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[ "> Thank you very much! :)\r\n\r\nThank you very much for your guidance and approving the PR. ", "2 Windows tests failed internally. Could you please help take a look? This probably happened because we ran on older CPUs that don't have AVX512_BF16.\r\n\r\n`//tensorflow/core/grappler/optimizers:auto_mixed_precision_test_cpu`\r\n```\r\n==================== Test output for //tensorflow/core/grappler/optimizers:auto_mixed_precision_test_cpu:\r\n2023-09-18 19:41:58.740895: I [tensorflow/core/util/port.cc:113](https://cs.corp.google.com/piper///depot/google3/tensorflow/core/util/port.cc?l=113&cl=566379082)] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n[==========] Running 5 tests from 1 test suite.\r\n...\r\n[ RUN ] AutoMixedPrecisionMklTest.InferFollowUpStreamAllow\r\n2023-09-18 19:41:58.891920: I tensorflow/core/grappler/clusters/single_machine.cc:361] Starting new session\r\n2023-09-18 19:41:58.938031: I tensorflow/core/grappler/optimizers/auto_mixed_precision.cc:2254] Converted 3/7 nodes to bfloat16 precision using 0 cast(s) to bfloat16 (excluding Const and Variable casts)\r\n2023-09-18 19:41:58.957392: W tensorflow/core/framework/op_kernel.cc:1839] OP_REQUIRES failed at mkl_conv_ops.cc:1096 : ABORTED: Operation received an exception:Status: 3, message: could not create a primitive descriptor for a convolution forward propagation primitive, in file tensorflow/core/kernels/mkl/mkl_conv_ops.cc:1093\r\n2023-09-18 19:41:58.960676: W tensorflow/core/framework/op_kernel.cc:1839] OP_REQUIRES failed at mkl_conv_ops.cc:1096 : ABORTED: Operation received an exception:Status: 3, message: could not create a primitive descriptor for a convolution forward propagation primitive, in file tensorflow/core/kernels/mkl/mkl_conv_ops.cc:1093\r\n2023-09-18 19:41:58.961610: F tensorflow/core/grappler/utils/grappler_test.cc:124] Non-OK-status: session->Run(run_options, inputs, node_names, node_names, &output_tensors, nullptr) status: ABORTED: Operation received an exception:Status: 3, message: could not create a primitive descriptor for a convolution forward propagation primitive, in file tensorflow/core/kernels/mkl/mkl_conv_ops.cc:1093\r\n\t [[{{node infer}}]]\r\n*** Received signal 22 ***\r\n*** BEGIN STACK TRACE POINTERS ***\r\n```\r\n\r\n`//tensorflow/core/grappler/optimizers:remapper_test_cpu`\r\n```\r\n==================== Test output for //tensorflow/core/grappler/optimizers:remapper_test_cpu:\r\n2023-09-18 19:41:52.977040: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n[==========] Running 51 tests from 13 test suites.\r\n...\r\n[ RUN ] RemapperFuseConvWithBias.Conv2D_BF16\r\n2023-09-18 19:41:55.405579: W tensorflow/core/framework/op_kernel.cc:1839] OP_REQUIRES failed at mkl_conv_ops.cc:1096 : ABORTED: Operation received an exception:Status: 3, message: could not create a primitive descriptor for a convolution forward propagation primitive, in file tensorflow/core/kernels/mkl/mkl_conv_ops.cc:1093\r\n2023-09-18 19:41:55.406251: F tensorflow/core/grappler/utils/grappler_test.cc:124] Non-OK-status: session->Run(run_options, inputs, node_names, node_names, &output_tensors, nullptr) status: ABORTED: Operation received an exception:Status: 3, message: could not create a primitive descriptor for a convolution forward propagation primitive, in file tensorflow/core/kernels/mkl/mkl_conv_ops.cc:1093\r\n\t [[{{node bias_add}}]]\r\n*** Received signal 22 ***\r\n*** BEGIN STACK TRACE POINTERS ***\r\n...\r\n```\r\n\r\n", "If you don't have older CPUs, I believe you can run with the env var `ONEDNN_MAX_CPU_ISA=AVX2`. (If setting in the shell doesn't work, try passing `--test_env=ONEDNN_MAX_CPU_ISA=AVX2` to bazel)", "> If you don't have older CPUs, I believe you can run with the env var `ONEDNN_MAX_CPU_ISA=AVX2`. (If setting in the shell doesn't work, try passing `--test_env=ONEDNN_MAX_CPU_ISA=AVX2` to bazel)\r\n\r\nThe above environment setup worked and we were able to replicate the issue, looking into it", "@mraunak, oneDNN [requires Intel AVX-512 instruction set](https://oneapi-src.github.io/oneDNN/dev_guide_data_types.html#intel-r-architecture-processors) for bfloat16 functionality. You'll need to add guards for older instruction sets, like Intel AVX2.\r\n", "> @mraunak, oneDNN [requires Intel AVX-512 instruction set](https://oneapi-src.github.io/oneDNN/dev_guide_data_types.html#intel-r-architecture-processors) for bfloat16 functionality. You'll need to add guards for older instruction sets, like Intel AVX2.\r\n\r\nHi @penpornk @vpirogov, the test cases have been resolved. Please review and let me know if any further change is needed. Thank you very much.", "Thank you for approving the changes. sorry, I again posted a commit to ensure no failures of models during the runtime.", "2 tests still failed in [Ubuntu CPU](https://source.cloud.google.com/results/invocations/a79e4ba7-e264-495a-9b8b-fe6147474e2c/log)\r\n1. `//tensorflow/python/grappler:auto_mixed_precision_test_cpu`\r\n```\r\n[ RUN ] AutoMixedPrecisionTest.test_conv3d1 ('mkl')\r\n2023-09-21 22:46:21.978741: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n2023-09-21 22:46:22.033759: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:386] MLIR V1 optimization pass is not enabled\r\n2023-09-21 22:46:22.061896: I tensorflow/core/grappler/optimizers/auto_mixed_precision.cc:2330] Running auto_mixed_precision_onednn_bfloat16 graph optimizer\r\n2023-09-21 22:46:22.062308: I tensorflow/core/grappler/optimizers/auto_mixed_precision.cc:1506] No allowlist ops found, nothing to do\r\n2023-09-21 22:46:22.068265: I tensorflow/core/grappler/optimizers/auto_mixed_precision.cc:2330] Running auto_mixed_precision_onednn_bfloat16 graph optimizer\r\n2023-09-21 22:46:22.068735: I tensorflow/core/grappler/optimizers/auto_mixed_precision.cc:2258] Converted 3/19 nodes to bfloat16 precision using 0 cast(s) to bfloat16 (excluding Const and Variable casts)\r\n2023-09-21 22:46:22.074911: E ./tensorflow/core/graph/mkl_graph_util.h:184] oneDNN BFloat16 support are only on platforms with AVX512. Falling back to default implementation if present.\r\n2023-09-21 22:46:22.100372: I tensorflow/core/grappler/optimizers/auto_mixed_precision.cc:2330] Running auto_mixed_precision_onednn_bfloat16 graph optimizer\r\n2023-09-21 22:46:22.100651: I tensorflow/core/grappler/optimizers/auto_mixed_precision.cc:1506] No allowlist ops found, nothing to do\r\n2023-09-21 22:46:22.106529: I tensorflow/core/grappler/optimizers/auto_mixed_precision.cc:2330] Running auto_mixed_precision_onednn_bfloat16 graph optimizer\r\n2023-09-21 22:46:22.106998: I tensorflow/core/grappler/optimizers/auto_mixed_precision.cc:2258] Converted 3/19 nodes to bfloat16 precision using 0 cast(s) to bfloat16 (excluding Const and Variable casts)\r\n/b/f/w/bazel-out/k8-opt/bin/tensorflow/python/grappler/auto_mixed_precision_test_cpu.runfiles/org_tensorflow/tensorflow/python/framework/test_util.py:3109: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\r\n return np.array(a)\r\nINFO:tensorflow:time(__main__.AutoMixedPrecisionTest.test_conv3d1 ('mkl')): 0.17s\r\nI0921 22:46:22.148852 140329108536320 test_util.py:2574] time(__main__.AutoMixedPrecisionTest.test_conv3d1 ('mkl')): 0.17s\r\n[ FAILED ] AutoMixedPrecisionTest.test_conv3d1 ('mkl')\r\n...\r\n```\r\n\r\n2. `//tensorflow/python/grappler:remapper_test_cpu`\r\n```\r\n======================================================================\r\nFAIL: test_matmul_biasadd_activation_fusion1 ('mkl') (__main__.RemapperTest)\r\nRemapperTest.test_matmul_biasadd_activation_fusion1 ('mkl')\r\ndecorated('mkl')\r\n----------------------------------------------------------------------\r\nTraceback (most recent call last):\r\n File \"/b/f/w/bazel-out/k8-opt/bin/tensorflow/python/grappler/remapper_test_cpu.runfiles/absl_py/absl/testing/parameterized.py\", line 318, in bound_param_test\r\n return test_method(self, testcase_params)\r\n File \"/b/f/w/bazel-out/k8-opt/bin/tensorflow/python/grappler/remapper_test_cpu.runfiles/org_tensorflow/tensorflow/python/framework/test_util.py\", line 1749, in decorated\r\n return f(self, *args, **kwargs)\r\n File \"/b/f/w/bazel-out/k8-opt/bin/tensorflow/python/grappler/remapper_test_cpu.runfiles/org_tensorflow/tensorflow/python/framework/test_util.py\", line 2264, in decorated\r\n return func(self, *args, **kwargs)\r\n File \"/b/f/w/bazel-out/k8-opt/bin/tensorflow/python/grappler/remapper_test_cpu.runfiles/org_tensorflow/tensorflow/python/framework/test_util.py\", line 2436, in decorated\r\n f(self, *args, **kwargs)\r\n File \"/b/f/w/bazel-out/k8-opt/bin/tensorflow/python/grappler/remapper_test_cpu.runfiles/org_tensorflow/tensorflow/python/grappler/remapper_test.py\", line 220, in test_matmul_biasadd_activation_fusion\r\n graph = self._VerifyValues(out, precision != dtypes.float32, fused_op,\r\n File \"/b/f/w/bazel-out/k8-opt/bin/tensorflow/python/grappler/remapper_test_cpu.runfiles/org_tensorflow/tensorflow/python/grappler/remapper_test.py\", line 149, in _VerifyValues\r\n self.assertTrue(found_fused_op)\r\nAssertionError: False is not true\r\n\r\n----------------------------------------------------------------------\r\n```", "@penpornk - These tests were failing earlier on your internal Windows running on Broadwell as oneDNN is becoming enabled by default for it. \r\nWith this change, we are disabling BF16 tests on CPUs with only AVX2 or earlier ISAs for which oneDNN doesn't have BF16 support. OneDNN has emulation support for AVX512 systems without BF16. \r\nTests are now disabled on both Linux and Windows. This should not affect Linux systems as oneDNN is not enabled by default and the tests will get bypassed", "Hi @penpornk, we are working on it and will have an update ASAP. To clean up the PR, we will split the changes into two PRs. This PR #61878 will have changes only to make oneDNN default on Windows. A separate PR which is in progress will be submitted to address all the test case", "@mraunak Sounds good. Thank you very much for the update! :)", "Hi @penpornk, this PR https://github.com/tensorflow/tensorflow/pull/61878 have following changes\r\n1. Make oneDNN default on Windows\r\n2. Update the 'Major Feature and Improvements' section of the release notes for TensorFlow 2.15.0 with the addition of a new feature of making oneDNN optimization default on the Windows Platform.\r\n\r\nAnother PR https://github.com/tensorflow/tensorflow/pull/62011 handles the test case failures on older CPU machines. \r\n\r\nPlease review and let us know if any further change is needed.", "Hi @cantonios the above feedback/copybara check failure has been addressed in the PR https://github.com/tensorflow/tensorflow/pull/62011. Request you to re-run the check and merge if the check passes" ]

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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/61877/checks?check_run_id=16839222307) 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 @terryheo Can you please review this PR ? Thank you!" ]

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[ "Hi @SuryanarayanaY can I also contribute to this issue \r\nI tried casting the int64 to float 64 to obtain the outputs\r\n`import tensorflow as tf`\r\n`tensor_int64 = tf.constant(52479845, tf.int64)`\r\n`tensor_float64 = tf.cast(tensor_int64, tf.float64)`\r\n`print(tensor_float64)`\r\n\r\nThis was giving the output as below :\r\n `tf.Tensor(52479845.0, shape=(), dtype=float64)`\r\n\r\n", "This is the standard limitation of floating point precision.\r\n\r\nIn IEEE 754 single precision (float), there are 23 bits allocated for the mantissa, allowing for approximately 7 decimal digits of precision.\r\n\r\nDue to this finite precision, not all real numbers can be represented exactly in the floating-point format. This limitation leads to rounding errors or inaccuracies in calculations, especially when dealing with numbers that require more precision than what can be represented within the given number of bits.\r\n\r\nFor more detail, visit https://en.wikipedia.org/wiki/IEEE_754", "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/61876\">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/61876\">No</a>\n" ]

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[ "## Change the float32 to float64 to keep the value same\r\n>>> tf.cast(tf.constant(52479843, tf.int32), tf.float64)\r\n<tf.Tensor: shape=(), dtype=float64, numpy=52479843.0>\r\n## same for this one too\r\n>>> tf.cast(tf.constant(52479845, tf.int32), tf.float64)\r\n<tf.Tensor: shape=(), dtype=float64, numpy=52479845.0>", "@BMMevius Changing the float32 to float64 would give the same result in casting. \r\n Please have a look at this [gist](https://colab.research.google.com/gist/sushreebarsa/0b30b3321838a98226cdd27448670b29/61875.ipynb). As per [this](https://github.com/tensorflow/tensorflow/blob/v2.13.0/tensorflow/python/ops/math_ops.py#L938-L1019) documentation, the operation supports data types (for `x` and `dtype`) of\r\n `uint8`, `uint16`, `uint32`, `uint64`, `int8`, `int16`, `int32`, `int64`,\r\n `float16`, `float32`, `float64`, `complex64`, `complex128`, `bfloat16`.\r\n In case of casting from complex types (`complex64`, `complex128`) to real\r\n types, only the real part of `x` is returned.\r\n\r\n```\r\ntf.cast(tf.constant(52479843, tf.int32), tf.float64)\r\n<tf.Tensor: shape=(), dtype=float64, numpy=52479843.0>\r\n\r\n\r\n```\r\n```\r\ntf.cast(tf.constant(52479845, tf.int32), tf.float64)\r\n<tf.Tensor: shape=(), dtype=float64, numpy=52479845.0>\r\n```\r\n\r\nThank you!", "I see that the documentation states that there is a loss of precision when converting from a python datatype to a tensorflow datatype but it also happens when converting from a `tf.int32` to a `tf.float32`, which was not indicated by the documentation.\r\nIf there is a loss in precision between certain tf types, which I believe is going on, it might be useful to add that to the documentation as well.", "@BMMevius Thank you for your response here. Could you please confirm if the original issue has been resolved.\r\nThank you! ", "The issues has not been resolved. Because the documentation is not complete about this precision issue.", "This is the standard limitation of floating point precision.\r\n\r\nIn IEEE 754 single precision (float), there are 23 bits allocated for the mantissa, allowing for approximately 7 decimal digits of precision.\r\n\r\nDue to this finite precision, not all real numbers can be represented exactly in the floating-point format. This limitation leads to rounding errors or inaccuracies in calculations, especially when dealing with numbers that require more precision than what can be represented within the given number of bits.\r\n\r\nFor more detail, visit https://en.wikipedia.org/wiki/IEEE_754 ", "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/61875\">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/61875\">No</a>\n" ]

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[ "Made moot by https://github.com/tensorflow/tensorflow/commit/de701b17dc2f1e83b17224b421e406dda38cdb8f" ]

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[ "Hello, @huan415 \r\nI installed Tensorflow 2.12 on Windows using python -m pip install tensorflow==2.12.0. Please refer to the screenshots\r\n\r\n", "@huan415 Could you please find the matching distribution for TF v2.12 as mentioned [here](https://www.tensorflow.org/install/source_windows#tested_build_configurations) in this official doc. 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/61873\">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/61873\">No</a>\n" ]

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[ "@cfRod @nSircombe ", "Fixed by https://github.com/tensorflow/tensorflow/commit/de701b17dc2f1e83b17224b421e406dda38cdb8f", "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/61872\">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/61872\">No</a>\n" ]

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[ "> Can you please note which clang version and any other dependencies that were needed to be installed so internal builds / containers can be updated.\r\n\r\nclang version: LLVM-15.0.7-win64.exe downloaded from https://github.com/llvm/llvm-project/releases/tag/llvmorg-15.0.7, \r\n$ set BAZEL_LLVM=LLVM installation root path e.g. C:/Program Files/LLVM\r\n$ set Path=LLVM installation root path\\bin e.g. C:/Program Files/LLVM/bin", "@MichaelHudgins, all the above-suggested changes have been made. Please review and let me know if any further change is needed", "@mraunak What test suites (bazel flags, etc.) have been run against this?", "> @mraunak What test suites (bazel flags, etc.) have been run against this?\r\n\r\nHi @angerson,\r\n\r\nbazel --output_user_root=d:/ --windows_enable_symlinks test ^\r\n--experimental_cc_shared_library --enable_runfiles --nodistinct_host_configuration ^\r\n--dynamic_mode=off --config=xla --config=short_logs --announce_rc ^\r\n--build_tag_filters=-no_pip,-no_windows,-no_oss,-gpu,-tpu --build_tests_only --config=monolithic ^\r\n--config=opt --copt=/std:c++latest --copt=/clang:-Weverything --linkopt=/FORCE:MULTIPLE ^\r\n--extra_execution_platforms=//tensorflow/tools/toolchains/win:x64_windows-clang-cl --compiler=clang-cl ^\r\n--repo_env=TF_PYTHON_VERSION=3.10 -k --test_output=errors ^\r\n--test_tag_filters=-no_windows,-no_oss,-gpu,-tpu --discard_analysis_cache ^\r\n--test_size_filters=small,medium --jobs=16 --test_timeout=300,450,1200,3600 --verbose_failures ^\r\n -- //tensorflow/... -//tensorflow/java/... -//tensorflow/lite/... -//tensorflow/compiler/... -//tensorflow/python/data/experimental/kernel_tests/... ^\r\n1>run.log 2>&1", "> Please also add a comment about the current plan to submit it with clang 15, and then move it to 17 skipping 16, as it is important to have linux and windows on the same version of clang.\r\n\r\nNote: \r\nCurrently, we have migrated the TensorFlow build from MSVC to the Clang-cl compiler with Clang version 15.0.7, it is compatible with the Windows platform unlike Clang 16.0 as we observed a few errors while using Clang 16 on Windows. Next, we will upgrade Clang 15.0.7 to Clang 17 skipping 16 due to compatibility issues" ]

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[ "Hi @Janus-Shiau \r\n\r\nThanks for reporting the issue.\r\n\r\nCould you please share your findings on latest stable version TF2.13 and nightly snapshots?\r\n\r\nThanks.", "Hi pjpratik,\r\n\r\nWe have tested both on TF2.13 and nightly snapshots. The results are as below.\r\n\r\ntf_2.7/2.11_mobileNet_unitTest@Android10: 150 ms = 0.15 seconds\r\ntf_2.11_mobileNet_unitTest@Android12: 4964 ms = 5 seconds\r\ntf_2.13_mobileNet_unitTest@Android12: 3681ms = 3.7 seconds\r\ntf_nightly_mobileNet_unitTest@Android12: 3880ms = 3.9 seconds\r\n\r\nWe also find that if we build tensorflow-lite from source with printf()-type message logs inside op_builder->PopulateSubGraph() called by the ops being built , all of the logs will be printed following the sequential order same as the code flow in the BuildGraph() function by the code running on Android 12 . However only half of the logs will be printed by the code running on Android 10, and they appear to be printed in an unordered manner. From the clues above, we suspect that the BuildGraph() function will be executed by multiple CPU threads on Android 10 but somehow only single thread on Android 12. The build id of Android 10 is “QKQ1.210528.001” and the the build id of Android 12 is “SKQ1.220804.001”\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.", "Hi,\r\n\r\nIs there any update?\r\n\r\nThanks.", "Hi @sirakiin,\r\n\r\nCan you please take a look? Attached is a project that should be close to reproducing it, but there's no emulator for Snapdragons. Thanks.\r\n\r\n[Test61870.zip](https://github.com/tensorflow/tensorflow/files/12823349/Test61870.zip)\r\n", "Hi, is there any update here?", "The default number of threads has changed in different versions of TF Lite.\r\nFor reliable performance, you should explicitly set the number of threads using the `SetNumThreads` method of `InterpreterBuilder` (not to be confused with the similarly named method of `Interpreter`, which is deprecated).\r\n\r\nUnfortunately at this point changing the default number of threads back to what it was in TensorFlow 2.7 would also cause new regressions for other apps that are relying on the new default.", "> The default number of threads has changed in different versions of TF Lite.\r\n\r\nMore specifically, TF Lite 2.7 used Eigen which defaulted to 4 threads, whereas later versions of TF Lite default to 1 thread.\r\n\r\nSo if your code has had a performance regression since TF Lite 2.7, and it appear to be related to threading, try calling `.SetNumThreads(4)` on your `InterpreterBuilder` instance before constructing the `Interpreter`." ]

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[ "@0x0L FYKI, If a DataSet's elements are RaggedTensors, then you can batch them, even if they don't have the same size. The result will be a RaggedTensor with one more ragged dimension. The reason that `ds.map(lambda x: x).padded_batch(2, padded_shapes=[None])` works is that the map converts the RaggedTensor elements into Tensor elements. I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/a9ee1d794fd2c7b86b8fd4139735fff9/61869.ipynb) could you please confirm the outcome here. Thank you!", "Precisely, the point is that\r\n\r\n```\r\nimport tensorflow as tf\r\nds = tf.data.Dataset.from_tensor_slices(tf.ragged.constant([[1, 2, 3], [4, 5]]))\r\n\r\nfor x in ds.map(tf.square).batch(2, drop_remainder=True):\r\n pass\r\n```\r\n\r\ncrashes with\r\n\r\n```\r\nInvalidArgumentError: {{function_node __wrapped__IteratorGetNext_output_types_1_device_/job:localhost/replica:0/task:0/device:CPU:0}} Cannot add tensor to the batch: number of elements does not match. Shapes are: [tensor]: [2], [batch]: [3] [Op:IteratorGetNext] name: \r\n```\r\n", "Hi @0x0L ,\r\n\r\nWhenever we apply any transformations on a Dataset by default it will try to transform into batches of Tensors and hence we get `TensorSpec` as output for `element_spec`.\r\n\r\nIf we need to do batching on a Ragged dataset we need to use ragged_batch() function explicitly to make it as Ragged batches.\r\n\r\nThe below code will work and it will output `RaggedTensorSpec` for `ds.element_spec`\r\n\r\n```\r\nds = ds.map(tf.square).ragged_batch(2, drop_remainder=True)\r\nds.element_spec\r\n```\r\nPlease refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/b6fd1b6d42ac02e6bbe3520d0a272b9f/61869.ipynb).\r\n\r\nHope this helps. Thanks!\r\n", "`ragged_batch` works but I'm more focused on the semantics of `map` as one could reasonably expects that `ds.map(lambda x: x)` is still `ds`.\r\n\r\nThe doc for `tf.data.Dataset.map` does not mention this behavior." ]

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[ "Hi @rsuderman Can you please review this PR ? Thank you!", "Would you mind rebasing on head? Seems a merge conflict", "> Would you mind rebasing on head? Seems a merge conflict\r\n\r\ndone", "Hi @jpienaar Can you please review this PR ? Thank you!" ]

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[ "@gpetters94 I was able to run 'import tensorflow.compat.v2 as tf' successfully on colab using tf-nightly , please find the gist [here](https://colab.research.google.com/gist/sushreebarsa/3490ac4673c9718664880c16936bacd0/61867.ipynb) and confirm the same?\r\nThank you!", "> @gpetters94 I was able to run 'import tensorflow.compat.v2 as tf' successfully on colab using tf-nightly , please find the gist [here](https://colab.research.google.com/gist/sushreebarsa/3490ac4673c9718664880c16936bacd0/61867.ipynb) and confirm the same? Thank you!\r\n\r\nYeah, I'm not getting it on the colab link. From looking at the `pip freeze` output there it looks like it's installing a previous version of `keras` - it's 2.13.0 there but 2.14.0 on my system. That's the one that pip pulls in, not sure why.", "It also seems to happen on my system with 2.13.1 and 2.12.0.", "@gpetters94 Thank you for your response!\r\nCould you please try to reinstall the latest TensorFlow version 2.13 instead of nightly version with compatible version as mentioned [here](https://www.tensorflow.org/install/source#tested_build_configurations). Please let us know if it works?\r\nThank you! ", "@sushreebarsa TF 2.13 seems to work fine.", "@gpetters94 Thank you for the confirmation!\r\nWe recommend to use the stable one as nightly will be failing in some cases. Please let us know if we can close the issue?\r\nThank you!", "@sushreebarsa ,\r\n\r\nAs per setup.py tf-nightly needs 'keras-nightly ~= 2.15.0.dev' . It should suppose to work with keras-nightly and if not working there might be some reason for that. Instead its better to find out the reason rather than suggesting to use only stable versions. By the way nightly builds made available to community to use the up to date features and it also provides chance to fix the bugs if any.\r\n\r\n@gpetters94 , May be you can try uninstalling existing tensorflow and keras versions and install tf-nightly and it will install keras-nightly as well. Please ensure keras-nightly version was installed. With this environment it will work. I do tested in [colab](https://colab.research.google.com/gist/JyotiPDLr/d2eea28be989794a92fca0b579899bdd/61867.ipynb) and it works fine.", "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/61867\">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/61867\">No</a>\n" ]

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[ "Moved from https://github.com/keras-team/tf-keras/issues/136\r\n\r\nHere is relevant solution that seems to stop problem:\r\n\r\n```python\r\ndef wipeit(model):\r\n def _give_handles(m):\r\n if hasattr(m, 'resource_handle'):\r\n yield m.resource_handle\r\n \r\n children = {}\r\n try:\r\n children = m._trackable_children()\r\n except:\r\n pass\r\n\r\n for child in children.values():\r\n yield from _give_handles(child)\r\n\r\n\r\n for handle in _give_handles(model):\r\n tf.raw_ops.DestroyResourceOp(resource=handle)\r\n\r\n\r\n...\r\n\r\n# during model load, after its loaded\r\nprev_model = loaded_model\r\nloaded_model = tf.saved_model.load(...)\r\n\r\nif prev_model:\r\n wipeit(prev_model)\r\n del prev_model\r\n```\r\n", "@sachinprasadhs,\r\nI was able to reproduce the issue on tensorflow v2.13, v2.12 and tf-nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/90480a04fd76ea1d0968fe7b9bde09f3/untitled1367.ipynb)." ]

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[ "@YangChenyuan , I have replicated the reported behaviour on GPU runtime. Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/ad9b6248a61404ed4572c5bb9261281d/61865_gpu.ipynb) for reference.\r\n\r\nIt seems there is inconsistency in tf.gather API , w.r.t jit compilation when applied out of range indices. We will have a look into it and update.\r\n\r\nThanks for reporting.\r\n", "To add more on CPU for out of range indices, exception will be raised. On GPU, for out of range indices '0' will be returned which will be intended.\r\n\r\nThe intended behaviour observed with `jit_compile=False`. With `jit_compile=True` we need to check why it's not consistent.", "Thanks for looking into this problem!", "It's not feasible to throw an error from XLA in this case, as gather could be fused with other operations. The implementation semantics assumes that all reads are in-range, and from what I recall it performs clamping others (@burmako should it be spec'd?)", "Yes, XLA's gather clamps out of bounds indices as discussed in https://github.com/openxla/stablehlo/blob/main/docs/spec.md#gather. Not sure about the semantics of TF's gather though.", "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/61865\">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/61865\">No</a>\n", "Why close this issue? Isn't it a problem that the semantic of tf.gather different from xla's? Tf's semantic is also very useful in some cases.\r\n\r\nSince gather and scatter are symmetric operations, and scatter ignores out-of-bound indices, it is reasonable for gather to also ignore out-of-bound indices.\r\n\r\nThe implementation of Gather in tensorrt stores zero for OOB access, so the behavior of XLA is inconsistent with tensorrt.\r\nhttps://docs.nvidia.com/deeplearning/tensorrt/api/c_api/classnvinfer1_1_1_i_gather_layer.html\r\n\r\n@cheshire @burmako ", "@x10000year if your proposal is to change HLO semantics to match that of TF, it's probably better to open a bug on openxla/stablehlo repo (or openxla/xla).", "@cheshire I think it's tf/jit's responsibility to correctly translate a tensorflow graph to a xla graph. If computation overhead is an issue for correct translation for the default behavior (store zero for OOB access), at least an option should be provided to specify the desired behavior for OOB access.", "In this case I agree it seems like a bridge issue/sharp corner, which should be at least documented.\r\n\r\n@burmako any thoughts?", "A few thoughts:\r\n\r\n1. The [TF Gather spec](https://www.tensorflow.org/api_docs/python/tf/gather) states the following:\r\n- “The values must be in range [0, params.shape[axis])”\r\n- “Indices are always validated on CPU, never validated on GPU.”\r\n- “Caution: On CPU, if an out of bound index is found, an error is raised. On GPU, if an out of bound index is found, a 0 is stored in the corresponding output value.”\r\n\r\nIn some ways this is behaving as spec’ed, it looks like GPU specifics are hand-wavy. I think the doc could be changed to “OOB on GPU is undefined behavior” since it seems implementation specific as to what the GPU behavior is. Namely, I’m not positive on the meaning of JIT but I believe no-JIT dispatches directly to kernels and JIT is XLA compiled?\r\n\r\n2. _“Should we sync HLO spec to TF?”_\r\n \r\nI think it’s best to avoid this given the caveats of the TF spec. I.e. the spec as-is states “must be in range” but also “can be out of range”.\r\n\r\n3. Can the TF/XLA bridge generate HLO that makes CPU/GPU behavior consistent?\r\n\r\nIt isn’t feasible to reliably detect OOB access statically in the bridge, since the bounds are an input argument. As such, runtime checking is needed, and we currently have no means of doing this.\r\n\r\nThere have been informal discussions on some sort of assertion ops in StableHLO which we could add this use case to the discussion (not sure if we have a ticket for this yet, will look). That said, perhaps raising errors from XLA is infeasible, and this wouldn’t work for this use case? Maybe something can be done with custom calls to validate bounds during runtime, but this would likely add a performance hit.", "How the gradient of scatter_add op works when OOB indices exist? OOB indices are valid in scatter_add specs. \r\n\r\nIs it better to provide an option to specify the behavior of OOB? like 1) store zeros; 2) undefine behavior. Can XLA compilation fuse gather with the operations of bound validation and storing zeros for OOB to avoid performance hit?\r\n\r\nPersonally I would expect all operators to avoid undefine behavior, as it is often source of troubles." ]

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[ "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61864\">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/61864\">No</a>\n", "closed as this is intended behaviour" ]

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[ "@YangChenyuan ,\r\n\r\nInside the `tf.function` python statements will be executed only in first stage i.e retracing stage while capturing tensorflow operations in graph and in second stage of execution only tensorflow operation will executed. This might be the reason for the exception raised. If you pass boolean tensor instead of Python bool and it works fine as expected.\r\n\r\nPlease refer to the attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/fa460fdb1a12cf2a630119223ffa8d2b/61863.ipynb#scrollTo=7trxEkCL7Hzl). Please let us know if you still got any queries.\r\n\r\nThank you!\r\n", "Hi @SuryanarayanaY, thanks for your further explanation! I wonder if the autograph **should** trace these primitive types in the model since such primitive types, like `bool`, `int`, are very basic and used everywhere. I expect that they will be used without any error when using XLA compilation.", "Hi @YangChenyuan ,\r\n\r\n`tf.cond(pred)` raises `TypeError` if `pred` is not a callable. Here `pred` is python bool and hence the error.", "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/61863\">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/61863\">No</a>\n" ]

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[ "Related: https://github.com/tensorflow/tensorflow/issues/61856", "@sachinprasadhs,\r\nI was able to reproduce the issue on tensorflow v2.12, v2.13 and tf-nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/133237432d8d43f4aeb8c31899c321e7/untitled1366.ipynb)." ]

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[ "hey @SuryanarayanaY this is my first contribution in tensorflow . if is there any thing else i have to add in the template please let me know. [here](https://gist.github.com/samthakur587/93ed3255ba8b0c81856e943576b85bb0) is my gist file.", "Hi @SuryanarayanaY, also do add the \"**View Source on Github**\" link in the [elu documentation](https://www.tensorflow.org/api_docs/python/tf/nn/elu) (all other tf.nn methods have their link for source code)", "Hi @samthakur587 , @VachanVY ,\r\n\r\nYeah the documentation seems wrong and also implementation. This is a low level API. I need to have a check on it. Yeah the source code link also not available now which we will have a look into.\r\n\r\nYou can use high level API for same which is [tf.keras.layers.ELU](https://www.tensorflow.org/api_docs/python/tf/keras/layers/ELU) and its working fine. Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/f03c6a59421529b640cceaea86c574dd/61861.ipynb) for same.\r\n\r\n\r\n", "Hi @SuryanarayanaY thanks for your comment. I am just exploring the tf activation function where i found this bug. its very helpful if you provide link of the code of this function and also any documentation where i can setup my testing environment for tf functions. I just want to understand the tf code and contribute to this repo.\r\n\r\nThanks \r\nsamunder singh", "Hii @SuryanarayanaY can you provide the link for the source code of this function. we can make a PR and correct this.", "Since it's not solved in new release 2.15", "Hi @samthakur587 ,\r\n\r\nThe source code as Python wrapper not available now and it is getting generated from C++ backend. It's available at C++ backend.If you can able to work on C++ you can try it out." ]

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[ "Hi @wsb252, I'm not sure if we support MacOS M2 for that workflow yet.\r\n\r\n@terryheo can you please take a look? Thanks." ]

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[ "Hi @ekdnam ,\r\n\r\nCould you please submit a minimal code snippet for reproduction of issue. Also the dataset `modified_train.txt` is missing.\r\n\r\nIt would be appreciated if you can able to submit minimal code snippet for reproduction instead of complete code.", "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/61859\">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/61859\">No</a>\n" ]

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[ "@inviul,\r\nCould you please confirm whether you are facing the issue on only Windows environment or other env. as well. When I tried to import the above mentioned, I was able to import the same.\r\n\r\nAlso there is the numpy dependency. Could please re-check if you have installed the correct numpy version .\r\n[tensorflow/tensorflow/tools/pip_package/setup.py](https://github.com/tensorflow/tensorflow/blob/v2.13.0/tensorflow/tools/pip_package/setup.py#L93)\r\n\r\n**'numpy >= 1.22, <= 1.24.3',** \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/61858\">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/61858\">No</a>\n" ]

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[ "Hi @yishuangP, can you please take a look? Thanks.", "Checking in if there is any update on this? ", "Yes I would also really like that!" ]

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[ "@SuryanarayanaY I was able to replicate the issue reported. Please find the gist [here](https://colab.research.google.com/gist/sushreebarsa/f0c6a28d05bd082dcb0f77e6ddb2f7af/xla-compiled-tf-reshape-throws-valueerror-shape-must-be-rank-1-but-is-rank-0.ipynb#scrollTo=7O8bcJB8WvA7). Thank you!", "Hi @dengyinlin ,\r\n\r\nYou need to pass shape as tuple to get it work as intended. With that modification the `tf.function` decorator works fine with both `jit_compile=True `or `jit_compile=False`. Please refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/402d747df5cf8d09e2ed0903ebeb4caf/61856.ipynb).", "Hi @SuryanarayanaY ,\r\nThanks for the reply! Changing to `(4,)` can indeed solve the issue.\r\n\r\nHowever, I also noticed that without compiling, `tf.reshape` can automatically convert the `(4)` to a tuple and the model can work fine. I think it would be good if such implicit conversion can be supported during compilation (`tf.function`), to ensure the model behavior is more consistent with or without compilation. ", "I agree to @dengyinlin comment. It seems while tracing inside tf.function, its not able to convert (4) to (4,). I think the behaviour should be consistent with or without tf.function and I believe it can be fixed.", "Hi @dengyinlin ,\r\n\r\nI agree to the fact that there should be consistency. We will deep dig into the issue and will update you. \r\n\r\nThank you!" ]

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[ "cc: @nitins17 " ]

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[ "Hi @FrancoisDn \r\n\r\nSorry for the delayed response. \r\n\r\nCan you please try in latest nightly snapshot and see if you are facing the issue?\r\n\r\nAlso, could you please provide the Android SDK and NDK versions being used to better understand the issue?\r\n\r\nAs per the [documentation](https://www.tensorflow.org/lite/android/delegates/nnapi#enable_nnapi_cpu_implementation),\r\n>A graph that can't be processed completely by an accelerator can fall back to the NNAPI CPU implementation. However, since this is typically less performant than the TensorFlow interpreter, this option is disabled by default in the NNAPI delegate for Android 10 (API Level 29) or above. To override this behavior, set setUseNnapiCpu to true in the NnApiDelegate.Options object.\r\n\r\nThanks.\r\n\r\n", " Hi @pjpratik,\r\nthank you for you reply.\r\nI use Android NDK 25.1.8937393 and Android SDK 33.\r\n\r\nRegarding your remark, it's interesting, but strange for my case. In C++, I think that the equivalent to `setUseNnapiCpu()` is attribute `disallow_nnapi_cpu` of struct `tflite::StatefulNnApiDelegate::Options`. Indeed, this attribute is set `true` by default, and I do not change in my code, so the fallback to NNAPI CPU should not be used.\r\nBut according to the TFLite log, it's the case! I have no explanation for that yet.\r\n\r\nAbout testing a newest version, indeed it's a very good idea. I thought about that, because I used version 2.12.0. I'm using Conan package system to easily get TFLite, but unfortunately the last stable version 2.13.0 is not yet available on Conan Center.\r\nSo I will try to get the version by hand to test if the problem is still present with 2.13.0 or the nightly build, but it will require more time than using conan.", " Hi @pjpratik,\r\nI just made a test with Tensorflow Lite last stable version 2.14.0 just released, and I have the exact same issue.\r\nThe NNAPI CPU delegate is used instead of the dedicated GPU/TPU on the Samsung S9.\r\n\r\n_Note that I used a Int8 model (because if float32, for the test I made, they will be executed on CPU, because seem not supported by GPU/TPU with NAPI)._", "Hi @FrancoisDn \r\n\r\nThanks for the information.\r\n\r\n@pkgoogle Could you please look at this issue?\r\n\r\nThanks.", "Hi @FrancoisDn, is it possible for you to share your model file to help us reproduce the issue? (If you can't share a full model, can you reduce/use a smaller/toy model to reproduce the issue?)", " Hi @pkgoogle,\r\nthank you for proposition.\r\n\r\nI just succeed to reproduce the problem with a model not confidential.\r\n\r\nI used a public yolov8n model : [yolov8n-I8-256x320.zip](https://github.com/tensorflow/tensorflow/files/12784276/yolov8n-I8-256x320.zip). This model was export from https://hub.ultralytics.com/models/vY89GpzxNSze7BxrYy72?tab=deploy in TFLite format with option `INT8 Quantization` and image Size `256x320`.\r\n\r\nWith that model here the behavior I have: \r\n - (OK) On Samsung Galaxy S8, the inference is done on GPU/TPU. I have this log:\r\n```Initialized TensorFlow Lite runtime.\r\nCreated TensorFlow Lite delegate for NNAPI.\r\nNNAPI SL driver did not implement SL_ANeuralNetworksDiagnostic_registerCallbacks!\r\nReplacing 253 node(s) with delegate (TfLiteNnapiDelegate) node, yielding 1 partitions.\r\n```\r\n\r\n- (NOK) On Samsung S9 and also new, on Google Pixel Tab with that yolo model, the inference is done on the CPU. I have this log on S9:\r\n```\r\nInitialized TensorFlow Lite runtime.\r\nCreated TensorFlow Lite delegate for NNAPI.\r\nCreated TensorFlow Lite XNNPACK delegate for CPU.\r\nReplacing 169 node(s) with delegate (TfLiteXNNPackDelegate) node, yielding 103 partitions.\r\n```\r\nAnd this log on Google Pixel Tab:\r\n```\r\nInitialized TensorFlow Lite runtime.\r\nCreated TensorFlow Lite delegate for NNAPI.\r\nNNAPI SL driver did not implement SL_ANeuralNetworksDiagnostic_registerCallbacks!\r\nReplacing 30 node(s) with delegate (TfLiteNnapiDelegate) node, yielding 6 partitions.\r\nNNAPI SL driver did not implement SL_ANeuralNetworksDiagnostic_registerCallbacks!\r\nNNAPI SL driver did not implement SL_ANeuralNetworksDiagnostic_registerCallbacks!\r\nNNAPI SL driver did not implement SL_ANeuralNetworksDiagnostic_registerCallbacks!\r\nCreated TensorFlow Lite XNNPACK delegate for CPU.\r\nReplacing 145 node(s) with delegate (TfLiteXNNPackDelegate) node, yielding 98 partitions.\r\n```\r\n\r\nAs we can see, on S9 and Google Pixel Tab, even If I ask to use a NNAPI delegate to run on GPU/TPU, in both case it's run on CPU (log `Created TensorFlow Lite XNNPACK delegate for CPU`).\r\nI can also confirmed that the inference time on Samsung S9 is slower (20 ms) than the time on S8 (14ms). So it confirmed the TFlite log which tell that NNAPI CPU is used.", "@Wawha, Thanks for the additional information, @miaout17, can you please take a look? Thanks.", "I saw similar things galaxy s23 phone.\r\nIt also has snapdragon 8 gen 2 SoC.\r\nAny progress on this issue?", "Hi @miaout17 and @pkgoogle,\r\nis there some news/progress about problem?\r\nCan you confirmed that it's a bug with TensorFlow Lite and Galaxy Tab S9/Snapdragon 8 Gen 2 ?", "I saw similar issue by use public model [mobilenet_v2_1.0_224.tflite](https://commondatastorage.googleapis.com/chromeos-localmirror/distfiles/ml-test-assets-0.0.6.tar.gz) on Pixel 7 pro.\r\nI use [TFLite Model Benchmark Tool](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/benchmark) to inference.\r\n\r\ncommand:\r\nadb shell /data/local/tmp/benchmark_model --verbose=true --graph=/data/local/tmp/mobilenet_v2_1.0_224.tflite --use_nnapi=true\r\n\r\nmessage:\r\nINFO: Loaded model /data/local/tmp/mobilenet_v2_1.0_224.tflite\r\nINFO: Initialized TensorFlow Lite runtime.\r\nINFO: Created TensorFlow Lite delegate for NNAPI.\r\nINFO: NNAPI delegate created.\r\nWARNING: NNAPI SL driver did not implement SL_ANeuralNetworksDiagnostic_registerCallbacks!\r\nINFO: **_Though NNAPI delegate is explicitly applied, the model graph will not be executed by the delegate._**\r\nINFO: Created TensorFlow Lite XNNPACK delegate for CPU.\r\nVERBOSE: Replacing 66 out of 66 node(s) with delegate (TfLiteXNNPackDelegate) node, yielding 1 partitions for the whole graph.", "not sure whether it helps to solve the problem, but there are some discussions about Qualcomm disabling NNAPI support for Snapdragon 8 Gen 2:\r\nhttps://ai-benchmark.net/index.php?threads/what-are-qualcomm-qnn-htp-dsp-delegates.44/", "Hi there! Any updates on this issue?\r\nI've been trying to run the sample model from https://www.tensorflow.org/lite/android/delegates/nnapi#use_supported_models_and_ops on the Samsung Galaxy S23 Ultra.\r\nHowever, I'm encountering a similar result. I've tried benchmark tools and the sample app with the NnApiDelegate, but in all cases, I see a full fallback on CPU. There are no signs of using NNAPI.\r\n", "I'm also getting a similar issue on a Pixel 7. Unfortunately I can't share the model but the error when running the benchmarking tool is as follows:\r\n```\r\nINFO: Initialized TensorFlow Lite runtime.\r\nINFO: Created TensorFlow Lite delegate for NNAPI.\r\nWARNING: NNAPI SL driver did not implement SL_ANeuralNetworksDiagnostic_registerCallbacks!\r\nWARNING: NNAPI SL driver did not implement SL_ANeuralNetworksDiagnostic_registerCallbacks!\r\nERROR: Failed to add NN API tensor: type INT64 is not supported.\r\nERROR: Restored original execution plan after delegate application failure.\r\nERROR: Failed to apply NNAPI delegate.\r\nERROR: Benchmarking failed.\r\n```\r\nAny help much appreciated." ]

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[ "Hi @bvschaik, are you willing to contribute a PR? https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/java are where the manifest files are. We will review your changes if you submit one.", "Hi @pkgoogle, Sure! I'm looking into it now. I see that there are 5 .aar files that are deployed to maven:\r\n\r\n- tensorflow-lite\r\n- tensorflow-lite-api\r\n- tensorflow-lite-select-tf-ops\r\n- tensorflow-lite-gpu\r\n- tensorflow-lite-gpu-api\r\n\r\nI'm only familiar with the play services version, which can contain either just `api` or both `api` and `gpu-api`.\r\n\r\nIs it possible that a single app can contain any combination of above libraries? If so, I'll have to create 5 manifest files with 5 different namespaces.", "Hi @bvschaik, my intuition tells me it's possible, the review process will ferret that out though, IMO let's iterate faster here and just give it a try and let's see how the review goes. Thanks for your help!", "Alright, I have opened a PR: #61877", "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/61853\">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/61853\">No</a>\n", "Using a 2 year old version of AGP (which didn't issue a warning yet) is not a solution." ]

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[ "Hi @desenSunUBW ,\r\n\r\nIt seems you are using older Tf1.x version build which is not supported now. Since then build configurations also changed.\r\n\r\nPlease test with latest versions as per tested configurations attached [here](https://www.tensorflow.org/install/source#tested_build_configurations).In case of problem please come back with complete configuration details like OS, bazel version, compiler version and build command etc which helps better to test and analyse the issue.\r\n\r\nThank you!", "> Hi @desenSunUBW ,\r\n> \r\n> It seems you are using older Tf1.x version build which is not supported now. Since then build configurations also changed.\r\n> \r\n> Please test with latest versions as per tested configurations attached [here](https://www.tensorflow.org/install/source#tested_build_configurations).In case of problem please come back with complete configuration details like OS, bazel version, compiler version and build command etc which helps better to test and analyse the issue.\r\n> \r\n> Thank you!\r\n\r\nHi @SuryanarayanaY , I upgraded my gcc version to 7.2.1 to use bazel-6.1.0 to compile interactive_graphviz in tensorflow r2.14. However, there were still some errors:\r\n```\r\nERROR: /root/.cache/bazel/_bazel_root/416f10ed22bfff0f5a7b019527c9dfc7/external/com_google_protobuf/BUILD.bazel:27:11: Compiling src/google/protobuf/implicit_weak_message.cc [for tool] failed: (Exit 1): gcc failed: error executing command (from target @com_google_protobuf//:protobuf_lite) /usr/bin/gcc -U_FORTIFY_SOURCE -fstack-protector -Wall -Wunused-but-set-parameter -Wno-free-nonheap-object -fno-omit-frame-pointer -g0 -O2 '-D_FORTIFY_SOURCE=1' -DNDEBUG -ffunction-sections ... (remaining 31 arguments skipped)\r\nexternal/com_google_protobuf/src/google/protobuf/implicit_weak_message.cc: In constructor 'constexpr google::protobuf::internal::ImplicitWeakMessageDefaultType::ImplicitWeakMessageDefaultType()':\r\nexternal/com_google_protobuf/src/google/protobuf/implicit_weak_message.cc:53:42: error: temporary of non-literal type 'google::protobuf::internal::ImplicitWeakMessage' in a constant expression\r\n : instance(ConstantInitialized{}) {}\r\n ^\r\nIn file included from external/com_google_protobuf/src/google/protobuf/implicit_weak_message.cc:31:0:\r\nexternal/com_google_protobuf/src/google/protobuf/implicit_weak_message.h:58:23: note: 'google::protobuf::internal::ImplicitWeakMessage' is not literal because:\r\n class PROTOBUF_EXPORT ImplicitWeakMessage : public MessageLite {\r\n ^~~~~~~~~~~~~~~~~~~\r\nexternal/com_google_protobuf/src/google/protobuf/implicit_weak_message.h:58:23: note: 'google::protobuf::internal::ImplicitWeakMessage' has a non-trivial destructor\r\nTarget //tensorflow/compiler/xla/tools:interactive_graphviz failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\n```\r\nI built interactive_graphviz with the command \"bazel build --experimental_repo_remote_exec tensorflow/compiler/xla/tools:interactive_graphviz\". It would be helpful if you can give me some advice.", "@desenSunUBW Upgrade to GCC 7.4 or newer version will fix the error.", "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/61851\">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/61851\">No</a>\n" ]

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[ "@Alwaysadil Thank you for raising the issue.\r\nPlease fill up the template with detailed information.\r\nHope you are using the latest version of TF , please follow the following to install the latest TF version, and restart the kernel. \r\n```\r\nimport tensorflow as tf\r\n\r\nif tf.__version__ != '2.13.0':\r\n !pip uninstall -y tensorflow\r\n !pip install tensorflow-gpu==2.13.0\r\n```\r\nFor more information on TFlite converter please follow the doc [here](https://www.tensorflow.org/lite/models/convert/). Thank you!", "Hi @sushreebarsa ,thanks for your response\r\n\r\nERROR: Could not find a version that satisfies the requirement tensorflow-gpu==2.13.0 (from versions: 2.8.0rc0, 2.8.0rc1, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0rc0, 2.9.0rc1, 2.9.0rc2, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0rc0, 2.10.0rc1, 2.10.0rc2, 2.10.0rc3, 2.10.0, 2.10.1, 2.11.0rc0, 2.11.0rc1, 2.11.0rc2, 2.11.0, 2.12.0)\r\nERROR: No matching distribution found for tensorflow-gpu==2.13.0\r\n\r\n\r\n!pip install tensorflow-gpu==2.12.0\r\nCollecting tensorflow-gpu==2.12.0\r\n Downloading tensorflow-gpu-2.12.0.tar.gz (2.6 kB)\r\n error: subprocess-exited-with-error\r\n \r\n × python setup.py egg_info did not run successfully.\r\n │ exit code: 1\r\n ╰─> See above for output.\r\n \r\n note: This error originates from a subprocess, and is likely not a problem with pip.\r\n Preparing metadata (setup.py) ... error\r\nerror: metadata-generation-failed\r\n\r\n× Encountered error while generating package metadata.\r\n╰─> See above for output.\r\n\r\nnote: This is an issue with the package mentioned above, not pip.\r\nhint: See above for details.", "Thank you for your response!\r\n@Alwaysadil Could you please fill up [this](https://github.com/tensorflow/tensorflow/issues/new/choose) template which will help us to analyze the issue?\r\nThank you!", "hi @sushreebarsa i didn't get what you said\r\nyou are saying to raise me a new issue?\r\n", "@Alwaysadil , No i am not asking to create a new ticket. I am asking to fill out all the details related to the issue. You may edit your first comment else please provide the following information;\r\n\r\nIssue type?\r\n\r\nHave you reproduced the bug with TensorFlow Nightly?\r\nSource?\r\nTensorFlow version?\r\nCustom code?\r\nOS platform and distribution?\r\nMobile device?\r\nPython version?\r\nBazel version?\r\nGCC/compiler version?\r\nCUDA/cuDNN version?\r\nGPU model and memory?\r\nCurrent behavior?\r\nExpected behaviour?\r\nThank you!", "Hi @sushreebarsa thank you for your response\r\n**please check once my code https://colab.research.google.com/drive/14_ajVH4r4NXN6cDw96XJNBtltoueoGfN?usp=sharing**\r\n**Issue type**:tflite lite model conversion with input signature as string data type \r\n\r\n**Have you reproduced the bug with TensorFlow Nightly**:No\r\n**TensorFlow version**:2.13.0\r\nPython version:3.10\r\n**Current behavio**r: tflite lite model conversion with input signature as string data type i am getting this error\r\nConverterError: /usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/func_graph.py:670:0: error:\r\n**Expected behaviour**:i want without any errors\r\nThank you!", "Hi @Alwaysadil, you have one or more custom ops in your model, so you need to identify which of your ops are custom and then follow the instructions here: https://www.tensorflow.org/lite/guide/ops_custom, give that a try and see how far you can get. Specifically from your Colab it looks like this one: tf.TFText>FastWordpieceDetokenize\r\n\r\nAlso please triple quote your code sections to communicate code better on github, it helps us a lot, thanks!\r\nex:\r\n\\```\r\nyour code here\r\n\\```\r\n\r\ntranslates to:\r\n```\r\nyour code here\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/61850\">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/61850\">No</a>\n" ]

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[ "Only because of tuple! I fixed!", "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/61849\">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/61849\">No</a>\n" ]

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[ "Same error here! Trying to use fastapi!", "Hi,\r\n\r\nCould you please create a new environment and install Tensorflow 2.13 using below commands. \r\nI was able to successfully install TensorFlow in 2.13 in MacOS M1.\r\n\r\n```\r\npython3 -m venv temp_tf\r\nsource temp_tf/bin/activate\r\npip install tensorflow==2.13\r\n\r\npip list\r\ntyping_extensions 4.5.0\r\n```\r\n\r\n", "That doesn't help, we need typing-extensions>=4.6.0 ", "As the error suggests in the title, for Tensorflow MacOS 2.13, typing-`extensions` should be <4.6.0 and >=3.6.6\r\n", "as described in my first post, if I use the proposed typing-extensions I cant use pydantic.\r\n\r\n\"pydantic-core 2.6.3 requires typing-extensions!=4.7.0,>=4.6.0, but you have typing-extensions 4.5.0 which is incompatible.\r\npydantic 2.3.0 requires typing-extensions>=4.6.1, but you have typing-extensions 4.5.0 which is incompatible.\"", "@kulinseth , Can we bump this version for tensorflow-macos. ", "I am also getting this error but I am using linux.", "Hi, is there a fix for the issue yet?\r\n\r\nERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\ntensorflow-macos 2.13.0 requires typing-extensions<4.6.0,>=3.6.6, but you have typing-extensions 4.8.0 which is incompatible.\r\n\r\nERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\npydantic-core 2.6.3 requires typing-extensions!=4.7.0,>=4.6.0, but you have typing-extensions 4.5.0 which is incompatible.\r\npydantic 2.3.0 requires typing-extensions>=4.6.1, but you have typing-extensions 4.5.0 which is incompatible.", "same problem", "no solution yet , even i am stucked ", "please bump version for typing-extension", "> please bump version for typing-extension\r\n\r\nWe should switch to using the official Tensorflow arm64 builds. They are available from 2.14 onwards.\r\ncc @learning-to-play , @rishikasinha-tf , @nitins17 ", "@vipervs , Could you please install TensorFlow as per the above comment from official Tensorflow PyPi using `pip install tensorflow==2.14` and close the issue. Thanks!\r\nhttps://pypi.org/project/tensorflow/#files", "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/61848\">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/61848\">No</a>\n" ]

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[ "Hi @halseycamilla Can you please review this PR ? Thank you!" ]

[]

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[ "Hy try those....\r\n\r\n1) Upgrade gcc-9 to gcc-11.\r\n2) Install protobuf \" pip install protobuf \"\r\n3) Install jdk-8 or jdk-11 anyone.\r\n\r\nThen again try to install.", "Is this using the docker install?\r\n\r\nIf so, using devel-gpu, I cannot install gcc-11 and protobuff is already installed. Ubuntu 20 is installed by devel-gpu. ", "@cuchio, \r\n\r\nThe error seems related to protobuf. COuld you please check the protobuf version installed and confirm us. Please find the supported protobuf versions for TF2.13v below.\r\n\r\n`'protobuf>=3.20.3,<5.0.0dev,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5',`\r\n\r\nAlso, Just to inform you that from Tf2.13v onwards we are using Clang16 as compiler which is replacement of GCC and it's our official tested configuration. Is that possible to use Clang instead of GCC ?\r\n\r\nThank you!", "I assume this is using the docker install?\r\n\r\nNote I have changed to devel-gpu as this is my aim. The command is as \r\n\r\ndocker run --gpus all -it -w /tensorflow_src -v $PWD:/mnt -e HOST_PERMS=\"$(id -u):$(id -g)\" \\\r\n tensorflow/tensorflow:devel-gpu bash\r\n\r\nI changed /tensorflow to /tensorflow_src.\r\n\r\nThen git pull to pull, I assume, the latest build.\r\n\r\nProtobuf version is 3.20.3\r\n\r\nRunning configure produces the following. I have left all as default except for CUDA (7.5 for my GPU). Leaving this as default does not change the following.\r\n\r\nAs you can see the clang directory is not visible.\r\n\r\nThanks for your help.\r\n\r\n-----\r\n\r\nYou have bazel 6.1.0 installed.\r\nPlease specify the location of python. [Default is /usr/bin/python3]: \r\n\r\n\r\nFound possible Python library paths:\r\n /usr/lib/python3/dist-packages\r\n /usr/local/lib/python3.8/dist-packages\r\nPlease input the desired Python library path to use. Default is [/usr/lib/python3/dist-packages]\r\n\r\nDo you wish to build TensorFlow with ROCm support? [y/N]: \r\nNo ROCm support will be enabled for TensorFlow.\r\n\r\nFound CUDA 11.2 in:\r\n /usr/local/cuda-11.2/targets/x86_64-linux/lib\r\n /usr/local/cuda-11.2/targets/x86_64-linux/include\r\nFound cuDNN 8 in:\r\n /usr/lib/x86_64-linux-gnu\r\n /usr/include\r\nFound TensorRT 7.2.2 in:\r\n /usr/lib/x86_64-linux-gnu\r\n /usr/include/x86_64-linux-gnu\r\n\r\n\r\nPlease specify a list of comma-separated CUDA compute capabilities you want to build with.\r\nYou can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus. Each capability can be specified as \"x.y\" or \"compute_xy\" to include both virtual and binary GPU code, or as \"sm_xy\" to only include the binary code.\r\nPlease note that each additional compute capability significantly increases your build time and binary size, and that TensorFlow only supports compute capabilities >= 3.5 [Default is: 3.5,7.0]: 7.5\r\n\r\n\r\nDo you want to use clang as CUDA compiler? [Y/n]: Y\r\nClang will be used as CUDA compiler.\r\n\r\nPlease specify clang path that to be used as host compiler. [Default is ]: \r\n\r\n\r\n\r\n\r\n", "@cuchio If you see your Python path that path. That means you need to install or upgrade some pkg. You don't have Clang installed that's why your Clang path default was empty.\r\n\r\n1. Install or upgrade gcc-9 to gcc-11 \"use sudo apt install\"\r\n2. Install or upgrade protobuf **remember version protobuf>=3.20.3,<5.0** \"use pip install or conda install\"\r\n3. Install or upgrade Clang-16 \"use pip install or conda install\"", "@sparrow84001 In the docker container I assume? \r\n\r\nI ask as I already have these installed on my machine and versions are correct.\r\n\r\nAlso, when in the container, I am not able to change to see or change different versions of tensorflow. git pull works though.\r\n\r\ngit ls-remote tensorflow . \r\nfatal: 'tensorflow' does not appear to be a git repository", "@cuchio Yes in docker container. Use \" apt install clang \"", "Seeing changes made in tensorflow today I installed the supported cuda,cudnn and clang packages today.\r\n\r\ncuda toolkit 12.2,\r\nlibcudnn8 8.9.4.25-1+cuda12.2\r\nllvm 17.\r\n\r\nCuda toolkit 12.1 was also installed for tensorRT. I also installed python3.9\r\n\r\nAll installations done with apt-get.\r\n\r\nRunning configure is fine except that is does not see or allow selection of CUDA 12.2 (see output below). My docker system has the cuda-toolkit 11.1, 11.2, 12.1 and 12.2. \r\n\r\nCan this be fixed by manually editing the .tf_configure.bazelrc to only contain the 12.2 paths? \r\n\r\nSee below the contents of this file on my system as created by ./configure\r\n\r\n./configure\r\nYou have bazel 6.1.0 installed.\r\nPlease specify the location of python. [Default is /usr/bin/python3]: /usr/bin/python3.9\r\n\r\n\r\nFound possible Python library paths:\r\n /usr/lib/python3/dist-packages\r\n /usr/local/lib/python3.9/dist-packages\r\nPlease input the desired Python library path to use. Default is [/usr/lib/python3/dist-packages]\r\n\r\nDo you wish to build TensorFlow with ROCm support? [y/N]: \r\nNo ROCm support will be enabled for TensorFlow.\r\n\r\nFound CUDA 11.2 in:\r\n /usr/local/cuda-11.2/targets/x86_64-linux/lib\r\n /usr/local/cuda-11.2/targets/x86_64-linux/include\r\nFound cuDNN 8 in:\r\n /usr/lib/x86_64-linux-gnu\r\n /usr/include\r\nFound TensorRT 8.6.1 in:\r\n /usr/lib/x86_64-linux-gnu\r\n /usr/include/x86_64-linux-gnu\r\n\r\nPlease specify a list of comma-separated CUDA compute capabilities you want to build with.\r\nYou can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus. Each capability can be specified as \"x.y\" or \"compute_xy\" to include both virtual and binary GPU code, or as \"sm_xy\" to only include the binary code.\r\nPlease note that each additional compute capability significantly increases your build time and binary size, and that TensorFlow only supports compute capabilities >= 3.5 [Default is: 3.5,7.0]: 7.5\r\n\r\nDo you want to use clang as CUDA compiler? [Y/n]: Y\r\nClang will be used as CUDA compiler.\r\n\r\nPlease specify clang path that to be used as host compiler. [Default is /usr/lib/llvm-17/bin/clang]: \r\n\r\nYou have Clang 17.0.0 installed.\r\n\r\nPlease specify optimization flags to use during compilation when bazel option \"--config=opt\" is specified [Default is -Wno-sign-compare]: \r\n\r\nWould you like to interactively configure ./WORKSPACE for Android builds? [y/N]: \r\nNot configuring the WORKSPACE for Android builds.\r\n\r\n\r\nFollowing is the .tf_configure.bazelrc contents.\r\n\r\nbuild --action_env PYTHON_BIN_PATH=\"/usr/bin/python3.9\"\r\nbuild --action_env PYTHON_LIB_PATH=\"/usr/lib/python3/dist-packages\"\r\nbuild --python_path=\"/usr/bin/python3.9\"\r\nbuild --config=tensorrt\r\nbuild --action_env TF_CUDA_VERSION=\"11.2\"\r\nbuild --action_env TF_CUDNN_VERSION=\"8\"\r\nbuild --action_env CUDA_TOOLKIT_PATH=\"/usr/local/cuda-11.2\"\r\nbuild --action_env TF_CUDA_COMPUTE_CAPABILITIES=\"7.5\"\r\nbuild --action_env LD_LIBRARY_PATH=\"/usr/local/cuda-11.0/targets/x86_64-linux/lib:/usr/local/cuda/extras/CUPTI/lib64:/usr/local/cuda/lib64:/usr/include/x86_64-linux-gnu:/usr/lib/x86_64-linux-gnu:/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/local/cuda/lib64/stubs:/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.2/lib64\"\r\nbuild --config=cuda_clang\r\nbuild --action_env CLANG_CUDA_COMPILER_PATH=\"/usr/lib/llvm-17/bin/clang\"\r\nbuild --config=cuda_clang\r\nbuild:opt --copt=-Wno-sign-compare\r\nbuild:opt --host_copt=-Wno-sign-compare\r\ntest --test_size_filters=small,medium\r\ntest --test_env=LD_LIBRARY_PATH\r\ntest:v1 --test_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-no_gpu,-oss_serial\r\ntest:v1 --build_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-no_gpu\r\ntest:v2 --test_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-no_gpu,-oss_serial,-v1only\r\ntest:v2 --build_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-no_gpu,-v1only\r\n", "I think I may have found a partial reason for the fault. \r\n\r\nIn short, after looking at the .bazelrc file I saw the following line.\r\n\r\nbuild:cuda_clang_official --action_env=CUDA_TOOLKIT_PATH=\"/usr/local/cuda-12.2\"\r\nbuild:unsupported_gpu_linux --action_env=CUDA_TOOLKIT_PATH=\"/usr/local/cuda-11.2\"\r\n\r\nTherefore I think my gpu card is not being recognised and hence 11.2 is selected.\r\n\r\nHowever, nvidia-smi produced:\r\n\r\n+---------------------------------------------------------------------------------------+\r\n| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 |\r\n|-----------------------------------------+----------------------+----------------------+\r\n| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\r\n| | | MIG M. |\r\n|=========================================+======================+======================|\r\n| 0 NVIDIA GeForce GTX 1650 On | 00000000:01:00.0 On | N/A |\r\n| 26% 33C P8 9W / 75W | 220MiB / 4096MiB | 0% Default |\r\n| | | N/A |\r\n+-----------------------------------------+----------------------+----------------------+\r\n \r\n+---------------------------------------------------------------------------------------+\r\n| Processes: |\r\n| GPU GI CI PID Type Process name GPU Memory |\r\n| ID ID Usage |\r\n|=======================================================================================|\r\n+---------------------------------------------------------------------------------------+\r\n\r\nAlso,\r\n\r\n1) The test mnistCUDNN from the cudnn_samples_v8 also was OK and \r\n2) nvcc -- version produced 12.2\r\n\r\nTherefore, I have manually changed the configuration paths to 12.2. However it still fails to build. I think this may have to do with clang as a it suggests sending a bug report to llvm. However, could it also be caused by the configuration setup being incorrect.\r\n\r\nThe .tf_configure.bazelrc file is as follows\r\n\r\nbuild --action_env PYTHON_BIN_PATH=\"/usr/bin/python3.9\"\r\nbuild --action_env PYTHON_LIB_PATH=\"/usr/lib/python3/dist-packages\"\r\nbuild --python_path=\"/usr/bin/python3.9\"\r\nbuild --config=tensorrt\r\nbuild --action_env TF_CUDA_VERSION=\"12.2\"\r\nbuild --action_env TF_CUDNN_VERSION=\"8\"\r\nbuild --action_env CUDA_TOOLKIT_PATH=\"/usr/local/cuda-12.2\"\r\nbuild --action_env TF_CUDA_COMPUTE_CAPABILITIES=\"7.5\"\r\nbuild --action_env LD_LIBRARY_PATH=\"/usr/local/cuda-12.2/lib64:/usr/local/cuda-12.2/targets/x86_64-linux/lib:/usr/local/cuda/extras/CUPTI/lib64:/usr/local/cuda/lib64:/usr/include/x86_64-linux-gnu:/usr/lib/x86_64-linux-gnu:/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/local/cuda/lib64/stubs\"\r\nbuild --config=cuda_clang\r\nbuild --action_env CLANG_CUDA_COMPILER_PATH=\"/usr/lib/llvm-17/bin/clang\"\r\nbuild --config=cuda_clang\r\nbuild:opt --copt=-Wno-sign-compare\r\nbuild:opt --host_copt=-Wno-sign-compare\r\ntest --test_size_filters=small,medium\r\ntest --test_env=LD_LIBRARY_PATH\r\ntest:v1 --test_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-no_gpu,-oss_serial\r\ntest:v1 --build_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-no_gpu\r\ntest:v2 --test_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-no_gpu,-oss_serial,-v1only\r\ntest:v2 --build_tag_filters=-benchmark-test,-no_oss,-oss_excluded,-no_gpu,-v1only\r\n\r\nThe dump is as follows\r\n\r\nERROR: /root/.cache/bazel/_bazel_root/43801f1e35f242fb634ebbc6079cf6c5/external/com_google_protobuf/BUILD.bazel:364:11: Compiling src/google/protobuf/compiler/cpp/service.cc [for tool] failed: (Segmentation fault): clang failed: error executing command (from target @com_google_protobuf//:protoc_lib) /usr/lib/llvm-17/bin/clang -MD -MF bazel-out/k8-opt-exec-50AE0418/bin/external/com_google_protobuf/_objs/protoc_lib/0/service.d ... (remaining 49 arguments skipped)\r\nPLEASE submit a bug report to https://github.com/llvm/llvm-project/issues/ and include the crash backtrace, preprocessed source, and associated run script.\r\nStack dump:\r\n0. Program arguments: /usr/lib/llvm-17/bin/clang -MD -MF bazel-out/k8-opt-exec-50AE0418/bin/external/com_google_protobuf/_objs/protoc_lib/0/service.d -frandom-seed=bazel-out/k8-opt-exec-50AE0418/bin/external/com_google_protobuf/_objs/protoc_lib/0/service.o -DBAZEL_CURRENT_REPOSITORY=\\\"com_google_protobuf\\\" -iquote external/com_google_protobuf -iquote bazel-out/k8-opt-exec-50AE0418/bin/external/com_google_protobuf -iquote external/zlib -iquote bazel-out/k8-opt-exec-50AE0418/bin/external/zlib -isystem external/com_google_protobuf/src -isystem bazel-out/k8-opt-exec-50AE0418/bin/external/com_google_protobuf/src -isystem external/zlib -isystem bazel-out/k8-opt-exec-50AE0418/bin/external/zlib -fmerge-all-constants -Wno-builtin-macro-redefined -D__DATE__=\\\"redacted\\\" -D__TIMESTAMP__=\\\"redacted\\\" -D__TIME__=\\\"redacted\\\" -fPIE -U_FORTIFY_SOURCE -D_FORTIFY_SOURCE=1 -fstack-protector -Wall -Wno-invalid-partial-specialization -fno-omit-frame-pointer -no-canonical-prefixes -DNDEBUG -g0 -O2 -ffunction-sections -fdata-sections --cuda-path=/usr/local/cuda-12.2 -g0 -w -Wno-sign-compare -g0 -std=c++17 -DHAVE_ZLIB -Woverloaded-virtual -Wno-sign-compare -c external/com_google_protobuf/src/google/protobuf/compiler/cpp/service.cc -o bazel-out/k8-opt-exec-50AE0418/bin/external/com_google_protobuf/_objs/protoc_lib/0/service.o\r\n1. <eof> parser at end of file\r\n2. Optimizer\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\n\r\nThanks for your help. I will also send a report to llvm.", "@cuchio ,\r\n\r\nAs bazelrc suggests to use cuda toolkit 12.2 at path `/usr/local/cuda-12.2`, could you tried installing cuda toolkit 12.2 and ensure that it exists in the specified path.\r\n\r\nFrom your .configure setting it seems you have cuda 11.2 installed. Could you please confirm you have upgraded to 12.2 version ? \r\n\r\nAlso please ignore the CUDA toolkit versions displayed in CUDA driver info. As per my knowledge it displays cuda version compatible with that driver and tensorflow won't access it.", "Hi,\r\n\r\nYes I have cuda toolkit 12.2 installed in Docker and I have it in my path.\r\n\r\nAs an alternative, today I tried to build on my root machine i.e. not using Docker. Instead, in a Python venv. Cuda toolkit-12.2 and 12.1 (for tensorrt) installed. llvm 17. This worked better in that it selected the 12.2 path. The build still fails, which I think is due to llvm 17 dependencies. I will try to resolve tomorrow with the latest and an earlier tensorflow version (pre llvm).\r\n\r\nA question regarding llvm. For the Docker build I chose the Ubuntu 20.04 version (as the image was clearly Ubuntu 20). For the other venv method I installed the 22.04 as that is what is on my machine. Is this correct, especially for the Docker build?\r\n\r\nHaving these problems raises another question. My motherboard is old (2008), which is why I need to compile tensorflow. Could the age be a factor in other ways such as an old bios? I assume not given Linux's back compatibility.", "Hi @cuchio ,\r\n\r\n> As an alternative, today I tried to build on my root machine i.e. not using Docker. Instead, in a Python venv. Cuda toolkit-12.2 and 12.1 (for tensorrt) installed. llvm 17. This worked better in that it selected the 12.2 path. The build still fails, which I think is due to llvm 17 dependencies. I will try to resolve tomorrow with the latest and an earlier tensorflow version (pre llvm).\r\n\r\nYou need to use LLVM16 for compilation.Please refer attached documentation [here](https://www.tensorflow.org/install/source#install_clang_recommended_linux_only).\r\n\r\n> A question regarding llvm. For the Docker build I chose the Ubuntu 20.04 version (as the image was clearly Ubuntu 20). \r\nFor the other venv method I installed the 22.04 as that is what is on my machine. Is this correct, especially for the Docker build?\r\n\r\nI don't think it should be a problem as long as you maintained the environment accordingly.\r\n\r\n> Having these problems raises another question. My motherboard is old (2008), which is why I need to compile tensorflow. Could the age be a factor in other ways such as an old bios? I assume not given Linux's back compatibility.\r\n\r\nYou CPU has to support AVX instructions set as tensorflow built on 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/61845\">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/61845\">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/61844/checks?check_run_id=16702631805) 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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[ "Hi @rohan100jain Can you please review this PR ? Thank you!", "Hi @rohan100jain Can you please review this PR ? Thank you!", "Thanks for the PR. However I don't think this is necessary for a bugfix or improves readability of the code. " ]

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[ "Hi @terryheo Can you please review this PR ? Thank you!", "Hi @terryheo Can you please review this PR ? Thank you!", "Hi @rascani Can you please review this PR ? Thank you!" ]

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[ "@ChurikiTenna Could you please provide the complete standalone code to replicate the issue reported here. 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/61840\">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/61840\">No</a>\n" ]

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[ "I am able to reproduce and confirm that it does work in for r2.13. @terryheo, can you please take a look? Thanks.", "Reproducible on master branch.", "@terryheo @pkgoogle \r\nIt's be still reproduced on `2.14.0` and master branch.", "Let me share my analysis, \r\nI found https://github.com/tensorflow/tensorflow/commit/599baefe9e2ac8d16202553f01f252dc48cbda72 (by @daniel-lang) brings error.\r\n\r\nBy this change, gemmlowp's CMake module add \"pthread\" to link_libraries(), but it's unwanted for Android target.\r\nhttps://github.com/google/gemmlowp/blob/master/contrib/CMakeLists.txt#L27\r\nThat should be excluded on Android by if statement, for example.\r\n\r\nOne of straight-forward solution is to fix gemmlowp CMake and then switch GIT_TAG.\r\nAnother possible solution is to avoid use gemmlowp's CMakeLists.txt. It's more simple but I'm not sure that's doable.", "The analysis sounds right to me.\r\n\r\nDo you want to send a PR to gemmlowp to fix this in gemmlowp?", "@fergushenderson \r\nYes, I've just submitted PR for gemmlowp. https://github.com/google/gemmlowp/pull/212 \r\n\r\nAlso I already checked this change of gemmlowp can be fix tflite build error. ", "I've submitted PR. https://github.com/tensorflow/tensorflow/pull/62330", "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/61839\">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/61839\">No</a>\n" ]

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[ "I have halted merging this PR internally since it has been marked as draft. @elfringham Please let me know when it is ready to be merged. ", "@nitins17 It should be OK now thanks. It just needed a little tweak to get past configure.py when applied to a container with only clang-17 installed.", "Thanks, I'll resume the merge process internally. " ]

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[ "@chrisvdt Could you please upgrade to the latest TF version 2.13 as the older versions are not actively supported. I was able to run the code successfully using TF v2.13 . Please find the gist [here](https://colab.research.google.com/gist/sushreebarsa/e914f851201383d0d71d8bc556bebbc4/advanced.ipynb#scrollTo=nOOLr8yP4h6Z) and confirm the same. Thank you!", "@sushreebarsa Yes I can confirm that it works now.\r\nI only realized later that I had to exactly follow the instructions on this page :\r\nhttps://www.tensorflow.org/install/pip#step-by-step_instructions\r\n\r\nthanks", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61832\">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/61832\">No</a>\n" ]

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[ "Hi @ N-Harish,\r\n\r\nIn Tensorflow there are 2 random seeds one is at global level set by `tf.random.set_seed(seed)` and another at **Op** level for eg. say` tf.random.uniform(..., seed=seed2)`. Together with both these seeds we can able to get deterministic results. If we don't set seed at Op level each process will generate its own random seed which may result in difference of outputs.\r\n\r\nI am not sure whether your code has both seeds set for all workflows. Please confirm whether your code has any other Op which needs to set the seed? Please check and confirm the problem with reproducible code. \r\n\r\nThanks!", "We are using tf dataset map method and shuffle method so do we need to set separate seed for that as well ?", "@N-Harish ,\r\n\r\nIf you are using tf.data.Dataset.[shuffle](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shuffle) method it has `seed` argument for which you can pass a constant value to enable determinism.\r\n\r\nAlso for [map](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#map) method you may need to pass `deterministic = True` to preserve the order.But this will have some performance cost.Please go through attached documentation for more details.\r\n\r\nThanks!", "we have added seed for randomflip, shuffle and set deterministic=true for map but still there are some differences in the models. Also tf dataset has num_parallel_calls set eqaul to tf.data.AUTOTUNE", "Hi @N-Harish ,\r\n\r\nCould you please confirm the difference in the results in terms of percentages rather than absolute difference? Also TF2.5v is quiet older and not having active support now, Could you able to test with latest versions and confirm the results. \r\n\r\nAlso please submit some the code snippet that can reproduce the issue so that we may check it on different environments.\r\nIt's hard to get at a solution without reproducible code snippet.\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/61831\">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/61831\">No</a>\n" ]

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[ "This could be due to the commit [Vendor XLA/TSL into TensorFlow as Bazel dependencies](https://github.com/tensorflow/tensorflow/commit/132c98b29c1a42298cd13055d1168c8d67177bc9)\r\nAnd the below files are affected for `numberic_types.h `\r\nhttps://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/numeric_types.h\r\nhttps://github.com/tensorflow/tensorflow/blob/master/third_party/xla/third_party/tsl/tsl/framework/numeric_types.h\r\n\r\n@jakeharmon8 , Could you please take a look into this. Thanks!", "Hi @sachinprasadhs,\r\n\r\nIt seems to work now.", "Thanks for confirming, could you pease close this issue. ", "I still got this error with 2.15.0rc0 on Windows. The package was installed using `pip`.\r\n\r\n```\r\n C:/Users/runneradmin/AppData/Local/Temp/pip-build-env-csrgykur/normal/Lib/site-packages/tensorflow/include\\tensorflow/core/framework/numeric_types.h(24,1): fatal error C1083: Cannot open include file: 'tsl/framework/numeric_types.h': No such file or directory [D:\\a\\deepmd-kit\\deepmd-kit\\build\\py37-none-win_amd64\\op\\deepmd_op.vcxproj]\r\n Building Custom Rule D:/a/deepmd-kit/deepmd-kit/source/op/CMakeLists.txt\r\n cmake_pch.cxx\r\n C:/Users/runneradmin/AppData/Local/Temp/pip-build-env-csrgykur/normal/Lib/site-packages/tensorflow/include\\tensorflow/core/framework/numeric_types.h(24,1): fatal error C1083: Cannot open include file: 'tsl/framework/numeric_types.h': No such file or directory [D:\\a\\deepmd-kit\\deepmd-kit\\build\\py37-none-win_amd64\\op\\op_grads.vcxproj]\r\n```\r\n\r\nThere are no errors on Linux and macOS, though.", "> There are no errors on Linux and macOS, though.\r\n\r\nI checked the Linux wheel and the Windows wheel. The Linux wheel contains `include/tsl` and `include/xla` directories, but the Windows wheel doesn't.", "This time I see:\r\n```\r\nfatal error: third_party/eigen3/unsupported/Eigen/CXX11/Tensor: No such file or directory\r\n 23 | #include \"third_party/eigen3/unsupported/Eigen/CXX11/Tensor\"```", "> I checked the Linux wheel and the Windows wheel. The Linux wheel contains `include/tsl` and `include/xla` directories, but the Windows wheel doesn't.\r\n\r\nHi @sachinprasadhs, this issue still exists in 2.15.0rc1. I know now Intel builds the Windows package. Is there some way to report to them?", "cc: @mraunak ", "Hi @njzjz and @sachinprasadhs, the above issue will be fixed and missing xla/tsl files will be available in the include folder in the next release TF 2.16.", "> the above issue will be fixed and missing xla/tsl files will be available in the include folder in the next release TF 2.16.\r\n\r\nI confirm it has been fixed in TF 2.16.", "Closing since it is fixed. Thank you for the confirmation", "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/61830\">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/61830\">No</a>\n" ]

[]

[]

[ "As always, the pylint and 'Code Check' failures are because of a pre-existing line being too long which cannot easily be fixed." ]

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[ "Hi @CorbinFoucart \r\n\r\nThe representative dataset might be causing the problem. Usually, this is a small subset of a few hundred samples randomly chosen. I have tried with random input data and I was succesfully able to invoke the interpreter and allocate the tensors.\r\n\r\nPlease find this [gist](https://colab.research.google.com/gist/pjpratik/0943560e00d3834ab6c9ee8e637114be/61828.ipynb).\r\n\r\nThanks.", "I appreciate the fast response @pjpratik -- Interesting, how would the nature of the dataset itself be causing a segfault? Does the representative dataset need to be a certain size?\r\n\r\nAnd correct me if I'm wrong, isn't a segfault under any representative dataset size considered a bug?\r\n\r\nThank you! ", "Hi @CorbinFoucart \r\n\r\nSorry for the delayed response.\r\n\r\nAs far as I know, there is not strict rule for representative dataset. It needs to be a small portion of the data samples to calibrate the ranges. \r\n\r\n@pkgoogle is this an intended behaviour? \r\n\r\nThanks.\r\n\r\n\r\n", "Hi @CorbinFoucart, can you resolve what tflite means in this gist? https://colab.sandbox.google.com/gist/pkgoogle/2667494c5beeb55a4e47da0b942e4a0f/61828.ipynb It seems like you installed or defined it somewhere else as it's not a known package to me, the next line redefines it but I am unable to resolve the original symbol.", "Sure, this is the tflite library built which I compiled from source as a wheel file.\r\n```python\r\nimport tflite as tfl\r\nprint(tfl)\r\n``` \r\nprints \r\n` module 'tflite' from '/home/_my_path_to_/tflite/__init__.py`\r\n\r\nwhich I built from source via docker with a command like \r\n```\r\ndocker run --rm -w /opt/tensorflow_src -v ${SCRIPT_DIR}/third_party/tensorflow_src:/opt/tensorflow_src -t tflite_wheel:latest \\\r\n bash -c \"PYTHON=python3 tensorflow/lite/tools/pip_package/build_pip_package_with_cmake.sh native\"\r\n```", "Hi @CorbinFoucart, I'm not familiar with your environment/workflow, where is the image \"tflite_wheel:latest\" coming from? How is $SCRIPT_DIR defined? Are you using a Dockerfile? Are you following a particular resource/link?", "Yes, I'm using a Dockerfile. Unfortunately, I can't share the process by which I'm doing the build. However, the last line is the relevant one--I'm just compiling tensorflow from source. So tflite is simply the tflite module as is typically packaged by tensorflow.", "Hi @CorbinFoucart, ok understood, can you help me understand where Model is coming from (The one where you call GetRootAsModel)?\r\n\r\n[gist](https://colab.sandbox.google.com/gist/pkgoogle/2987b4cd34bd29b2d33b51cf0b0488ce/61828)\r\n\r\nYour original code is not resolving for me and I tried a couple of different things, using tf.keras.Model, tflite.tflite.Model, tflite.Interpreter.Model. None of these work.", "In this case, that line is irrelevant (it does some flatbuffer reading) and can be commented out; I should have removed it earlier. I reproduce the segfault with that line removed--I can write the TfL model to file, instantiate the Interpreter object, and `interpreter.allocate_tensors()` still results in the segfault.\r\n\r\n\r\n```python\r\n# ...\r\n_tflite_quant_model = converter.convert()\r\n#tfl_model = Model.Model.GetRootAsModel(_tflite_quant_model, 0) \r\n\r\n# write model to file\r\nmodel_file = \"segfault_test.tfl\"\r\nwith open(model_file, \"wb\") as fp:\r\n# ... \r\n...\r\n\r\n```\r\n\r\n\r\nMy apologies for the confusion!", "Thanks!, I was able to replicate with and without tf-nightly, [gist](https://colab.sandbox.google.com/gist/pkgoogle/2987b4cd34bd29b2d33b51cf0b0488ce/61828.ipynb#scrollTo=mqwm4Z2KzbBa).\r\n\r\n@yijie-yang, can you please take a look? Thanks!", "Sorry I'm not very familiar with the representation data related problem. I think MOT team will be more helpful with this issue. Jaesung, could you take a look at this plz? @abattery", "Hi everyone,\r\n\r\n@abattery I wanted to follow up with this in case it's dropped off your radar. Thank you!", "Hi everyone, is there any update on how to fix this error? I'm also facing the same problem ...", "I'm looking more into this... currently it is failing this check which effectively throws an ABORT, double_multiplier=243.18696458117677 for the model that gets saved in the above gist, https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/kernels/internal/quantization_util.cc#L117", "@CorbinFoucart, the original code sample uses identical tensors, so effectively in your representative datasets, this operation produces zero tensors for the output. Our automatic quantization scaling ends up giving the output a scale of 7.84313681e-09. This is effectively too small and causes the assert to trigger. This gist replaces the identical tensors with random tensors and it is able to run through correctly: [gist](https://colab.sandbox.google.com/gist/pkgoogle/02a7dc93b9f89eda92eb65102e2a5af5/61828.ipynb). I'll work on fixing this (as it shouldn't crash) but I wanted to let you know the information for now.\r\n\r\n@HajarAva, If what I just said doesn't apply to you, please create a new issue with your specific problem.", "@pkgoogle : Thanks for your quick response, I've made a new issue and explained my problem [here](https://github.com/tensorflow/tensorflow/issues/64296). ", "Hi @CorbinFoucart, this should now be resolved .. here's a gist showing it no longer crashes with tf-nightly: [gist](https://colab.sandbox.google.com/gist/pkgoogle/02a7dc93b9f89eda92eb65102e2a5af5/61828.ipynb#scrollTo=mqwm4Z2KzbBa). The change is here: https://github.com/tensorflow/tensorflow/commit/ea158b22e6e3449cf73e784ca8fbce5ec6c1656f. Please test with nightly and let us know if it resolves your issue. Thanks.", "@pkgoogle -- yes, this resolves the crash. Thanks for the patch.", "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/61828\">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/61828\">No</a>\n" ]

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[ "@drewshark you are correct it accepts tf.float32, tf.complex64, tf.float64, tf.complex128 and doesn't accept tf.float64\r\n\r\n```python\r\nif tensor.dtype == dtypes.float32 or tensor.dtype == dtypes.complex64:\r\n out_dtype = dtypes.complex64\r\nelif tensor.dtype == dtypes.float64 or tensor.dtype == dtypes.complex128:\r\n out_dtype = dtypes.complex128\r\ne, _ = gen_linalg_ops.eig(tensor, Tout=out_dtype, compute_v=False, name=name)\r\nreturn e\r\n```\r\n", "Can I take a shot at this issue?", "hey @sahusiddharth can I try to solve this one", "@sachinprasadhs I was able to replicate the issue reported here. Please find the attached [gist](https://colab.research.google.com/gist/sushreebarsa/75e17bf472b2ebfbccb4fa4f6a48feef/61827.ipynb).\r\nThank you!" ]

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[ "Hi @drewshark,\r\n\r\nI have replicated the behaviour for multiple inputs and my observations follows below.\r\n\r\n1. The output values returned by TF compared to Numpy or Scipy is different wrt Order.Also this order is inconsistent some times TF output order inline with Numpy and Scipy and sometimes not. But as per Numpy and Scipy documentation you can find a note that the order of output Eigen values need not have any specific order. Please find notes of [Numpy](https://numpy.org/doc/stable/reference/generated/numpy.linalg.eigvals.html#:~:text=Returns,for%20real%20matrices.) and [Scipy](https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.eigvals.html#scipy.linalg.eigvals:~:text=Returns%3A,homogeneous_eigvals%3DTrue.) for same.\r\n\r\n<img width=\"702\" alt=\"Screenshot 2023-09-11 at 12 40 12 PM\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/521c36c1-b24d-45f7-9cb3-afd1b73749ce\">\r\n\r\n\r\n\r\n\r\n\r\n\r\n<img width=\"779\" alt=\"Screenshot 2023-09-11 at 12 38 38 PM\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/e1991149-7fb3-4e37-8f2e-32e6f4c827e0\">\r\n\r\n\r\n3. The values more or less same with some precisional differences of **e-05** order which can be ignored as these precision levels might be different for different backends and also varies w.r.t the hardware also.\r\n\r\nI hope wrt the order of results the documentation states the documentation states that its need not be same. I am curious to know any use case that might get effects with this order.\r\n\r\n", "I am curious to know how the order of eigen values generated may affect for the case of batch normalization applied on inputs.\r\n\r\n@drewshark , Could you please confirm whether you are looking the differences in results wrt precision or order ?", "Hi @SuryanarayanaY,\r\n\r\nI am looking the differences in results wrt both precision and order and I agree with you that the precision difference is very close so the problem may only be the order problem. I observe this difference because I am calling the eigvals API and only use the first result returned by it. " ]

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[ "Hello, @LuisFMCuriel ! Could you please have a look at this [gist](https://colab.research.google.com/gist/sushreebarsa/ad0f5f56ae553c4c706de7d1ff732dce/swarmymcqlearny.ipynb#scrollTo=stXA1XfmHCOx) where I tried to replicate the error reported? Please confirm the result.\r\nThank you!", "Hi @sushreebarsa ! Thanks for the quick replay. Yes, I confirm the result. Running the example for each (for example for 60 episodes), the tensorflow implementation has an elapsed time of 00:00:37 whereas the pytorch implementation has an elapsed time of only 00:00:09, having roughly the same reward (these numbers might change since seed is not set, but the behaviour is the same every time you run it). Is this a problem with my implementation of the neural gradient optimization? The program gives you the optimization time, and it looks to be approximately the same for both, so I am not sure where is the timing bottleneck. \r\n\r\nThanks again for the time!!" ]

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[ "@i-chaochen I have no way to test whether this change is ok, can you please confirm whether this change makes sense?" ]

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[ "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61823\">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/61823\">No</a>\n" ]

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[ "@sparrow84001 Could you please elaborate on the steps you followed and fill up the template [here](https://github.com/tensorflow/tensorflow/issues/new?assignees=&labels=&projects=&template=tensorflow_issue_template.yaml).?\r\nIf you are using WSL2 for GPU access for a later version of 2.10 on windows, then please make sure that you have followed the steps mentioned [here](https://www.tensorflow.org/install/pip#windows-wsl2). Thank you! \r\n", "1) I tried Check this #60144 that uses **onednn (intel opt)**\r\n2) That's why I use build from source. I want to use **cudnn (nvidia opt)** .\r\n3) errors \r\n\r\n **1st error**\r\n\r\n \" ERROR: Traceback (most recent call last):\r\n File \"/home/n3/.cache/bazel/_bazel_n3/6f14c15d85335b4cb3c9403635dfd45f/external/build_bazel_rules_apple/apple/internal/transition_support.bzl\", line 547, column 46, in <toplevel>\r\n _apple_platform_split_transition = transition(Error in transition: Invalid transition input '//command_line_option:incompatible_enable_apple_toolchain_resolution'. Cannot transition on --experimental_* or --incompatible_* options\" \"\r\n\r\n **2nd error**\r\n\r\n \" ERROR: /mnt/d/TensorflowC/tensorflow/WORKSPACE:88:14: error loading package '@com_github_grpc_grpc//src/compiler': at /home/n3/.cache/bazel/_bazel_n3/6f14c15d85335b4cb3c9403635dfd45f/external/com_github_grpc_grpc/bazel/grpc_build_system.bzl:28:6: at /home/n3/.cache/bazel/_bazel_n3/6f14c15d85335b4cb3c9403635dfd45f/external/build_bazel_rules_apple/apple/ios.bzl:33:5: at /home/n3/.cache/bazel/_bazel_n3/6f14c15d85335b4cb3c9403635dfd45f/external/build_bazel_rules_apple/apple/internal/ios_rules.bzl:95:5: initialization of module 'apple/internal/transition_support.bzl' failed and referenced by '//external:grpc_python_plugin' \"\r\n\r\n\r\n4) Check the attached video.\r\n\r\nhttps://github.com/tensorflow/tensorflow/assets/37022413/b8bba37c-6335-494c-ab44-64e12f4dc94c\r\n\r\n", "Same issue happens here, trying to build tensorflow inside a docker container. Seems not limited to WSL2" ]

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