the tflite imagenet evaluation tool to replicate the issue reported here ?\r\n> \r\n> Thank you!\r\n\r\n@tiruk007 Please find the detailed steps I exercised towards building the provided tflite imagenet evaluation tool:\r\n\r\n- clone tensorflow from github:\r\n\r\n> $ git clone \"https://github.com/tensorflow/tensorflow.git\"\r\n> $ cd tensorflow\r\n\r\n- Download ILSVRC validation dataset consisting of 50k images from http://image-net.org/request: ILSVRC2012_img_val https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar\r\n- Download ILSVRC 2012 dev kit: ILSVRC2012_devkit_t12 (tasks1&2) https://image-net.org/data/ILSVRC/2012/ILSVRC2012_devkit_t12.tar.gz\r\n- Generate Ground Truth Labels:\r\n\r\n> python /tensorflow/lite/tools/evaluation/tasks/imagenet_image_classification/generate_validation_labels.py \\\r\n> --ilsvrc_devkit_dir=path/to/ILSVRC2012_devkit_t12 \\\r\n> --validation_labels_output=output_labels.txt\r\n\r\n- Download imagenet labels from https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt\r\n\r\n- Build and run on Ubuntu desktop:\r\n\r\n> bazel run -c opt \\\r\n> -- \\\r\n> //tensorflow/lite/tools/evaluation/tasks/imagenet_image_classification:run_eval \\\r\n> --model_file=mbnv2_model_test1.tflite \\\r\n> --ground_truth_images_path=path/to/ILSVRC2012_img_val \\\r\n> --ground_truth_labels=path/to/output_labels.txt \\\r\n> --model_output_labels=path/to/ImageNetLabels.txt \\\r\n> --output_file_path=accuracy_output.txt \\\r\n> --num_images=0 # Run on all images.\r\n> \r\n\r\n\r\nWhen i run this on a tflite model sourced from tfhub: https://storage.googleapis.com/download.tensorflow.org/models/tflite_11_05_08/mobilenet_v2_1.0_224.tgz\r\ni get the below results:\r\n\r\n> INFO: Num evaluation runs: 50000\r\n> INFO: Preprocessing latency: avg=3790.96(us), std_dev=0(us)\r\n> INFO: Inference latency: avg=6675.06(us), std_dev=206(us)\r\n> INFO: Top-1 Accuracy: 0.71922\r\n> INFO: Top-2 Accuracy: 0.82604\r\n> INFO: Top-3 Accuracy: 0.86718\r\n> INFO: Top-4 Accuracy: 0.8892\r\n> INFO: Top-5 Accuracy: 0.90452\r\n> INFO: Top-6 Accuracy: 0.9152\r\n> INFO: Top-7 Accuracy: 0.9234\r\n> INFO: Top-8 Accuracy: 0.9298\r\n> INFO: Top-9 Accuracy: 0.93518\r\n> INFO: Top-10 Accuracy: 0.9394\r\n\r\nTop1 ~72% which is very close to actual mobilenetv2 accuracy on imagenet.\r\n\r\nBut when i run the same tool on the converted tflite model which I had mentioned in issue, i get accuracy of 43% \r\n", "Is it because of the way image is being processed before bring provided as input to the model?\r\nI convert the model in the similar way as the mnist example provided here:\r\nhttps://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/performance/post_training_integer_quant.ipynb\r\n\r\ndo i need to further preprocess the images?", "Hi @tiruk007 @pjpratik \r\nI changed my representative dataset, and used images from imagenet dataset for my representative dataset to convert model to int8 and uint8 format. \r\nOn checking the accuracy against the imagenet evaluation tool, Igot the following accuracies:\r\n\r\n**accuracy for INT8 tflite:**\r\n\r\n> INFO: Num evaluation runs: 50000\r\n> INFO: Preprocessing latency: avg=3807.62(us), std_dev=0(us)\r\n> INFO: Inference latency: avg=5723.94(us), std_dev=96(us)\r\n> INFO: Top-1 Accuracy: 0.63438\r\n> INFO: Top-2 Accuracy: 0.7515\r\n> INFO: Top-3 Accuracy: 0.80124\r\n> INFO: Top-4 Accuracy: 0.83126\r\n> INFO: Top-5 Accuracy: 0.8516\r\n> INFO: Top-6 Accuracy: 0.86576\r\n> INFO: Top-7 Accuracy: 0.87676\r\n> INFO: Top-8 Accuracy: 0.88508\r\n> INFO: Top-9 Accuracy: 0.89138\r\n> INFO: Top-10 Accuracy: 0.89808\r\n> \r\n\r\nBut interesting thing to note is if i don't convert this model to int8 tflite and let it remain as default float32 tflite model, my accuracies are way lower:\r\n\r\n**accuracy for fp32 tflite:** \r\n> \r\n> INFO: Num evaluation runs: 50000\r\n> INFO: Preprocessing latency: avg=3837.7(us), std_dev=0(us)\r\n> INFO: Inference latency: avg=16046.3(us), std_dev=486(us)\r\n> INFO: Top-1 Accuracy: 0.43192\r\n> INFO: Top-2 Accuracy: 0.5436\r\n> INFO: Top-3 Accuracy: 0.60002\r\n> INFO: Top-4 Accuracy: 0.6364\r\n> INFO: Top-5 Accuracy: 0.66124\r\n> INFO: Top-6 Accuracy: 0.68194\r\n> INFO: Top-7 Accuracy: 0.69918\r\n> INFO: Top-8 Accuracy: 0.71268\r\n> INFO: Top-9 Accuracy: 0.72398\r\n> INFO: Top-10 Accuracy: 0.73382\r\n\r\nthis is odd. shouldn't top1 accuracies of float32 be higher than INT8? mobilenetv2 fp32 should give me ~71% whereas int8 should give me about ~63% according to data mentioned here: https://www.tensorflow.org/lite/performance/model_optimization#quantization\r\n\r\n", "Hi @suyash-narain \r\n\r\nThe input images for the tensorflow hub model are expected to have color values in the range [0,1], following the [common image input](https://www.tensorflow.org/hub/common_signatures/images#input) conventions as per the [documentation](https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/5).\r\n\r\nAlso, we might have to apply `softmax` activation as the model returns raw logits as per the this [colab](https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/image_classification.ipynb). Please check this [example](https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/image_classification.ipynb) on image classification using tensorflow hub.\r\n\r\nThanks.", "Hi @pjpratik \r\n\r\nthank you for your reply. So you mean to say I cannot use the imagenet classification evaluation tool for tflite with models sourced from tfhub since they require input image color values in range [0,1] but the imagenet tool fails with that?\r\nthe output from the imagenet tool seems like softmax activation is already applied as i get top classifications between [0-1]\r\n\r\nhow can i use the tflite imagenet classification tool against this mobilenetv2 modle sourced from tfhub to get correct accuracies? \r\n\r\nif i source the model from keras application layers as below:\r\n\r\n> model = tf.keras.applications.mobilenet_v2.MobileNetV2(\r\n> input_shape=None,\r\n> alpha=1.0,\r\n> include_top=True,\r\n> weights='imagenet',\r\n> input_tensor=None,\r\n> pooling='avg',\r\n> classes=1000,\r\n> classifier_activation='softmax'\r\n> )\r\n> converter = tf.lite.TFLiteConverter.from_keras_model(model)\r\n> tflite_file = \"mobilenet_v2_keras_model.tflite\"\r\n> with open(tflite_file, 'wb') as f:\r\n> f.write(converter.convert())\r\n\r\nin this case i get perfect accuracies (~71.9%) as mentioned in official documentation) while running against imagenet evaluation tool provided by tflite. \r\nSo what different thing i need to do against models sourced from tfhub to get similar accuracies as i am getting with keras application layer model?\r\n\r\nthanks", "Hi @suyash-narain \r\n\r\nI have observed that the default normalization the tool does is for the output range `[-1,1]`. \r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/0c7968df89e4e3edb7c48d6b0f50e3a9a477f81a/tensorflow/lite/tools/evaluation/stages/image_preprocessing_stage.h#L168\r\n\r\nThe Mobilenetv2 from keras application applies the `tf` normalization which normalizes to the output range `[-1,1]`. Please check the source code below\r\n\r\nhttps://github.com/keras-team/keras-applications/blob/06fbeb0f16e1304f239b2296578d1c50b15a983a/keras_applications/mobilenet.py#L84\r\n\r\nhttps://github.com/keras-team/keras-applications/blob/06fbeb0f16e1304f239b2296578d1c50b15a983a/keras_applications/imagenet_utils.py#L42\r\n\r\nFor the tool to normalize the data in `[0,1]`, replace \r\n\r\n `AddNormalizationStep(127.5, 1.0 / 127.5);` with\r\n\r\n`AddNormalizationStep(0.0, 1.0 / 255.0);` and run the tool. I have tested with fp32 model converted from tf hub with code below\r\n```\r\nm = tf.keras.Sequential([hub.KerasLayer(\"https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/5\")])\r\n\r\nm.build([None, 224, 224, 3])# Batch input shape.\r\n\r\n\r\nconverter = tf.lite.TFLiteConverter.from_keras_model(m)\r\nconverter.optimizations = [tf.lite.Optimize.DEFAULT]\r\n\r\ntflite_file = \"mbnv2_model_fp32_test1.tflite\"\r\nwith open(tflite_file, 'wb') as f:\r\n f.write(converter.convert())\r\n``` \r\nand obtained the following results.\r\n\r\n<img width=\"416\" alt=\"Screenshot 2023-03-30 at 4 57 41 PM\" src=\"https://user-images.githubusercontent.com/118897289/228825703-aab2094d-1e47-4995-8712-c8fde0d29fb5.png\">\r\n\r\nThanks.", "thanks @pjpratik \r\nthis worked for me. \r\n\r\nIs there a similar evaluation tool for pretrained tf models as well?\r\nI am trying to use the same pretrained tf model on the same dataset https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar \r\nand ground truth labels generated using \r\n\r\n> python /tensorflow/lite/tools/evaluation/tasks/imagenet_image_classification/generate_validation_labels.py\r\n> --ilsvrc_devkit_dir=path/to/ILSVRC2012_devkit_t12\r\n> --validation_labels_output=output_labels.txt\r\n\r\nHow do i preprocess such a dataset wherein i have 50000 images in a test directory and a labels.txt file containing ground truth labels for these 50000 images and use the same against the pretrained mobilenet_v2 saved_model downloaded from: https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/5\r\n\r\nthanks", "Hi @suyash-narain, glad it worked.\r\n\r\nI see that a ticket has already been opened #60181 for that issue and it is being tracked there. \r\n\r\nFeel free to close this issue if it is resolved.\r\n\r\nThanks.", "Hi @pjpratik \r\n\r\nthanks for your reply. I had one more question. What are the official accuracies for mobilenet_v2 and quantized mobilenet_v2 on imagenet?\r\n\r\nfor fp32, I know it should be 72.9%\r\n\r\nfor int8/uint8 it is 63.7% (post training quantization) or 70.9% (quantization aware training) as mentioned here https://www.tensorflow.org/lite/performance/model_optimization#quantization\r\n\r\nbut when I evaluate the accuracy on quantized mobilenet_v2 model sourced from https://tfhub.dev/tensorflow/lite-model/mobilenet_v2_1.0_224_quantized/1/default/, I get accuracy of 71.5% which is higher than both the mentioned methods.\r\n\r\nWhen I quantize model sourced from https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/5 and perform post training quantization on the same using a representative dataset consisting of 100 images from imagenet dataset, I still get a 71.30% accuracy for int8 and 71.25% accuracy for uint8 quantized tflite model when in reality i should be getting 63% right?\r\n\r\nBut when I perform post training quantization on model sourced from keras application layers,\r\n\r\n> model = tf.keras.applications.mobilenet_v2.MobileNetV2(\r\n> input_shape=None,\r\n> alpha=1.0,\r\n> include_top=True,\r\n> weights='imagenet',\r\n> input_tensor=None,\r\n> pooling='avg',\r\n> classes=1000,\r\n> classifier_activation='softmax'\r\n> )\r\n\r\nin this case, I get an accuracy of 63.43% on int8 and 63.69% on uint8 quantized models while running the imagenet evaluation tool. \r\n\r\nwhich models were the original accuracies as mentioned in documentation obtained upon? \r\n\r\nthanks", "Hi @suyash-narain \r\n\r\nI think the reported accuracies are using the test set of 100,000 images whereas here it is being tested on val data. Incase of quantized tflite models, the models might have been trained using QAT and when tested on val data might report higher accuracies as. Inorder to reproduce the reported accuracies, we might need to replicate the experiments with the same test data the models are tested on. \r\n\r\nThanks.", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "> \r\n\r\nthanks @pjpratik \r\nI will close this issue", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60072\">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/60072\">No</a>\n" ]

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[ "Hi @trickiwoo, apologies for the delay.\r\nI was able to replicate the issue in Colab using TF v2.11. Please find the gist [here](https://colab.sandbox.google.com/gist/synandi/411295c65f63287aa3070ca3944eb389/60071.ipynb). It seems like we have to dig deep into the issue, we'll update here soon. Thank you! ", "`None` is a placeholder for uniformly zero.", "@trickiwoo , As per the above comment,` tf.experimental.numpy.fabs` is working as intended. Please fell free to close the issue. Thanks!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "Thanks! I am closing the issue as it is intended behavior.", "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/60071\">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/60071\">No</a>\n" ]

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[]

[]

[ "Do not merge yet" ]

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[ "This error typically occurs when using an older version of TensorFlow with code that was written for a newer version. In this case, it looks like the code is trying to access the **GraphKeys** attribute of the **tensorflow** module, which was removed in **TensorFlow 2.0**.\r\n\r\nTo fix this error, you can try replacing **tf.GraphKey**s with **tf.compat.v1.GraphKeys** throughout your code. This should allow it to run with TensorFlow 2.0 and newer versions.\r\n\r\nand there can be also other issue like It's possible that the CleverHans library is not installed on your system or it is not installed correctly. You can try reinstalling it using the following command:\r\n```\r\n!pip install cleverhans==3.1.0\r\n```", "Hello, thanks for replying. I saw a description like that on internet, I\ntried it but it seems like it doesn't work on google colab. Maybe i need to\ntry more deeply again.\n Thanks anyway.\n\nOn Sat, Mar 25, 2023, 9:29 PM Durgesh Patel ***@***.***>\nwrote:\n\n> This error typically occurs when using an older version of TensorFlow with\n> code that was written for a newer version. In this case, it looks like the\n> code is trying to access the *GraphKeys* attribute of the *tensorflow*\n> module, which was removed in *TensorFlow 2.0*.\n>\n> To fix this error, you can try replacing *tf.GraphKey*s with\n> *tf.compat.v1.GraphKeys* throughout your code. This should allow it to\n> run with TensorFlow 2.0 and newer versions.\n>\n> and there can be also other issue like It's possible that the CleverHans\n> library is not installed on your system or it is not installed correctly.\n> You can try reinstalling it using the following command:\n>\n> !pip install cleverhans==3.1.0\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/tensorflow/tensorflow/issues/60069#issuecomment-1483915384>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/A5WEGCKITXYOQOMH3CK7UXDW55ISZANCNFSM6AAAAAAWEHG2AM>\n> .\n> You are receiving this because you authored the thread.Message ID:\n> ***@***.***>\n>\n", "@fabixs Deprecated apis are causing this issue in your implementation. Could you try to use the newer apis by replacing tf.GraphKeys with tf.compat.v1.GraphKeys in your code. Please install the Cleverhans again and rerun the kernel which will install the libraries. Thank you!", " @sushreebarsa I appreciate your reply. But I don't have any line\ntf.GraphKeys in my code, that's why I'm pretty confused. Looks, this is the\nheader of the code right here:\ngoogle colab points out an error from line 4 to line 9 .\n1)import logging\n2)import numpy as np\n3)import tensorflow.compat.v1 as tf\ntf.disable_v2_behavior()\n\n4)from cleverhans.attacks import SaliencyMapMethod, FastGradientMethod\n5)from cleverhans.utils_tf import model_train , model_eval, batch_eval,\n model_argmax\n6)from cleverhans.attacks_tf import jacobian_graph\n7)from cleverhans.utils import other_classes\n8)from cleverhans.attacks import CarliniWagnerL2\n9)from cleverhans.utils_keras import KerasModelWrapper\n\n\n This error is :\n\nAttributeError: module 'tensorflow' has no attribute 'GraphKeys'\n\nNot only I installed Cleverhans3.1.0 but I also installed tensorflow\n2.11.0, keras 2.11.0, and all required packages\nAs I said before, I don't have any place in my code where I used\ntf.GraphKey.\nThis error is gonna drive me crazy, I have already tried several methods\nwithout getting a good solution.\nThanks in advance to anyone who can suggest me a better way to solve this\nproblem.\n\n\n\n\n\n\n\n\n\nOn Sat, Mar 25, 2023, 9:29 PM Durgesh Patel ***@***.***>\nwrote:\n\n> This error typically occurs when using an older version of TensorFlow with\n> code that was written for a newer version. In this case, it looks like the\n> code is trying to access the *GraphKeys* attribute of the *tensorflow*\n> module, which was removed in *TensorFlow 2.0*.\n>\n> To fix this error, you can try replacing *tf.GraphKey*s with\n> *tf.compat.v1.GraphKeys* throughout your code. This should allow it to\n> run with TensorFlow 2.0 and newer versions.\n>\n> and there can be also other issue like It's possible that the CleverHans\n> library is not installed on your system or it is not installed correctly.\n> You can try reinstalling it using the following command:\n>\n> !pip install cleverhans==3.1.0\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/tensorflow/tensorflow/issues/60069#issuecomment-1483915384>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/A5WEGCKITXYOQOMH3CK7UXDW55ISZANCNFSM6AAAAAAWEHG2AM>\n> .\n> You are receiving this because you authored the thread.Message ID:\n> ***@***.***>\n>\n", "@SuryanarayanaY I was able to replicate the issue on colab, please find the [gist](https://colab.research.google.com/gist/sushreebarsa/a23b461c0c330971a08016f929926905/60069.ipynb) here. \r\nThank you!", "@fabixs ,\r\n\r\nIs that third party library `cleverhans` using TF 1. versions ? I am sorry to say we are not supporting TF1.x versions currently. Also this is related to third party library `cleverhans` which we are not the right team to address. If you can share a code snippet(2.X version) that can reproduce this error without any third party libraries we will have a look. \r\n\r\nThanks !\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60069\">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/60069\">No</a>\n" ]

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[ "Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/60068/checks?check_run_id=12197353885) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.", "Hi @Suyash0096 Can you please sign CLA. Thank you!" ]

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[ "@tilakrayal Do you mind if I take on this issue? Looks like a good beginner issue to me", "@melissamullen I am able to reproduce this from the master build. Let me see what's the right approach here", "@melissamullen,\r\nThank you for raising the issue. I tried to execute the given code and it was executed as mentioned. The **Uniform_row_length** method can be used to create RaggedTensors with multiple uniform outer dimensions. For example, a **RaggedTensor** with shape `[2, 2, None]` can be constructed with this method from a RaggedTensor values with shape `[4, None]`\r\n```\r\nvalues = tf.ragged.constant([[1, 2, 3], [4], [5, 6], [7, 8, 9, 10, 9], [7, 8, 9, 10, 11, 12]])\r\nprint(values.shape)\r\n\r\n\r\nrt1 = tf.RaggedTensor.from_uniform_row_length(values, 5)\r\nprint(rt1)\r\n\r\nprint(rt1.shape)\r\n\r\noutput:\r\n-------------\r\n\r\n(5, None)\r\n<tf.RaggedTensor [[[1, 2, 3], [4], [5, 6], [7, 8, 9, 10, 9], [7, 8, 9, 10, 11, 12]]]>\r\n(1, 5, None)\r\n```\r\n\r\n\r\n**tf.math.Reduce_mean** reduces `input_tensor` along the dimensions given in axis by computing the mean of elements across the dimensions in axis. Unless keepdims is true, the rank of the tensor is reduced by 1 for each of the entries in axis, which must be unique. If keepdims is true, the reduced dimensions are retained with length 1.\r\n```\r\n#print(\"Averaging along ragged dimension (axis = 2)...\\n\")\r\n\r\n# Reduce mean along ragged axis\r\nprint(\"* Averaged Tensor: *\\n\")\r\navg_ragged_dim = tf.reduce_mean(rt1, axis=2)\r\nprint(f\"avg_ragged_dim = {avg_ragged_dim}\\n\")\r\nprint(f\"Shape of avg_ragged_dim: {avg_ragged_dim.shape}\\n\")\r\n\r\noutput:\r\n---------------\r\navg_ragged_dim = <tf.RaggedTensor [[2.0, 4.0, 5.5, 8.6, 9.5]]>\r\n\r\nShape of avg_ragged_dim: (1, None)\r\n```\r\nKindly find the gist of it here. [Gist1](https://colab.research.google.com/gist/tilakrayal/e67baf0e4eb5a5dbfa6d00082ed3f1ba/untitled1051.ipynb) [Gist2](https://colab.research.google.com/gist/tilakrayal/b9876f684d7c6d607333e1355c5cface/untitled1050.ipynb) Thank you!\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60067\">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/60067\">No</a>\n" ]

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[ "Please don't use \"update <file>\" commit messages. These make it harder to understand at a glance at commit history what the changes are.\r\n\r\nhttps://cbea.ms/git-commit/" ]

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[ "**NoneTensorSpec** is not a public API in TensorFlow, so it is not directly accessible from the public API. However, you can access it through the private module **_tensor_spec**, like this:\r\n\r\n```\r\nfrom tensorflow.python.framework import tensor_spec as _tensor_spec\r\nimport tensorflow as tf\r\n\r\nspec = tf.TensorSpec(shape=(2, 3), dtype=tf.float32)\r\nspec_with_none = tf.TensorSpec(shape=(2, 3), dtype=tf.float32, name='maybe_none')\r\nspecs = [spec, spec_with_none]\r\n\r\n# Find specs with NoneTensorSpec\r\nnone_specs = [s for s in specs if isinstance(s, _tensor_spec.NoneTensorSpec)]\r\n\r\n# Replace NoneTensorSpec with None\r\nfor i, s in enumerate(specs):\r\n if isinstance(s, _tensor_spec.NoneTensorSpec):\r\n specs[i] = None\r\n```", "@pat749, Thanks for your time and effort on responding to the issue.\r\n@hamzamerzic , Can you take a look into the above response, also have you checked `tf.TensorSpec `https://www.tensorflow.org/api_docs/python/tf/TensorSpec?version=nightly if that is something which you are looking for. Thanks!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "Thanks. I just tried this in a public Colab running TF 2.12.0 and I get an `AttributeError: module 'tensorflow.python.framework.tensor_spec' has no attribute 'NoneTensorSpec'`. I looked at the nightly `tf.TensorSpec` API as well, but there is no mention of `NoneTensorSpec`.\r\n\r\nBut it does look like the following works:\r\n\r\n```python\r\nfrom tensorflow.python.data.util import structure\r\n\r\nstructure.NoneTensorSpec()\r\n```" ]

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[ "> \r\n\r\nThanks a lot for the help, but unfortunately the problem is still exisit. The averaged checkpoint have changed almost every name of the variables (1. change the '/' to '.S' ; 2. add '.ATTRIBUTES/VARIABLE_VALUE' to each variable again). Here is the var_list of single checkpoint and the averaged checkpoint with tf.train.list_variables (Left is the single checkpoint and the right is the averaged checkpoint). And I checked the single checkpoint and averaged checkpoint in TF1, they have the same variable name, what's wrong with it? \r\n\r\n\r\n", "@yjiangling Thank you for raising an issue!\r\nCould you please specify the TF version you are using and provide the standalone code to replicate this one?\r\nThank you!", "> @yjiangling Thank you for raising an issue! Could you please specify the TF version you are using and provide the standalone code to replicate this one? Thank you!\r\n\r\nOK, I use the TensorFlow2.2 and TensorFlow2.4. You can use the attach scripts to replicate the issue (the name of the variable of the averaged checkpoint replace the string \"/\" with \".S\"). Please delete the suffix of \".txt\" before run it.\r\n\r\n[avg_checkpoints.py.txt](https://github.com/tensorflow/tensorflow/files/11075893/avg_checkpoints.py.txt)\r\n[gen_checkpoints.py.txt](https://github.com/tensorflow/tensorflow/files/11075897/gen_checkpoints.py.txt)\r\n\r\n\r\nThanks a lot for the help!!!", "@yjiangling Thank you for the response!\r\nFYI, the older version of TF is not actively supported. It is now recommended to use the latest TF version. I am trying to replicate it in the latest TF version and update you soon. Thank you!\r\n", "@yjiangling I tried to replicate this issue on the latest TF version 2.12 and faced different results. Could you please check this [gist](https://colab.research.google.com/gist/sushreebarsa/e6b241f88e3721b38bec0195ef9fd7b8/60064.ipynb) and confirm the same?\r\nThank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "> @yjiangling I tried to replicate this issue on the latest TF version 2.12 and faced different results. Could you please check this [gist](https://colab.research.google.com/gist/sushreebarsa/e6b241f88e3721b38bec0195ef9fd7b8/60064.ipynb) and confirm the same? Thank you!\r\n\r\nThanks a lot for the detail experiments and the replay, I'm sorry for the late reply. I solve this problem with the following step:\r\n1. build model\r\n`model = Model()\r\npred = model.predict() # may use model.call() is also ok`\r\n\r\n2. create train Checkpoint\r\n`ckpt = tf.train.Checkpoint(model=model)`\r\n\r\n3. restore parameter of each checkpoint, get weights and average\r\n```\r\nfor checkpoint in checkpoints:\r\n ckpt.restore(checkpoint).expect_partial()\r\n weights = model.get_weights()\r\n swa_weithts += weights\r\nswa_weithts = [weight/len(checkpoints) for weight in swa_weithts]\r\n```\r\n\r\n4. set weights and save model\r\n```\r\nmodel.set_weights(swa_weithts)\r\nckpt.save(save_path)\r\n```\r\n\r\nMust use model.call() or model.predict() one time, otherwise can't get the weights of the checkpoint, I still do not understand the reason, maybe someone may look into it who is interested.", "> This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.\r\n\r\nThank you, the problem have been solved, you can feel free to close it.", "@yjiangling Thank you for the update!\r\nClosing the issue as the issue has been resolved. \r\nThank you!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60064\">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/60064\">No</a>\n" ]

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[ "It seems like there are two import statements for **CTCModel** from different locations:\r\n```\r\nfrom keras_ctcmodel.CTCModel import CTCModel as CTCModel\r\nfrom CTCModel import CTCModel\r\n```\r\nYou should remove one of the import statements, depending on where the **CTCModel** class is defined in your project.", "\nOk, I understood, thanks a lot.\n________________________________\nDe : Durgesh Patel ***@***.***>\nEnvoyé : samedi 25 mars 2023 21:41\nÀ : tensorflow/tensorflow ***@***.***>\nCc : Achraf EL ASRI ***@***.***>; Author ***@***.***>\nObjet : Re: [tensorflow/tensorflow] CTCModel (Issue #60063)\n\n\nIt seems like there are two import statements for CTCModel from different locations:\n\nfrom keras_ctcmodel.CTCModel import CTCModel as CTCModel\nfrom CTCModel import CTCModel\n\n\nYou should remove one of the import statements, depending on where the CTCModel class is defined in your project.\n\n—\nReply to this email directly, view it on GitHub<https://github.com/tensorflow/tensorflow/issues/60063#issuecomment-1483917680>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/ASD4HAUQYP7L3LQMAYB7753W55KANANCNFSM6AAAAAAWDSQCY4>.\nYou are receiving this because you authored the thread.Message ID: ***@***.***>\n", "@MrAchraf10 Could you please see the installation instructions as below \r\n```\r\n$ pip install keras-ctcmodel\r\n\r\nOR\r\n\r\n$ git clone https://github.com/cyprienruffino/CTCModel\r\n python setup.py install --user\r\n\r\n\r\n```\r\nLet us know if you still face the issue for importing CTCModel using the following \r\n```\r\nfrom keras_ctcmodel.CTCModel import CTCModel as CTCModel\r\n\r\n```\r\nThank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60063\">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/60063\">No</a>\n" ]

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[ "Hi @Eva-An, please try MSVC 2019 (set BAZEL_VC=C:\\Program Files(x86)\\Microsoft Visual Studio\\2019\\Enterprise\\VC). " ]

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[ "Here is a log with the linker errors. It looks like we will need to make sure libtensorflow_cc exports all of these undefined TF symbols via `roots`.\r\nFor the missing mlir symbols, TF hides symbols matching `*mlir*` using the linker scripts to prevent duplication bugs, so we should have tf serving depend on mlir directly in those cases.\r\n\r\n[tfs_build.log](https://github.com/tensorflow/tensorflow/files/11054607/tfs_build.log)\r\n" ]

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[ "loop @aice-support", "@sushreebarsa \r\nany update for this question?", "I have replicated the reported behaviour. With Tf 2.10v I found many files along with xplane.pb being built but with TF2.12 and tf-nightly there is only one file i.e. xplane.pb. I have attached minimal code snippet for replicating the behaviour in attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/dc945ea54171d35a0bd6a65a0d2e6b2b/60060.ipynb).\r\n\r\n**With TF 2.10:**\r\n\r\n<img width=\"1512\" alt=\"Screenshot 2023-04-12 at 2 53 17 PM\" src=\"https://user-images.githubusercontent.com/116063290/231417640-4c1a9ac2-a481-4b5b-96ed-18fd877175ea.png\">\r\n\r\n\r\n**With TF2.12:**\r\n\r\n<img width=\"1512\" alt=\"Screenshot 2023-04-12 at 2 50 09 PM\" src=\"https://user-images.githubusercontent.com/116063290/231417430-2188e423-965d-4b01-b751-087bda35f86c.png\">\r\n\r\n@louie-tsai , I need to check the reasons for this difference in behaviour and will update you if ay workaround. Thanks", "@SuryanarayanaY \r\nthanks. It also works for me to generate trace.json file from xplane.pb file from a tool/script.", "Hi @louie-tsai ,\r\n\r\nHave you used any tool outside Tensorflow for this? Please confirm and also if issue resolved please feel free to close the issue. Thanks!", "@SuryanarayanaY \r\nOh. I haven't had any solution by using other tool.\r\nIf you have a tool to convert it, that option would also work for me.\r\n", "Hi @louie-tsai ,\r\n\r\nThe reason for this behavior after 2.10 version because of the changes made here in the commit https://github.com/tensorflow/tensorflow/commit/52992fc29f00fc743e07e16067f6418af6c489ff.\r\n\r\nThe detailed discussion can be found here https://github.com/tensorflow/tensorflow/issues/58738.\r\n", "@sachinprasadhs \r\nis there a way to get the json.gz from xplane.pb?\r\njson.gz file is helpful for our debugging. We have had some analysis tools depending on it already.\r\nthanks", "@cliveverghese, Could you please take a look into the above request. Thanks!", "You could use pywrap_profiler functions defined in https://github.com/tensorflow/tensorflow/blob/f62622072d3b6de1790861ba749a1bfa043bd1dd/tensorflow/python/profiler/internal/profiler_wrapper.cc to extract the trace.json. \r\n\r\nReferences to how this can be invoked can be found here\r\nhttps://github.com/tensorflow/profiler/blob/85dcfd10656d623330b11c3bbb8afed6418ec533/plugin/tensorboard_plugin_profile/convert/raw_to_tool_data.py\r\n\r\nThis would look like \r\n```\r\npywrap_profiler.xspace_to_tools_data(xspace_file_path, \"trace_viewer\")\r\n```", "@cliveverghese's comment got me to finally get the `.json` out of this annoying file format. I made this Python script (with a patch from @louie-tsai):\r\n\r\n```py\r\nimport argparse\r\n\r\n# Create argument parser\r\nparser = argparse.ArgumentParser(description=\"Convert raw file to tool data\")\r\nparser.add_argument(\"input_file\", help=\"Input file path\")\r\nparser.add_argument(\"output_file\", help=\"Output JSON file path\")\r\n\r\n# Parse the arguments\r\nargs = parser.parse_args()\r\n\r\n# Process the input file\r\nprint(\"\\033[32mImport TensorFlow...\\033[0m\")\r\nimport tensorboard_plugin_profile.convert.raw_to_tool_data as rttd\r\ninput_file = args.input_file\r\nprint(\"\\033[32mXSpace to Tool Data...\\033[0m\")\r\ntv = rttd.xspace_to_tool_data([input_file], \"trace_viewer^\", {'tqx': ''})\r\n\r\nif isinstance(tv, tuple):\r\n tv = tv[0]\r\n\r\n# Write the processed data to the output file\r\noutput_file = args.output_file\r\nprint(\"\\033[32mWriting file...\\033[0m\")\r\nwith open(output_file, \"w\") as f:\r\n f.write(tv)\r\n\r\nprint(\"\\033[32mDone!\\033[0m\")\r\n\r\n```", "thanks a lot! It works for me.\r\nWe will start using this API for our analysis tool.", "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/60060\">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/60060\">No</a>\n", "output of xspace_to_tool_data is tuple data format for my case, so I need to only get first item of the tuple data.", "> output of xspace_to_tool_data is tuple data format for my case, so I need to only get first item of the tuple data.\r\n\r\nI now had this problem too. I put an if in the code to support multiple versions of TensorFlow.\r\nI also added colors for fun. I updated the code in my original answer." ]

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[ "@trickiwoo Though i was able to replicate the issue [here](https://colab.research.google.com/gist/sushreebarsa/5af7133e302705296c699040acddd4de/60059.ipynb), please confirm if this is a duplicate ticket of [this](https://github.com/tensorflow/tensorflow/issues/59960) issue. If so please close this thread as we will follow up with the other one. Thank you!", "Hi @sushreebarsa , thanks for replicating it! It was a similar issue but for different APIs", "@trickiwoo Thank you for the confirmation.\r\n@SuryanarayanaY Could you please have a look at this issue?\r\nThank you!" ]

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[ "Hi @tatwaichong Can you please resolve conflicts? Thank you!", "Hi @tatwaichong Any update on this PR? Please. Thank you!", "I believe this is waiting an upstream change to be submitted and would be integrated along with that.", "This was landed successfully as part off the LLVM integrate." ]

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[ "Note I also opened an Issue at Keras since I am not sure where its fit better.", "@benHeid,\r\nThank you for opening this issue. Development of keras moved to another [repository](https://github.com/keras-team/keras/issues). \r\n\r\nCould you please feel free to close this issue, as this issue is already tracking there in Keras repo.\r\nhttps://github.com/keras-team/tf-keras/issues/230\r\n\r\n Thank you!\r\n", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60057\">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/60057\">No</a>\n" ]

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[ "Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/60056/checks?check_run_id=12153354161) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.", "Hi @gharibian Can you please review this PR ? Thank you!", "Hi @gharibian Can you please review this PR ? Thank you!", "Hi @gharibian Can you please review this PR ? Thank you!", "Hi @gharibian Can you please review this PR ? Thank you!", "Hi @gharibian Can you please review this PR ? Thank you!", "Hi @gharibian Can you please review this PR ? Thank you!", "Hi @gharibian Can you please review this PR ? Thank you!", "You're right, I added the test back with the requested functionality." ]

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[ "@Fabiran \r\nCould you please provide a reproducible code or a toy example to reproduce the issue reported here ?\r\n\r\nThank you!", "Hi, @tiruk007, here is my data [N6.csv](https://github.com/tensorflow/tensorflow/files/11049537/N6.csv) and code.\r\n## model process\r\nimport time\r\nimport numpy as np\r\nimport pandas as pd\r\nimport tensorflow as tf\r\nfrom sklearn.metrics import mean_squared_error, mean_absolute_error\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.preprocessing import StandardScaler\r\nfrom tensorflow.python.keras.layers import LSTM, Dense, Dropout\r\nfrom tensorflow.python.keras.models import Sequential\r\n\r\n#read data\r\ndata = pd.read_csv('N6.csv')\r\n\r\ndata_HR = data['HR'].values\r\n\r\n#z_escore\r\nscaler = StandardScaler()\r\nscaler.fit(data_HR.reshape(-1, 1))\r\ndata_std = scaler.transform(data_HR.reshape(-1, 1))\r\n\r\n#split\r\ndata_for_training, data_for_testing = train_test_split(data_std, test_size=0.2, shuffle=False)\r\n\r\ndata_for_training, data_for_val = train_test_split(data_for_training, test_size=0.2, shuffle=False)\r\n\r\n\r\ndef createXY(dataset, n_past):\r\n dataX = []\r\n dataY = []\r\n for i in range(n_past, len(dataset)):\r\n dataX.append(dataset[i - n_past:i])\r\n dataY.append(dataset[i])\r\n return np.array(dataX), np.array(dataY)\r\n\r\n\r\nwindow_size = 30\r\n\r\ntrainX, trainY = createXY(data_for_training, window_size)\r\nvalX, valY = createXY(data_for_val, window_size)\r\ntestX, testY = createXY(data_for_testing, window_size)\r\n\r\n#reshape to 3D\r\ntrainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))\r\nvalX = np.reshape(valX, (valX.shape[0], valX.shape[1], 1))\r\ntestX = np.reshape(testX, (testX.shape[0], testX.shape[1], 1))\r\n\r\n#build model\r\nmodel = Sequential()\r\nmodel.add(LSTM(30, activation='relu', return_sequences=False, input_shape=(window_size, 1)))\r\nmodel.add(Dropout(0.2))\r\nmodel.add(Dense(1))\r\nmodel.compile(loss='mean_squared_error', optimizer='adam')\r\n\r\nepochs = 25\r\nhistory = model.fit(trainX, trainY, epochs=epochs, batch_size=32, verbose=1, validation_data=(valX, valY))\r\n\r\nmodel.summary()\r\n\r\nprediction_std = model.predict(testX)\r\nprediction = scaler.inverse_transform(prediction_std)\r\n\r\n#convert into tflite\r\nconverter = tf.lite.TFLiteConverter.from_keras_model(model)\r\nconverter.optimizations = [tf.lite.Optimize.DEFAULT]\r\ntflite_model = converter.convert()\r\nwith open(f\"model1.tflite\", \"wb\") as f:\r\n f.write(tflite_model)\r\n\r\n\r\n## arduino nano 33 ble sense\r\n\r\n#include <TensorFlowLite.h>\r\n#include \"model.h\"\r\n#include \"tensorflow/lite/micro/all_ops_resolver.h\"\r\n#include \"tensorflow/lite/micro/micro_interpreter.h\"\r\n#include \"tensorflow/lite/micro/micro_log.h\"\r\n#include \"tensorflow/lite/micro/system_setup.h\"\r\n#include \"tensorflow/lite/schema/schema_generated.h\"\r\n\r\n\r\n// Globals, used for compatibility with Arduino-style sketches.\r\nnamespace {\r\nconst tflite::Model* model = nullptr;\r\ntflite::MicroInterpreter* interpreter = nullptr;\r\nTfLiteTensor* input = nullptr;\r\nTfLiteTensor* output = nullptr;\r\nTfLiteTensor* output_type = nullptr;\r\nTfLiteTensor* output_precip = nullptr;\r\nconstexpr int kTensorArenaSize = 100*1024;\r\n// Keep aligned to 16 bytes for CMSIS\r\nalignas(16) uint8_t tensor_arena[kTensorArenaSize];\r\n} // namespace\r\n\r\n// The name of this function is important for Arduino compatibility.\r\nvoid setup() {\r\n tflite::InitializeTarget();\r\n\r\n // Map the model into a usable data structure. This doesn't involve any\r\n // copying or parsing, it's a very lightweight operation.\r\n model = tflite::GetModel(g_model);\r\n if (model->version() != TFLITE_SCHEMA_VERSION) {\r\n MicroPrintf(\r\n \"Model provided is schema version %d not equal \"\r\n \"to supported version %d.\\n\",\r\n model->version(), TFLITE_SCHEMA_VERSION);\r\n }\r\n\r\n // This pulls in all the operation implementations we need.\r\n // NOLINTNEXTLINE(runtime-global-variables)\r\n static tflite::AllOpsResolver resolver;\r\n\r\n // Build an interpreter to run the model with.\r\n static tflite::MicroInterpreter static_interpreter(\r\n model, resolver, tensor_arena, kTensorArenaSize);\r\n interpreter = &static_interpreter;\r\n\r\n // Allocate memory from the tensor_arena for the model's tensors.\r\n TfLiteStatus allocate_status = interpreter->AllocateTensors();\r\n if (allocate_status != kTfLiteOk) {\r\n MicroPrintf(\"AllocateTensors() failed\");\r\n return;\r\n }\r\n\r\n // Obtain a pointer to the model's input tensor\r\n input = interpreter->input(0);\r\n input->dims->data[0] = 1;\r\n input->dims->data[1] = 30;\r\n input->dims->data[2] = 1;\r\n input->dims->data[3] = 1;\r\n\r\n MicroPrintf(\"size:%d data:%d data1:%d data2:%d \\n\",input->dims->size,input->dims->data[0],input->dims->data[1],input->dims->data[2],input->type);\r\n Serial.println(input->type);\r\n \r\n\r\n\r\n float input_data[1][30][1] = {\r\n {\r\n {0.14},\r\n {0.95},\r\n {0.33},\r\n {0.25},\r\n {0.29},\r\n {0.26},\r\n {0.88},\r\n {0.78},\r\n {0.78},\r\n {0.5},\r\n {0.59},\r\n {0.65},\r\n {0.38},\r\n {0.27},\r\n {0.3},\r\n {0.27},\r\n {0.5},\r\n {0.01},\r\n {0.275},\r\n {0.333},\r\n {0.237},\r\n {0.54},\r\n {0.91},\r\n {0.96},\r\n {0.83},\r\n {0.023},\r\n {0.87},\r\n {0.534},\r\n {0.32},\r\n {0.50}\r\n }\r\n }; // Put your input data here\r\n for (int i = 0; i < 30; i++) {\r\n input->data.f[i] = input_data[0][i][0];\r\n }\r\n\r\n TfLiteStatus invoke_status = interpreter->Invoke();\r\n if (invoke_status != kTfLiteOk) {\r\n MicroPrintf(\"Invoke failed\\n\");\r\n }\r\n\r\n output = interpreter->output(0);\r\n output_type = interpreter->output(1);\r\n output_precip = interpreter->output(0);\r\n\r\n MicroPrintf(\"size:%d data:%d data1:%d type:%c\\n\",output->dims->size,output->dims->data[0],output->dims->data[1],input->type);\r\n\r\n Serial.println(output_type->type);\r\n Serial.println(output_precip->type);\r\n float output_val = output->data.f[0];\r\n\r\n MicroPrintf(\"Output value: %f\\n\", output_val);\r\n \r\n}\r\n\r\nvoid loop() {}", "According to this [1825](https://github.com/tensorflow/tflite-micro/issues/1825) and [patch](https://github.com/tensorflow/tflite-micro/files/11012191/0001-changes-to-add-to-support-int32-types-for-LSTM.patch)， I have tried to modify the CMSIS-NN/add.cpp, the error didn't run, but the output values is NaN. \r\n@tiruk007\r\n\r\n", "@pjpratik \r\nCould you please look into this\r\n\r\nThank you!", "Hi @Fabiran \r\n\r\nI see that you are using TF 2.3. We recommend you to use latest stable version TF 2.11 and also fusion code lab for Keras LSTM conversion which can be found [here](https://www.tensorflow.org/lite/models/convert/rnn#example).\r\n\r\nIn your case,\r\n\r\n```\r\nrun_model = tf.function(lambda x: model(x))\r\n# This is important, let's fix the input size.\r\nBATCH_SIZE = 1\r\nSTEPS = window_size\r\nINPUT_SIZE = 1\r\nconcrete_func = run_model.get_concrete_function(\r\n tf.TensorSpec([BATCH_SIZE, STEPS, INPUT_SIZE], model.inputs[0].dtype))\r\n\r\n# model directory.\r\nMODEL_DIR = \"keras_lstm\"\r\nmodel.save(MODEL_DIR, save_format=\"tf\", signatures=concrete_func)\r\n\r\nconverter = tf.lite.TFLiteConverter.from_saved_model(MODEL_DIR)\r\ntflite_model = converter.convert()\r\nwith open(f\"model1.tflite\", \"wb\") as f:\r\n f.write(tflite_model)\r\n````\r\nCan you please try this and let us know if the issue still persists.\r\n\r\nThanks.", "> Hi @Fabiran\r\n> \r\n> I see that you are using TF 2.3. We recommend you to use latest stable version TF 2.11 and also fusion code lab for Keras LSTM conversion which can be found [here](https://www.tensorflow.org/lite/models/convert/rnn#example).\r\n> \r\n> In your case,\r\n> \r\n> ```\r\n> run_model = tf.function(lambda x: model(x))\r\n> # This is important, let's fix the input size.\r\n> BATCH_SIZE = 1\r\n> STEPS = window_size\r\n> INPUT_SIZE = 1\r\n> concrete_func = run_model.get_concrete_function(\r\n> tf.TensorSpec([BATCH_SIZE, STEPS, INPUT_SIZE], model.inputs[0].dtype))\r\n> \r\n> # model directory.\r\n> MODEL_DIR = \"keras_lstm\"\r\n> model.save(MODEL_DIR, save_format=\"tf\", signatures=concrete_func)\r\n> \r\n> converter = tf.lite.TFLiteConverter.from_saved_model(MODEL_DIR)\r\n> tflite_model = converter.convert()\r\n> with open(f\"model1.tflite\", \"wb\") as f:\r\n> f.write(tflite_model)\r\n> ```\r\n> \r\n> Can you please try this and let us know if the issue still persists.\r\n> \r\n> Thanks.\r\n\r\nHello, @pjpratik. It is the same issue. tensorflow==2.11 and use get_concrete_function.\r\n\r\n\r\n\r\n", "Maybe I find a solution, acorrding to this [issue](https://github.com/tensorflow/tflite-micro/issues/1825) and [patch](https://github.com/tensorflow/tflite-micro/files/11012191/0001-changes-to-add-to-support-int32-types-for-LSTM.patch), and take your advice, what' more, I find that the SRAM of arduino nano 33 ble sense is possibly not enough to run the model, so I reduce the LSTM unit and window size, eg: model.add(LSTM(***20***, activation='relu', return_sequences=False, input_shape=(window_size, 1))). And this model needs to be further optimized. Whether this issue has been specifically resolved remains to be considered.", "Hi @Fabiran Thanks for the information. Is the issue resolved by making those changes?\r\n\r\nI have tested the code in python and found no issue while performing inference using `interpretr.invoke` with provided values.The tflite output was aligned to the model's output. Please find the gist [here](https://colab.research.google.com/gist/pjpratik/6292148fc3144b2cdd4ca6d9a1cc1bd7/60055.ipynb).\r\n\r\nThanks.", "Hi, @pjpratik . It is resolved, thanks for your help!", "Hi @Fabiran, glad it is resolved.\r\n\r\nPlease feel free to close the issue.\r\n\r\nThanks.", "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/60055\">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/60055\">No</a>\n" ]

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[ "Hi @mayankagarwals\r\nApologies for the delay. At the moment you can use TF v2.11 whl file available [here](https://www.tensorflow.org/install/pip#package_location). As far as I know, `tensorflow-2.13.0-cp38-cp38-macosx_12_0_x86_64.whl` is not available in the official document. Hence, you are getting the above error message. Kindly use the stable wheel file which is `tensorflow-2.11.0-cp38-cp38-macosx_10_14_x86_64.whl`. Thank you!", "Hey @synandi \r\n\r\nI was actually trying to build from source with the intention of contributing to tensorflow. While I was not able to build the mac binary, I got around it by spinning up a linux docker container, mounting my local directory there and building linux binary. Thanks for the response though :) \r\n\r\nClosing the issue as I think mac binaries are unstable", "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/60054\">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/60054\">No</a>\n" ]

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[ "@suyash-narain \r\nCould you please try to convert the model by using `hub.load()` or `hub.KerasLayer()` as per [Doc1](https://www.tensorflow.org/hub/migration_tf2#summary_of_the_new_api) & [Doc2](https://tfhub.dev/tensorflow/efficientnet/b7/classification/1) then try to evaluate tf-lite model. Please find the gist [here](https://colab.research.google.com/gist/tiruk007/a64d6cf4de9f727456208d5e4611c31d/untitled163.ipynb) for reference and let us know if still issue persists.\r\n\r\nThank you !", "> @suyash-narain Could you please try to convert the model by using `hub.load()` or `hub.KerasLayer()` as per [Doc1](https://www.tensorflow.org/hub/migration_tf2#summary_of_the_new_api) & [Doc2](https://tfhub.dev/tensorflow/efficientnet/b7/classification/1) then try to evaluate tf-lite model. Please find the gist [here](https://colab.research.google.com/gist/tiruk007/a64d6cf4de9f727456208d5e4611c31d/untitled163.ipynb) for reference and let us know if still issue persists.\r\n> \r\n> Thank you !\r\n\r\nHi, i tried to convert the model using `hub.load()` or `hub.KerasLayer()` as specified in the colab gist sent by you. I still get the same error. \r\n\r\nif i use the below method:\r\n\r\n> import tensorflow as tf\r\n> import tensorflow_hub as hub\r\n> \r\n> m = tf.keras.Sequential([hub.KerasLayer(\"https://tfhub.dev/tensorflow/efficientnet/b7/classification/1\")])\r\n> m.build([None, 600, 600, 3])# Batch input shape.\r\n> \r\n> m.save('model')\r\n> \r\n> converter = tf.lite.TFLiteConverter.from_saved_model(\"/content/model\")\r\n> converter.target_spec.supported_ops = [\r\n> tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.\r\n> tf.lite.OpsSet.SELECT_TF_OPS, # enable TensorFlow ops.\r\n> ]\r\n> \r\n> tflite_file = \"effnetB7_model.tflite\"\r\n> with open(tflite_file, 'wb') as f:\r\n> f.write(converter.convert()) \r\n\r\nthe input shape becomes (1,600,600,3) as desired, and has the output shape (1,1000). The official ImageNetLabels.txt available at https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt has total of 1001 labels ranging from (0-1000). So in that way, the output shape for this model should be (1,1001). When I run it against tflite imagenet evaluation tool, i get the same error` 'model_output_ not set correctly'`. If i remove 0-background label from label file to bring total labels to 1000, i still get the same error. \r\n\r\nI get similar error results if i use `hub.load()`", "@suyash-narain \r\nCould you please provide detailed steps of tflite imagenet evaluation test to replicate the issue reported here ?\r\n\r\nThank you!", "@tiruk007 Please find the detailed steps I exercised towards building the provided tflite imagenet evaluation tool:\r\n\r\n1. clone tensorflow from github:\r\n\r\n> $ git clone \"https://github.com/tensorflow/tensorflow.git\"\r\n> $ cd tensorflow\r\n\r\n 2. Download ILSVRC validation dataset consisting of 50k images from http://image-net.org/request: ILSVRC2012_img_val https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar\r\n\r\n3. Download ILSVRC 2012 dev kit: ILSVRC2012_devkit_t12 (tasks1&2) https://image-net.org/data/ILSVRC/2012/ILSVRC2012_devkit_t12.tar.gz\r\n\r\n4. Generate Ground Truth Labels:\r\n\r\n> python /tensorflow/lite/tools/evaluation/tasks/imagenet_image_classification/generate_validation_labels.py \\\r\n> --ilsvrc_devkit_dir=path/to/ILSVRC2012_devkit_t12 \\\r\n> --validation_labels_output=output_labels.txt\r\n\r\n5. Download imagenet labels from https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt\r\n\r\n6. Build and run on Ubuntu desktop:\r\n\r\n \r\n\r\n> bazel run -c opt\r\n> -- \r\n> //tensorflow/lite/tools/evaluation/tasks/imagenet_image_classification:run_eval\r\n> --model_file=mbnv2_model_test1.tflite\r\n> --ground_truth_images_path=path/to/ILSVRC2012_img_val\r\n> --ground_truth_labels=path/to/output_labels.txt\r\n> --model_output_labels=path/to/ImageNetLabels.txt\r\n> --output_file_path=accuracy_output.txt\r\n> --num_images=0 # Run on all images.\r\n", "Hi @suyash-narain \r\n\r\nSorry for the delayed response.\r\n\r\nAs the tool for evaluation is based on ILSVRC 2012 task, the classes with shape [1,1001] are only accepted for the evaluation. The tensorflow hub provides modules trained on ImageNet (ILSVRC-2012-CLS) which return the output shapes as `[1,1001]` and can be evaluated using the tool.\r\n\r\nPlease find the list of models [here](https://tfhub.dev/google/collections/image/1).\r\n\r\nThanks.", "Hi @pjpratik \r\nYour reply confuses me. If i use a mobilenetv2 tflite model created using keras application layers as below:\r\n\r\n> model = tf.keras.applications.mobilenet_v2.MobileNetV2(\r\n> input_shape=None,\r\n> alpha=1.0,\r\n> include_top=True,\r\n> weights='imagenet',\r\n> input_tensor=None,\r\n> pooling='avg',\r\n> classes=1000,\r\n> classifier_activation='softmax'\r\n> )\r\n> converter = tf.lite.TFLiteConverter.from_keras_model(model)\r\n> tflite_file = \"mobilenet_v2_keras_model.tflite\"\r\n> with open(tflite_file, 'wb') as f:\r\n> f.write(converter.convert())\r\n\r\ni get output model shape as [1.1000]. I run this model against imagenet labels having 1000 classes from 1-1000 (removing 0-background), and it works. Same way, if i run efficientnetlite4 downloaded from https://tfhub.dev/tensorflow/efficientnet/lite4/classification/2, and run it against imagenet labels having 1000 classes from 1-1000 after removing 0-background class, it still works. \r\nIts just the efficientnet B7 downloaded from tfhub and converted to tflite that fails. even the classification accuracy on individual images is off the charts. \r\nSo what changes can i make to execute efficientnet B7 perfectly against imagenet evaluation tool, or increase accuracy of tflite model. I think this model was also trained against imagenet dataset.\r\n\r\nthanks\r\n\r\n> Hi @suyash-narain\r\n> \r\n> Sorry for the delayed response.\r\n> \r\n> As the tool for evaluation is based on ILSVRC 2012 task, the classes with shape [1,1001] are only accepted for the evaluation. The tensorflow hub provides modules trained on ImageNet (ILSVRC-2012-CLS) which return the output shapes as `[1,1001]` and can be evaluated using the tool.\r\n> \r\n> Please find the list of models [here](https://tfhub.dev/google/collections/image/1).\r\n> \r\n> Thanks.\r\n\r\n", "Hi @suyash-narain Thanks for the clarification.\r\n\r\nSorry for the delayed response. I was able to reproduce this issue. Please find the screenshot here.\r\n\r\n<img width=\"565\" alt=\"Screenshot 2023-04-06 at 12 27 18 PM\" src=\"https://user-images.githubusercontent.com/118897289/230296057-ce4de44c-1743-4b4b-943d-996bda7c3826.png\">\r\n\r\n\r\n@sachinprasadhs Could you please look into this? Thanks." ]

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[ "### Potential workaround\r\n\r\nIt looks like removing `--config=monolithic` from the build command fixes the issue.\r\n\r\n(I am not 100% sure because there is another error, but it seems it is now a completely different error regarding python installation/dirs...)", "Still wondering how it was supposed to work with `--config=monolithic`.", "Hi @Damine, \r\nThank you for reporting the issue! We are trying to replicate the issue on our end. could you please share complete steps you've followed to build tensorflow?\r\nI was successfully able to build tensorflow with the below code\r\n ```\r\nbazel build --verbose_failures //tensorflow/tools/pip_package:build_pip_package\r\n```\r\nKindly refer to the screenshot below.\r\n\r\n\r\nThank you!\r\n\r\n ", "Yep, I think it works fine **without** `--config=monolithic`.", "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/60052\">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/60052\">No</a>\n" ]

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[ "Hi @trickiwoo,\r\n\r\nAs TensorArray is a wrapper class for dynamic sized,, per-time-step, Tensor arrays. Also from documentation this class is meant to be used with dynamic iteration primitives such as `while_loop` and `map_fn`. \r\n\r\nIt supports gradient `back-propagation` via special \"flow\" control flow dependencies. Please also refer to [tf.raw_ops](https://www.tensorflow.org/api_docs/python/tf/raw_ops) which confirms it supports gradient.\r\n\r\nFor your case mentioned since they are not using any dynamic iteration primitives, you can use `tf.stack()` to collect all elements at once and then apply gradients or use `tf.read()` iteratively to collect elements one by one and apply gradients.\r\nHowever the correct way to use TensorArray is by using with dynamic iteration primitives such as `while_loop` and `map_fn`.\r\n\r\nI just added a simple [gist](https://colab.research.google.com/gist/SuryanarayanaY/c84d303dd7d569bcfe4c73bf79a8a2be/60048.ipynb#scrollTo=NfqXvYrilJqE) here for your reference. Please let us know if it helps.\r\n\r\nThank you!", "Hi @SuryanarayanaY , thanks for your detailed inputs! \r\n\r\nI've tried your example with the `for` loop and it works well with the gradient computation:\r\n\r\n```\r\nv = tf.Variable(1)\r\n@tf.function\r\ndef f(x):\r\n ta = tf.TensorArray(tf.int32, size=0, dynamic_size=True)\r\n for i in tf.range(x):\r\n v.assign_add(i)\r\n ta = ta.write(i, v)\r\n return ta\r\nf(5)\r\n```\r\nAnd made a few modifications as follows. It turns out that without dynamic iteration primitives, `tf.read()`, or `tf.stack()`, it doesn't throw any error, contrasting the case I reported before. The usage of TensorArray, and whether it supports back propagation seems vague. I think it would be great if there could have a consistently defined behavior or produce a better error message.\r\n```\r\nimport os\r\nimport tensorflow as tf\r\nimport numpy as np\r\n\r\nv = tf.Variable(1)\r\n@tf.function\r\ndef f(x):\r\n ta = tf.TensorArray(tf.int32, size=4, dynamic_size=True)\r\n ta = ta.write(0, v)\r\n return ta\r\n\r\n\r\nwith tf.GradientTape() as tape:\r\n tape.watch(v)\r\n t = f(5)\r\ngradient = tape.jacobian(t, v)\r\n```", "Hi @trickiwoo ,\r\n\r\nCould I know what error message you want to display?(I assuming you want to display the error regarding the usage of TensorArray w.r.t Gradients). Due to performance reasons all the checks are not getting validated. If you want to propose some changes to documentation or want to accommodate user error or want to add some more example codes please feel free to propose a PR and Team may happily review it. \r\n\r\nThanks!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60048\">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/60048\">No</a>\n" ]

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[ "@cheshire There were some issues rebasing the other [PR](https://github.com/tensorflow/tensorflow/pull/60002) branch. So I created a this PR with the same changes and closed the other PR. Please approve this PR. Sorry about the confusion. I am resolving the build issue in the tests. This is due to introduction of new cudnn frontend.", "I think @akuegel is also fixing this internally.", "> I think @akuegel is also fixing this internally.\r\n\r\nI just checked, and there are differences in the fixes. Let's import again and see whether additional fixes will be needed, and if not, we can take it as is.", "This has been merged. I had to also apply some fixes suggested by ClangTidy as it was also blocking the submit.", "Unfortunately this change needs to be rolled back, it seems it breaks JAX build under CUDA 11.4 and CuDNN 8.2", "> Unfortunately this change needs to be rolled back, it seems it breaks JAX build under CUDA 11.4 and CuDNN 8.2\r\n\r\n@akuegel what are the issues? We can work on fixing them and re-merge.", "Can we re-open this PR to work on fixing the issues? I don't see a way to re-open this. ", "It fails to build under CUDA 11.4 /CuDNN 8.2:\r\n\r\n```\r\nexternal/xla/xla/stream_executor/cuda/cuda_dnn.cc: At global scope:\r\nexternal/xla/xla/stream_executor/cuda/cuda_dnn.cc:3500:5: error: 'cudnnBackendTensorReordering_t' has not been declared\r\n 3500 | cudnnBackendTensorReordering_t cudnn_tensor_order_type =\r\n | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\nexternal/xla/xla/stream_executor/cuda/cuda_dnn.cc:3501:9: error: 'CUDNN_TENSOR_REORDERING_NONE' was not declared in this scope; did you mean 'cudnn_frontend::cudnnBackendTensorReordering_t::CUDNN_TENSOR_REORDERING_NONE'?\r\n 3501 | CUDNN_TENSOR_REORDERING_NONE,\r\n | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\n | cudnn_frontend::cudnnBackendTensorReordering_t::CUDNN_TENSOR_REORDERING_NONE\r\nIn file included from bazel-out/k8-opt/bin/external/cudnn_frontend_archive/_virtual_includes/cudnn_frontend/third_party/cudnn_frontend/include/cudnn_frontend_ConvDesc.h:35,\r\n from bazel-out/k8-opt/bin/external/cudnn_frontend_archive/_virtual_includes/cudnn_frontend/third_party/cudnn_frontend/include/cudnn_frontend.h:100,\r\n from external/xla/xla/stream_executor/cuda/cuda_dnn.cc:56:\r\nbazel-out/k8-opt/bin/external/cudnn_frontend_archive/_virtual_includes/cudnn_frontend/third_party/cudnn_frontend/include/cudnn_frontend_utils.h:376:5: note: 'cudnn_frontend::cudnnBackendTensorReordering_t::CUDNN_TENSOR_REORDERING_NONE' declared here\r\n 376 | CUDNN_TENSOR_REORDERING_NONE,\r\n | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\n\r\netc.\r\n```\r\n\r\n", "@akuegel After reopening the PR, why does it now have only one file? Was this partially merged?", "It looks like only the last commit got reverted. I don't think the issue (of build failing with CUDA 11.4 and CuDNN 8.2) will have resolved with reverting the last commit only.", "> \r\n\r\nActually all of it was reverted:\r\n\r\nhttps://github.com/tensorflow/tensorflow/commit/7a4d2e8b1a503a45ff86d40b0387fc832302379f\r\n\r\nWhy it doesn't show properly merged in this PR, I don't know :-(\r\nMaybe it is easier to create a new PR?", "> > \r\n> \r\n> Actually all of it was reverted:\r\n> \r\n> [7a4d2e8](https://github.com/tensorflow/tensorflow/commit/7a4d2e8b1a503a45ff86d40b0387fc832302379f)\r\n> \r\n> Why it doesn't show properly merged in this PR, I don't know :-( Maybe it is easier to create a new PR?\r\n\r\nYeah..I guess. Let me do that.", "@akuegel @cheshire opened a new PR https://github.com/tensorflow/tensorflow/pull/60139" ]

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[ "@DEKHTIARJonathan ", "Thank you.\r\n\r\nI merged master branch few minutes ago. Now, there hasn't be any conflict yet.", "There is a silent conflict in the latest merge commit. [These lines](https://github.com/tensorflow/tensorflow/blob/10cbe6ef7ad60cdeef873e6a99be86b0ad80693c/tensorflow/compiler/tf2tensorrt/common/utils.cc#L216-L220) are repeated [here](https://github.com/tensorflow/tensorflow/blob/10cbe6ef7ad60cdeef873e6a99be86b0ad80693c/tensorflow/compiler/tf2tensorrt/common/utils.cc#L235-L239). ", "OK, I fixed." ]

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[ "@aboubezari thanks for the PR! Good catch.\r\n\r\nWould you mind adding one tiny unittest that shows without your fix it fails and with your fix it works?\r\nThat way I can merge it and make sure it doesn't happen again", "> @aboubezari thanks for the PR! Good catch.\r\n> \r\n> Would you mind adding one tiny unittest that shows without your fix it fails and with your fix it works? That way I can merge it and make sure it doesn't happen again\r\n\r\nI'm working on building the unittests properly on my machine, will get back to you, thanks", "@DEKHTIARJonathan I tried reproducing the issue on `convert_nodes_test.cc`, but I'm realizing that this is not a conversion-time error, but this is something that happens when we attempt to execute the engine at inference time, so the UT is not useful at the moment. \r\n\r\nSince this is a low-risk change, what do you think about merging it as is? ", "@nluehr @pjannaty @poulsbo @DEKHTIARJonathan friendly ping here, what do we think is the best next step forward?", "> I tried reproducing the issue on convert_nodes_test.cc, but I'm realizing that this is not a conversion-time error, but this is something that happens when we attempt to execute the engine at inference time, so the UT is not useful at the moment.\r\n\r\nTF-TRT code is not executed at runtime. There is conversion aka. segmentation time and engine build time (which happens prior the first execution).\r\n\r\nIt should still be possible to write a unittest.\r\n\r\nIf you can identify which layer is leading to the problem, we should be able to replicate very simply with a unittest.\r\n\r\nIndeed the problem is fairly simple and low risk, however it is of good practice to merge a change/bugfix with a unittest :)\r\nIf you are not able to write the unittest please provide a 1-2 layers networks that shows the issues. Don't forget to set `minimum_segment_size=1`\r\n\r\nFeel free to use this template to show us the problem:\r\n\r\n```python\r\nimport tensorflow as tf\r\n\r\ntf.get_logger().setLevel('INFO')\r\n\r\nINPUT_SIZE = (512, 5000)\r\nSAVED_MODEL_DIR=\"/tmp/1234\"\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\r\n # Create a basic model instance\r\n model = tf.keras.models.Sequential([\r\n tf.keras.layers.Dense(4096, activation='relu'),\r\n ])\r\n \r\n # Build model - randomly initialized\r\n _ = model(tf.random.uniform(INPUT_SIZE))\r\n\r\n # Save Model\r\n model.save(SAVED_MODEL_DIR)\r\n \r\n # =================== INFERENCE ================ #\r\n from tensorflow.python.saved_model import signature_constants\r\n from tensorflow.python.saved_model import tag_constants\r\n from tensorflow.python.compiler.tensorrt import trt_convert as trt\r\n\r\n converter = trt.TrtGraphConverterV2(\r\n input_saved_model_dir=SAVED_MODEL_DIR,\r\n input_saved_model_tags=[tag_constants.SERVING],\r\n input_saved_model_signature_key=signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY,\r\n precision_mode=trt.TrtPrecisionMode.FP32,\r\n minimum_segment_size=1\r\n )\r\n\r\n func = converter.convert()\r\n\r\n converter.summary()\r\n\r\n data = tf.random.uniform(INPUT_SIZE)\r\n for step in range(1, 20):\r\n print(f\"Step: {step + 1}/50\")\r\n _ = func(data).numpy()\r\n```", "Hey @DEKHTIARJonathan, \r\nI wanted to confirm whether this is an inference time error not, so I re-ran my own test case with a custom layer. \r\nAs you can see below, the engine is created successfully, and when we execute the tensorrt network created by the converter, the inference fails in tensorrt and falls back to the native segment, which I don't want. With this change, the failure is resolved. \r\nI am working on a reproducible error with native tensorflow components. Based on this, do you have recommendations on how to catch it in a unittest? \r\nHere's the log:\r\n```\r\nW20230410 06:52:48.092698 2334411 trt_optimization_pass.cc:186] Calibration with FP32 or FP16 is not implemented. Falling back to use_calibration = False.Note that the default value of use_calibration is True.\r\nW20230410 06:52:48.093089 2334411 segment.cc:956] \r\n\r\n################################################################################\r\nTensorRT unsupported/non-converted OP Report:\r\n - Identity -> 3x\r\n - [Count: 3x] excluded by segmenter option. Most likely an input or output node.\r\n\r\n - Placeholder -> 3x\r\n - [Count: 3x] excluded by segmenter option. Most likely an input or output node.\r\n\r\n - NoOp -> 2x\r\n - [Count: 2x] Op type NoOp is not supported.\r\n\r\n--------------------------------------------------------------------------------\r\n - Total nonconverted OPs: 8\r\n - Total nonconverted OP Types: 3\r\nFor more information see https://docs.nvidia.com/deeplearning/frameworks/tf-trt-user-guide/index.html#supported-ops.\r\n################################################################################\r\nW20230410 06:52:48.093191 2334411 segment.cc:1284] The environment variable TF_TRT_MAX_ALLOWED_ENGINES=20 has no effect since there are only 1 TRT Engines with at least minimum_segment_size=1 nodes.\r\nI20230410 06:52:48.093217 2334411 convert_graph.cc:795] Number of TensorRT candidate segments: 1\r\nI20230410 06:52:48.101868 2334411 convert_graph.cc:909] Replaced segment 0 consisting of 1 nodes by TRTEngineOp_000_000.\r\nWARNING: Logging before InitGoogleLogging() is written to STDERR\r\nI20230410 06:52:48.296857 2334263 utils.cc:100] Linked TensorRT version: 8.5.1\r\nI20230410 06:52:48.296913 2334263 utils.cc:102] Loaded TensorRT version: 8.5.1\r\nW20230410 06:52:52.116518 2334263 trt_logger.cc:36] TF-TRT Warning: DefaultLogger CUDA lazy loading is not enabled. Enabling it can significantly reduce device memory usage. See `CUDA_MODULE_LOADING` in https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars\r\nE20230410 06:52:52.150035 2334263 trt_logger.cc:40] DefaultLogger 4: [network.cpp::validate::2760] Error Code 4: Internal Error (Repeated layer name: TRTEngineOp_000_000/PartitionedCall/model/tf.nuro_detection_output/DetectionOutput-shuffle:SHUFFLE (layers must have distinct names))\r\nW20230410 06:52:52.204959 2334263 trt_engine_op.cc:1046] TF-TRT Warning: Engine creation for TRTEngineOp_000_000 failed. The native segment will be used instead. Reason: INTERNAL: Failed to build TensorRT engine\r\nW20230410 06:52:52.205046 2334263 trt_engine_op.cc:887] TF-TRT Warning: Engine retrieval for input shapes: [[1,1600], [1,4000], [1,2000]] failed. Running native segment for TRTEngineOp_000_000\r\nI0410 06:52:52.370252 2334179 builder_impl.py:780] Assets written to: /tmp/tmp3mqez0ti/test_model_trt/assets\r\nTRTEngineOP Name Device # Nodes # Inputs # Outputs Input DTypes Output Dtypes Input Shapes Output Shapes \r\n================================================================================================================================================================\r\nTRTEngineOp_000_000 device:GPU:0 1 3 3 ['float32', 'f ... ['float32', 'i ... [[1, 1600], [1 ... [[200, 8], [1] ...\r\n\r\n - DetectionOutput: 1x\r\n\r\n================================================================================================================================================================\r\n[*] Total number of TensorRT engines: 1\r\n[*] % of OPs Converted: 14.29% [1/7]\r\n***\r\nStarting tensorrt vs. tf inference:\r\nW20230410 06:52:55.758549 2334264 trt_logger.cc:36] TF-TRT Warning: DefaultLogger CUDA lazy loading is not enabled. Enabling it can significantly reduce device memory usage. See `CUDA_MODULE_LOADING` in https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars\r\nE20230410 06:52:55.789471 2334264 trt_logger.cc:40] DefaultLogger 4: [network.cpp::validate::2760] Error Code 4: Internal Error (Repeated layer name: PartitionedCall/TRTEngineOp_000_000/PartitionedCall/model/tf.nuro_detection_output/DetectionOutput-shuffle:SHUFFLE (layers must have distinct names))\r\nW20230410 06:52:55.837152 2334264 trt_engine_op.cc:1046] TF-TRT Warning: Engine creation for PartitionedCall/TRTEngineOp_000_000 failed. The native segment will be used instead. Reason: INTERNAL: Failed to build TensorRT engine\r\nW20230410 06:52:55.837267 2334264 trt_engine_op.cc:887] TF-TRT Warning: Engine retrieval for input shapes: [[1,1600], [1,4000], [1,2000]] failed. Running native segment for PartitionedCall/TRTEngineOp_000_000\r\n```\r\n", "Actually, I do this conversion warning: \r\n```\r\nTF-TRT Warning: Engine creation for TRTEngineOp_000_000 failed. The native segment will be used instead. Reason: INTERNAL: Failed to build TensorRT engine\r\n```\r\nLet me keep trying to resurface this :) ", "Ok, I realized what I was missing: I was not calling `BuildAndRun` in the unittest to actually build & test the network. \r\n\r\nI've added the unittest and confirmed that it fails without my change and passes with it. \r\n\r\n@nluehr @pjannaty @poulsbo @DEKHTIARJonathan can we take one more look? It should be ready 👍 ", "## Unittest results \r\n\r\n**Command**: \r\n```\r\nbazel build -c opt --copt=\"-Wno-error=switch\" tensorflow/compiler/tf2tensorrt:convert_nodes_test && bazel-bin/tensorflow/compiler/tf2tensorrt/convert_nodes_test_gpu --gtest_filter=\"OpConverterTest.DuplicateSqueeze\"\r\n```\r\n\r\n**With my change:**\r\n```\r\nNote: Google Test filter = OpConverterTest.DuplicateSqueeze\r\n[==========] Running 1 test from 1 test suite.\r\n[----------] Global test environment set-up.\r\n[----------] 1 test from OpConverterTest\r\n[ RUN ] OpConverterTest.DuplicateSqueeze\r\n2023-04-10 16:09:25.417162: I tensorflow/compiler/tf2tensorrt/common/utils.cc:104] Linked TensorRT version: 8.6.0\r\n2023-04-10 16:09:25.417259: I tensorflow/compiler/tf2tensorrt/common/utils.cc:106] Loaded TensorRT version: 8.6.0\r\n2023-04-10 16:09:25.463796: W tensorflow/compiler/tf2tensorrt/convert/op_converter_registry.cc:55] Overwriting TF->TRT Abs op converter with priority 1 using another converter with priority 2\r\n2023-04-10 16:09:25.468067: I tensorflow/compiler/tf2tensorrt/convert/convert_nodes.cc:1330] [TF-TRT] Sparse compute capability: enabled.\r\n[ OK ] OpConverterTest.DuplicateSqueeze (323 ms)\r\n[----------] 1 test from OpConverterTest (323 ms total)\r\n\r\n[----------] Global test environment tear-down\r\n[==========] 1 test from 1 test suite ran. (323 ms total)\r\n[ PASSED ] 1 test.\r\n```\r\n\r\n**Without my change:** \r\n```\r\nNote: Google Test filter = OpConverterTest.DuplicateSqueeze\r\n[==========] Running 1 test from 1 test suite.\r\n[----------] Global test environment set-up.\r\n[----------] 1 test from OpConverterTest\r\n[ RUN ] OpConverterTest.DuplicateSqueeze\r\n2023-04-10 16:13:13.607505: I tensorflow/compiler/tf2tensorrt/common/utils.cc:104] Linked TensorRT version: 8.6.0\r\n2023-04-10 16:13:13.607600: I tensorflow/compiler/tf2tensorrt/common/utils.cc:106] Loaded TensorRT version: 8.6.0\r\n2023-04-10 16:13:13.654402: W tensorflow/compiler/tf2tensorrt/convert/op_converter_registry.cc:55] Overwriting TF->TRT Abs op converter with priority 1 using another converter with priority 2\r\n2023-04-10 16:13:13.658712: I tensorflow/compiler/tf2tensorrt/convert/convert_nodes.cc:1330] [TF-TRT] Sparse compute capability: enabled.\r\n2023-04-10 16:13:13.695280: E tensorflow/compiler/tf2tensorrt/utils/trt_logger.cc:87] DefaultLogger 4: [network.cpp::validate::2783] Error Code 4: Internal Error (Repeated layer name: /my_unary-shuffle:SHUFFLE (layers must have distinct names))\r\ntensorflow/compiler/tf2tensorrt/convert/convert_nodes_test.cc:9891: Failure\r\nExpected equality of these values:\r\n ::tsl::OkStatus()\r\n Which is: OK\r\n (BuildAndRun(input_data, &outputs))\r\n Which is: INTERNAL: Failed to build TensorRT engine\r\n[ FAILED ] OpConverterTest.DuplicateSqueeze (214 ms)\r\n[----------] 1 test from OpConverterTest (214 ms total)\r\n\r\n[----------] Global test environment tear-down\r\n[==========] 1 test from 1 test suite ran. (214 ms total)\r\n[ PASSED ] 0 tests.\r\n[ FAILED ] 1 test, listed below:\r\n[ FAILED ] OpConverterTest.DuplicateSqueeze\r\n\r\n 1 FAILED TEST\r\n```", "@DEKHTIARJonathan quick ping, let me know if there's anything else, I think we're close here :) \r\n\r\n", "@nluehr @pjannaty @poulsbo @DEKHTIARJonathan another friendly ping :)\r\nCould we take a look please? We should be all good now with unittest coverage. \r\n\r\n", "@nluehr @pjannaty @poulsbo @DEKHTIARJonathan one last ping for a while. I'm going on vacation for 2-3 weeks after today so would be great to take a look in that time, thanks!", "LGTM approved" ]

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[ "LGTM. Thanks." ]

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[ "Regarding the model accuracy for model_3 : (1, 10, 1) x (1, 10, 3) , I think updating this condition for 3d shapes will resolve it.\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/1b8f5c396f0c016ebe81fe1af029e6f205c926a4/tensorflow/lite/delegates/gpu/common/model_builder.cc#L814\r\n\r\nIt would be helpful if there can be clarity on broadcasting of (1, 10, 1) and (3).", "Hi @gokulkrishna98 Thanks for reporting the issue.\r\n\r\nCould you please share a reproducible code/gist in order to expedite trouble shooting process.\r\n\r\nThanks.", "Hi @pjpratik,\r\nI am doing inferencing of mul layer of shape (1, 1600, 1) x (384) via TFliteGpuDelegateV2 through samsung galaxy s22.\r\nusing the steps mentioned here: https://www.tensorflow.org/lite/guide/inference#load_and_run_a_model_in_c\r\nand https://www.tensorflow.org/lite/performance/gpu#delegate_serialization\r\n\r\n```\r\n// Load the model\r\nstd::unique_ptr<tflite::FlatBufferModel> model =\r\n tflite::FlatBufferModel::BuildFromFile(filename);\r\n\r\n// Build the interpreter\r\ntflite::ops::builtin::BuiltinOpResolver resolver;\r\nstd::unique_ptr<tflite::Interpreter> interpreter;\r\ntflite::InterpreterBuilder(*model, resolver)(&interpreter);\r\n\r\n// Resize input tensors, if desired.\r\ninterpreter->AllocateTensors();\r\n\r\nfloat* input = interpreter->typed_input_tensor<float>(0);\r\n// Fill `input`.\r\n\r\ninterpreter->Invoke();\r\n\r\nfloat* output = interpreter->typed_output_tensor<float>(0);\r\n```\r\n\r\nThere is accuracy issue during this inferencing when setting tfliteGpuDelegateV2 instead of default xnnpack (cpu). The accuracy issue report can be seen in my previous message.", "@gokulkrishna98 Thanks for the information.\r\n\r\nHave you tried on TF 2.11 and TF Nightly and see if the issue still exists?\r\n\r\nThanks.", "Yes, I have also tested it on tf2.11, the same issue persists. I believe there has been no change in elementwise parser by commit history in nightly, so I don’t think testing is required there.", "Also, https://github.com/tensorflow/tensorflow/issues/60043#issuecomment-1476327924,\r\n\r\n here the input tensor 1 should be the greater one, but the channel size is checked for equality ", "@gokulkrishna98 Thanks for the information.\r\n\r\n@sachinprasadhs Could you please look into this issue? Thanks.", "I'm not entirely clear what you mean by `{input1 - [1, 1600, 1] and input2 - [384]}` (this is not even mathematically well-formed?), but note that we're quite ad hoc when it comes to broadcast, because whatever seems so natural and the norm in Python doesn't necessarily mean that's the norm in C++. In fact, all the broadcast situations have to be hand-coded in an ad-hoc fashion. Having said that, we assume (probably not the best assumption) that the left operand is \"bigger\" and the right operand is \"smaller\", and that's not even universally true; I think we support that in ADD but not necessarily in MUL. You have to inspect the code; it's been a while since I last checked it. IIRC, the logic used to be something like:\r\n\r\n```\r\nif (right operand shape > left operand shape) {\r\n swap(left, right); // so that right is always bigger, and the right is always smaller\r\n}\r\n\r\nwhere > is defined as, \r\n\r\nreturn l.b > r.b || l.h > r.h || l.w > r.w || l.c > r.c; // C operator precedence to be reflected\r\n```\r\n\r\nnow, your tensor (1, 1600, 1) will be probably expanded to (1, 1, 1600, 1) and the 384 will be expanded to (1, 1, 1, 384). Due to the definition of >, (1, 1600, 1) will be probably considered as a bigger tensor (because 1600 gets compared to 1 before 1 > 384 is evaluated) and the 384 the smaller tensor. Now... then comes one of the bad assumptions. We use the dimension of the bigger tensor to do the math, so ... we will probably only use (1, 1, 1600, 1) and the 1st value of (1, 1, 1, 384) is used to do multiplication along the 1600 elements.\r\n\r\nSo that's the bug, and you are welcome to add a proper broadcasting that solves your problem, or restructure your network to bypass the situation. I don't remember whether GPU supports EXPAND_DIMS, but one way would be to expand (1, 1600, 1) to (1, 1600, 384) and then maybe multiply with a tensor with an explicit shape of (1, 1, 384).\r\n\r\ntl;dr Broadcast in GPU delegate is broken; don't rely on it. Be more explicit.", "Hi @impjdi thanks for the reply. \r\n\r\nEven I thought so 1,1600, 1 and 384 is not well formed but the logic is implemented in tensorflow core op and as well as in tensorflowlite-cpu op. It broadcasts each element in 2nd dim of first input and elements in 384 (i.e 1 1600, 1 and 1 1600, 384), to result in [1,1600, 384]. So wanted a clear idea to why there is limitation in GPU implementation for better clarity while writing a model.\r\n\r\nRegarding the broadcasting error, I even interchanged both the inputs, still the error occurs for the case with 1d input, I feel there is limitation during GPU kernels or GPU broadcasting ( I couldn't find its code through grep, so cant comment on that).\r\n\r\nfor the case of \r\nmodel_3 : (1, 10, 1) x (1, 10, 3) => accuracy issue occurs. (mean absolute error (mae) = 2.44e-01).\r\nwe can just update this statement https://github.com/tensorflow/tensorflow/issues/60043#issuecomment-1476327924, it resolves the issue. (change the == comparison along channel dims to <=)\r\n\r\nI tried to do include broadcast_to layer to prevent this, but this results in pack operation in tflite which does not have gpu implementation in r2.8.\r\n\r\nLooks like the only way is to do manual broadcasting, but this layer is introduced when trying to do keras.layerNormalization on 3d input and converting this to Tflite. So manual broadcasting seems difficult here, hence some help is needed.\r\n\r\n\r\nAlso, Is there any documentation or study material regarding tensorflowlite convertor or Tflite architecture in general, would like to do opensource contribution in the future. It would be tremendously helpful in understanding the codebase." ]