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Isuru0x01

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[ "It seems the bug can be fixed by changing the code in the DLProgbar class in data_utils.py from:\r\n\r\n```python\r\n def __call__(self, block_num, block_size, total_size):\r\n if not self.progbar:\r\n if total_size == -1:\r\n total_size = None\r\n self.progbar = Progbar(total_size)\r\n```\r\nto:\r\n```python\r\n def __call__(self, block_num, block_size, total_size):\r\n if total_size == -1:\r\n total_size = None\r\n if not self.progbar:\r\n self.progbar = Progbar(total_size)\r\n```\r\n", "The crash is caused by the fact that total_size is -1 but the progbar has been created so it isn't turned into None. But the code later assumes only None but not -1 means size unknown.", "@freemant2000,\r\nI tried to execute the code in an alternative approach and **tf.keras.utils.get_file** working as expected & executed the code without any issue/error. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/2a8f050485f2640a22ed5e13ce77f1e9/untitled1211.ipynb).\r\n\r\nAlso it was mentioned above to modify the code, but the code was working without any issues. Could you please provide any unit test case for the statement mentioned. It will be easy to analyse and debug the issue. Thank you!", "As shown in the colab tests, it fails on http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data but works on some other URLs because the former doesn't include a Content-Length header in the HTTP response, which causes the code to fail.", "I have made a unit test demonstrating the problem. Please find the files attached.\r\n[DLProgbar-unit-test.zip](https://github.com/tensorflow/tensorflow/files/11832255/DLProgbar-unit-test.zip)\r\n", "Hi, \r\n\r\nI was able to execute the code in Tf-nightly without any issues, please find the attached Gist [here](https://gist.github.com/sachinprasadhs/02ecff6555b025e0b3aef0ef4e6dec8b) for reference. Thanks!\r\n\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60932\">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/60932\">No</a>\n" ]

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[ "Hi @BealZephyr ,\r\n\r\nThanks for reporting the issue. The problem might be related to `zlib` version. To confirm that could you try build with TF2.10 version and confirm whether it works ?\r\n\r\nAlternatively please replace the below code in master branch\r\nhttps://github.com/tensorflow/tensorflow/blob/master/tensorflow/workspace2.bzl#LL578C1-L585C1\r\nwith \r\nhttps://github.com/tensorflow/tensorflow/blob/r2.10/tensorflow/workspace2.bzl#L564-L571 \r\n\r\nand also the below line from master branch\r\nhttps://github.com/tensorflow/tensorflow/blob/master/third_party/png.BUILD#LL64C1\r\nto \r\nhttps://github.com/tensorflow/tensorflow/blob/r2.10/third_party/png.BUILD#L64\r\n\r\nAfter the above changes try to build again and let us know the outcome.\r\n\r\nThanks!", "Hi @SuryanarayanaY ,\r\n\r\nThank you for your prompt response. I followed your recommendations and tried to build with [TF2.10](https://github.com/tensorflow/tensorflow/tree/r2.10), but it still failed. The error message appears as follows:\r\n\r\n```\r\nERROR: /workdir2/tensorflow/tensorflow/compiler/mlir/hlo/BUILD:2091:11: no such target '@llvm-project//mlir:AsmParser': target 'AsmParser' not declared in package 'mlir' (did you mean 'Parser'?) defined by /root/.cache/bazel/_bazel_root/1bd8bd6e381aed66bea8b87fef9055ba/external/llvm-project/mlir/BUILD.bazel and referenced by '//tensorflow/compiler/mlir/hlo:propagate_static_shapes_to_kernel'\r\nERROR: Analysis of target '//tensorflow/compiler/mlir:tf-opt' failed; build aborted:\r\nINFO: Elapsed time: 0.493s\r\nINFO: 0 processes.\r\nFAILED: Build did NOT complete successfully (2 packages loaded, 0 targets configured)\r\n```\r\nI also tried to build with [master](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/compiler/mlir) branch by replacing workspace2.bzl and png.BUILD, and I encountered the following error:\r\n\r\n```\r\nERROR: /root/.cache/bazel/_bazel_root/1bd8bd6e381aed66bea8b87fef9055ba/external/llvm-project/llvm/BUILD.bazel:184:11: no such package '@llvm_zstd//': The repository '@llvm_zstd' could not be resolved: Repository '@llvm_zstd' is not defined and referenced by '@llvm-project//llvm:Support'\r\nWARNING: Download from https://golang.org/dl/?mode=json&include=all failed: class java.io.IOException connect timed out\r\nERROR: Analysis of target '//tensorflow/compiler/mlir:tf-opt' failed; build aborted:\r\nINFO: Elapsed time: 0.302s\r\nINFO: 0 processes.\r\nFAILED: Build did NOT complete successfully (1 packages loaded, 0 targets configured)\r\n currently loading: tensorflow/lite/schema\r\n Fetching repository @stablehlo; starting\r\n```\r\nI tried a lot, but it didn't work. .Can you take a look at it for me？", "I just want to mention that I had the same problem as @BealZephyr and the suggested change (which I see are already implmented on the main branch) did not help. ", "HI, Same problem as the OP. In my case, the error message is as follows: \r\n\r\n``` \r\n no such package '@llvm_zlib//': The repository '@llvm_zlib' could not be resolved: Repository '@llvm_zlib' is not defined and referenced by '@llvm-project//llvm:Support'\r\nERROR: Analysis of target '//tensorflow/compiler/mlir:tf-opt' failed; build aborted:\r\n\r\n```", "Related to #62384 ", "> ```\r\n> no such package '@llvm_zlib//': The repository '@llvm_zlib' could not be resolved: Repository '@llvm_zlib' is not defined and referenced by '@llvm-project//llvm:Support'\r\n> ERROR: Analysis of target '//tensorflow/compiler/mlir:tf-opt' failed; build aborted:\r\n> ```\r\n\r\nI am getting the same error.\r\n\r\nSince tensorflow is compiled with `clang-17`. I thought if I checkout the tag `llvmorg-17.0.6` in `llvm-project`, it might work. Still no luck. \r\n\r\nHow do we do this?", "This could be a possible workaround?\r\n\r\nhttps://github.com/tensorflow/tensorflow/issues/69367#issuecomment-2155693927" ]

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[ "I was able reproduce this issue. Please find the [gist](https://colab.research.google.com/gist/pjpratik/98e2a454aafb91de2aa202bd1b7d9b2f/60929.ipynb).\r\n\r\n@pkgoogle Could you please check if it is intended behaviour.\r\n\r\nThanks.", "Hello,\r\n\r\nbroadcasting is working for some simpler cases: [gist](https://colab.sandbox.google.com/gist/pkgoogle/35a12ccd0f2fca2ae8892b50db4260cd/60929.ipynb)\r\n\r\nSo this appears to be a legitimate bug.\r\n\r\n@miaout17, can you please take a look? Thanks.", "Any update on this?" ]

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[ "We are seeing similar issue recently with TF2.12, and it could be reproduced with this short script in a multi-gpu setup (we reproduced this on a machine with 2 A100 gpu, CUDA11.8, cuDNN8.6):\r\n\r\n```\r\nimport numpy as np\r\nimport tensorflow as tf\r\n\r\n# tf.debugging.set_log_device_placement(True)\r\n\r\nprint(\"multi gpu mirrored strategy test sanity check:\")\r\n\r\ndef run_fft(img):\r\n print(tf.signal.fft2d(tf.experimental.numpy.copy(img)))\r\n\r\nstrategy = tf.distribute.MirroredStrategy()\r\nrand_mat = tf.random.uniform(\r\n (1, 1, 686, 686),\r\n minval=0.0,\r\n maxval=1.0,\r\n dtype=tf.dtypes.float32,\r\n seed=None\r\n )\r\nimg = tf.cast(rand_mat, tf.complex64)\r\nwith strategy.scope():\r\n strategy.run(run_fft, args=(img,))\r\n```\r\nOutput with TF2.9:\r\n```\r\ntf.Tensor(\r\n[[[[ 2.35175906e+05 +0.j -1.30122070e+02 +71.79061j\r\n 2.00236359e+02 -40.830986j ... 2.86872749e+01-174.89594j\r\n 2.00236313e+02 +40.831j -1.30122101e+02 -71.790565j]\r\n [ 2.59338074e+01 -47.23604j 1.37966949e+02 +27.436068j\r\n 1.20167496e+02+118.6681j ... 1.09129379e+02 -69.504425j\r\n 9.90038986e+01+118.647736j -6.46898956e+01 -98.40455j ]\r\n [ 1.74128876e+02+181.25378j -2.27663452e+02 -70.84499j\r\n -3.25569305e+01+143.9032j ... 1.96747189e+01-331.84482j\r\n 8.72258377e+01+154.79916j 7.57820816e+01 -84.29112j ]\r\n ...\r\n [-8.93064423e+01 -94.69147j 2.13672241e+02+246.61185j\r\n 2.80917282e+01-143.79309j ... 2.39895905e+02 +28.519115j\r\n 9.76210938e+01 -32.177147j 5.52456627e+01-292.4319j ]\r\n [ 1.74128815e+02-181.2538j 7.57820969e+01 +84.29112j\r\n 8.72257385e+01-154.79922j ... -1.00665627e+02 -92.76081j\r\n -3.25569763e+01-143.90318j -2.27663406e+02 +70.8449j ]\r\n [ 2.59337730e+01 +47.23597j -6.46899033e+01 +98.40452j\r\n 9.90038757e+01-118.64778j ... -2.73922150e+02+190.94734j\r\n 1.20167503e+02-118.66812j 1.37966995e+02 -27.436005j]]]], shape=(1, 1, 686, 686), dtype=complex64)\r\ntf.Tensor(\r\n[[[[ 2.35175906e+05 +0.j -1.30122070e+02 +71.79061j\r\n 2.00236359e+02 -40.830986j ... 2.86872749e+01-174.89594j\r\n 2.00236313e+02 +40.831j -1.30122101e+02 -71.790565j]\r\n [ 2.59338074e+01 -47.23604j 1.37966949e+02 +27.436068j\r\n 1.20167496e+02+118.6681j ... 1.09129379e+02 -69.504425j\r\n 9.90038986e+01+118.647736j -6.46898956e+01 -98.40455j ]\r\n [ 1.74128876e+02+181.25378j -2.27663452e+02 -70.84499j\r\n -3.25569305e+01+143.9032j ... 1.96747189e+01-331.84482j\r\n 8.72258377e+01+154.79916j 7.57820816e+01 -84.29112j ]\r\n ...\r\n [-8.93064423e+01 -94.69147j 2.13672241e+02+246.61185j\r\n 2.80917282e+01-143.79309j ... 2.39895905e+02 +28.519115j\r\n 9.76210938e+01 -32.177147j 5.52456627e+01-292.4319j ]\r\n [ 1.74128815e+02-181.2538j 7.57820969e+01 +84.29112j\r\n 8.72257385e+01-154.79922j ... -1.00665627e+02 -92.76081j\r\n -3.25569763e+01-143.90318j -2.27663406e+02 +70.8449j ]\r\n [ 2.59337730e+01 +47.23597j -6.46899033e+01 +98.40452j\r\n 9.90038757e+01-118.64778j ... -2.73922150e+02+190.94734j\r\n 1.20167503e+02-118.66812j 1.37966995e+02 -27.436005j]]]], shape=(1, 1, 686, 686), dtype=complex64)\r\n```\r\n\r\nOutput with TF2.12:\r\n```\r\ntf.Tensor(\r\n[[[[ 2.3510438e+05 +0.j -2.3397424e+02 +95.00578j\r\n -2.8460379e+02 +96.456604j ... 3.8885651e+00+184.90929j\r\n -2.8460379e+02 -96.45657j -2.3397423e+02 -95.00578j ]\r\n [ 4.0683159e+01-196.26071j -3.3500740e+01+253.18457j\r\n 1.2614692e+02+251.45973j ... 1.1127013e+02 +73.44855j\r\n -9.9558403e+01+301.1387j -3.7669452e+02+258.1033j ]\r\n [-2.9365808e+02 -26.070122j 1.1248355e+02+219.34619j\r\n -4.5030090e+01-103.50034j ... 6.4451477e+01-142.80096j\r\n -2.9005927e+02 -39.51029j -1.2654387e+02 +66.92355j ]\r\n ...\r\n [-5.9005920e+01-109.54649j -2.5984564e+02 -8.701363j\r\n -1.3211296e+02 -64.48026j ... 1.5030142e+02+232.03638j\r\n -2.1768060e+02 -23.536972j -4.0744392e+01-186.61447j ]\r\n [-2.9365802e+02 +26.070091j -1.2654389e+02 -66.92351j\r\n -2.9005920e+02 +39.51022j ... 1.3489523e+02 -29.27546j\r\n -4.5030064e+01+103.50032j 1.1248353e+02-219.34612j ]\r\n [ 4.0683189e+01+196.26067j -3.7669446e+02-258.1033j\r\n -9.9558449e+01-301.1387j ... 9.9753399e+00 +51.120373j\r\n 1.2614685e+02-251.45963j -3.3500755e+01-253.18451j ]]]], shape=(1, 1, 686, 686), dtype=complex64)\r\ntf.Tensor(\r\n[[[[0.+0.j 0.+0.j 0.+0.j ... 0.+0.j 0.+0.j 0.+0.j]\r\n [0.+0.j 0.+0.j 0.+0.j ... 0.+0.j 0.+0.j 0.+0.j]\r\n [0.+0.j 0.+0.j 0.+0.j ... 0.+0.j 0.+0.j 0.+0.j]\r\n ...\r\n [0.+0.j 0.+0.j 0.+0.j ... 0.+0.j 0.+0.j 0.+0.j]\r\n [0.+0.j 0.+0.j 0.+0.j ... 0.+0.j 0.+0.j 0.+0.j]\r\n [0.+0.j 0.+0.j 0.+0.j ... 0.+0.j 0.+0.j 0.+0.j]]]], shape=(1, 1, 686, 686), dtype=complex64)\r\n\r\n```", "@sachinprasadhs any update here? ", "@reedwm is there any update for this issue? Thanks!", "@hubingallin started looking into this issue.", "A bisect shows 877e6ecbd43957d8e7313efb61e962149f6231cb (cl/505171308) as the culrpit. I'm guessing the issue is that the FFT plan cache does not include the device in the cache key (probably cufftHandle is associated with the current context's device, or maybe even a stream). So running the same FFT on multiple devices would reuse the cufftHandle\r\n\r\n@cantonios can you fix this?", "Looks like @reedwm was right, it's because the `cufftHandle` is created on one device, cached, then attempted to be used on another physical device. We can add the device ID to the plan cache key to address this. The fix should be out soon.", "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/60926\">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/60926\">No</a>\n" ]

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[ "I am able to replicate on tf-nightly consistently, here is the [gist](https://colab.sandbox.google.com/gist/pkgoogle/87b4aa9dda67f8bdead474d0bf56476d/60925.ipynb)\r\n\r\nunsure if the reshape is incorrect, but output should be the same after conversion.\r\n\r\nHi, @arfaian can you please take a look?" ]

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[ "Hi @rohan100jain Can you please review this PR ? Thank you!", "Hi @rohan100jain Can you please review this PR ? Thank you!", "Hi @aaudiber Can you please review this PR ? Thank you!", "Hi @aaudiber Can you please review this PR ? Thank you!", "Hi @aaudiber Can you please review this PR ? Thank you!", "Hi @Nov11 Can you please resolve conflicts? Thank you!", "This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.", "This PR was closed because it has been inactive for 14 days since being marked as stale. Please reopen if you'd like to work on this further." ]

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[ "Hi @Erida-Bendo ,\r\n\r\nIt seems the issue is related to Jupyter notebook environment. I can able to execute the code on google colab successfully without error and attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/7b3fd146e2d519119de447994f25997e/pix2pix.ipynb) for reference.\r\n\r\nWe are not officially supporting Jupyter notebook instructions. You may refer the similar issues from #60790 and SO [link1](https://stackoverflow.com/questions/76101948/tensorflow-gpu-recognized-in-the-terminal-but-not-in-the-jupyter-notebook).\r\n", "Hi @SuryanarayanaY, thank you for your reply.\r\nDifferently from the attached issues, my jupyter notebook does recognize GPU. The error only appears when calling the 'fit' function, the rest of the notebook runs without issues.\r\nDo you maybe have some suggestions on what could be some steps I could try for tackling the issue?\r\nThanks :)", "@Erida-Bendo , Could you tried installing `conda install nb_conda_kernels` and let us know.\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/60923\">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/60923\">No</a>\n" ]

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[ "Note that the issue is not the dependencies, but an error in LLVM\r\n\r\n```\r\nERROR: /home/...../.cache/bazel/_bazel_root/9c8aaa3c6704dd9093a3ba260ef8cc3f/external/llvm-project/llvm/BUILD.bazel:356:11: Compiling llvm/lib/TableGen/TableGenBackendSkeleton.cpp failed: undeclared inclusion(s) in rule '@llvm-project//llvm:TableGen':\r\nthis rule is missing dependency declarations for the following files included by 'llvm/lib/TableGen/TableGenBackendSkeleton.cpp':\r\n 'bazel-out/k8-opt/bin/external/llvm-project/llvm/Demangle.cppmap'\r\n 'bazel-out/k8-opt/bin/external/llvm_terminfo/terminfo.cppmap'\r\n 'bazel-out/k8-opt/bin/external/llvm_zlib/zlib.cppmap'\r\n```\r\n\r\nProject dependencies are specified as coming from multiple URLs (the upstream and the mirror). Bazel tries both and reports failure when the first attempt fails, but then look at the second URL and downloads the file from there. The build will be broken much faster if the dependency cannot be downloaded from _both_ upstream and the mirror.", "I'm getting the same error while trying to build tensorflow 2.13 (CPU build)\r\nINFO: Found 1 target...\r\nERROR: /root/.cache/bazel/_bazel_root/129f64c1bf7a2ba048cf317d8c31c367/external/llvm-project/llvm/BUILD.bazel:605:10: Compiling llvm/utils/TableGen/Attributes.cpp [for host] failed: undeclared inclusion(s) in rule '@llvm-project//llvm:llvm-tblgen':\r\nthis rule is missing dependency declarations for the following files included by 'llvm/utils/TableGen/Attributes.cpp':\r\n 'bazel-out/host/bin/external/llvm-project/llvm/Demangle.cppmap'\r\n 'bazel-out/host/bin/external/llvm_terminfo/terminfo.cppmap'\r\n 'bazel-out/host/bin/external/llvm_zlib/zlib.cppmap'\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\nINFO: Elapsed time: 57.973s, Critical Path: 13.61s\r\nINFO: 355 processes: 159 internal, 196 local.\r\nFAILED: Build did NOT complete successfully", "This is a nearly daily occurrence. You can wait until a fix lands or try building either from a stable release branch (`r2.13` for example), or if you want from the main branch, trying to find a commit from before a \"LLVM integrate\" one.\r\n\r\nThough now with the XLA and TSL separation, this might also be harder to do, you'd need to keep all of these in sync.", "Also, closing this one, since the original issue has been resolved.", "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/60922\">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/60922\">No</a>\n" ]

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[ "@cheyennee,\r\nFor some of the API's which were mentioned above, I can see the description is available on the official document. Could you please be more specific where the parameters are missing type descriptions. It will be easy to analyse the issue. \r\n\r\nEg:\r\n\r\nhttps://www.tensorflow.org/api_docs/python/tf/keras/metrics/RootMeanSquaredError\r\n\r\n\r\nThank you!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60921\">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/60921\">No</a>\n" ]

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[ "Hi @Glfrey ,\r\n\r\nThanks for reporting. Unfortunately the attached drive file can't accessible for me. Could you please provide the access. I request you to provide colab gist for ease of access with a minimal code snippet which can help us to look into the issue.\r\n\r\nThanks!", "Hi @SuryanarayanaY,\r\n\r\nThanks for your rapid response. I've transferred it over to colab here's the link https://colab.research.google.com/drive/1CoKr7DNBT3VDqSQlUAPfZPH0pYlj1-cq?usp=sharing\r\n\r\n", "> Note: This API is new and only available via pip install tf-nightly.\r\n\r\nhttps://www.tensorflow.org/api_docs/python/tf/keras/metrics/F1Score", "@DenisVorotyntsev Yes I'm aware, my tests were with the nightly builds but thank you for sharing. ", "@Glfrey, the error you mentioned in the first post doesn't match the error in the notebook, which confused me. \r\n\r\nI looked into F1, and it seems it required input to be one-hot-encoded to make the metric work. I made a conversion to ohe in the [notebook](https://colab.research.google.com/drive/1HRLJBvKhwAUAoKkZxMF7csh_GG3M9w9Y?usp=sharing) and it works. I also added a custom metric class that converts labels to ohe on the fly, which could lead to less memory consumption. ", "@DenisVorotyntsev huh, that's weird. I'm definitely on the nightly build at the moment I just checked. Either way, you're absolutely right. I completely overlooked the requirement for one-hot-encoding. My conversion to that representation of my labels has enabled it to work, so thank you very much!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60920\">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/60920\">No</a>\n" ]

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[ "@nourama30,\r\nCould you please try to follow the below steps mentioned:\r\n\r\nEdit ./etc/conda/activate.d/env_vars.sh as follows:\r\n\r\n```\r\n#!/bin/sh\r\n\r\nexport LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib\r\n```\r\n\r\nEdit ./etc/conda/deactivate.d/env_vars.sh as follows:\r\n\r\n```\r\n#!/bin/sh\r\n\r\nunset LD_LIBRARY_PATH\r\n```\r\n\r\nhttps://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#macos-and-linux\r\n\r\nAlso Could you please refer this documentation [source](https://www.tensorflow.org/install/pip#linux:~:text=your%20conda%20environment.-,export%20LD_LIBRARY_PATH%3D%24LD_LIBRARY_PATH%3A%24CONDA_PREFIX/lib/,CONDA_PREFIX/lib/%27%20%3E%20%24CONDA_PREFIX/etc/conda/activate,-.d/).\r\n\r\n`export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/`\r\n\r\nand Could you please try to install latest stable tensorflow v2.12 and let us know if you are facing same issue. 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/60918\">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/60918\">No</a>\n" ]

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[ "Hi @vrunm ,\r\n\r\nThe issue seems related to Memory resources and not problem with Tensorflow. If using one hot encoding it creates a very large sparse tensor which may require higher memory resources. As you have set `histogram_freq=1` it will create additional computations for weight histograms of each layer which needs higher memory resources. \r\n\r\nYou may try setting `histogram_freq=0` and check if the problem still exists then we need to check your code which is causing the large tensor computations.If no problem then its clear case of Higher memory requirement due to Histogram computations .\r\n\r\nThanks!\r\n", "The problem still exists with `histogram_freq=0`. I will look into making the code having large tensor computations.", "If the problem exists with histogram_freq=0 then the problem might be related to memory intensive computations due to higher input tensors.With large input tensors this is expected behaviour. However providing the code snippet shall confirm the same.", "Setting histogram_freq=0 did solve the problem to some extent, but I am still surprised by the very high memory requirements for the histogram computations. ", "@vrunm ,\r\n\r\nSetting `histogram_freq=1` needs storage of all weights into memory and for Larger models the weights are more and takes more memory as well. As you are using VGG16 model which has 138 million parameters which itself takes higher memory resource to store them. Any additional computations like histograms will add more load on memory.\r\n\r\nThanks!\r\n\r\n", "For these kind of computations what should be the value of histogram_freq? It worked for VGG16 but for models like ResNet and EfficientNet it did not work. What should be the value for those models?", "Hi @vrunm ,\r\n\r\nIt's all depends upon the Inputs sizes and Memory resources available. OOM errors are depends upon the input sizes and the Memory resources. Tensorflow can't have control on this. It has to be taken care by the users. \r\n\r\nMay be you can reduce `batch_size` in model.fit which is by default 32(you may try batch_size=16) . Also use the below code before importing Tensorflow in your code block.This may help if the OOM is due to Memory fragmentation.\r\n\r\n```\r\nimport os\r\nos.environ[‘TF_GPU_ALLOCATOR’] = ‘cuda_malloc_async’\r\n```\r\n", "I tried using a smaller batch size and using the above code but still ran into this error. I am also experimenting with smaller CNN networks to reduce memory computations and also make the model train faster.", "Hi @vrunm ,\r\n\r\nCould you please confirm the `batch_size` you used and let us knothe error. Maybe you can check with smaller networks since one_hot encoding is a large Tensor and may cause OOM error.", "I had tried using a batch size of 16 and 8 and still ran into this error.", "Could you please update the issue thread here https://github.com/keras-team/tf-keras/issues/168", "Sure will update it there.", "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/60917\">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/60917\">No</a>\n" ]

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[ "@deepcoder \r\nIn order to expedite the trouble-shooting process, please provide the example code snippet to reproduce the issue reported here. Thank you!", "As I stated in my opening post, running any of the example programs that you provide in the Tensorflow CUDA docker image caused the failures referencing the ZMQStream message. In terms of helping you further with this, unfortunately for that, I have moved on to another docker image I found on Docker Hub that provides recent Tensorflow 2 version, cuda support and so far is working fine for me. ", "Hi, \r\n\r\nThanks for reporting the issue.\r\n\r\nFeel free to close the issue if the issue is resolved in the latest docker version.\r\n\r\nFor our latest docker images, please follow https://hub.docker.com/r/tensorflow/tensorflow/tags", "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/60916\">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/60916\">No</a>\n" ]

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[ "Hi @AviadCo \r\n\r\nCan you try with latest stable version `r2.13` and let us know if you still face the error?\r\n\r\nAlso, try `bazel clean --expunge` before executing `bazel build -c opt tensorflow/compiler/mlir:tf-opt`\r\n\r\nThere is no such docker build as of now for the `tf-opt` binary.\r\n\r\nThanks.", "Hi @pjpratik , thanks for the response!\r\nAfter moving to branch `r2.13` I manage to build tf-opt with the following change:\r\nin `tensorflow/tensorflow.bzl`:\r\n```\r\n@@ -939,7 +939,8 @@ def _create_symlink(src, dest, visibility = None):\r\n outs = [src],\r\n srcs = [dest],\r\n output_to_bindir = 1,\r\n- cmd = \"ln -sf $$(realpath --relative-to=$(RULEDIR) $<) $@\",\r\n+ cmd = \"ln -sf $$(basename $<) $@\",\r\n```\r\n\r\nAt least for me the symbolic link is refering to not existing path when running with `ln -sf $$(realpath --relative-to=$(RULEDIR) $<) $@`", "Moreover, when I ran the lit tests using:\r\n`bazel test --override_repository=\"llvm-raw=${LLVM_SRC}\" -c opt -t //tensorflow/compiler/mlir/... --spawn_strategy=sandboxed -j 60`\r\nWhere `${LLVM_SRC}` was defined to my llvm-project clone, I had to remove some references to `\"@llvm-project//mlir:run_lit.sh\"` in filegroups in the following files:\r\n```\r\n/tensorflow/compiler/mlir/lite/stablehlo/tests/BUILD\r\n/tensorflow/compiler/mlir/quantization/stablehlo/tests/BUILD\r\n/tensorflow/compiler/mlir/quantization/tensorflow/tests/BUILD\r\n/tensorflow/compiler/mlir/tfr/BUILD\r\n/tensorflow/compiler/mlir/tools/kernel_gen/tests/BUILD\r\n/tensorflow/compiler/mlir/tools/kernel_gen/tests/tf_to_kernel/BUILD\r\n```", "Hi @AviadCo \r\n\r\nThanks for the fix. Please feel free to create a PR for the same. \r\n\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "> tensorflow/tensorflow.bzl\r\n\r\n\r\n\r\n> Hi @pjpratik , thanks for the response! After moving to branch `r2.13` I manage to build tf-opt with the following change: in `tensorflow/tensorflow.bzl`:\r\n> \r\n> ```\r\n> @@ -939,7 +939,8 @@ def _create_symlink(src, dest, visibility = None):\r\n> outs = [src],\r\n> srcs = [dest],\r\n> output_to_bindir = 1,\r\n> - cmd = \"ln -sf $$(realpath --relative-to=$(RULEDIR) $<) $@\",\r\n> + cmd = \"ln -sf $$(basename $<) $@\",\r\n> ```\r\n> \r\n> At least for me the symbolic link is refering to not existing path when running with `ln -sf $$(realpath --relative-to=$(RULEDIR) $<) $@`\r\n\r\nWorked for me with the latest `nightly` branch, thanks!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60915\">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/60915\">No</a>\n" ]

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[ "@gg4u I was able to run this official [example code](https://keras.io/examples/generative/vq_vae/) on colab as you can see [here](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/generative/ipynb/vq_vae.ipynb). Please make sure that you are using compatible versions for build configurations. Please let us know the cuda version you used ?\r\nThank you!", "I got the same error...any solutions yet? I tried to reinstall cudnn, nvidia driver, and cuda but didn't help at all.", "I am also facing same error tried uninstalling and reinstalling cuda and cudnn, downgraded tensorflow and tried. nothing is working. Please help to close this issue. I am facing this issue from 2 weeks\r\n`Loaded runtime CuDNN library: 8.5.0 but source was compiled with: 8.6.0. CuDNN library needs to have matching major version and equal or higher minor version. If using a binary install, upgrade your CuDNN library. If building from sources, make sure the library loaded at runtime is compatible with the version specified during compile configuration.\r\n2023-06-27 12:07:13.521171: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at conv_ops_fused_impl.h:625 : UNIMPLEMENTED: DNN library is not found.`", "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/60913\">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/60913\">No</a>\n", "@Gnanendra18 @Jerry-UtopiaCompression Could you please let us know if you are still facing the issue?\r\nThank you!", "Hi,\r\nI got the same issue.\r\nThe problem was due to the fact the version of the package `nvidia-cudnn-cu11` was 8.5.0.\r\nThis package should be upgraded (`pip install -U nvidia-cudnn-cu11`).", "@znacer that did the trick indeed! I've got 8.6.0 actually installed as a ubuntu package but the python package takes priority. That was an old one and needed updating.", "I'm in Docker linux and have no idea how to push this forward, I tried upgrade cudnn and tf, downgraded tf, changed docker image from 11 to 12, only thing I found strange, that in TF compatibility matrix there is no CUDNN 8.5 , so have no idea which version of TF i should take.", "@zba Hi Alexey, \r\n\r\nIt is well tricky. What I ended up with is doing this in my `dockerfile`\r\n\r\n`RUN pip install -v --default-timeout=120 --no-cache-dir -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu118`\r\n\r\nand this in my `requirements.txt`\r\n\r\n`nvidia-cudnn-cu11==8.6.*`\r\n\r\nFor the `dockerfile` the only thing that matters is (I think)\r\n\r\n`RUN pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu118`\r\n\r\nand you could also do\r\n\r\n`RUN pip install nvidia-cudnn-cu11==8.6.* --extra-index-url https://download.pytorch.org/whl/cu118`\r\n\r\nI hope this works for you. Let us know!\r\n\r\nEdit: This was using `FROM tensorflow/tensorflow:2.14.0-gpu` as my base image. I think it was installing PyTorch that bungled it.", "> RUN pip install nvidia-cudnn-cu11==8.6.* --extra-index-url https://download.pytorch.org/whl/cu118\r\n\r\nHi, thank you, it works, fyi my base image nvidia/cuda:11.8.0-devel-ubuntu22.04 ", "Hi,\r\nI got the same error. \r\nubuntu 22.04 , cuda version 12.2 , cudnn 8.8.1, I got the error in tensorflow version 2.15.0 as per documentation cuda 12.2 is supported for tenserflow 2.15.0\r\nI haven't done any changes for cuda version\r\nJust downgraded tenserflow with cuda and cudnn using the command \"pip install tensorflow[and-cuda]==2.14.0\" so the issue has sorted\r\n", "Got the same error with tensorflow 2.15. Fixed it downgrading to 2.14, tf 2.15 installer is broken. It seems that can't recognize nvidia-cudnn-cu11==8.9.4.* installed with pip." ]

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[ "@sachinprasadhs Any response?", "@dentiny , The request has been filed internally, once any new update is available, I will update it here. Thank you." ]

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[ "Please don't spam!" ]

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[ "Please don't spam!", "Please don't spam!" ]

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[ "Can I work on this issue?\r\n", "Hi @kentmchenry ,\r\n\r\nThanks for your observation and I believe you are right. `Model.fit` is meant for training on the inputs for certain epochs. The second reference of model.fit should be actually replaced with model.call .\r\n\r\nIf you are willing to contribute PR please feel free to do. Thanks!", "@SuryanarayanaY working it...", "@SuryanarayanaY PR is here: https://github.com/tensorflow/docs/pull/2236", "Hi @kentmchenry ,\r\n\r\nThanks for the PR. I can see the changes also approved by Engg team. Thanks for reporting and also for PR.", "Welcome, glad I could help", "@kentmchenry ,\r\n\r\nThe PR has been merged now. Could we mark it as resolved now. Thanks!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60907\">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/60907\">No</a>\n" ]

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[ "@abhaskumarsinha Thank you for raising the issue. \r\nIn order to expedite the trouble-shooting process, please provide the complete code snippet to reproduce the issue reported here. Thank you!!", "Hello @sushreebarsa Apologies for the late reply.\r\n\r\nThank you for looking into the issue. A sample reproducible code is given below:\r\n```\r\nimport tensorflow as tf\r\nimport numpy as np\r\nimport os\r\n\r\ntpu = tf.distribute.cluster_resolver.TPUClusterResolver()\r\ntf.config.experimental_connect_to_cluster(tpu)\r\ntf.tpu.experimental.initialize_tpu_system(tpu)\r\nstrategy = tf.distribute.TPUStrategy(tpu)\r\n\r\n# Generate random training data\r\nnum_samples = 1000\r\ninput_dim = 10\r\noutput_dim = 1\r\n\r\nx_train = np.random.random((num_samples, input_dim))\r\ny_train = np.random.random((num_samples, output_dim))\r\n\r\n# Convert the training data into TensorFlow datasets\r\nbatch_size = 32\r\ntrain_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(batch_size).repeat()\r\n\r\n# Custom training loop\r\nepochs = 10\r\n\r\nwith strategy.scope():\r\n # Define the model\r\n model = tf.keras.Sequential([\r\n tf.keras.layers.Dense(32, activation='relu', input_shape=(input_dim,)),\r\n tf.keras.layers.Dense(output_dim)\r\n ])\r\n\r\n # Define the loss function\r\n loss_object = tf.keras.losses.MeanSquaredError(reduction=tf.keras.losses.Reduction.NONE)\r\n\r\n # Define the optimizer\r\n optimizer = tf.keras.optimizers.Adam()\r\n\r\n\r\n for epoch in range(epochs):\r\n for inputs, labels in train_dataset:\r\n with tf.GradientTape() as tape:\r\n predictions = model(inputs, training=True)\r\n loss = loss_object(labels, predictions)\r\n gradients = tape.gradient(loss, model.trainable_variables)\r\n\r\n\r\n # Create custom gradients\r\n\r\n grads = []\r\n for g in gradients:\r\n grads += [tf.ones((tf.shape(g)))]\r\n\r\n optimizer.apply_gradients(zip(grads, model.trainable_variables))\r\n```\r\n\r\nHere's a reproducible Colab Link: https://colab.research.google.com/drive/1yj7pGuFS-Ottf1eM9ua3a_cCf3VdoU0Z?usp=sharing\r\n\r\nPlease look into the issue and update us with any possible solution (even temporarily) to deal with the issue.\r\n\r\nThank You.", "@abhaskumarsinha I was able to replicate the issue in TF v[2.12](https://colab.research.google.com/gist/sushreebarsa/c44e879509f73128a791c9957fe16306/60906.ipynb), [v2.13](https://colab.research.google.com/gist/sushreebarsa/c495b13f6eeb80c1408d7f96f94dbb91/60906.ipynb#scrollTo=AHBpLHNWX6kS) and faced **TypeError** in [tf-nightly](https://colab.research.google.com/gist/sushreebarsa/91e477da76917eba3668cf87a94ac49e/60906.ipynb#scrollTo=4DiN9Sz7X_KB). Please find the gist here. Could you please confirm the same? Thank you!", "Hello @sushreebarsa \r\n\r\nYes. I think the issue here is common for 2.12/13. I'm not sure about tf-nightly (for some reason I can't use them on my device due to some custom configurations, so I can confirm the same thing on Google Colab) but those modules certainly need a check. Thank you.", "@abhaskumarsinha Thank you for the response!\r\n@sachinprasadhs Could you please take a look at this issue?\r\nThank you!" ]

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[ "@minkooseo,\r\nThank you for opening this issue. Development of keras moved to another [repository](https://github.com/keras-team/keras/issues). \r\n\r\nCould you please post this issue on keras-team/keras [repo](https://github.com/keras-team/keras/issues).\r\nTo know more please refer:\r\nhttps://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999\r\nThank you!\r\n", "Ok. I will." ]

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[ "I have noticed that When i train just for 1 epoch and quantize it to int8, I lose 2-3% but when I train for more epoch I lose more then 30%. Why is the case.", "Hi @TayyabaZainab0807 \r\n\r\nCould you please provide the standalone code to reproduce the issue?\r\n\r\nThe quantization aware training is supposed to give better model accuracy than post training quantization. Please refer to [document](https://www.tensorflow.org/model_optimization/guide/quantization/training) for QAT and let us know if it helps.\r\n\r\nThanks.", "Hello @TayyabaZainab0807 \r\n\r\nCould you please try to use OPTIMIZE_FOR_SIZE while optimization and use the data type of float for input and output to get the better accuracy. \r\nAnd also, try this :\r\n def generate_representative_dataset():\r\n for i in range(x_train.shape[0]):\r\n print(i, end=\"\\r\")\r\n yield [tf.expand_dims(x_train[i], axis=0)]\r\n\r\n\r\nI am not sure about this, But you can try this once \r\n", "> Hi @TayyabaZainab0807\r\n> \r\n> Could you please provide the standalone code to reproduce the issue?\r\n> \r\n> The quantization aware training is supposed to give better model accuracy than post training quantization. Please refer to [document](https://www.tensorflow.org/model_optimization/guide/quantization/training) for QAT and let us know if it helps.\r\n> \r\n> Thanks.\r\n\r\nThank you for your reply.\r\nHere is a colab notebook:\r\nhttps://colab.research.google.com/drive/1iw4vJKyMuFTnOY6hyDFj2PJ2ZhBJlWDE?usp=sharing\r\n\r\n\r\nThis are the results for my trail. I get different accuracy every time, the difference in accuracy can be from 1% - more then 50%:\r\nTesting X_test_results_quantized.csv\r\nTP= 145\r\nTN= 1039\r\nFP= 0\r\nFN= 1316\r\naccuracy: 0.4736\r\n\r\n\r\nTesting X_test_results_normal.csv\r\nTP= 1441\r\nTN= 1028\r\nFP= 11\r\nFN= 20\r\naccuracy: 0.9876", "\r\n> OPTIMIZE_FOR_SIZE\r\n\r\n@netra2109 Thanks for your reply. I removed the int input and outputs but still face the same challenge. I didn't understand what you meant by OPTIMIZE_FOR_SIZE.\r\n\r\nDoes this go somewhere here?\r\nconverter.optimizations = [tf.lite.Optimize.DEFAULT]", "\r\n@TayyabaZainab0807 \r\n\r\nyeah, adjust the optimizations if needed. \r\n converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]\r\n\r\n", "> @TayyabaZainab0807\r\n> \r\n> yeah, adjust the optimizations if needed. converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]\r\n\r\nOPTIMIZE_FOR_SIZE also didn't work for me, still got 44% accuracy.", "Hi @TayyabaZainab0807 \r\n\r\nThe issue with your code is not initialising the interpreter while testing the samples in the loop. \r\n\r\nPlease find the [gist](https://colab.research.google.com/gist/pjpratik/e8926a25ac83179fb29717dc1657608f/60903.ipynb) which obtained `98.04%` accuracy on the converted quantized tflite model.\r\n\r\nThanks.", "It seems to work now..Thank you very much. Is there any documentation on this? I would like to read a bit into that.", "Hi @TayyabaZainab0807 \r\n\r\nGlad it worked. Please check the [documentation](https://www.tensorflow.org/lite/guide/inference#running_a_model) on steps involved in running a model in TFLite.\r\n\r\nPlease feel free to close the issue since it is resolved.\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/60903\">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/60903\">No</a>\n", "Hello, \r\n\r\nThank you for your input on this. I am facing exactly the same issue (working with MobilenetV3 on int8 quantisation and observing big accuracy drop / kind of random results depending on a particular attempt - both with PTQ and QAT). The proposed solution did not work, may I please double check if it solved the issues in case of @TayyabaZainab0807 ? Any chance you could please elaborate why it is required to call the following on every iteration? I thought it would be okay to call it only once before the cycle? @pjpratik \r\n`interpreter = tf.lite.Interpreter(model_path='keras_lstm/model_int8.tflite')\r\n interpreter.allocate_tensors()` \r\n\r\nThank you very much." ]

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[ "@galaturka Thank you for opening this issue. You are using an older version of TF which is not actively supported. Could you please provide the entire tflite model to debug the issue if that persists in the newer version?\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.", "@galaturka I experience the problem. Are you able to figure this out yet?", "Hi @galaturka \r\n\r\nCan you try with latest nightly snapshot and see if you are still facing the issue?\r\n\r\n```\r\ndependencies {\r\n implementation 'org.tensorflow:tensorflow-lite:0.0.0-nightly-SNAPSHOT'\r\n // This dependency adds the necessary TF op support.\r\n implementation 'org.tensorflow:tensorflow-lite-select-tf-ops:0.0.0-nightly-SNAPSHOT'\r\n}\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/60902\">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/60902\">No</a>\n", "Hi @galaturka were you able to resolve this issue? i am currently facing it.\r\n" ]

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[ "@bergentruckung,\r\nHave you tried the above query after trying the **bazel clean --expunge** command. Could you try once and try repeating the above steps. And also make sure you follow the steps mentioned [here](https://www.tensorflow.org/install/source).\r\n\r\nAlso usually users get this error `PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: `unable to find valid certification path to requested target due to the system firewall. The system firewall restricts the application to connect to external systems. The firewall requires a valid certificate to allow access to the external systems.\r\n\r\nCould you please confirm, whether you're installing [Bazel](https://bazel.build/install) normally or via Bazelisk because [Bazelisk](https://github.com/bazelbuild/bazelisk) is an easy way to install Bazel and automatically downloads the correct Bazel version for TensorFlow and if you're using Bazelisk then please download manually and please follow below steps:\r\n\r\n1. wget https://github.com/bazelbuild/bazelisk/releases/download/v1.16.0/bazelisk-darwin-arm64 (If you get any error with respect to certificate then you can use this command wget --no-check-certificate https://github.com/bazelbuild/bazelisk/releases/download/v1.16.0/bazelisk-darwin-arm64\r\n\r\n2. `chmod +x bazelisk-darwin-arm64`\r\n\r\n3. `sudo mv bazelisk-darwin-arm64 /usr/local/bin/bazel`\r\n\r\nAfter that please try to run the below steps:\r\n\r\n```\r\n1. whereis bazel\r\n2. whereis bazelisk\r\n```\r\nAlso https://bazel.build/install/compile-source#bootstrap-unix.\r\nplease try to comment out 'for' statement and rebuild a binary version of bazel:\r\nhttps://github.com/bazelbuild/bazel/blob/master/src/main/cpp/blaze.cc#L1015\r\nThen use this to build tensorflow. Thank you!", "Thanks, @tilakrayal.\r\n>Have you tried the above query after trying the bazel clean --expunge command. Could you try once and try repeating the above steps. And also make sure you follow the steps mentioned [here](https://www.tensorflow.org/install/source).\r\n\r\nYes, I've tried after running `bazel clean --expunge` as well. Also, I've been following the exact same docs that you linked.\r\n\r\n>Also usually users get this error PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target due to the system firewall. The system firewall restricts the application to connect to external systems. The firewall requires a valid certificate to allow access to the external systems.\r\n\r\nWhat can I do to bypass this? What's the cert that is causing the problem? Since the error seems to indicate a java application (I'm guessing bazel?), is it possible to add the required cert to its java keystore/cert bundle?\r\n\r\n>Could you please confirm, whether you're installing [Bazel](https://bazel.build/install) normally or via [Bazelisk](https://github.com/bazelbuild/bazelisk) because Bazelisk is an easy way to install Bazel and automatically downloads the correct Bazel version for TensorFlow and if you're using Bazelisk then please download manually and please follow below steps:\r\n\r\nYes, I'm using `bazelisk` to install the correct version of `bazel`.", "@tilakrayal, were you able to check this?", "Any updates here?", "@sachinprasadhs, any updates here?" ]

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[ "Hi @mjugl ,\r\n\r\nI think the problem is not with Tensorflow.I tested the same code in colab and it executes fine.[Gist](https://colab.research.google.com/gist/SuryanarayanaY/39ae6bc8ded874147e9ef57171a6111f/60900.ipynb) attached for reference.\r\n\r\nI request you to please refer the attached pip instructions guide to check the Hardware requirements and GPU setup process. I have seen from the attached logs GPU was not enabled which may be causing the problem.Please verify the GPU steps and confirm the same. \r\n\r\nComing to GPU drivers you can check the Nvidia website for compatible GPU driver for your GPU.Also Tensorflow tested configurations for CUDA and cuDNN can be found [here](https://www.tensorflow.org/install/source#gpu).\r\n\r\nThanks!", "Hi @SuryanarayanaY,\r\n\r\n> I have seen from the attached logs GPU was not enabled which may be causing the problem.Please verify the GPU steps and confirm the same.\r\n\r\nI reinstalled the graphics drivers and verified they work. `nvidia-smi` reports that the GPU is indeed active. I'm also fairly confident the graphics drivers were correctly installed before. Unfortunately, I can only provide a screenshot of the CLI for now.\r\n\r\n\r\n\r\n> Also Tensorflow tested configurations for CUDA and cuDNN can be found [here](https://www.tensorflow.org/install/source#gpu).\r\n\r\nGood point. I found that we're running CUDA 11.4 which is not officially tested with TF 2.12. After the graphics driver re-installation I set up a fresh Tensorflow install as shown in the docs, this time with TF 2.11. I'm getting the same soft lockup (and an unresponsive server) when performing the \"Verify install\" step.\r\n\r\n\r\n\r\nYou can also see that it is being shown in the process list of the `nvidia-smi` screenshot above.", "Okay I just verified that this issue is not unique to Tensorflow. I ran the bandwidth test from the CUDA samples repository and found it also leads to a CPU soft lockup. So I can confirm that this is likely not an issue with Tensorflow.\r\n\r\nOn the other hand, I've no idea where to go from here. The issue persists and I've no idea what to look for next.", "Hi @mjugl ,\r\n\r\nI am also quite confident that the issue might be related to your own environment as it is not replicable from our side. I have tried it on GCP VM and also can get desired result as per logs attached below.\r\n\r\n```\r\n(bazel) suryanarayanay@surya-ubuntu20:~$ python3 -c \"import tensorflow as tf; print(tf.test.gpu_device_name())\"\r\n2023-06-28 05:24:05.535235: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-06-28 05:24:06.474920: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-06-28 05:24:09.452020: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-06-28 05:24:09.499115: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-06-28 05:24:09.500874: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-06-28 05:24:11.554723: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-06-28 05:24:11.556761: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-06-28 05:24:11.558432: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-06-28 05:24:11.560108: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /device:GPU:0 with 13623 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\r\n/device:GPU:0\r\n(bazel) suryanarayanay@surya-ubuntu20:~$ python3 -c \"import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))\"\r\n2023-06-28 05:26:39.033522: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-06-28 05:26:39.988490: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-06-28 05:26:42.978403: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-06-28 05:26:43.025696: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-06-28 05:26:43.027424: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-06-28 05:26:43.029879: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-06-28 05:26:43.031511: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-06-28 05:26:43.033072: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-06-28 05:26:44.111462: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-06-28 05:26:44.113411: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-06-28 05:26:44.115093: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-06-28 05:26:44.116645: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13623 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\r\ntf.Tensor(1441.14, shape=(), dtype=float32)\r\n(bazel) suryanarayanay@surya-ubuntu20:~$ \r\n```\r\n\r\nThe issue is specific to your own environment which I even don't have a clue on this.I believe the issue might be related to OS. I am attaching few references from Internet for similar issues which are related to OS.Please refer [link1](https://askubuntu.com/questions/1264859/watchdog-bug-soft-lockup-cpu6-stuck-for-23s) and [link2](https://bugs.launchpad.net/ubuntu/+source/linux/+bug/1989521). \r\n\r\nPlease refer the above links and maybe you can get more help from internet community. As the issues is not relevant to TF can we mark it as closed. \r\n\r\nThanks!", "Hi, thanks for investigating this further. The issue has been resolved and it didn't have anything to do with Tensorflow in the end.\r\n\r\nFor anyone who might see this in the future: our IT department forgot to inform us that the licensing process to access the GPU has changed very recently and that we needed to make some modification to our guest VM. The most likely explanation is that the GPU slows down after extended unlicensed use and probably stops working entirely, leading to the lockup we experienced.\r\n\r\nWe haven't encountered this issue since we reconfigured our guest VM. Again thanks so much for taking the time to investigate even though this really was an issue on our behalf.", "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/60900\">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/60900\">No</a>\n" ]

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[]

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[ "Hi @jpienaar and @rsuderman, please could you help to review when you have time.\r\nThank you!" ]

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[ "Hi @Alavandar08! Could you please have a look at this [link](https://www.tensorflow.org/install/source#tested_build_configurations) and make sure the compatible versions are matching yours? Please let us know the exact command you have used so that we could replicate the error reported here. \r\nThank you!", "Hi @sushreebarsa I have tried the compatible versions provided with in the [link](https://www.tensorflow.org/install/source#cpu). Still I am observing the same issue as mentioned above.\r\n\r\ncommand I used to build TF from source:\r\n1. Installing prerequisites (Env, bazel version etc.,)\r\n2. Configuring the Build: ./configure\r\n3. Building the package: bazel build //tensorflow/tools/pip_package:build_pip_package\r\n\r\nI have created a [PR](https://github.com/tensorflow/tensorflow/pull/60874) to revert specific commit [60872](https://github.com/tensorflow/tensorflow/pull/60872) (After reverting commit in local, I was able to build the package).\r\n\r\nPlease let me know the fix for the above issue.\r\n\r\nThank you!", "As mentioned in the previous comment.\r\n\r\nFor the above PR that got merged master had failed through CI phase\r\n\r\n\r\nEven in CI we are observing the same error\r\n\r\n", "I am also facing the same issue. Not able to build the tensorflow in linux. If i checkout the commit id 76addf724a4, build goes through.", "@penpornk ", "Hi @Alavandar08 ,\r\n\r\nThe proposed PR by you has been closed as there won't be any fresh release of Tf2.12. This seems to be resolved in TF2.13 onwards. Could you please check with latest version and let us know if this is still a problem.\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/60897\">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/60897\">No</a>\n" ]

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[ "Please don't spam" ]

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[ "Hi @mergian ,\r\n\r\nThanks for reporting. I think reproduction of this issue needs non-NVIDIA GPUs,If I am not wrong. I am assuming that you have non-GPU pluggable devices which are actually identified as GPU by TF which is the problem,right?\r\n\r\nI did refer some resources [gpu-plugins ](https://www.tensorflow.org/install/gpu_plugins) and [pluggable-device-for-tensorflow.md](https://github.com/tensorflow/community/blob/master/rfcs/20200624-pluggable-device-for-tensorflow.md).As per my understanding from here if the pluggable device registered as GPU then it shall be added to GPU list and implementation done accordingly. \r\n\r\nAs per my understanding there might be non GPU pluggable devices also which can be usable by TF. At least this is my understanding from the `.readme` file attached earlier in this comment and correct me if I am wrong. In Your proposed code all the devices which are not registered as GPU will be ignored right.I think in that case we need to check for more. Please share your thoughts .", "Dear @SuryanarayanaY \r\n\r\nyes you are correct.\r\n\r\nOur PluggableDevice plugin adds non-GPU devices. The code that I marked however assumes all PluggableDevices to be GPUs, therefore the `visible_devices` list will be populated with invalid ids, which then later cause errors when being checked internally.\r\n\r\nMy proposal is, that for this particular code pice, that looks for TensorFlow-Native-GPUs and PluggableDevice-GPUs and combines them, to check if the `.device_type` is really `GPU`.\r\n\r\nIf needed it should be possible to create a fake PluggableDevice-Plugin, that creates non existing devices. Then you could reproduce this error even without having our devices available. But I think could take some hours to do so.", "Hi @mergian ,\r\n\r\nThanks for confirmation. With your proposal only PluggableDevice-GPUs can be detectable y TF. I doubt whether this implementation only limited to GPUs as .[readme](https://github.com/tensorflow/community/blob/master/rfcs/20200624-pluggable-device-for-tensorflow.md) talks about more than GPU type devices and I may be wrong also here. I would like to hear from dev team on this.But I feel there is some cases not covered in implementation.\r\n\r\nThanks!\r\n\r\n", "No. My bugfix aims at supporting also other PluggableDevice types. Currently, when you mix GPUs and Non-GPUs, you'll get the above mentioned error, as within this GPU specific initialization function, it's not properly distinguishing between GPUs and other device types, and therefore initializes this \"gpu_options\" data structure with invalid values.\r\n\r\nI build a demonstrator for this. Here is a fake PluggableDevice, that creates 2 \"NO_GPU\" devices.\r\n\r\n```cpp\r\n#include <tensorflow/c/experimental/stream_executor/stream_executor.h>\r\n\r\nstatic void get_device_count(const SP_Platform* platform, int* device_count, TF_Status* status) {\r\n\t*device_count = 2;\r\n\tTF_SetStatus(status, TF_OK,\t\"\");\r\n}\r\n\r\nstatic void create_device(const SP_Platform* platform, SE_CreateDeviceParams* params, TF_Status* status) {\r\n\tparams->device->device_handle\t= 0;\r\n\tparams->device->hardware_name\t= \"TEST DEVICE\";\r\n\tparams->device->device_vendor\t= \"SOME VENDOR\";\r\n\tparams->device->pci_bus_id\t\t= \"0000:00:00.0\";\r\n\tTF_SetStatus(status, TF_OK,\t\"\");\r\n}\r\n\r\nstatic int32_t get_numa_node(const SP_Device* device) {\r\n\treturn device->ordinal;\r\n}\r\n\r\nstatic int64_t get_memory_bandwidth(const SP_Device* device) {\r\n\treturn -1;\r\n}\r\n\r\nstatic double get_gflops(const SP_Device* device) {\r\n\treturn -1;\r\n}\r\n\r\nstatic void create_device_fns(const SP_Platform* platform, SE_CreateDeviceFnsParams* params, TF_Status* status) {\r\n\tparams->device_fns->get_gflops\t\t\t\t= &get_gflops;\r\n\tparams->device_fns->get_memory_bandwidth\t= &get_memory_bandwidth;\r\n\tparams->device_fns->get_numa_node\t\t\t= &get_numa_node;\r\n\tTF_SetStatus(status, TF_OK,\t\"\");\r\n}\r\n\r\nstatic uint64_t nanoseconds(SP_Timer timer) {\r\n\treturn 0;\r\n}\r\n\r\nstatic void destroy_device(const SP_Platform* platform, SP_Device* device) {\r\n}\r\n\r\nstatic void destroy_device_fns(const SP_Platform* platform, SP_DeviceFns* device_fns) {\r\n}\r\n\r\nstatic void destroy_platform(SP_Platform* platform) {\r\n}\r\n\r\nstatic void destroy_platform_fns(SP_PlatformFns* platform_fns) {\r\n}\r\n\r\nstatic void destroy_stream_executor(const SP_Platform* platform, SP_StreamExecutor* stream_executor) {\r\n}\r\n\r\nstatic void destroy_timer_fns(const SP_Platform* platform, SP_TimerFns* timer_fns) {\r\n}\r\n\r\nstatic void destroy_event(const SP_Device* device, SP_Event event) {\r\n}\r\n\r\nstatic void record_event_helper(SP_Event event) {\r\n}\r\n\r\nstatic void create_event(const SP_Device* device, SP_Event* event, TF_Status* status) {\r\n\tTF_SetStatus(status, TF_OK,\t\"\");\r\n}\r\n\r\nstatic void create_timer_fns(const SP_Platform* platform, SP_TimerFns* timer, TF_Status* status) {\r\n\ttimer->nanoseconds = &nanoseconds;\r\n\tTF_SetStatus(status, TF_OK,\t\"\");\r\n}\r\n\r\nstatic SE_EventStatus get_event_status(const SP_Device* device, SP_Event event) {\t\r\n\treturn SE_EventStatus();\r\n}\r\n\r\nstatic void record_event(const SP_Device* device, SP_Stream stream, SP_Event event, TF_Status* status) {\r\n\tTF_SetStatus(status, TF_OK,\t\"\");\r\n}\r\n\r\nstatic void wait_for_event(const SP_Device* const device, SP_Stream stream, SP_Event event, TF_Status* const status) {\r\n\tTF_SetStatus(status, TF_OK,\t\"\");\r\n}\r\n\r\nstatic void allocate(const SP_Device* device, uint64_t size, int64_t memory_space, SP_DeviceMemoryBase* mem) {\r\n}\r\n\r\nstatic void deallocate(const SP_Device* device, SP_DeviceMemoryBase* memory) {\r\n}\r\n\r\nstatic void* host_memory_allocate(const SP_Device* device, uint64_t size) {\r\n\treturn 0;\r\n}\r\n\r\nstatic void host_memory_deallocate(const SP_Device* device, void* ptr) {\r\n}\r\n\r\nstatic TF_Bool get_allocator_stats(const SP_Device* device, SP_AllocatorStats* stats) {\r\n\treturn 1;\r\n}\r\n\r\nstatic TF_Bool device_memory_usage(const SP_Device* device, int64_t* free, int64_t* total) {\r\n\treturn 1;\r\n}\r\n\r\nstatic void create_stream(const SP_Device* device, SP_Stream* stream, TF_Status* status) {\r\n\tTF_SetStatus(status, TF_OK,\t\"\");\r\n}\r\n\r\nstatic void destroy_stream(const SP_Device* device, SP_Stream stream) {\r\n}\r\n\r\nstatic void create_stream_dependency(const SP_Device* device, SP_Stream dependent, SP_Stream other, TF_Status* status) {\r\n\tTF_SetStatus(status, TF_OK,\t\"\");\r\n}\r\n\r\nstatic void get_stream_status(const SP_Device* device, SP_Stream stream, TF_Status* status) {\r\n}\r\n\r\nstatic void memcpy_dtoh(const SP_Device* device, SP_Stream stream, void* host_dst, const SP_DeviceMemoryBase* device_src, uint64_t size, TF_Status* status) {\r\n\tTF_SetStatus(status, TF_OK,\t\"\");\r\n}\r\n\r\nstatic void memcpy_htod(const SP_Device* device, SP_Stream stream, SP_DeviceMemoryBase* device_dst, const void* host_src, uint64_t size, TF_Status* status) {\r\n\tTF_SetStatus(status, TF_OK,\t\"\");\r\n}\r\n\r\nstatic void memcpy_dtod(const SP_Device* device, SP_Stream stream, SP_DeviceMemoryBase* device_dst, const SP_DeviceMemoryBase* device_src, uint64_t size, TF_Status* status) {\r\n\tTF_SetStatus(status, TF_OK,\t\"\");\r\n}\r\n\r\nstatic void sync_memcpy_dtoh(const SP_Device* device, void* host_dst, const SP_DeviceMemoryBase* device_src, uint64_t size, TF_Status* status) {\r\n\tTF_SetStatus(status, TF_OK,\t\"\");\r\n}\r\n\r\nstatic void sync_memcpy_htod(const SP_Device* device, SP_DeviceMemoryBase* device_dst, const void* host_src, uint64_t size, TF_Status* status) {\r\n\tTF_SetStatus(status, TF_OK,\t\"\");\r\n}\r\n\r\nstatic void sync_memcpy_dtod(const SP_Device* device, SP_DeviceMemoryBase* device_dst, const SP_DeviceMemoryBase* device_src, uint64_t size, TF_Status* status) {\r\n\tTF_SetStatus(status, TF_OK,\t\"\");\r\n}\r\n\r\nstatic void block_host_for_event(const SP_Device* device, SP_Event event, TF_Status* status) {\r\n}\r\n\r\nstatic void block_host_until_done(const SP_Device* device, SP_Stream stream, TF_Status* status) {\r\n\tTF_SetStatus(status, TF_OK,\t\"\");\r\n}\r\n\r\nstatic void synchronize_all_activity(const SP_Device* device, TF_Status* status) {\r\n\tTF_SetStatus(status, TF_OK,\t\"\");\r\n}\r\n\r\nstatic TF_Bool host_callback(const SP_Device* device, SP_Stream stream, SE_StatusCallbackFn callback_fn, void* callback_arg) {\r\n\treturn true;\r\n}\r\n\r\nstatic void create_timer(const SP_Device* device, SP_Timer* timer, TF_Status* status) {\r\n}\r\n\r\nstatic void destroy_timer(const SP_Device* device, SP_Timer timer) {\r\n}\r\n\r\nstatic void start_timer(const SP_Device* device, SP_Stream stream, SP_Timer timer, TF_Status* status) {\r\n\tTF_SetStatus(status, TF_OK,\t\"\");\r\n}\r\n\r\nstatic void stop_timer(const SP_Device* device, SP_Stream stream, SP_Timer timer, TF_Status* status) {\r\n\tTF_SetStatus(status, TF_OK,\t\"\");\r\n}\r\n\r\nstatic void mem_zero(const SP_Device* device, SP_Stream stream, SP_DeviceMemoryBase* location, uint64_t size, TF_Status* status) {\r\n\tTF_SetStatus(status, TF_OK,\t\"\");\r\n}\r\n\r\nstatic void memset8(const SP_Device* device, SP_Stream stream, SP_DeviceMemoryBase* location, uint8_t pattern, uint64_t size, TF_Status* status) {\r\n\tTF_SetStatus(status, TF_OK,\t\"\");\r\n}\r\n\r\nstatic void memset32(const SP_Device* device, SP_Stream stream, SP_DeviceMemoryBase* location, uint32_t pattern, uint64_t size, TF_Status* status) {\r\n\tTF_SetStatus(status, TF_OK,\t\"\");\r\n}\r\n\r\nvoid create_stream_executor(const SP_Platform* platform, SE_CreateStreamExecutorParams* params, TF_Status* status) {\r\n\tparams->stream_executor->allocate\t\t\t\t\t= &allocate;\r\n\tparams->stream_executor->block_host_for_event\t\t= &block_host_for_event;\r\n\tparams->stream_executor->block_host_until_done\t\t= &block_host_until_done;\r\n\tparams->stream_executor->create_event\t\t\t\t= &create_event;\r\n\tparams->stream_executor->create_stream\t\t\t\t= &create_stream;\r\n\tparams->stream_executor->create_stream_dependency\t= &create_stream_dependency;\r\n\tparams->stream_executor->create_timer\t\t\t\t= &create_timer;\r\n\tparams->stream_executor->deallocate\t\t\t\t\t= &deallocate;\r\n\tparams->stream_executor->destroy_event\t\t\t\t= &destroy_event;\r\n\tparams->stream_executor->destroy_stream\t\t\t\t= &destroy_stream;\r\n\tparams->stream_executor->destroy_timer\t\t\t\t= &destroy_timer;\r\n\tparams->stream_executor->device_memory_usage\t\t= &device_memory_usage;\r\n\tparams->stream_executor->get_allocator_stats\t\t= &get_allocator_stats;\r\n\tparams->stream_executor->get_event_status\t\t\t= &get_event_status;\r\n\tparams->stream_executor->get_stream_status\t\t\t= &get_stream_status;\r\n\tparams->stream_executor->host_callback\t\t\t\t= &host_callback;\r\n\tparams->stream_executor->host_memory_allocate\t\t= &host_memory_allocate;\r\n\tparams->stream_executor->host_memory_deallocate\t\t= &host_memory_deallocate;\r\n\tparams->stream_executor->memcpy_dtod\t\t\t\t= &memcpy_dtod;\r\n\tparams->stream_executor->memcpy_dtoh\t\t\t\t= &memcpy_dtoh;\r\n\tparams->stream_executor->memcpy_htod\t\t\t\t= &memcpy_htod;\r\n\tparams->stream_executor->record_event\t\t\t\t= &record_event;\r\n\tparams->stream_executor->start_timer\t\t\t\t= &start_timer;\r\n\tparams->stream_executor->stop_timer\t\t\t\t\t= &stop_timer;\r\n\tparams->stream_executor->sync_memcpy_dtod\t\t\t= &sync_memcpy_dtod;\r\n\tparams->stream_executor->sync_memcpy_dtoh\t\t\t= &sync_memcpy_dtoh;\r\n\tparams->stream_executor->sync_memcpy_htod\t\t\t= &sync_memcpy_htod;\r\n\tparams->stream_executor->synchronize_all_activity\t= &synchronize_all_activity;\r\n\tparams->stream_executor->wait_for_event\t\t\t\t= &wait_for_event;\r\n\tparams->stream_executor->mem_zero\t\t\t\t\t= &mem_zero;\r\n\tparams->stream_executor->memset\t\t\t\t\t\t= &memset8;\r\n\tparams->stream_executor->memset32\t\t\t\t\t= &memset32;\r\n\tTF_SetStatus(status, TF_OK,\t\"\");\r\n}\r\n\r\nextern \"C\" void SE_InitPlugin(SE_PlatformRegistrationParams* params, TF_Status* status) {\r\n\tparams->destroy_platform\t\t\t\t\t\t= &destroy_platform;\r\n\tparams->destroy_platform_fns\t\t\t\t\t= &destroy_platform_fns;\r\n\r\n\tparams->platform->name\t\t\t\t\t\t\t= \"SOME_PLATFORM_NAME\";\r\n\tparams->platform->type\t\t\t\t\t\t\t= \"NO_GPU\";\r\n\tparams->platform->supports_unified_memory\t\t= 0;\r\n\tparams->platform->use_bfc_allocator\t\t\t\t= 0;\r\n\tparams->platform->force_memory_growth\t\t\t= 0;\r\n\r\n\tparams->platform_fns->create_device\t\t\t\t= &create_device;\r\n\tparams->platform_fns->create_device_fns\t\t\t= &create_device_fns;\r\n\tparams->platform_fns->create_stream_executor\t= &create_stream_executor;\r\n\tparams->platform_fns->create_timer_fns\t\t\t= &create_timer_fns;\r\n\tparams->platform_fns->destroy_device\t\t\t= &destroy_device;\r\n\tparams->platform_fns->destroy_device_fns\t\t= &destroy_device_fns;\r\n\tparams->platform_fns->destroy_stream_executor\t= &destroy_stream_executor;\r\n\tparams->platform_fns->destroy_timer_fns\t\t\t= &destroy_timer_fns;\r\n\tparams->platform_fns->get_device_count\t\t\t= &get_device_count;\r\n}\r\n\r\nextern \"C\" void TF_InitKernel(void) {\r\n}\r\n```\r\n\r\nYou can trigger the compilation + error using this:\r\n```bash\r\n#!/bin/bash\r\n\r\npython_path=$(python3 -m site --user-site)\r\n\r\ng++ -I $python_path/tensorflow/include -fpic -c pluggable.cpp -o pluggable.o\r\ng++ -shared -L $python_path/tensorflow/ -l:libtensorflow_framework.so.2 pluggable.o -o $python_path/tensorflow-plugins/liberror.so\r\n\r\npython3 -c \"import tensorflow as tf; print('', *tf.config.list_physical_devices(), '', sep='\\n'); tf.device('/GPU:0')\"\r\n```\r\n\r\nI get the output:\r\n```\r\n2023-06-19 09:15:25.980567: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-06-19 09:15:27.181142: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n\r\nPhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')\r\nPhysicalDevice(name='/physical_device:NO_GPU:0', device_type='NO_GPU')\r\nPhysicalDevice(name='/physical_device:NO_GPU:1', device_type='NO_GPU')\r\nPhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')\r\n\r\nTraceback (most recent call last):\r\n File \"<string>\", line 1, in <module>\r\n File \"/.../.local/lib/python3.8/site-packages/tensorflow/python/framework/ops.py\", line 5577, in device_v2\r\n return device(device_name)\r\n File \"/.../.local/lib/python3.8/site-packages/tensorflow/python/framework/ops.py\", line 5526, in device\r\n return context.device(device_name_or_function)\r\n File \"/.../.local/lib/python3.8/site-packages/tensorflow/python/eager/context.py\", line 2348, in device\r\n ensure_initialized()\r\n File \"/.../.local/lib/python3.8/site-packages/tensorflow/python/eager/context.py\", line 2143, in ensure_initialized\r\n context().ensure_initialized()\r\n File \"/.../.local/lib/python3.8/site-packages/tensorflow/python/eager/context.py\", line 583, in ensure_initialized\r\n context_handle = pywrap_tfe.TFE_NewContext(opts)\r\ntensorflow.python.framework.errors_impl.InvalidArgumentError: 'visible_device_list' listed an invalid Device id '2' but visible device count is 2\r\n```\r\n\r\nWith my proposed bugfix, the GPU device correctly gets selected:\r\n```\r\n2023-06-19 09:17:32.876242: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-06-19 09:17:36.355710: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n\r\nPhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')\r\nPhysicalDevice(name='/physical_device:NO_GPU:0', device_type='NO_GPU')\r\nPhysicalDevice(name='/physical_device:NO_GPU:1', device_type='NO_GPU')\r\nPhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')\r\n\r\n2023-06-19 09:17:38.938987: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform NO_GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\r\n2023-06-19 09:17:38.939062: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:272] Created TensorFlow device (/job:localhost/replica:0/task:0/device:NO_GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: SOME_PLATFORM_NAME, pci bus id: <undefined>)\r\n2023-06-19 09:17:39.512542: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 7371 MB memory: -> device: 0, name: Quadro P4000, pci bus id: 0000:65:00.0, compute capability: 6.1\r\n```\r\n" ]

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[ "@akuegel would you review our hipblas_lt PR, please? Thanks in advance!", "@ekuznetsov139 Not sure whether you saw my review comments? I got asked by @i-chaochen about this PR, but I think this now needs the changes from your side until I can approve it.", "> @ekuznetsov139 Not sure whether you saw my review comments? I got asked by @i-chaochen about this PR, but I think this now needs the changes from your side until I can approve it.\r\n\r\nHi @akuegel Thanks for the feedback. I am going to split this big PR to smaller ones due to some upstream changes. And I will do the changes based on your feedback as well.\r\n\r\nWe're going to push it to XLA first (https://github.com/openxla/xla/pull/3953). Thanks in advance!", "Hi @ekuznetsov139 Can you please rebase your branch and resolve conflicts? Thank you!", "This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.", "This PR was closed because it has been inactive for 14 days since being marked as stale. Please reopen if you'd like to work on this further." ]

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[ "Hi @woojinn8, thanks for reporting your issue. Help me understand your environment a little bit better, are you building for Android on a linux system? Are you testing your model on a linux system or an android system? Have you reviewed this link? https://www.tensorflow.org/lite/guide/ops_select If you are testing your converted model on android please follow the instructions there, otherwise I think you are testing on a linux system from the context of everything. Have you tried enabling SELECT_TF_OPS before conversion?\r\n\r\nexample:\r\n```python\r\nimport tensorflow as tf\r\n\r\nconverter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)\r\nconverter.target_spec.supported_ops = [\r\n tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.\r\n tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.\r\n]\r\ntflite_model = converter.convert()\r\nopen(\"converted_model.tflite\", \"wb\").write(tflite_model)\r\n```", "> Hi @woojinn8, thanks for reporting your issue. Help me understand your environment a little bit better, are you building for Android on a linux system? Are you testing your model on a linux system or an android system? Have you reviewed this link? https://www.tensorflow.org/lite/guide/ops_select If you are testing your converted model on android please follow the instructions there, otherwise I think you are testing on a linux system from the context of everything. Have you tried enabling SELECT_TF_OPS before conversion?\r\n> \r\n> example:\r\n> \r\n> ```python\r\n> import tensorflow as tf\r\n> \r\n> converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)\r\n> converter.target_spec.supported_ops = [\r\n> tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.\r\n> tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.\r\n> ]\r\n> tflite_model = converter.convert()\r\n> open(\"converted_model.tflite\", \"wb\").write(tflite_model)\r\n> ```\r\n\r\nThank you so much for reply. \r\nTo explain in more detail, \r\n1) I build my code building for Android on a linux system\r\n2) And I testing my model on android system\r\n -> I copied the built executable file to an ARM core embedded device with Android 8.1 installed and excute.\r\n3) I also read about ops_select page. So, I used the exact same code you provided as a example for conversion and successfully converted my TF model to a TFLite model.\r\n -> I enable \"tf.lite.OpsSet.TFLITE_BUILTINS\" & \"tf.lite.OpsSet.SELECT_TF_OPS\" when i convert.\r\n \r\nIn this environment, I am still encountering the following error message.\r\n```\r\nERROR: Select TensorFlow op(s), included in the given model, is(are) not supported by this interpreter. Make sure you apply/link the Flex delegate before inference. For Android, it can be resolved by adding...\r\nERROR: Node number 1348 (FlexErf) failed to prepare.\r\nI am unable to identify the cause of the issue. I would greatly appreciate it if someone could help me.\r\n```\r\n", "Hi @woojinn8, Thanks for the information, have you followed these instructions? https://www.tensorflow.org/lite/guide/ops_select#android_aar did you build a custom aar or did you use the big one here? https://central.sonatype.com/artifact/org.tensorflow/tensorflow-lite-select-tf-ops/2.12.0\r\n\r\nThis should be how you link the flex ops on android as stated on the documentation:\r\n\r\n```java\r\ndependencies {\r\n implementation 'org.tensorflow:tensorflow-lite:0.0.0-nightly-SNAPSHOT'\r\n // This dependency adds the necessary TF op support.\r\n implementation 'org.tensorflow:tensorflow-lite-select-tf-ops:0.0.0-nightly-SNAPSHOT'\r\n}\r\n```", "> Hi @woojinn8, Thanks for the information, have you followed these instructions? https://www.tensorflow.org/lite/guide/ops_select#android_aar did you build a custom aar or did you use the big one here? https://central.sonatype.com/artifact/org.tensorflow/tensorflow-lite-select-tf-ops/2.12.0\r\n> \r\n> This should be how you link the flex ops on android as stated on the documentation:\r\n> \r\n> ```java\r\n> dependencies {\r\n> implementation 'org.tensorflow:tensorflow-lite:0.0.0-nightly-SNAPSHOT'\r\n> // This dependency adds the necessary TF op support.\r\n> implementation 'org.tensorflow:tensorflow-lite-select-tf-ops:0.0.0-nightly-SNAPSHOT'\r\n> }\r\n> ```\r\nHi @pkgoogle Thanks to reply.\r\n\r\nI kindly request your understanding as I am not familiar with Android and Java, so even if my questions may be incorrect, I would appreciate your patience and understanding.\r\nThe information you have shown is understood as the settings required for using Select TensorFlow operators when creating an Android application using Java.\r\n\r\nWhat I want to do is similar to [this link](https://github.com/tensorflow/tensorflow/issues/55536), but the difference is that the link runs on Ubuntu, whereas I want to run it on Android.\r\n\r\nIn my case, I provide a .so library file which built in C for android OS. And I want users to use it either in C or in Java using JNI. \r\nIn this case, do I still need the setup you described? If so, linking the \"libtensorflowlite_flex.so\" file in a sample implemented in C is not possible?.\r\n", "Hi @woojinn8, no worries, it seems I misidentified your workflow previously, you are using C++ with JNI to use C++/tflite on Android, is that correct? Are you doing this entirely outside of Android studio? If so, I recommend you try adjusting your workflow to use Android Studio, as package/linking issues are better managed there. Here's a very simple Android Project which uses JNI to call C++ code, perhaps you can use it as a skeleton to get what you started working: [HelloWorldJNI.zip](https://github.com/tensorflow/tensorflow/files/11873357/HelloWorldJNI.zip). Let me know if that helps or if you run into issues trying out this workflow.\r\n", "Thank you @pkgoogle Thank you for advice. I'll try to use Android Studio.", "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/60893\">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/60893\">No</a>\n" ]

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[ "Hi @YaoJiayi \r\n\r\nI was able to reproduce this issue. Please find the gist [here](https://colab.research.google.com/gist/pjpratik/5adbb5fe6d6eaae645b5f2862c14f097/60892.ipynb).\r\n\r\n@pkgoogle Could you please look into this issue? \r\n\r\nThanks.", "I tried the flex op option just in case it converts better:\r\n\r\n[gist](https://colab.sandbox.google.com/gist/pkgoogle/54790a01e7daf4faafc424143a934380/60892.ipynb)\r\n\r\nIt is likely the conversion doesn't account properly for softplus having an out of bounds intermediary value.\r\n\r\nHi @haozha111, can you please take a look? Thanks.", "Just adding info:\r\n\r\nTensorflow performs this approximation/optimization here:\r\nhttps://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/softplus_op.h#32\r\n```c++\r\nstruct Softplus {\r\n // Computes Softplus activation.\r\n //\r\n // features: any shape.\r\n // activations: same shape as \"features\".\r\n void operator()(const Device& d, typename TTypes<T>::ConstTensor features,\r\n typename TTypes<T>::Tensor activations) {\r\n // Choose a threshold on x below which exp(x) may underflow\r\n // when added to 1, but for which exp(x) is always within epsilon of the\r\n // true softplus(x). Offset of 2 from machine epsilon checked\r\n // experimentally for float16, float32, float64. Checked against\r\n // softplus implemented with numpy's log1p and numpy's logaddexp.\r\n static const T threshold =\r\n Eigen::numext::log(Eigen::NumTraits<T>::epsilon()) + T(2);\r\n // Value above which exp(x) may overflow, but softplus(x) == x\r\n // is within machine epsilon.\r\n auto too_large = features > features.constant(-threshold);\r\n // Value below which exp(x) may underflow, but softplus(x) == exp(x)\r\n // is within machine epsilon.\r\n auto too_small = features < features.constant(threshold);\r\n auto features_exp = features.exp();\r\n activations.device(d) = too_large.select(\r\n features, // softplus(x) ~= x for x large\r\n too_small.select(features_exp, // softplus(x) ~= exp(x) for x small\r\n features_exp.log1p()));\r\n }\r\n};\r\n```", "I am having the same problem here with Convolutional layers with softplus activation function at the end of the network as a regressor model. For prediction with lower values, the converted model turns out nicely. Here is examples of [[predicted]] [label]\r\n```\r\n[[87.694496]] [83]\r\n[[79.957184]] [80]\r\n[[71.28555]] [70]\r\n[[72.062096]] [70]\r\n[[71.2224]] [70]\r\n```\r\nHowever, for the prediction of higher values, a different converted model cannot handle this problem. See examples:\r\n```\r\n[[inf]] [125]\r\n[[inf]] [124]\r\n[[inf]] [140]\r\n[[inf]] [135]\r\n[[inf]] [138]\r\n```" ]

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[ "@alexrosen45 thanks for your efforts on https://github.com/tensorflow/tensorflow/issues/60884 , it was nice to see your reasoning, even if I was well aware of where and how the problem was happening. However what you propose is not a solution to the problem. I was aware of the parameter you suggested to change and did not use it for a very good reason. In effect, by setting converter.target_spec.supported_types = [tf.int8] as you suggest, you are telling the converter to ignore the fact that we want 16-8 quantization and and forcing it to quantize at int8 only. This should not be set automatically or checked in the manner you proposed.", "@DerryFitz I see, thanks for clarifying. Running\r\n```\r\nimport tensorflow as tf\r\nimport numpy as np\r\n\r\ninp = tf.keras.Input([1, 1], batch_size = 1, name = \"input_0\")\r\nx = tf.keras.layers.LSTM(inp.shape[2], return_sequences = True)(inp)\r\nmodel_lstm = tf.keras.Model(inputs=inp, outputs=x)\r\n\r\n# pick some representative dataset\r\nrep_data = tf.data.Dataset.from_tensor_slices(np.float32(np.random.random_sample((10,1,1,1))))\r\n\r\ndef representative_dataset():\r\n for data in rep_data:\r\n yield [data]\r\n\r\nconverter = tf.lite.TFLiteConverter.from_keras_model(model_lstm)\r\nconverter.representative_dataset = representative_dataset\r\nconverter.optimizations = [tf.lite.Optimize.DEFAULT]\r\nconverter.target_spec.supported_ops = [tf.lite.OpsSet.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8]\r\ntflite_model = converter.convert()\r\n\r\n# allocate tensors\r\ninterpreter = tf.lite.Interpreter(model_content=tflite_model)\r\ninterpreter.allocate_tensors()\r\n```\r\nwhich should accomplish what you want to do gives ```RuntimeError: Quantization to 16x8-bit not yet supported for op: 'UNIDIRECTIONAL_SEQUENCE_LSTM'```, a better error message than before. This should be addressed in a separate PR. I'm going to keep this PR open anyways because a supported types argument seems cleaner, and I'll get on support for 16-8 quantization for LSTMs soon.", "Hi @JunyoungLim Can you please review this PR ? Thank you!", "Hi @JunyoungLim 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!", "Hi @rascani Can you please review this PR ? Thank you!", "Hi @alexrosen45 Can you please resolve conflicts? Thank you!", "This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.", "This PR was closed because it has been inactive for 14 days since being marked as stale. Please reopen if you'd like to work on this further." ]

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[ "Hi @chrisc36 ,\r\n\r\nI have replicated the same behaviour on Mac M1 and logs attached below for reference.\r\n\r\n```\r\n(tf-metal) suryanarayanay-macbookpro:~ suryanarayanay$ python Downloads/60890.py\r\n2.14.0-dev20230515\r\nMetal device set to: Apple M1 Pro\r\n\r\nsystemMemory: 16.00 GB\r\nmaxCacheSize: 5.33 GB\r\n\r\n2023-06-16 12:29:47.379748: W tensorflow/core/common_runtime/type_inference.cc:339] Type inference failed. This indicates an invalid graph that escaped type checking. Error message: INVALID_ARGUMENT: type mismatch for node 'TensorSliceDataset': expected a subtype of:\r\ntype_id: TFT_PRODUCT\r\nargs {\r\n type_id: TFT_DATASET\r\n args {\r\n type_id: TFT_PRODUCT\r\n args {\r\n type_id: TFT_TENSOR\r\n args {\r\n type_id: TFT_LEGACY_VARIANT\r\n }\r\n }\r\n args {\r\n type_id: TFT_TENSOR\r\n args {\r\n type_id: TFT_FLOAT\r\n }\r\n }\r\n }\r\n}\r\n\r\n got:\r\ntype_id: TFT_PRODUCT\r\nargs {\r\n type_id: TFT_DATASET\r\n args {\r\n type_id: TFT_PRODUCT\r\n args {\r\n type_id: TFT_RAGGED\r\n args {\r\n type_id: TFT_STRING\r\n }\r\n }\r\n args {\r\n }\r\n }\r\n}\r\n\r\n \r\n\twhile updating its output type.\r\n{'labels': array([b'a', b'b'], dtype=object), 'vals': 0.1, 'other': array([0.1, 0.2], dtype=float32)}\r\n{'labels': array([b'a'], dtype=object), 'vals': 0.2, 'other': array([0.1, 0.2], dtype=float32)}\r\n(tf-metal) suryanarayanay-macbookpro:~ suryanarayanay$ \r\n```\r\nIf the issue is specific to Mac M1 then this should be handled by the Apple team itself. Let me check the same code on Linux and confirm the same.\r\n\r\nThanks!", "@chrisc36 ,\r\n\r\nThe same behaviour reflected in Linux Environment also as per logs below. Needs to dig more to confirm the behaviour.\r\n\r\n```\r\n(tf) suryanarayanay@surya-ubuntu22-cuda-test:~$ python 60890.py\r\n2023-06-16 08:54:44.652393: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n2023-06-16 08:54:45.563585: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:7704] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2023-06-16 08:54:45.563639: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2023-06-16 08:54:45.565976: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1520] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n2023-06-16 08:54:45.980611: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-06-16 08:54:47.800431: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2.14.0-dev20230523\r\n2023-06-16 08:54:52.254785: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 38238 MB memory: -> device: 0, name: NVIDIA A100-SXM4-40GB, pci bus id: 0000:00:04.0, compute capability: 8.0\r\n2023-06-16 08:54:53.029857: I tensorflow/core/common_runtime/executor.cc:1210] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_2' with dtype float and shape [2]\r\n [[{{node Placeholder/_2}}]]\r\n2023-06-16 08:54:53.056549: W tensorflow/core/common_runtime/type_inference.cc:339] Type inference failed. This indicates an invalid graph that escaped type checking. Error message: INVALID_ARGUMENT: type mismatch for node 'TensorSliceDataset': expected a subtype of:\r\ntype_id: TFT_PRODUCT\r\nargs {\r\n type_id: TFT_DATASET\r\n args {\r\n type_id: TFT_PRODUCT\r\n args {\r\n type_id: TFT_TENSOR\r\n args {\r\n type_id: TFT_LEGACY_VARIANT\r\n }\r\n }\r\n args {\r\n type_id: TFT_TENSOR\r\n args {\r\n type_id: TFT_FLOAT\r\n }\r\n }\r\n }\r\n}\r\n\r\n got:\r\ntype_id: TFT_PRODUCT\r\nargs {\r\n type_id: TFT_DATASET\r\n args {\r\n type_id: TFT_PRODUCT\r\n args {\r\n type_id: TFT_RAGGED\r\n args {\r\n type_id: TFT_STRING\r\n }\r\n }\r\n args {\r\n }\r\n }\r\n}\r\n\r\n \r\n while updating its output type.\r\n{'labels': array([b'a', b'b'], dtype=object), 'vals': 0.1, 'other': array([0.1, 0.2], dtype=float32)}\r\n{'labels': array([b'a'], dtype=object), 'vals': 0.2, 'other': array([0.1, 0.2], dtype=float32)}\r\n(tf) suryanarayanay@surya-ubuntu22-cuda-test:~$ \r\n```\r\n", "Thanks for looking into this, I am also seeing this on linux machines.\r\n\r\nFrom my perspective It would be great if we could get confirmation that this warning can be safely ignored, otherwise it is really just annoyance." ]

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[ "Please consult the first paragraph of [TF 2.12 release notes](https://github.com/tensorflow/tensorflow/releases/tag/v2.12.0):\r\n\r\n> Removed redundant packages tensorflow-gpu and tf-nightly-gpu. These packages were removed and replaced with packages that direct users to switch to tensorflow or tf-nightly respectively. Since TensorFlow 2.1, the only difference between these two sets of packages was their names, so there is no loss of functionality or GPU support. See https://pypi.org/project/tensorflow-gpu for more details.", "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/60886\">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/60886\">No</a>\n" ]

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[ "I reproduced the error on Ubuntu with Python 3.8. Here's a snippet of what you were trying to do (without quantization) which runs fine:\r\n```\r\nimport tensorflow as tf\r\n\r\ninp = tf.keras.Input([1, 1], batch_size = 1, name = \"input_0\")\r\nx = tf.keras.layers.LSTM(inp.shape[2], return_sequences = True)(inp)\r\nmodel_lstm = tf.keras.Model(inputs=inp, outputs=x)\r\n\r\n# convert the model to TFLite\r\nconverter = tf.lite.TFLiteConverter.from_keras_model(model_lstm)\r\ntflite_model = converter.convert()\r\n\r\n# allocate tensors\r\ninterpreter = tf.lite.Interpreter(model_content=tflite_model)\r\ninterpreter.allocate_tensors()\r\n```", "Adding onto this, we can get close to what you were trying to do before using the following script\r\n```\r\nimport tensorflow as tf\r\nimport numpy as np\r\n\r\ninp = tf.keras.Input([1, 1], batch_size = 1, name = \"input_0\")\r\nx = tf.keras.layers.LSTM(inp.shape[2], return_sequences = True)(inp)\r\nmodel_lstm = tf.keras.Model(inputs=inp, outputs=x)\r\n\r\n# pick some representative dataset\r\nrep_data = tf.data.Dataset.from_tensor_slices(np.float32(np.random.random_sample((10,1,1,1))))\r\n\r\ndef representative_dataset():\r\n for data in rep_data:\r\n yield [data]\r\n\r\n# convert model to TFLite with representative dataset\r\nconverter = tf.lite.TFLiteConverter.from_keras_model(model_lstm)\r\nconverter.representative_dataset = representative_dataset\r\nconverter.optimizations = [tf.lite.Optimize.DEFAULT]\r\ntflite_model = converter.convert()\r\n\r\n# allocate tensors\r\ninterpreter = tf.lite.Interpreter(model_content=tflite_model)\r\ninterpreter.allocate_tensors()\r\n```\r\nBut, we can see that your error occurs when trying to replace ```tflite_model = converter.convert()``` with an extra step\r\n```\r\nconverter.target_spec.supported_ops = [\r\n tf.lite.OpsSet.TFLITE_BUILTINS,\r\n tf.lite.OpsSet.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8\r\n]\r\ntflite_model = converter.convert()\r\n```\r\nSo, it seems that TF Lite is encountering an issue when trying to apply the operation set ```EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8``` to your LSTM model. In particular, it seems to fail to prepare the LSTM layer (```UNIDIRECTIONAL_SEQUENCE_LSTM``` operation in your error message) when this operation set is used. For now, you can try using full integer or float quantization, and I'll try to see if this is a simple fix only pertaining to this specific kind of quantization.", "Here are some comments from whomever implemented ```EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8``` in the ```OpsSet``` class.\r\n\r\nConvert model using only TensorFlow Lite operations with quantized int8\r\n weights, int16 activations and int64 bias.\r\n Specifying this will throw an error for operations that do not yet have\r\n quantized implementations.\r\n This quantization mode may be used in models for super-resolution,\r\n audio signal processing or image de-noising. It improves accuracy\r\n significantly, but only slightly increases the model size.\r\n WARNING: These ops are currently experimental and have not yet been\r\n finalized.\r\n They are only compatible with CPU execution, and have not been optimized for\r\n production.", "@DerryFitz I have found a fix. Try adding ```converter.target_spec.supported_types = [tf.int8]```. For example,\r\n```\r\nimport tensorflow as tf\r\nimport numpy as np\r\n\r\ninp = tf.keras.Input([1, 1], batch_size = 1, name = \"input_0\")\r\nx = tf.keras.layers.LSTM(inp.shape[2], return_sequences = True)(inp)\r\nmodel_lstm = tf.keras.Model(inputs=inp, outputs=x)\r\n\r\n# pick some representative dataset\r\nrep_data = tf.data.Dataset.from_tensor_slices(np.float32(np.random.random_sample((10,1,1,1))))\r\n\r\ndef representative_dataset():\r\n for data in rep_data:\r\n yield [data]\r\n\r\n# convert model to TFLite with representative dataset\r\nconverter = tf.lite.TFLiteConverter.from_keras_model(model_lstm)\r\nconverter.representative_dataset = representative_dataset\r\nconverter.optimizations = [tf.lite.Optimize.DEFAULT]\r\nconverter.target_spec.supported_types = [tf.int8]\r\nconverter.target_spec.supported_ops = [\r\n tf.lite.OpsSet.TFLITE_BUILTINS,\r\n tf.lite.OpsSet.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8\r\n]\r\ntflite_model = converter.convert()\r\n\r\n# allocate tensors\r\ninterpreter = tf.lite.Interpreter(model_content=tflite_model)\r\ninterpreter.allocate_tensors()\r\n```\r\nPerhaps this should be implemented for you automatically, seeing as you couldn't find this documented anywhere.", "@alexrosen45 thanks for your efforts on this, it was nice to see your reasoning, even if I was well aware of where and how the problem was happening. However what you propose is not a solution to the problem. I was aware of the parameter you suggested to change and did not use it for a very good reason. In effect, by setting converter.target_spec.supported_types = [tf.int8] as you suggest, you are telling the converter to ignore the fact that we want 16-8 quantization and and forcing it to quantize at int8 only. ", "Hi @DerryFitz \r\n\r\nI was able to reproduce this issue. Please find the gist [here](https://colab.research.google.com/gist/pjpratik/aa003c553efb3d9446deced37ff02d3b/60884.ipynb).\r\n\r\nThe similar issues on support for LSTM on 16x8 quantization is being tracked here [#55267](https://github.com/tensorflow/tensorflow/issues/55267), [#59626](https://github.com/tensorflow/tensorflow/issues/59626)\r\n\r\n@pkgoogle Can you please look into this. Thank you.", "I was also able to reproduce using the same gist, seems like a legitimate bug.\r\n\r\nHi @zichuan-wei, can you please take a look?", "Hi @DerryFitz , \r\n\r\nwe're wondering if you may be able to resolve your issue by using [AI-Edge-Torch](https://github.com/google-ai-edge/ai-edge-torch), you can find more information here: [googleblog](https://developers.googleblog.com/en/ai-edge-torch-high-performance-inference-of-pytorch-models-on-mobile-devices/).\r\n\r\nI have actually created a simple script for converting an LSTM model here:\r\n```\r\nimport torch\r\nimport torchvision\r\nimport ai_edge_torch\r\n\r\nrnn = torch.nn.LSTM(10, 20, 2)\r\nsample_inputs = (torch.randn(5, 3, 10),)\r\n\r\nedge_model = ai_edge_torch.convert(rnn.eval(), sample_inputs)\r\nedge_model.export(\"rnn.tflite\")\r\n```\r\nIf you want to, you can actually try visualizing the result in [model-explorer](https://github.com/google-ai-edge/model-explorer) as well.\r\n\r\nPlease try them out and let us know if this resolves your issue. If you still need further help, feel free to open a new issue at the respective repos." ]

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[ "Hi @mergian ,\r\n\r\nThanks for reporting. I tried to replicate the issue with the provided code snippet but getting error. Could you please refer to attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/e9750c420ce8d0a08521e217e1e7f918/60883.ipynb) and confirm if something is missing. Thanks.\r\n\r\n", "Hey @SuryanarayanaY \r\n\r\nYes, you need to replace the `MODE` with a number `0`, `1`, `2`, or `3` to reproduce the different test cases I mention above. So:\r\n```bash\r\n!nvprof --print-gpu-trace --openacc-profiling off python3 error.py 0\r\n!nvprof --print-gpu-trace --openacc-profiling off python3 error.py 1\r\n!nvprof --print-gpu-trace --openacc-profiling off python3 error.py 2\r\n!nvprof --print-gpu-trace --openacc-profiling off python3 error.py 3\r\n```", "Hi @mergian ,\r\n\r\nThanks for inputs. I have replicated the reported behaviour and attaching [gist](https://colab.research.google.com/gist/SuryanarayanaY/0ae66b486a0e666486d003f262cd56fc/60883_r1.ipynb) for reference.\r\n\r\nThough initial checks seems there are lot of memory copies but need to dig more to confirm if they are really needed or not .\r\n\r\nThanks!" ]

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[ "pip tests are no longer supported" ]

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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/60881/checks?check_run_id=14286428921) 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 @antnsi It looks like your PR relates to the Keras component. Please submit it to the github.com/keras-team/keras repository instead. Thankyou.\r\n@fchollet, @qlzh727" ]

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[ "Tried again using last night's nightly ... same exact error as above when trying to import TFQ. Also, the quantum scripts (/scripts/test_all.sh) all failed --- output is included below:\r\n\r\nTesting All Bazel py_test and cc_tests.\r\nLoading: \r\nLoading: 0 packages loaded\r\nAnalyzing: 130 targets (0 packages loaded, 0 targets configured)\r\nWARNING: /root/.cache/bazel/_bazel_root/bcb09a07ff13a4e9505bab5f8eac883a/external/local_config_tf/BUILD:9259:8: target 'libtensorflow_framework.so' is both a rule and a file; please choose another name for the rule\r\nWARNING: /root/.cache/bazel/_bazel_root/bcb09a07ff13a4e9505bab5f8eac883a/external/local_config_tf/BUILD:9269:8: target 'test_log_pb2.py' is both a rule and a file; please choose another name for the rule\r\nDEBUG: Rule 'qsim' indicated that a canonical reproducible form can be obtained by modifying arguments sha256 = \"a975f83605bb5240b65cff20847b0e9173549951267f96d69b852c6bd941a676\"\r\nDEBUG: Repository qsim instantiated at:\r\n /content/quantum/WORKSPACE:35:13: in <toplevel>\r\nRepository rule http_archive defined at:\r\n /root/.cache/bazel/_bazel_root/bcb09a07ff13a4e9505bab5f8eac883a/external/bazel_tools/tools/build_defs/repo/http.bzl:355:31: in <toplevel>\r\nINFO: Analyzed 130 targets (0 packages loaded, 0 targets configured).\r\nINFO: Found 83 targets and 47 test targets...\r\n[2 / 17] [Prepa] BazelWorkspaceStatusAction stable-status.txt ... (7 actions, 3 running)\r\n[10 / 25] Compiling tensorflow_quantum/core/ops/tfq_calculate_unitary_op.cc; 1s local ... (8 actions running)\r\n[11 / 25] Compiling tensorflow_quantum/core/ops/tfq_calculate_unitary_op.cc; 3s local ... (8 actions, 7 running)\r\n[17 / 28] Compiling tensorflow_quantum/core/ops/tfq_calculate_unitary_op.cc; 4s local ... (8 actions running)\r\n[18 / 29] Compiling tensorflow_quantum/core/ops/tfq_calculate_unitary_op.cc; 10s local ... (8 actions, 7 running)\r\n[18 / 29] Compiling tensorflow_quantum/core/ops/tfq_calculate_unitary_op.cc; 12s local ... (8 actions running)\r\n[18 / 29] 1 / 47 tests; Compiling tensorflow_quantum/core/ops/tfq_calculate_unitary_op.cc; 15s local ... (8 actions running)\r\nINFO: From Compiling tensorflow_quantum/core/ops/tfq_calculate_unitary_op.cc:\r\nIn file included from tensorflow_quantum/core/ops/tfq_calculate_unitary_op.cc:32:\r\n./tensorflow_quantum/core/src/util_qsim.h:496:13: warning: 'void tfq::BalanceTrajectory(const int&, const int&, std::vector<std::vector<int> >*)' defined but not used [-Wunused-function]\r\n 496 | static void BalanceTrajectory(const int& num_samples, const int& num_threads,\r\n | ^~~~~~~~~~~~~~~~~\r\n./tensorflow_quantum/core/src/util_qsim.h:450:13: warning: 'void tfq::BalanceTrajectory(const std::vector<std::vector<int> >&, const int&, std::vector<std::vector<int> >*)' defined but not used [-Wunused-function]\r\n 450 | static void BalanceTrajectory(const std::vector<std::vector<int>>& num_samples,\r\n | ^~~~~~~~~~~~~~~~~\r\n[31 / 46] 2 / 47 tests; Compiling tensorflow_quantum/core/ops/tfq_adj_grad_op.cc; 18s local ... (8 actions, 7 running)\r\n[32 / 46] 2 / 47 tests; Compiling tensorflow_quantum/core/ops/tfq_adj_grad_op.cc; 21s local ... (8 actions running)\r\nINFO: From Compiling tensorflow_quantum/core/ops/tfq_simulate_samples_op.cc:\r\ntensorflow_quantum/core/ops/tfq_simulate_samples_op.cc: In member function 'void tfq::TfqSimulateSamplesOp::ComputeLarge(const std::vector<int>&, int, int, const std::vector<std::vector<qsim::GateFused<qsim::Gate<float, qsim::Cirq::GateKind> > > >&, tensorflow::OpKernelContext*, tensorflow::TTypes<signed char, 3>::Tensor*)':\r\ntensorflow_quantum/core/ops/tfq_simulate_samples_op.cc:157:23: warning: comparison of integer expressions of different signedness: 'int' and 'std::vector<std::vector<qsim::GateFused<qsim::Gate<float, qsim::Cirq::GateKind> > > >::size_type' {aka 'long unsigned int'} [-Wsign-compare]\r\n 157 | for (int i = 0; i < fused_circuits.size(); i++) {\r\n | ~~^~~~~~~~~~~~~~~~~~~~~~~\r\ntensorflow_quantum/core/ops/tfq_simulate_samples_op.cc:166:25: warning: comparison of integer expressions of different signedness: 'int' and 'std::vector<qsim::GateFused<qsim::Gate<float, qsim::Cirq::GateKind> > >::size_type' {aka 'long unsigned int'} [-Wsign-compare]\r\n 166 | for (int j = 0; j < fused_circuits[i].size(); j++) {\r\n | ~~^~~~~~~~~~~~~~~~~~~~~~~~~~\r\ntensorflow_quantum/core/ops/tfq_simulate_samples_op.cc:175:22: warning: comparison of integer expressions of different signedness: 'uint64_t' {aka 'long unsigned int'} and 'int' [-Wsign-compare]\r\n 175 | while (q_ind < nq) {\r\n | ~~~~~~^~~~\r\ntensorflow_quantum/core/ops/tfq_simulate_samples_op.cc:182:22: warning: comparison of integer expressions of different signedness: 'uint64_t' {aka 'long unsigned int'} and 'const int' [-Wsign-compare]\r\n 182 | while (q_ind < max_num_qubits) {\r\n | ~~~~~~^~~~~~~~~~~~~~~~\r\ntensorflow_quantum/core/ops/tfq_simulate_samples_op.cc: In lambda function:\r\ntensorflow_quantum/core/ops/tfq_simulate_samples_op.cc:222:27: warning: comparison of integer expressions of different signedness: 'int' and 'std::vector<qsim::GateFused<qsim::Gate<float, qsim::Cirq::GateKind> > >::size_type' {aka 'long unsigned int'} [-Wsign-compare]\r\n 222 | for (int j = 0; j < fused_circuits[i].size(); j++) {\r\n | ~~^~~~~~~~~~~~~~~~~~~~~~~~~~\r\ntensorflow_quantum/core/ops/tfq_simulate_samples_op.cc:231:24: warning: comparison of integer expressions of different signedness: 'uint64_t' {aka 'long unsigned int'} and 'int' [-Wsign-compare]\r\n 231 | while (q_ind < nq) {\r\n | ~~~~~~^~~~\r\ntensorflow_quantum/core/ops/tfq_simulate_samples_op.cc:238:24: warning: comparison of integer expressions of different signedness: 'uint64_t' {aka 'long unsigned int'} and 'const int' [-Wsign-compare]\r\n 238 | while (q_ind < max_num_qubits) {\r\n | ~~~~~~^~~~~~~~~~~~~~~~\r\nIn file included from tensorflow_quantum/core/ops/tfq_simulate_samples_op.cc:38:\r\n./tensorflow_quantum/core/src/util_qsim.h: At global scope:\r\n./tensorflow_quantum/core/src/util_qsim.h:496:13: warning: 'void tfq::BalanceTrajectory(const int&, const int&, std::vector<std::vector<int> >*)' defined but not used [-Wunused-function]\r\n 496 | static void BalanceTrajectory(const int& num_samples, const int& num_threads,\r\n | ^~~~~~~~~~~~~~~~~\r\n./tensorflow_quantum/core/src/util_qsim.h:450:13: warning: 'void tfq::BalanceTrajectory(const std::vector<std::vector<int> >&, const int&, std::vector<std::vector<int> >*)' defined but not used [-Wunused-function]\r\n 450 | static void BalanceTrajectory(const std::vector<std::vector<int>>& num_samples,\r\n | ^~~~~~~~~~~~~~~~~\r\nINFO: From Compiling tensorflow_quantum/core/ops/tfq_simulate_state_op.cc:\r\nIn file included from tensorflow_quantum/core/ops/tfq_simulate_state_op.cc:33:\r\n./tensorflow_quantum/core/src/util_qsim.h:496:13: warning: 'void tfq::BalanceTrajectory(const int&, const int&, std::vector<std::vector<int> >*)' defined but not used [-Wunused-function]\r\n 496 | static void BalanceTrajectory(const int& num_samples, const int& num_threads,\r\n | ^~~~~~~~~~~~~~~~~\r\n./tensorflow_quantum/core/src/util_qsim.h:450:13: warning: 'void tfq::BalanceTrajectory(const std::vector<std::vector<int> >&, const int&, std::vector<std::vector<int> >*)' defined but not used [-Wunused-function]\r\n 450 | static void BalanceTrajectory(const std::vector<std::vector<int>>& num_samples,\r\n | ^~~~~~~~~~~~~~~~~\r\nINFO: From Compiling tensorflow_quantum/core/ops/tfq_simulate_expectation_op.cc:\r\nIn file included from tensorflow_quantum/core/ops/tfq_simulate_expectation_op.cc:34:\r\n./tensorflow_quantum/core/src/util_qsim.h:496:13: warning: 'void tfq::BalanceTrajectory(const int&, const int&, std::vector<std::vector<int> >*)' defined but not used [-Wunused-function]\r\n 496 | static void BalanceTrajectory(const int& num_samples, const int& num_threads,\r\n | ^~~~~~~~~~~~~~~~~\r\n./tensorflow_quantum/core/src/util_qsim.h:450:13: warning: 'void tfq::BalanceTrajectory(const std::vector<std::vector<int> >&, const int&, std::vector<std::vector<int> >*)' defined but not used [-Wunused-function]\r\n 450 | static void BalanceTrajectory(const std::vector<std::vector<int>>& num_samples,\r\n | ^~~~~~~~~~~~~~~~~\r\nINFO: From Compiling tensorflow_quantum/core/ops/tfq_simulate_sampled_expectation_op.cc:\r\ntensorflow_quantum/core/ops/tfq_simulate_sampled_expectation_op.cc: In member function 'void tfq::TfqSimulateSampledExpectationOp::ComputeLarge(const std::vector<int>&, const std::vector<std::vector<qsim::GateFused<qsim::Gate<float, qsim::Cirq::GateKind> > > >&, const std::vector<std::vector<tfq::proto::PauliSum> >&, const std::vector<std::vector<int> >&, tensorflow::OpKernelContext*, tensorflow::TTypes<float, 1>::Matrix*)':\r\ntensorflow_quantum/core/ops/tfq_simulate_sampled_expectation_op.cc:178:23: warning: comparison of integer expressions of different signedness: 'int' and 'std::vector<std::vector<qsim::GateFused<qsim::Gate<float, qsim::Cirq::GateKind> > > >::size_type' {aka 'long unsigned int'} [-Wsign-compare]\r\n 178 | for (int i = 0; i < fused_circuits.size(); i++) {\r\n | ~~^~~~~~~~~~~~~~~~~~~~~~~\r\ntensorflow_quantum/core/ops/tfq_simulate_sampled_expectation_op.cc:191:25: warning: comparison of integer expressions of different signedness: 'int' and 'std::vector<qsim::GateFused<qsim::Gate<float, qsim::Cirq::GateKind> > >::size_type' {aka 'long unsigned int'} [-Wsign-compare]\r\n 191 | for (int j = 0; j < fused_circuits[i].size(); j++) {\r\n | ~~^~~~~~~~~~~~~~~~~~~~~~~~~~\r\ntensorflow_quantum/core/ops/tfq_simulate_sampled_expectation_op.cc:194:25: warning: comparison of integer expressions of different signedness: 'int' and 'std::vector<tfq::proto::PauliSum>::size_type' {aka 'long unsigned int'} [-Wsign-compare]\r\n 194 | for (int j = 0; j < pauli_sums[i].size(); j++) {\r\n | ~~^~~~~~~~~~~~~~~~~~~~~~\r\ntensorflow_quantum/core/ops/tfq_simulate_sampled_expectation_op.cc: In lambda function:\r\ntensorflow_quantum/core/ops/tfq_simulate_sampled_expectation_op.cc:276:29: warning: comparison of integer expressions of different signedness: 'int' and 'std::vector<qsim::GateFused<qsim::Gate<float, qsim::Cirq::GateKind> > >::size_type' {aka 'long unsigned int'} [-Wsign-compare]\r\n 276 | for (int j = 0; j < fused_circuits[cur_batch_index].size(); j++) {\r\n | ~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\nIn file included from tensorflow_quantum/core/ops/tfq_simulate_sampled_expectation_op.cc:37:\r\n./tensorflow_quantum/core/src/util_qsim.h: At global scope:\r\n./tensorflow_quantum/core/src/util_qsim.h:496:13: warning: 'void tfq::BalanceTrajectory(const int&, const int&, std::vector<std::vector<int> >*)' defined but not used [-Wunused-function]\r\n 496 | static void BalanceTrajectory(const int& num_samples, const int& num_threads,\r\n | ^~~~~~~~~~~~~~~~~\r\n./tensorflow_quantum/core/src/util_qsim.h:450:13: warning: 'void tfq::BalanceTrajectory(const std::vector<std::vector<int> >&, const int&, std::vector<std::vector<int> >*)' defined but not used [-Wunused-function]\r\n 450 | static void BalanceTrajectory(const std::vector<std::vector<int>>& num_samples,\r\n | ^~~~~~~~~~~~~~~~~\r\n[37 / 47] 2 / 47 tests; Compiling tensorflow_quantum/core/ops/tfq_adj_grad_op.cc; 24s local ... (8 actions running)\r\nINFO: From Compiling tensorflow_quantum/core/ops/tfq_adj_grad_op.cc:\r\nIn file included from tensorflow_quantum/core/ops/tfq_adj_grad_op.cc:35:\r\n./tensorflow_quantum/core/src/util_qsim.h:496:13: warning: 'void tfq::BalanceTrajectory(const int&, const int&, std::vector<std::vector<int> >*)' defined but not used [-Wunused-function]\r\n 496 | static void BalanceTrajectory(const int& num_samples, const int& num_threads,\r\n | ^~~~~~~~~~~~~~~~~\r\n./tensorflow_quantum/core/src/util_qsim.h:450:13: warning: 'void tfq::BalanceTrajectory(const std::vector<std::vector<int> >&, const int&, std::vector<std::vector<int> >*)' defined but not used [-Wunused-function]\r\n 450 | static void BalanceTrajectory(const std::vector<std::vector<int>>& num_samples,\r\n | ^~~~~~~~~~~~~~~~~\r\nFAIL: //tensorflow_quantum/python/layers/high_level:noisy_pqc_test (see /root/.cache/bazel/_bazel_root/bcb09a07ff13a4e9505bab5f8eac883a/execroot/__main__/bazel-out/k8-opt/testlogs/tensorflow_quantum/python/layers/high_level/noisy_pqc_test/test.log)\r\nERROR: /content/quantum/tensorflow_quantum/python/layers/high_level/BUILD:100:8: Testing //tensorflow_quantum/python/layers/high_level:noisy_pqc_test failed: Test failed, aborting\r\nERROR: Test failed, aborting\r\nINFO: Elapsed time: 27.041s, Critical Path: 26.23s\r\nINFO: 46 processes: 30 internal, 16 local.\r\nINFO: Build completed, 1 test FAILED, 46 total actions\r\nINFO: Build completed, 1 test FAILED, 46 total actions\r\nTesting failed, please correct errors before proceeding.\r\n{==================== Test output for //tensorflow_quantum/python/layers/high_level:noisy_pqc_test:\r\nTraceback (most recent call last):\r\n File \"/root/.cache/bazel/_bazel_root/bcb09a07ff13a4e9505bab5f8eac883a/execroot/__main__/bazel-out/k8-opt/bin/tensorflow_quantum/python/layers/high_level/noisy_pqc_test.runfiles/__main__/tensorflow_quantum/python/layers/high_level/noisy_pqc_test.py\", line 30, in <module>\r\n from tensorflow_quantum.python.layers.high_level import noisy_pqc\r\n File \"/root/.cache/bazel/_bazel_root/bcb09a07ff13a4e9505bab5f8eac883a/execroot/__main__/bazel-out/k8-opt/bin/tensorflow_quantum/python/layers/high_level/noisy_pqc_test.runfiles/__main__/tensorflow_quantum/python/layers/high_level/noisy_pqc.py\", line 25, in <module>\r\n from tensorflow_quantum.python.layers.circuit_construction import elementary\r\n File \"/root/.cache/bazel/_bazel_root/bcb09a07ff13a4e9505bab5f8eac883a/execroot/__main__/bazel-out/k8-opt/bin/tensorflow_quantum/python/layers/high_level/noisy_pqc_test.runfiles/__main__/tensorflow_quantum/python/layers/circuit_construction/elementary.py\", line 24, in <module>\r\n class AddCircuit(tf.keras.layers.Layer):\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/util/lazy_loader.py\", line 58, in __getattr__\r\n module = self._load()\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/util/lazy_loader.py\", line 41, in _load\r\n module = importlib.import_module(self.__name__)\r\n File \"/usr/lib/python3.10/importlib/__init__.py\", line 126, in import_module\r\n return _bootstrap._gcd_import(name[level:], package, level)\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/__init__.py\", line 21, in <module>\r\n from keras import models\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/models/__init__.py\", line 18, in <module>\r\n from keras.engine.functional import Functional\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/engine/functional.py\", line 26, in <module>\r\n from keras import backend\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/backend.py\", line 35, in <module>\r\n from keras.engine import keras_tensor\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/engine/keras_tensor.py\", line 19, in <module>\r\n from keras.utils import object_identity\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/utils/__init__.py\", line 53, in <module>\r\n from keras.utils.feature_space import FeatureSpace\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/utils/feature_space.py\", line 20, in <module>\r\n from keras.engine import base_layer\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/engine/base_layer.py\", line 44, in <module>\r\n from keras.saving.legacy.saved_model import layer_serialization\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/saving/legacy/saved_model/layer_serialization.py\", line 23, in <module>\r\n from keras.saving.legacy.saved_model import save_impl\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/saving/legacy/saved_model/save_impl.py\", line 34, in <module>\r\n from keras.saving.legacy.saved_model import load as keras_load\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/saving/legacy/saved_model/load.py\", line 29, in <module>\r\n from keras.protobuf import saved_metadata_pb2\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/protobuf/saved_metadata_pb2.py\", line 5, in <module>\r\n from google.protobuf.internal import builder as _builder\r\nImportError: cannot import name 'builder' from 'google.protobuf.internal' (/usr/local/lib/python3.10/dist-packages/google/protobuf/internal/__init__.py)\r\n================================================================================\r\n//tensorflow_quantum/core/ops:batch_util_test NO STATUS\r\n//tensorflow_quantum/core/ops:circuit_execution_ops_test NO STATUS\r\n//tensorflow_quantum/core/ops:cirq_ops_test NO STATUS\r\n//tensorflow_quantum/core/ops:tfq_adj_grad_op_test NO STATUS\r\n//tensorflow_quantum/core/ops:tfq_ps_util_ops_test NO STATUS\r\n//tensorflow_quantum/core/ops:tfq_simulate_ops_gpu_test NO STATUS\r\n//tensorflow_quantum/core/ops:tfq_simulate_ops_test NO STATUS\r\n//tensorflow_quantum/core/ops:tfq_unitary_op_test NO STATUS\r\n//tensorflow_quantum/core/ops:tfq_utility_ops_test NO STATUS\r\n//tensorflow_quantum/core/ops/math_ops:fidelity_op_test NO STATUS\r\n//tensorflow_quantum/core/ops/math_ops:inner_product_grad_test NO STATUS\r\n//tensorflow_quantum/core/ops/math_ops:inner_product_op_test NO STATUS\r\n//tensorflow_quantum/core/ops/math_ops:simulate_mps_test NO STATUS\r\n//tensorflow_quantum/core/ops/noise:noisy_expectation_op_test NO STATUS\r\n//tensorflow_quantum/core/ops/noise:noisy_sampled_expectation_op_test NO STATUS\r\n//tensorflow_quantum/core/ops/noise:noisy_samples_op_test NO STATUS\r\n//tensorflow_quantum/core/serialize:op_serializer_test NO STATUS\r\n//tensorflow_quantum/core/serialize:serializable_gate_set_test NO STATUS\r\n//tensorflow_quantum/core/serialize:serializer_test NO STATUS\r\n//tensorflow_quantum/core/src:adj_util_test NO STATUS\r\n//tensorflow_quantum/core/src:circuit_parser_qsim_test NO STATUS\r\n//tensorflow_quantum/core/src:program_resolution_test NO STATUS\r\n//tensorflow_quantum/core/src:util_qsim_test NO STATUS\r\n//tensorflow_quantum/datasets:cluster_state_test NO STATUS\r\n//tensorflow_quantum/datasets:spin_system_test NO STATUS\r\n//tensorflow_quantum/python:util_test NO STATUS\r\n//tensorflow_quantum/python/differentiators:adjoint_test NO STATUS\r\n//tensorflow_quantum/python/differentiators:differentiator_test NO STATUS\r\n//tensorflow_quantum/python/differentiators:gradient_test NO STATUS\r\n//tensorflow_quantum/python/differentiators:linear_combination_test NO STATUS\r\n//tensorflow_quantum/python/differentiators:parameter_shift_test NO STATUS\r\n//tensorflow_quantum/python/differentiators:parameter_shift_util_test NO STATUS\r\n//tensorflow_quantum/python/layers/circuit_construction:elementary_test NO STATUS\r\n//tensorflow_quantum/python/layers/circuit_executors:expectation_test NO STATUS\r\n//tensorflow_quantum/python/layers/circuit_executors:input_checks_test NO STATUS\r\n//tensorflow_quantum/python/layers/circuit_executors:sample_test NO STATUS\r\n//tensorflow_quantum/python/layers/circuit_executors:sampled_expectation_test NO STATUS\r\n//tensorflow_quantum/python/layers/circuit_executors:state_test NO STATUS\r\n//tensorflow_quantum/python/layers/circuit_executors:unitary_test NO STATUS\r\n//tensorflow_quantum/python/layers/high_level:controlled_pqc_test NO STATUS\r\n//tensorflow_quantum/python/layers/high_level:noisy_controlled_pqc_test NO STATUS\r\n//tensorflow_quantum/python/layers/high_level:pqc_test NO STATUS\r\n//tensorflow_quantum/python/optimizers:rotosolve_minimizer_test NO STATUS\r\n//tensorflow_quantum/python/optimizers:spsa_minimizer_test NO STATUS\r\n//tensorflow_quantum/core/serialize:op_deserializer_test PASSED in 12.4s\r\n//tensorflow_quantum/python:quantum_context_test PASSED in 5.9s\r\n//tensorflow_quantum/python/layers/high_level:noisy_pqc_test FAILED in 10.4s\r\n /root/.cache/bazel/_bazel_root/bcb09a07ff13a4e9505bab5f8eac883a/execroot/__main__/bazel-out/k8-opt/testlogs/tensorflow_quantum/python/layers/high_level/noisy_pqc_test/test.log\r\n\r\nExecuted 3 out of 47 tests: 2 tests pass, 1 fails locally and 44 were skipped.\r\nThere were tests whose specified size is too big. Use the --test_verbose_timeout_warnings command line option to see which ones these are.}", "Output from today's run using the most recent nightly build. I am just using CPU, no GPU due to the length of compile time, and I decided to try branch instead of master to see if that made a difference. It did not, same error persisted. Quantum smoke tests are all still failing, and the tfq import still bombs out with the same pauli error. \r\n\r\nSee attached output\r\n[061723_tfq_import_err_v2.11.0-0-gd5b57ca93e5.txt](https://github.com/tensorflow/tensorflow/files/11780212/061723_tfq_import_err_v2.11.0-0-gd5b57ca93e5.txt)\r\n", "Attempted nightly build using TF 2.12 and Python 3.9, but TFQ compile failed. I saved the colab to github for your review as the error was quite profound:\r\n\r\nhttps://github.com/liv4unix/quantum/blob/master/TF12_TFQ_nightly_bld_061823_bazel-out_gcc-err.ipynb", "Hi @liv4unix! I faced the same issue as reported here. By any chance did you try to downgrade the TF version and try to build TF Quantum? There are some version incompatibility related issues occurring it seems. Please have a look at this [doc](https://www.tensorflow.org/quantum/install) and let me know if that helps?\r\nThank you!", "Thank you for the response. I have attempted all steps, in each section of that tutorial using TF11 (and even tried TF12, just to see what would happen, yes, I know it's unsupported). None of the documented steps work, TFQ does not import in Colab any longer, as far as I can tell. \r\n\r\nThe latest daily build from source is still compiling using Py 3.9, but I am including a link for the pip install for py3.8. Here, you will see that TFQ will not import on the pip install in Colab: \"no module named Cirq\"\r\n\r\nhttps://github.com/liv4unix/quantum/blob/master/TF11-TFQnightly-Py38_062023.ipynb", "@SuryanarayanaY I have replicated the issue on colab using TF [2.12](https://colab.research.google.com/gist/sushreebarsa/8f34148c7ed663110c278f75a10101fc/tf11-tfqnightly-py38_062023.ipynb#scrollTo=-rDlWfjzocwG) and [nightly](https://colab.research.google.com/gist/sushreebarsa/120780e30884cfa07e409dfe2e22551b/tf11-tfqnightly-py38_062023.ipynb#scrollTo=-rDlWfjzocwG), please find the attached gists for reference. \r\nThank you!", "Last night's build from source, using 06202023_nightly image is complete and TFQ still does not import into my Colab [Pro+] environment:\r\n\r\n1) tensorflow-quantum does NOT show up in \"pip list\" as an installed package\r\n2) the same \"cannot import name 'pauli_sum_pb2' from 'tensorflow_quantum.core.proto'\" persists on the TFQ import\r\n3) the TFQ/bazel installation confirmation scripts (./scripts/test_all.sh) continue to FAIL\r\n\r\nAll Colab job output is here:\r\nhttps://github.com/liv4unix/quantum/blob/master/TF_TFQ_Build_from_Source_Nightly_06212023.ipynb\r\n\r\nFYI:\r\nThis is a CPU TF/TFQ build. However, my QNN|ANN uses QPU|GPU|CPU so once we get TFQ import working for CPU, I will need to build from source using GPU so that I can test TFQ/import once more using GPU system. The GPU|Cuda TF/TFQ build from source takes ~12 hours.", "Attempted to roll back to Python 3.7 but the TFQ requirements file bombs out. It appears python 3.8 is the min required version for TFQ contrary to the install guide:\r\nhttps://www.tensorflow.org/quantum/install\r\n\r\nOutput from \"Py 3.7, tf-nightly-2.11.0, and quantum.git\" can be found here:\r\nhttps://github.com/liv4unix/quantum/blob/master/TF_TFQ_Build_from_Source_Nightly_06222023.ipynb", "While I've tested the pip installs for TFQ many times, I've never actually uploaded the colab notebooks. Both python 3.8 and 3.9 fail to import TFQ with an identical error:\r\n\r\nModuleNotFoundError: No module named 'tensorflow_quantum'\r\n\r\nThis is differentiated from the pauli error observed when attempting to import TFQ, when building from source.\r\n\r\nPython 3.9:\r\nhttps://github.com/liv4unix/quantum/blob/master/Pip_install_Py39_TF_TFQ_062423.ipynb\r\nPython 3.8\r\nhttps://github.com/liv4unix/quantum/blob/master/Pip_install_Py38_TF_TFQ_062423.ipynb", "Hi Michael,\r\n\r\nAny ETA on when an update will be available?\r\n\r\nJust tested latest [pip](Pip_install_Py38_TF211_TFQ073dev_070623.), nothing new ... and I won't bother testing nightlies until I hear from you as those builds hurt.\r\n\r\nI was really hoping to submit this latest (year's worth of) Cirq|TFQ work to QCE23 Poster Committee, and they just extended the Poster deadline to July 20, so if there is any possible chance we could get some kind of a fix in place, so that I could finish my poster submission, well, that would be really-really great. \r\nhttps://qce.quantum.ieee.org/2023/home/authors/submission-deadlines/\r\n\r\nThis latest QNN work builds off last year's published work, which was also presented at QCE22 (where I met Alex).\r\nhttps://ieeexplore.ieee.org/document/9951271\r\n\r\nWould also be great to meet you at QCE23 this year (hint!hint!), otherwise, thanks to you and your team for all the incredible work on the TF|TFQ stack!\r\n\r\n\r\n", "After going through all my previous build jobs, the issue appears to be that bazel config is defaulting to Colab's built-in Python 10 - bazel is not using the version of Python in the activated environment (py 3.8) I cannot figure out how to override. \r\n\r\nI am attempting to once again hard-code the 3.8 python paths into the bazel config in the TF11 nightly build. Will upload the output once the build is complete.", "I was able to getTF11 bazel config script to execute successfully when I entered the FQPN for the activated py38_quantum_env lib and bin paths \r\n\r\nHowever TFQ/nightly now bombs out with a new error on bazel build:\r\n\r\n\r\nERROR: /content/quantum/tensorflow_quantum/core/src/BUILD:123:11: Compiling tensorflow_quantum/core/src/program_resolution.cc failed: (Exit 1): gcc failed: error executing command /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 54 arguments skipped)\r\nIn file included from bazel-out/k8-opt/bin/external/local_config_tf/include/tensorflow/tsl/platform/status.h:37,\r\n from bazel-out/k8-opt/bin/external/local_config_tf/include/tensorflow/core/platform/status.h:23,\r\n from bazel-out/k8-opt/bin/external/local_config_tf/include/tensorflow/core/lib/core/status.h:19,\r\n from ./tensorflow_quantum/core/src/program_resolution.h:25,\r\n from tensorflow_quantum/core/src/program_resolution.cc:16:\r\nbazel-out/k8-opt/bin/external/local_config_tf/include/tensorflow/tsl/protobuf/error_codes.pb.h:12:2: error: #error This file was generated by a newer version of protoc which is\r\n 12 | #error This file was generated by a newer version of protoc which is\r\n | ^~~~~\r\nbazel-out/k8-opt/bin/external/local_config_tf/include/tensorflow/tsl/protobuf/error_codes.pb.h:13:2: error: #error incompatible with your Protocol Buffer headers. Please update\r\n 13 | #error incompatible with your Protocol Buffer headers.\r\n\r\n\r\nFull notebook is uploaded here:\r\n\r\nhttps://github.com/liv4unix/quantum/blob/master/TF_TFQ_Build_from_Source_Nightly_070823.ipynb", "Is anyone even working on this? \r\n\r\nIs there any way for me to access Sycamore without using Colab? I am desperate to continue my research guys. Please help." ]

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[ "I'm unsure, maybe both variables are required" ]

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[ "@antnsi,\r\nI have raised the PR in Keras repo for the issue https://github.com/keras-team/keras/pull/18233 and the PR has been assigned for reviewing and once it is merged this issue will move to closed status. Thank you!", "@antnsi,\r\nThe PR which was raised for the typo error on the `policy.py` file was merged and the typo was also replaced with **bfloat16**\r\nhttps://github.com/keras-team/keras/pull/18233\r\nhttps://github.com/keras-team/keras/blob/master/keras/mixed_precision/policy.py\r\n\r\nCould you please take a look and confirm whether the issue was resolved. Thank you!", "@tilakrayal Should changes in the Keras codebase be somehow reflected in the TF repository? Apparently, it's not automatically propagated, and the mistype [is in master branch](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/mixed_precision/policy.py#L195). Sorry, I'm not very familiar with the TF release process. Either way, the issue can be closed as the Keras code was fixed.", "@antnsi,\r\nAny new Keras PR or issue should be open in [github.com/keras-team/keras](http://github.com/keras-team/keras), instead of [github.com/tensorflow/tensorflow](http://github.com/tensorflow/tensorflow). Keras development is fully moving to [github.com/keras-team/keras](http://github.com/keras-team/keras).\r\n\r\nhttps://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999\r\n\r\nThank you!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/60878\">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/60878\">No</a>\n", "Hi, The code fix in the Keras repo is the one you should be referring to, the tensorflow/keras code is deprecated and will be deleted in future, once all the dependency is taken care of.\r\n\r\n> @tilakrayal Should changes in the Keras codebase be somehow reflected in the TF repository? Apparently, it's not automatically propagated, and the mistype [is in master branch](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/mixed_precision/policy.py#L195). Sorry, I'm not very familiar with the TF release process. Either way, the issue can be closed as the Keras code was fixed.\r\n\r\n" ]

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[ "I used your script and I could install python3.8 in the google Colab environment but then while importing tensorflow_quantum it shows ```ModuleNotFoundError: No module named 'tensorflow_quantum'```\r\nFor reference I have mentioned link to my [colab notebook](https://colab.research.google.com/drive/11hnCAIOAKn938fuVLxhIueMlbaMhQ_dG?usp=sharing)", "Should be opened in the quantum repo, not TF.", "Hi @anikde ,\r\n\r\nCould you please report the issue at [tensorflow-quantum](https://github.com/tensorflow/quantum/issues) repo as instructed above.\r\n\r\nThanks!", "Hi @SuryanarayanaY @mihaimaruseac, I have raised the issue in tensorflow-quantum repo as directed.", "Hi @anikde ,\r\n\r\nPlease refer to attached [readme.md](https://github.com/tensorflow/quantum/blob/master/docs/install.md) file from tensorflow-quantum for installation instructions. \r\n\r\n**Requirements**\r\n\r\n> pip 19.0 or later (requires manylinux2010 support)\r\n> [TensorFlow == 2.11.0](https://www.tensorflow.org/install/pip)\r\n\r\nIt seems this works only with tensorflow==2.11. Also please check other requirements mentioned in readme.md are fulfilled. Could you please have a look at those instructions and let us know if it helps.\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/60877\">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/60877\">No</a>\n", "@SuryanarayanaY I have tired installing the updated version of tensorflow but that too didn't work. I have attached the [link to my notebook](https://colab.research.google.com/drive/11hnCAIOAKn938fuVLxhIueMlbaMhQ_dG?authuser=1&usp=drive_open#scrollTo=aMujKtZb7oDA) for reference." ]

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[ "@bchetioui , Could you please take a look into this, it is related to your commit https://github.com/tensorflow/tensorflow/commit/1bd12b4863759e44da4139628973f372655b14f6", "@sachinprasadhs @ychensha Hi, just to confirm, did you narrow down the failure to my commit? It is a very small change which doesn't add/remove any dependency to any file, and has nothing to do with TFRT---I'd be surprised if it had anything to do with the failure reported here.", "Understood, Thank you!" ]

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[ "Dependabot tried to update this pull request, but something went wrong. We're looking into it, but in the meantime you can retry the update by commenting `@dependabot rebase`.", "Dependabot tried to update this pull request, but something went wrong. We're looking into it, but in the meantime you can retry the update by commenting `@dependabot rebase`.", "Dependabot tried to update this pull request, but something went wrong. We're looking into it, but in the meantime you can retry the update by commenting `@dependabot rebase`.", "Dependabot tried to update this pull request, but something went wrong. We're looking into it, but in the meantime you can retry the update by commenting `@dependabot rebase`.", "Dependabot tried to update this pull request, but something went wrong. We're looking into it, but in the meantime you can retry the update by commenting `@dependabot rebase`.", "Dependabot tried to update this pull request, but something went wrong. We're looking into it, but in the meantime you can retry the update by commenting `@dependabot rebase`.", "OK, I won't notify you again about this release, but will get in touch when a new version is available. If you'd rather skip all updates until the next major or minor version, let me know by commenting `@dependabot ignore this major version` or `@dependabot ignore this minor version`.\n\nIf you change your mind, just re-open this PR and I'll resolve any conflicts on it." ]

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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/60874/checks?check_run_id=14278950141) 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.", "Closing since there won't be a new 2.12 release" ]

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[ "Hi @K-Bion \r\n\r\ntf.app.run is deprecated in TF 2.x, please use TF [tf.compat.v1.app.run](https://www.tensorflow.org/api_docs/python/tf/compat/v1/app/run) for TF 2.12.\r\n\r\nThanks.\r\n", "> Hi @K-Bion\r\n> \r\n> tf.app.run is deprecated in TF 2.x, please use TF [tf.compat.v1.app.run](https://www.tensorflow.org/api_docs/python/tf/compat/v1/app/run) for TF 2.12.\r\n> \r\n> Thanks.\r\n\r\nIt worked. Thank you very much.", "Hi @K-Bion \r\n\r\nGlad it worked. Please feel free to close the issue since it is resolved.\r\n\r\nThanks." ]

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