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[ "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59712\">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/59712\">No</a>\n" ]

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[ "@codejaeger can you re-execute with this defined first:\r\n```\r\nexport TF_TRT_SHOW_DETAILED_REPORT=1\r\n```\r\n\r\nPlease copy the log output here. Thanks", "Hi,\r\n\r\nThanks for the quick response. I have used the flag you mentioned and collected the logs below.\r\n\r\nWhile using `quantize_and_dequantize` at the `trt_func = converter.convert()` step\r\n```shell\r\n2023-02-17 04:15:02.325189: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-02-17 04:15:02.334523: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-02-17 04:15:02.335893: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-02-17 04:15:02.337785: I tensorflow/core/platform/cpu_feature_guard.cc:194] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE3 SSE4.1 SSE4.2 AVX\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-02-17 04:15:02.338256: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-02-17 04:15:02.339644: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-02-17 04:15:02.341033: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-02-17 04:15:02.463483: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-02-17 04:15:02.464255: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-02-17 04:15:02.464901: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-02-17 04:15:02.465529: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1637] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 20554 MB memory: -> device: 0, name: NVIDIA A10, pci bus id: 0000:04:00.0, compute capability: 8.6\r\n2023-02-17 04:15:02.913897: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-02-17 04:15:02.914577: I tensorflow/core/grappler/devices.cc:66] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 1\r\n2023-02-17 04:15:02.914799: I tensorflow/core/grappler/clusters/single_machine.cc:358] Starting new session\r\n2023-02-17 04:15:02.915229: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-02-17 04:15:02.915866: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-02-17 04:15:02.916488: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-02-17 04:15:02.917169: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-02-17 04:15:02.917785: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-02-17 04:15:02.918368: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1637] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 20554 MB memory: -> device: 0, name: NVIDIA A10, pci bus id: 0000:04:00.0, compute capability: 8.6\r\nINFO:tensorflow:Clearing prior device assignments in loaded saved model\r\nINFO:tensorflow:Automatic mixed precision will be used on the whole TensorFlow Graph. This behavior can be deactivated using the environment variable: TF_TRT_EXPERIMENTAL_FEATURES=deactivate_mixed_precision.\r\nMore information can be found on: https://www.tensorflow.org/guide/mixed_precision.\r\n2023-02-17 04:15:03.126010: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-02-17 04:15:03.126716: I tensorflow/core/grappler/devices.cc:66] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 1\r\n2023-02-17 04:15:03.126943: I tensorflow/core/grappler/clusters/single_machine.cc:358] Starting new session\r\n2023-02-17 04:15:03.127281: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-02-17 04:15:03.127933: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-02-17 04:15:03.128545: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-02-17 04:15:03.129232: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-02-17 04:15:03.129848: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-02-17 04:15:03.130423: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1637] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 20554 MB memory: -> device: 0, name: NVIDIA A10, pci bus id: 0000:04:00.0, compute capability: 8.6\r\n2023-02-17 04:15:03.143752: I tensorflow/core/grappler/optimizers/auto_mixed_precision.cc:2359] Running auto_mixed_precision graph optimizer\r\n2023-02-17 04:15:03.152657: I tensorflow/core/grappler/optimizers/auto_mixed_precision.cc:1195] Automatic Mixed Precision Grappler Pass Summary:\r\n\r\nTotal processable nodes: 46\r\nRecognized nodes available for conversion: 11\r\nTotal nodes converted: 6\r\nTotal FP16 Cast ops used (excluding Const and Variable casts): 10\r\nAllowlisted nodes converted: 5\r\nDenylisted nodes blocking conversion: 0\r\nNodes blocked from conversion by denylisted nodes: 0\r\n\r\nFor more information regarding mixed precision training, including how to make automatic mixed precision aware of a custom op type, please see the documentation available here:\r\nhttps://docs.nvidia.com/deeplearning/frameworks/tensorflow-user-guide/index.html#tfamp\r\n\r\n\r\n2023-02-17 04:15:03.158267: I tensorflow/compiler/tf2tensorrt/convert/trt_optimization_pass.cc:206] [TF-TRT] Using explicit QDQ mode\r\n2023-02-17 04:15:03.159885: W tensorflow/compiler/tf2tensorrt/convert/ops/quantization_ops.cc:146] QuantizeAndDequantizeV2: StatefulPartitionedCall/sequential/custom_conv2d_1/custom_conv2d_1q1_w has narrow_range=true, but for TensorRT conversion, narrow_range=false is recommended.\r\n2023-02-17 04:15:03.160112: W tensorflow/compiler/tf2tensorrt/convert/ops/quantization_ops.cc:146] QuantizeAndDequantizeV2: StatefulPartitionedCall/sequential/custom_conv2d_2/custom_conv2d_2q1_w has narrow_range=true, but for TensorRT conversion, narrow_range=false is recommended.\r\n2023-02-17 04:15:03.160292: W tensorflow/compiler/tf2tensorrt/convert/ops/quantization_ops.cc:146] QuantizeAndDequantizeV2: StatefulPartitionedCall/sequential/custom_conv2d_3/custom_conv2d_3q1_w has narrow_range=true, but for TensorRT conversion, narrow_range=false is recommended.\r\n2023-02-17 04:15:03.160497: W tensorflow/compiler/tf2tensorrt/convert/ops/quantization_ops.cc:146] QuantizeAndDequantizeV2: StatefulPartitionedCall/sequential/custom_conv2d_4/custom_conv2d_4q1_w has narrow_range=true, but for TensorRT conversion, narrow_range=false is recommended.\r\n2023-02-17 04:15:03.160682: W tensorflow/compiler/tf2tensorrt/convert/ops/quantization_ops.cc:146] QuantizeAndDequantizeV2: StatefulPartitionedCall/sequential/custom_conv2d_5/custom_conv2d_5q1_w has narrow_range=true, but for TensorRT conversion, narrow_range=false is recommended.\r\n2023-02-17 04:15:03.160711: W tensorflow/compiler/tf2tensorrt/convert/ops/quantization_ops.cc:146] QuantizeAndDequantizeV2: StatefulPartitionedCall/sequential/custom_conv2d_1/custom_conv2d_1_q1_i has narrow_range=true, but for TensorRT conversion, narrow_range=false is recommended.\r\n2023-02-17 04:15:03.160782: W tensorflow/compiler/tf2tensorrt/convert/ops/quantization_ops.cc:146] QuantizeAndDequantizeV2: StatefulPartitionedCall/sequential/custom_conv2d_2/custom_conv2d_2_q1_i has narrow_range=true, but for TensorRT conversion, narrow_range=false is recommended.\r\n2023-02-17 04:15:03.160815: W tensorflow/compiler/tf2tensorrt/convert/ops/quantization_ops.cc:146] QuantizeAndDequantizeV2: StatefulPartitionedCall/sequential/custom_conv2d_3/custom_conv2d_3_q1_i has narrow_range=true, but for TensorRT conversion, narrow_range=false is recommended.\r\n2023-02-17 04:15:03.160846: W tensorflow/compiler/tf2tensorrt/convert/ops/quantization_ops.cc:146] QuantizeAndDequantizeV2: StatefulPartitionedCall/sequential/custom_conv2d_4/custom_conv2d_4_q1_i has narrow_range=true, but for TensorRT conversion, narrow_range=false is recommended.\r\n2023-02-17 04:15:03.160876: W tensorflow/compiler/tf2tensorrt/convert/ops/quantization_ops.cc:146] QuantizeAndDequantizeV2: StatefulPartitionedCall/sequential/custom_conv2d_5/custom_conv2d_5_q1_i has narrow_range=true, but for TensorRT conversion, narrow_range=false is recommended.\r\n2023-02-17 04:15:03.160929: W tensorflow/compiler/tf2tensorrt/segment/segment.cc:952] \r\n\r\n################################################################################\r\nTensorRT unsupported/non-converted OP Report:\r\n - Conv2D -> 5x\r\n - [Count: 5x] Conv2D expects kernel of dimension 4\r\n\r\n - NoOp -> 2x\r\n - [Count: 2x] Op type NoOp is not supported.\r\n\r\n - Identity -> 1x\r\n - [Count: 1x] excluded by segmenter option. Most likely an input or output node.\r\n\r\n - Placeholder -> 1x\r\n - [Count: 1x] excluded by segmenter option. Most likely an input or output node.\r\n\r\n--------------------------------------------------------------------------------\r\n - Total nonconverted OPs: 9\r\n - Total nonconverted OP Types: 4\r\nFor more information see https://docs.nvidia.com/deeplearning/frameworks/tf-trt-user-guide/index.html#supported-ops.\r\n################################################################################\r\n\r\n2023-02-17 04:15:03.161089: W tensorflow/compiler/tf2tensorrt/segment/segment.cc:1280] The environment variable TF_TRT_MAX_ALLOWED_ENGINES=20 has no effect since there are only 5 TRT Engines with at least minimum_segment_size=3 nodes.\r\n2023-02-17 04:15:03.161110: I tensorflow/compiler/tf2tensorrt/convert/convert_graph.cc:799] Number of TensorRT candidate segments: 5\r\n2023-02-17 04:15:03.162706: I tensorflow/compiler/tf2tensorrt/convert/convert_graph.cc:916] Replaced segment 0 consisting of 3 nodes by TRTEngineOp_000_000.\r\n2023-02-17 04:15:03.162745: W tensorflow/compiler/tf2tensorrt/convert/convert_graph.cc:919] TF-TRT Warning: Cannot replace segment 1 consisting of 16 nodes by TRTEngineOp_000_001 reason: Segment has no inputs (possible constfold failure) (keeping original segment).\r\n2023-02-17 04:15:03.162816: I tensorflow/compiler/tf2tensorrt/convert/convert_graph.cc:916] Replaced segment 2 consisting of 3 nodes by TRTEngineOp_000_002.\r\n2023-02-17 04:15:03.162881: I tensorflow/compiler/tf2tensorrt/convert/convert_graph.cc:916] Replaced segment 3 consisting of 3 nodes by TRTEngineOp_000_003.\r\n2023-02-17 04:15:03.162950: I tensorflow/compiler/tf2tensorrt/convert/convert_graph.cc:916] Replaced segment 4 consisting of 3 nodes by TRTEngineOp_000_004.\r\n2023-02-17 04:15:03.165975: I tensorflow/core/grappler/optimizers/auto_mixed_precision.cc:1533] No allowlist ops found, nothing to do\r\n2023-02-17 04:15:03.167998: I tensorflow/core/grappler/optimizers/auto_mixed_precision.cc:1533] No allowlist ops found, nothing to do\r\n2023-02-17 04:15:03.169708: I tensorflow/core/grappler/optimizers/auto_mixed_precision.cc:1533] No allowlist ops found, nothing to do\r\n2023-02-17 04:15:03.171302: I tensorflow/core/grappler/optimizers/auto_mixed_precision.cc:1533] No allowlist ops found, nothing to do\r\n```\r\n\r\nThe `converter.build(input_fn=input_fn)` step with this flag being set seemed to suppress all the warnings I was getting without it.\r\n\r\nI noticed something else, while checking the converted model summary i.e. the converter shows the input and output dtypes to be in `float16` while upon checking the original model before TF-TRT conversion I can see the layer `dtypes` are inferred to be `float32`. \r\n\r\n```shell\r\n>>> converter.summary()\r\nTRTEngineOP Name Device # Nodes # Inputs # Outputs Input DTypes Output Dtypes Input Shapes Output Shapes \r\n================================================================================================================================================================\r\n\r\n----------------------------------------\r\n\r\nTRTEngineOp_000_000 device:GPU:0 5 1 1 ['float16'] ['float16'] [[-1, 112, 112 ... [[-1, 112, 112 ...\r\n\r\n - Cast: 2x\r\n - Const: 2x\r\n - QuantizeAndDequantizeV2: 1x\r\n\r\n----------------------------------------\r\n\r\nTRTEngineOp_000_002 device:GPU:0 5 1 1 ['float16'] ['float16'] [[-1, 56, 56, 64]] [[-1, 56, 56, 64]]\r\n\r\n - Cast: 2x\r\n - Const: 2x\r\n - QuantizeAndDequantizeV2: 1x\r\n\r\n----------------------------------------\r\n\r\nTRTEngineOp_000_003 device:GPU:0 5 1 1 ['float16'] ['float16'] [[-1, 28, 28, 64]] [[-1, 28, 28, 64]]\r\n\r\n - Cast: 2x\r\n - Const: 2x\r\n - QuantizeAndDequantizeV2: 1x\r\n\r\n----------------------------------------\r\n\r\nTRTEngineOp_000_004 device:GPU:0 5 1 1 ['float16'] ['float16'] [[-1, 14, 14, 64]] [[-1, 14, 14, 64]]\r\n\r\n - Cast: 2x\r\n - Const: 2x\r\n - QuantizeAndDequantizeV2: 1x\r\n\r\n================================================================================================================================================================\r\n[*] Total number of TensorRT engines: 4\r\n[*] % of OPs Converted: 41.67% [20/48]\r\n```\r\nIs this some automatic behavior of TF-TRT that can be suppressed?", "Any updates regarding this issue? @DEKHTIARJonathan are the logs above helpful to solve the issue or is anything more required?", "Just a quick note that TF-TRT with explicit quantize & dequantize is actually still experimental and not really supported. I just finished working through some of the issues you mentioned above, including Conv2D not being supported (for me it was because it had a tensor input). \r\n\r\n@DEKHTIARJonathan is there any active effort on this right now? Would be happy to join forces and add the fixes I've found", "I've opened another issue here: https://github.com/tensorflow/tensorflow/issues/60168\r\nI think that the fixes I've already implemented are real bugs and can be used to help here as well @codejaeger. \r\n" ]

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[ "Hi, @SuperSecureHuman \r\n\r\nAplogize for the delay and It seems like there is some problem with `cudatoolkit` and the `cudnn` version could be incompatible with your tensorflow version. If possible could you please help me with the output of these two commands `nvcc --version` and `nvidia-smi` and as per our [official documentation](https://www.tensorflow.org/install/source#gpu) with Tested build configurations for `tensorflow-2.11.0`,`CUDA==11.2` and `cuDNN==8.1` should work. Thank you!", "Hey!\r\n\r\nHere you go\r\n\r\nNvidia-smi\r\n\r\n\r\n\r\n\r\nnvcc --version\r\n\r\n```\r\nnvcc --version\r\nnvcc: NVIDIA (R) Cuda compiler driver\r\nCopyright (c) 2005-2022 NVIDIA Corporation\r\nBuilt on Tue_Mar__8_18:18:20_PST_2022\r\nCuda compilation tools, release 11.6, V11.6.124\r\nBuild cuda_11.6.r11.6/compiler.31057947_0\r\n```\r\n\r\n\r\nCuda 11.2 still has the library version at 11, while TF looks for libcublas10.\r\n\r\n\r\n\r\n\r\nAnyway, loading 11.2/8.1 and running the same training script on two nodes.\r\n\r\n", "I have no idea what happened, but the training went fine. Even with cuda 11.6\r\n\r\nBecause of the queue in the HPC, I am unable to have an interactive session for longer time, through which I can look into this once again.\r\n\r\nI'll update once again here, after I try it again", "I am unable to reproduce the bug now. You may close the issue if needed. I'll reopen if I get it again.", "Hi, @SuperSecureHuman \r\n\r\nThank you for the confirmation and it seems like the issue got resolved if you're able to reproduce the bug again, so I would request you to please post new issue and we will look into that issue. Could you please confirm if this issue is resolved for you now ? Please feel free to close the issue if it is resolved ? Thank you!", "This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n", "I was not able to reproduce the issue again, I'll create a new issue if I get it again.", "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/59710\">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/59710\">No</a>\n" ]

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[ "@priyansh4320 \r\n I tried to reproduce the issue on colab using TF v2.11 but facing different error could you please provide all dependencies and find the gist [here](https://colab.research.google.com/gist/tiruk007/b861319a02bcbea01a7a2a80154f906b/untitled134.ipynb) for reference.\r\n\r\nThank you !", "@tiruk007 i assume that you have also uploaded the dataset via the link https://drive.google.com/file/d/1pKAJ-PF42nRB2kw0ZdMAkg1i2fUSvF-y/view?usp=sharing to your google drive.\r\n\r\n1. my file path to the dataset is this : /content/drive/MyDrive/ATS- gigadata/model_gigadata_new.csv \r\n2. here I have create another folder \"ATS-gigadata\" where my file is located\r\n3. you can use this path for yourself: /content/drive/MyDrive/model_gigadata_new.csv\r\n4. since uploading a file to google drive will appear in \"MyDrive\" by default\r\n5. regarding dependencies , all dependencies are provided in file itself ", "This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n", "@priyansh4320 \r\nSorry for the late reply, Could you please provide an accessible dataset to replicate the issue reported here ?\r\n\r\nThank you!\r\n", "@tiruk007 \r\ndataset link : https://drive.google.com/file/d/1pKAJ-PF42nRB2kw0ZdMAkg1i2fUSvF-y/view?usp=sharing", "@SuryanarayanaY \r\nI was able to reproduce the issue on Colab using 2.11. please find the gist [here](https://colab.research.google.com/gist/tiruk007/98ddf5d0ede5ad6f6ad06168ed1e09f5/untitled148.ipynb) for reference.\r\n\r\nThank you !", "@tiruk007 ok, now can you please provide me solution to resolve this error . i am not able to move forward in my project.\r\n", "Hi, Apologies for the delayed response, I tried to check the issue. Model definition looks fine, the only thing you want to try is data preprocessing part like take a relook into the tokenizer part.\r\nAlso, I did not get the use of below logic.\r\n```\r\n#increasing length of word index to change the shape\r\ni=1\r\nwhile i<=10:\r\n word_index['person organization gpe ',i] = 1000+i\r\n i+=1\r\n```\r\n\r\n", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59709\">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/59709\">No</a>\n" ]

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[ "cc @eric-k256 @rsuderman ", "There is actually no implementation of the `dtype.isUnsigned()` [Reference](https://mlir.llvm.org/doxygen/classmlir_1_1quant_1_1QuantizedType.html) . Simply using getStorageTypeMin/Max for much cleaner implementation.\r\n\r\nJerry " ]

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[ "@ZIFENG278 try this below \r\nIt looks like there might be an issue with the TensorFlow Lite build for your BeagleBone Black Wireless. The error message you're getting suggests that there is an unresolved symbol when loading the shared library.\r\n\r\nHere are some suggestions to resolve this issue:\r\n\r\nEnsure that you have built the TensorFlow Lite wheel file correctly for your platform by following the instructions in the [guide](https://www.tensorflow.org/lite/guide/build_cmake_pip).\r\n\r\nDouble-check the compilation flags you used when building the wheel file, as you mentioned that you changed the -mfpu=neon-vpfv4 flag to -mfpu=neon. Ensure that this change is compatible with your platform's architecture and requirements.\r\n\r\nTry rebuilding the wheel file and reinstalling it on your BeagleBone Black Wireless. Make sure to remove any previous installations of tflite_runtime before reinstalling. \r\n\r\nIf the issue persists, consider posting a detailed description of your build process, the flags you used, and the platform information on the [TensorFlow GitHub issues](https://github.com/tensorflow/tensorflow/issues) page. The TensorFlow team or the community members might be able to help you with your specific issue.\r\n\r\nGood luck, and I hope you manage to resolve the issue!", "@Adesoji1 Thanks for your answer,\r\n\r\nThe problem I found was that I built the wheel in a conda=py3.7 environment, which could have been problematic, the guide mentions it uses docker to build it so I ignored that\r\n\r\nThis time I built the wheel in my bash environment after a git pull and it installed successfully in my beaglebone black wireless.\r\n\r\nThanks for help.", "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/59707\">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/59707\">No</a>\n" ]

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[]

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[ "The CPU behavior matches NumPy. The result overflows the underlying data type in NumPy.\r\n```\r\n>>> import numpy as np\r\n>>> np.power(2, 62)\r\n4611686018427387904\r\n>>> np.power(2, 63)\r\n-9223372036854775808\r\n>>> np.power(2, 64)\r\n0\r\n>>> np.power(2, 128)\r\n0\r\n```\r\n\r\nOn GPU, the result is wrapping for some reason:\r\n```\r\nwith tf.device(\"gpu\"):\r\n print(tf.experimental.numpy.power(2, 63))\r\n print(tf.experimental.numpy.power(2, 64))\r\n print(tf.experimental.numpy.power(2, 65))\r\n print(tf.experimental.numpy.power(2, 66))\r\n print(tf.experimental.numpy.power(2, 67))\r\n...\r\n\r\ntf.Tensor(-9223372036854775808, shape=(), dtype=int64)\r\ntf.Tensor(1, shape=(), dtype=int64)\r\ntf.Tensor(2, shape=(), dtype=int64)\r\ntf.Tensor(4, shape=(), dtype=int64)\r\ntf.Tensor(8, shape=(), dtype=int64)\r\n```\r\n\r\nI haven't gotten too deep into the implementation (I'm new), but CPU and GPU seem to follow the same path all the way through `gen_math_ops.py._pow()`'s call to `pywrap_tfe.TFE_Py_FastPathExecute()`. I don't have the time to figure out what happens in `TFE_Py_FastPathExecute_C()` and maybe `GetOp()` are doing based on the device.", "@trickiwoo Thanks for reporting this issue.\r\n\r\nI was able to reproduce this behaviour on TF 2.11 and TF Nightly 2.13.0-dev20230215. \r\n\r\nHowever, `tf.math.pow` has not shown any such behaviour on GPU. Please find the [gist](https://colab.research.google.com/gist/pjpratik/cc9e46c992bcc4631133f57064f9e7db/59705.ipynb) here.\r\n\r\n@tilakrayal Could you please look into this. Thanks!", "Right, `tf.experimental.numpy.pow()` converts its scalar arguments to `tf.Tensor` before passing to `gen_math_ops._pow()`.\r\n\r\n`tf.math.pow()` passes them as scalars. That seems to make a difference in the GPU case, but apparently not in the CPU case.", "Tried with Apple M1 GPU and it is giving the expected result.\r\nSeems to be issue only when NVIDIA GPU is used. It needs more investigation.\r\n```\r\nwith tf.device(\"gpu\"):\r\n x_t = tf.experimental.numpy.power(2, 64)\r\n print(x_t)\r\n```\r\n`tf.Tensor(0, shape=(), dtype=int64)`", "This results is a signed int overflow, which is undefined behavior. Different compilers can choose to do different things, so your program output is unpredictable, which is what you're seeing here.", "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/59705\">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/59705\">No</a>\n" ]

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[ "Hi, @trickiwoo \r\n\r\nAplogize for the delay and I was able to replicate the issue and it's returning different values for the same code on `CPU` and `GPU`, for your reference I have added [gist-file-tf2.11](https://colab.research.google.com/gist/gaikwadrahul8/097cea61a28081a64601a762e40903bb/-59704.ipynb), [gist-file-tf2.12.0rc0 ](https://colab.research.google.com/gist/gaikwadrahul8/d1a846e1a60c10eb1b4f806014427aa4/-59704-tensorflow-2-12-0rc0.ipynb) and [gist-file-tf-nightly-2.13.0.dev20230216](https://colab.research.google.com/gist/gaikwadrahul8/453761c41c9b0dd0bf84a207e3ed2035/-59704.ipynb) so we will have to dig more into this issue and we'll update you soon here, Thank you for noticing this issue. Thank you!", "Would this be undefined by the specification anyway? The [tf.image.convert_image_dtype](https://www.tensorflow.org/api_docs/python/tf/image/convert_image_dtype) documentation says floating-point values are expected to be in the range `[0, 1)`. If so, then I'd expect inconsistent behavior and perhaps a warning message for out-of-range values.", "Thanks for getting back to me! @gaikwadrahul8 @nrwahl2 \r\nYes, the input values are expected to be in the range [0, 1), but it seems that the CPU converts negative numbers to 180, and it doesn't make sense. It would be great if it throws exceptions or outputs 0 as the GPU does. ", "This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n", "Hi, @trickiwoo \r\n\r\nApologize for the delay and I tried to execute the same code on CPU by passing `saturate=True` argument to `tf.image.convert_image_dtype()` and it's giving the same result as GPU and for your reference I have added gist file [here](https://colab.research.google.com/gist/SuryanarayanaY/5e193ed69f1cad5de8b0561dc05046d7/untitled55.ipynb) and by default value is `saturate=False` and you can refer [official documentation ](https://www.tensorflow.org/api_docs/python/tf/image/convert_image_dtype)\r\n\r\nCould you please try from your end by passing `saturate=True` argument to `tf.image.convert_image_dtype()` and check whether is it giving the same result as GPU ? Thank you!", "Thanks for the detailed explanation. Yes, passing the `saturate=True` argument would be the proper usage.", "@trickiwoo \r\n\r\nThank you for the confirmation and If you need any further assistance please let us know ? or Could you please confirm if this issue is resolved for you ? Please feel free to close the issue if it is resolved ? Thank you!", "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/59704\">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/59704\">No</a>\n" ]

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[ "> LGTM, just reordering the imports back as this would break Copybara otherwise\r\n\r\nNo problem. Why's that?", "Internally the imports get rewritten based on where the code is situated in the Google monorepo and the type of the code (OSS -> third_party). Plus, code linters want the imports sorted." ]

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[ "The result of casting a negative float to an int is undefined according to the C++ standard:\r\n> A prvalue of a floating-point type can be converted to a prvalue of an\r\ninteger type. The conversion truncates; that is, the fractional part is\r\ndiscarded.\r\n> The behavior is undefined if the truncated value cannot be represented\r\nin the destination type.\r\n\r\nhttps://github.com/cplusplus/draft/blob/5ef31f3fc1531d9e6e923cb57bf6e5ecec59ed4e/source/expressions.tex#L949-L958\r\n\r\nIt's mentioned in more readable language here (unofficial):\r\n* https://cplusplus.com/doc/tutorial/typecasting/\r\n* https://embeddeduse.com/2013/08/25/casting-a-negative-float-to-an-unsigned-int/\r\n\r\nPerhaps TF could either print a warning message or work around it by either:\r\n* taking absolute value of the floating-point value first, or\r\n* casting to a signed int type first", "@tilakrayal \r\n \r\nI was able to reproduce the issue on Colab using TF v2.11 and tf-nightly. Please find the gist of [2.11](https://colab.research.google.com/gist/tiruk007/fb8c930fed1e0f533966180067793815/2_11.ipynb) and [tf-nightly](https://colab.research.google.com/gist/tiruk007/538982863653d5f46152ee2f853ef874/untitled134.ipynb) for reference.\r\n\r\nThank you !", "Hi, @trickiwoo \r\n\r\nApologize for the delay and @nrwahl2, Thank you for the detailed explanation and suggested workaround, I really appreciate your efforts and time, I was able to replicate the same issue with `tensorflow 2.12.0rc0` and I have added [gist-file](https://colab.research.google.com/gist/gaikwadrahul8/6f28a09e97fd4597c26ad01d4a7cc54a/-59702.ipynb) for your reference. \r\n\r\nIt is undefined behaviour in `C99` if the floating point number is` less than or equal to -1.0`. If it's in the range `(-1.0, 0.0),` the resulting value will be 0.\r\n\r\n**From C99, §6.3.1.4, paragraph 1**\r\n\r\n`When a finite value of real floating type is converted to an integer type other than _Bool, the fractional part is discarded (i.e., the value is truncated toward zero). If the value of the integral part cannot be represented by the integer type, the behavior is undefined`\r\n\r\nWe'll have to dig more into this issue and we'll update you soon. Thank you! ", "This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n", "Hi, @sachinprasadhs \r\n\r\nCould you please look into this issue ? Thank you!" ]

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[ "@chewman19 I was able to execute the code from [Load video data](https://www.tensorflow.org/tutorials/load_data/video) tutorial succesfully using TF v 2.11. Kindly refer to the gist [here](https://colab.sandbox.google.com/gist/synandi/a9e43199c8c958f5f3178ce7d4e0ff14/59701.ipynb). For better assistance please share the colab gist in which you are executing the code from Load video data tutorial. Thank you!", "This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n", "\r\nClosing as stale. Please reopen if you'd like to work on this further.\r\n\r\n", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59701\">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/59701\">No</a>\n" ]

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[ "[Lstm3.zip](https://github.com/tensorflow/tensorflow/files/10745602/Lstm3.zip)\r\n\r\n\r\nTrained model:\r\n`model = keras.models.load_model('Lstm3')`", "Hi @danielstankw,\r\n\r\nThe prediction time in TensorFlow depends on several factors, some of them are:\r\n\r\n1. Model complexity: Models with a large number of layers or parameters require more computation and memory, which can increase the prediction time.\r\n2. Input data size: Larger input data requires more computation and memory to process, which can increase the prediction time.\r\n3. Hardware specifications: The speed and memory of the CPU, GPU, or TPU being used to run the model can significantly affect the prediction time.\r\n4. Optimization techniques: TensorFlow provides various optimization techniques to speed up model prediction, such as quantization, pruning, and model compression.\r\n\r\nPlease refer to [Pruning in Keras](https://www.tensorflow.org/model_optimization/guide/pruning/pruning_with_keras), [Quantization aware training](https://www.tensorflow.org/model_optimization/guide/quantization/training_comprehensive_guide) and [model compression](https://www.tensorflow.org/tutorials/optimization/compression). Thank you!\r\n\r\n", "@synandi \r\nThank you for your reply, \r\n1. I have tried a single LSTM layer with 4 units instead of the network given above, and the time of prediction was of the same order of magnitude. This leads me to believe that the issue is related to the slow performance of LSTM layer implementation in Tensorflow. \r\nThis seems to be the case, considering the fact that I have seen other Github Issue posts being made about LSTM performance issues.\r\n\r\n2 & 3. My PC specs are pretty good. A friend of mine implemented complex LSTM using Pytorch instead and was able to achieve great performance with no issue in prediction time delay. As mentioned before, it seems like the issue is related to LSTM not being optimized in TF rather then complexity of model/ data. \r\n\r\n4. I will have a look into the links provided - thanks. If that does not help I guess will have to move on to Pytorch which is a bummer as I like the ease of implementation in TF much more ;/", "@danielstankw, as per the keras documentation please try adding an extra argument to the LSTM as `unroll=True`.\r\n> unroll: Boolean (default False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences.\r\n \r\nLet us know if it is still an issue and if the links provided were helpful? Thank you! ", "This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n", "Closing 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/59698\">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/59698\">No</a>\n" ]

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[ "Hi @nluehr Can you please check @cantonios's comments and keep us posted ? Thank you!" ]

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[ "Hi @gaikwadrahul8 -- have you been able to reproduce this issue? Just want to make sure it doesn't get lost", "Hi, @taylora-mitre \r\n\r\nApologize for the delay and I was able to replicate the issue on Google colab with `Tensorflow==2.11` so I have added gist file for your reference [here](https://colab.research.google.com/gist/gaikwadrahul8/2360833ea46b4519353b601dc2da64dd/-59696.ipynb) and if training loss is converges to zero and still the validation loss is high this may indicate that the model is underfitting. It seems like mine and yours training loss and validation loss are different because you're generating random dataset in your code and you're using the same dataset as validation_data.\r\n\r\nJust to confirm, you're expecting both training loss and validation loss should be same because you're using same identical training and validation dataset Am I Right ? or Am I misunderstanding something here so please correct me ? Thank you!\r\n\r\n", "@gaikwadrahul8 \r\n\r\nYes, that's correct. I am expecting the train and validation loss to be the same, since I'm using identical datasets. Or, if not perfectly identical, at least very close -- I am not expecting them to differ by several orders of magnitude as in your gist and mine", "@gaikwadrahul8 what are the next steps for this issue?", "Hi, @taylora-mitre \r\n\r\nApologize for the delay and if you're following ResNet-18 model architecture with your customized functions and as you mentioned earlier training loss converging towards zero but still the validation loss high for random dataset with identical training and validation datasets it means that model is in underfitting but ideally we shouldn't use same training dataset for model validation.\r\n\r\nIf you want to build a reliable and robust model, you need to split your dataset into the training, validation, and test sets and If you don't, your results will be biased, and you'll end up with a false impression of better model accuracy.\r\n\r\nThe purpose of Validation set is our model training process is not a one-time process. We have to train multiple models by trying different combinations of hyperparameters. Then, we evaluate the performance of each model on the validation set. Therefore, the validation test is useful for hyperparameter tuning or selecting the best model out of different models\r\n\r\nYou're training your model only for 10 epochs so I don't think so you've built reliable, robust and generalise model so it will give you best result on validation dataset even though it's identical with training dataset in 10 epochs so could you please try for more number of epochs on GPU because the original ResNet-18 model architecture is complex and it has trained for some days with GPU, you can refer this [link](https://stackoverflow.com/questions/48051670/training-resnet-deep-neural-network-from-scratch), I hope it will help you to resolve your issue.\r\n\r\nIf I'm missing something here please let me know ? Thank you!", "@gaikwadrahul8 \r\n\r\nIt seems to me that you're missing the point of this issue entirely. I am not trying to build a robust, generalizable model; this is a toy example meant to demonstrate a specific technical issue.\r\n\r\nDo you truly think that the model is underfitting here? If anything, the model should be drastically *over-fitting* since we only use one `(X, y)` pair. In fact, this is by design! I am demonstrating that the expected behavior (extreme overfitting, leading to zero training and validation loss) does not happen here. This is the core of the issue.\r\n\r\nAgain, let me say that I am NOT trying to build a robust, production-ready, generalizable model in this simple toy example. I am trying a prove a point, which I believe you may have missed. For that reason, splitting the dataset into unique train/validation/test sets is not applicable here; similarly, training for longer times is not applicable here.", "Hi, @taylora-mitre \r\n\r\nApologize for the delayed response and I'm really sorry if I misunderstood your issue here so as you are using identical training and validation sets so you're expecting training and validation loss should be very close to zero or at least there shouldn't be difference by several orders of magnitude so (extreme overfitting, leading to zero training and validation loss) does not happening here, We'll have to dig more into this issue and we'll update you soon, Thank you for noticing this issue. Thank you!", "Yes, this is my issue. Thanks for the response. Let me know if you come up with anything for this!", "Hi, @taylora-mitre \r\n\r\nApologize for the delayed response and I found [stack-overflow-answer-1](https://stackoverflow.com/questions/49165351/why-val-loss-is-different-from-training-loss-when-use-the-same-training-data-as) and [stack-overflow-answer-2](https://stackoverflow.com/questions/55444737/validation-loss-much-higher-than-training-loss) which may help you to understand why training and validation loss shouldn't be very close to zero or at least there shouldn't be difference by several orders of magnitude on identical training and validation datasets and in your case it's leading to `Data Leakage` where the Machine Learning Model is already aware of some part of test data after training.This causes the problem of overfitting. I hope it will help you to solve your 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.", "For anyone who comes across this in the future, the issue is the `BatchNormalization` layers in the model creation. Replacing these with another kinds of normalization (such as e.g. `GroupNormalization`), or just removing them entirely, leads to the expected behavior in this toy example.\r\n\r\nFor real-life applications of ML, it is recommended to use an alternative kind of normalization rather than dropping it entirely.", "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/59696\">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/59696\">No</a>\n" ]

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[ "Hi, @clime \r\n\r\nApologize for the delay and as far I know, I think this warning can be safely ignored as you can find the same warning even in [Tensorflow-Tutorial](https://www.tensorflow.org/text/tutorials/text_generation#export_the_generator) and that warnings are that function could not be traced. It should be safe to ignore.\r\n\r\n`model.save() `will not only try to save the weights/variables, but also trace functions of the model and save their graph representations. This is what is failing for that function, and the warnings tell you about that.\r\n\r\nIf you want to continue training a model, you probably want to recreate the model from Python code anyway, and load/save just the weights. The `tf.keras.callbacks.BackupAndRestore `does this automatically at the end of each epoch. So if you interrupt the training script, next time you start it, it should automatically pick up from the beginning of the interrupted epoch.\r\n\r\nAs far as know these functions are needed while training the model I think those warnings are coming while training the model.\r\n\r\nYou can refer this [Stack overflow answer](https://stackoverflow.com/questions/65697623/tensorflow-warning-found-untraced-functions-such-as-lstm-cell-6-layer-call-and) also which supports ignoring those warnings.\r\n\r\nIf you're looking to disable `absl INFO and WARNING log `messages please use below code snippet:\r\n\r\n```\r\nimport absl.logging\r\nabsl.logging.set_verbosity(absl.logging.ERROR)\r\n```\r\nI hope I answered your questions and if I miss something here, Please let us know ? Thank you!", "Thanks, but I don't want to suppress the warning and I would also prefer not to ignore it. There should be a clean way to get rid of them. \r\n\r\nMy questions are:\r\n\r\n1) Why traces of these particular functions cannot be saved?\r\n2) If I decide to save the model without traces, will I be able to later load the model without recompiling and use it for evaluation?\r\n3) If I compile the model (`load_model(compile=True)`), will these jit functions be regenerated?\r\n4) Why would the get_config() method be only needed for all custom layers/models (I don't actually have any custom layers or models) when I disable saving the traces? Why is this `get_config()` method relevant at all?\r\n", "This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n", "ping :)", "Hi, @clime \r\n\r\nApologize for the delay and I found similar issue [#47554](https://github.com/tensorflow/tensorflow/issues/47554), If you save your model in `SavedModel` format which is the more comprehensive save format that saves the model architecture, weights, and the traced Tensorflow subgraphs of the call functions. This enables Keras to restore both built-in layers as well as custom objects and if saved model reloads without any change in model architecture, layers or Parameters then there shouldn't be any problem.\r\n\r\nIf you're saving your model in` .h5` format then you won't get any such warnings \r\n\r\nConfiguration of a Sequential model or Functional API model, These types of models are explicit graphs of layers their configuration is always available in a structured form and `get_config()` and `from_config()` API's calling will return a Python dict containing the configuration of the model. The same model can then be reconstructed via `Sequential.from_config(config)` (for a Sequential model) or `Model.from_config(config) `(for a Functional API model).\r\n\r\nIf you save the model in `SavedModel` format without traces, you can load the model without recompilation by doing `load_model(compile=False)` and if you compile the model `load_model(compile=True)` as far I know these jit functions warnings may come, you can refer our official documentation for [Save and load Keras models ](https://www.tensorflow.org/guide/keras/save_and_serialize#savedmodel_format) and also please refer this [answer](https://stackoverflow.com/questions/65697623/tensorflow-warning-found-untraced-functions-such-as-lstm-cell-6-layer-call-and#:~:text=If%20you%20ignore,environment%20after%20training.) on stackoverflow, I hope I'm able to answer your questions. \r\n\r\nIf you still need any further help please let us know ? Thank you!\r\n\r\n\r\n\r\n\r\n\r\n", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59694\">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/59694\">No</a>\n" ]

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[ "https://lore.kernel.org/distributions/[email protected]/T/#u\r\n\r\ntl;dr it might be worth holding off.", "Hi @RamanSggs, In order to expedite the trouble shooting process, please fill the issue [template](https://github.com/tensorflow/tensorflow/issues/new/choose) and share complete reproducible code. Thank you!", "This should be handled by #59695 ", "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/59693\">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/59693\">No</a>\n" ]

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[ "@turgut-baba Thanks for reporting the issue.\r\n\r\nI've tried running the commands \r\n\r\n`bazel build -c opt --config=elinux_aarch64 //tensorflow/lite:libtensorflowlite.so --fat_apk_cpu=arm64-v8a`\r\nand\r\n`bazel build -c opt --config=elinux_aarch64 //tensorflow/lite:libtensorflowlite.so` on Linux/Ubuntu 20.04, with Bazel 5.3.0 and was able to build successfully. Please find the [gist](https://colab.research.google.com/gist/pjpratik/eda786aa4b7b0471a09465a4b19561c7/-59692.ipynb#scrollTo=D2pgHogEk3O0) here. \r\n\r\nHave you tried with Android NDK 19c which is [recommended](https://www.tensorflow.org/lite/android/lite_build#set_up_build_environment_without_docker). Thanks!\r\n\r\n", "Hello, I will try it with bazel 5.3.0 and update you, however due to my projects needs, I can't use NDK versions below 25.", "Running `bazel build -c opt --config=elinux_aarch64 //tensorflow/lite:libtensorflowlite.so --fat_apk_cpu=arm64-v8a` with bazel 5.3.0 causes this error on the `v2.12.0-rc0` and `r2.12` and `master` branch :\r\n```\r\nStarting local Bazel server and connecting to it...\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=80\r\nINFO: Reading rc options for 'build' from /home/tb/Desktop/tensorflow/tensorflow_src/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /home/tb/Desktop/tensorflow/tensorflow_src/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility\r\nINFO: Reading rc options for 'build' from /home/tb/Desktop/tensorflow/tensorflow_src/.tf_configure.bazelrc:\r\n 'build' options: --action_env PYTHON_BIN_PATH=/usr/bin/python3 --action_env PYTHON_LIB_PATH=/usr/lib/python3/dist-packages --python_path=/usr/bin/python3\r\nINFO: Reading rc options for 'build' from /home/tb/Desktop/tensorflow/tensorflow_src/.bazelrc:\r\n 'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils\r\nINFO: Found applicable config definition build:short_logs in file /home/tb/Desktop/tensorflow/tensorflow_src/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /home/tb/Desktop/tensorflow/tensorflow_src/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:elinux_aarch64 in file /home/tb/Desktop/tensorflow/tensorflow_src/.bazelrc: --config=elinux --cpu=aarch64 --distinct_host_configuration=true\r\nINFO: Found applicable config definition build:elinux in file /home/tb/Desktop/tensorflow/tensorflow_src/.bazelrc: --crosstool_top=@local_config_embedded_arm//:toolchain --host_crosstool_top=@bazel_tools//tools/cpp:toolchain\r\nINFO: Found applicable config definition build:linux in file /home/tb/Desktop/tensorflow/tensorflow_src/.bazelrc: --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wno-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-array-bounds --copt=-Wunused-result --copt=-Werror=unused-result --copt=-Wswitch --copt=-Werror=switch --copt=-Wno-error=unused-but-set-variable --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --distinct_host_configuration=false --experimental_guard_against_concurrent_changes\r\nINFO: Found applicable config definition build:dynamic_kernels in file /home/tb/Desktop/tensorflow/tensorflow_src/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS\r\nINFO: Analyzed target //tensorflow/lite:libtensorflowlite.so (89 packages loaded, 10700 targets configured).\r\nINFO: Found 1 target...\r\nERROR: /home/tb/Desktop/tensorflow/tensorflow_src/tensorflow/lite/kernels/internal/BUILD:864:11: Compiling tensorflow/lite/kernels/internal/mfcc.cc failed: (Exit 1): aarch64-none-linux-gnu-gcc failed: error executing command /home/tb/.cache/bazel/_bazel_tb/f8bd8a53c316ccb93548129bbd0ade7a/external/aarch64_linux_toolchain/bin/aarch64-none-linux-gnu-gcc -fstack-protector -g0 -O2 -DNDEBUG -ffunction-sections -fdata-sections ... (remaining 52 arguments skipped)\r\nIn file included from /home/tb/.cache/bazel/_bazel_tb/f8bd8a53c316ccb93548129bbd0ade7a/external/aarch64_linux_toolchain/aarch64-none-linux-gnu/include/c++/11.3.1/bits/specfun.h:46,\r\n from /home/tb/.cache/bazel/_bazel_tb/f8bd8a53c316ccb93548129bbd0ade7a/external/aarch64_linux_toolchain/aarch64-none-linux-gnu/include/c++/11.3.1/cmath:1935,\r\n from /home/tb/.cache/bazel/_bazel_tb/f8bd8a53c316ccb93548129bbd0ade7a/external/aarch64_linux_toolchain/aarch64-none-linux-gnu/include/c++/11.3.1/math.h:36,\r\n from tensorflow/lite/kernels/internal/mfcc.cc:16:\r\n/home/tb/.cache/bazel/_bazel_tb/f8bd8a53c316ccb93548129bbd0ade7a/external/aarch64_linux_toolchain/aarch64-none-linux-gnu/include/c++/11.3.1/limits:1673:7: internal compiler error: Illegal instruction\r\n 1673 | min() _GLIBCXX_USE_NOEXCEPT { return __FLT_MIN__; }\r\n | ^~~\r\n0x7f1c93401d5f ???\r\n\t./signal/../sysdeps/unix/sysv/linux/x86_64/sigaction.c:0\r\nPlease submit a full bug report,\r\nwith preprocessed source if appropriate.\r\nPlease include the complete backtrace with any bug report.\r\nSee <https://bugs.linaro.org/> for instructions.\r\nERROR: /home/tb/Desktop/tensorflow/tensorflow_src/tensorflow/lite/kernels/internal/BUILD:864:11: Compiling tensorflow/lite/kernels/internal/mfcc_dct.cc failed: (Exit 1): aarch64-none-linux-gnu-gcc failed: error executing command /home/tb/.cache/bazel/_bazel_tb/f8bd8a53c316ccb93548129bbd0ade7a/external/aarch64_linux_toolchain/bin/aarch64-none-linux-gnu-gcc -fstack-protector -g0 -O2 -DNDEBUG -ffunction-sections -fdata-sections ... (remaining 52 arguments skipped)\r\nIn file included from /home/tb/.cache/bazel/_bazel_tb/f8bd8a53c316ccb93548129bbd0ade7a/external/aarch64_linux_toolchain/aarch64-none-linux-gnu/include/c++/11.3.1/bits/specfun.h:46,\r\n from /home/tb/.cache/bazel/_bazel_tb/f8bd8a53c316ccb93548129bbd0ade7a/external/aarch64_linux_toolchain/aarch64-none-linux-gnu/include/c++/11.3.1/cmath:1935,\r\n from /home/tb/.cache/bazel/_bazel_tb/f8bd8a53c316ccb93548129bbd0ade7a/external/aarch64_linux_toolchain/aarch64-none-linux-gnu/include/c++/11.3.1/math.h:36,\r\n from tensorflow/lite/kernels/internal/mfcc_dct.cc:18:\r\n/home/tb/.cache/bazel/_bazel_tb/f8bd8a53c316ccb93548129bbd0ade7a/external/aarch64_linux_toolchain/aarch64-none-linux-gnu/include/c++/11.3.1/limits:1673:7: internal compiler error: Illegal instruction\r\n 1673 | min() _GLIBCXX_USE_NOEXCEPT { return __FLT_MIN__; }\r\n | ^~~\r\n0x7ff7ee25dd5f ???\r\n\t./signal/../sysdeps/unix/sysv/linux/x86_64/sigaction.c:0\r\nPlease submit a full bug report,\r\nwith preprocessed source if appropriate.\r\nPlease include the complete backtrace with any bug report.\r\nSee <https://bugs.linaro.org/> for instructions.\r\nERROR: /home/tb/.cache/bazel/_bazel_tb/f8bd8a53c316ccb93548129bbd0ade7a/external/ruy/ruy/BUILD:189:11: Compiling ruy/tune.cc failed: (Exit 1): aarch64-none-linux-gnu-gcc failed: error executing command /home/tb/.cache/bazel/_bazel_tb/f8bd8a53c316ccb93548129bbd0ade7a/external/aarch64_linux_toolchain/bin/aarch64-none-linux-gnu-gcc -fstack-protector -g0 -O2 -DNDEBUG -ffunction-sections -fdata-sections ... (remaining 62 arguments skipped)\r\nIn file included from /home/tb/.cache/bazel/_bazel_tb/f8bd8a53c316ccb93548129bbd0ade7a/external/aarch64_linux_toolchain/aarch64-none-linux-gnu/include/c++/11.3.1/chrono:41,\r\n from external/ruy/ruy/time.h:19,\r\n from external/ruy/ruy/tune.h:48,\r\n from external/ruy/ruy/tune.cc:16:\r\n/home/tb/.cache/bazel/_bazel_tb/f8bd8a53c316ccb93548129bbd0ade7a/external/aarch64_linux_toolchain/aarch64-none-linux-gnu/include/c++/11.3.1/limits:1673:7: internal compiler error: Illegal instruction\r\n 1673 | min() _GLIBCXX_USE_NOEXCEPT { return __FLT_MIN__; }\r\n | ^~~\r\n0x7f82f4e2dd5f ???\r\n\t./signal/../sysdeps/unix/sysv/linux/x86_64/sigaction.c:0\r\nPlease submit a full bug report,\r\nwith preprocessed source if appropriate.\r\nPlease include the complete backtrace with any bug report.\r\nSee <https://bugs.linaro.org/> for instructions.\r\nERROR: /home/tb/.cache/bazel/_bazel_tb/f8bd8a53c316ccb93548129bbd0ade7a/external/XNNPACK/BUILD.bazel:5371:19: Compiling src/subgraph/static-constant-pad.c failed: (Exit 1): aarch64-none-linux-gnu-gcc failed: error executing command /home/tb/.cache/bazel/_bazel_tb/f8bd8a53c316ccb93548129bbd0ade7a/external/aarch64_linux_toolchain/bin/aarch64-none-linux-gnu-gcc -fstack-protector -g0 -O2 -DNDEBUG -ffunction-sections -fdata-sections ... (remaining 93 arguments skipped)\r\nIn file included from external/XNNPACK/src/xnnpack/compute.h:14,\r\n from external/XNNPACK/src/xnnpack/operator.h:18,\r\n from external/XNNPACK/src/subgraph/static-constant-pad.c:6:\r\nexternal/XNNPACK/src/xnnpack/math.h: In function 'min':\r\nexternal/XNNPACK/src/xnnpack/math.h:35:3: internal compiler error: Illegal instruction\r\n 35 | return XNN_UNPREDICTABLE(b < a) ? b : a;\r\n | ^~~~~~\r\n0x7f0820113d5f ???\r\n\t./signal/../sysdeps/unix/sysv/linux/x86_64/sigaction.c:0\r\n0x7f08200fed09 __libc_start_main\r\n\t../csu/libc-start.c:308\r\nPlease submit a full bug report,\r\nwith preprocessed source if appropriate.\r\nPlease include the complete backtrace with any bug report.\r\nSee <https://bugs.linaro.org/> for instructions.\r\nERROR: /home/tb/.cache/bazel/_bazel_tb/f8bd8a53c316ccb93548129bbd0ade7a/external/XNNPACK/BUILD.bazel:5371:19: Compiling src/subgraph/global-average-pooling.c failed: (Exit 1): aarch64-none-linux-gnu-gcc failed: error executing command /home/tb/.cache/bazel/_bazel_tb/f8bd8a53c316ccb93548129bbd0ade7a/external/aarch64_linux_toolchain/bin/aarch64-none-linux-gnu-gcc -fstack-protector -g0 -O2 -DNDEBUG -ffunction-sections -fdata-sections ... (remaining 93 arguments skipped)\r\nIn file included from external/XNNPACK/src/xnnpack/compute.h:14,\r\n from external/XNNPACK/src/xnnpack/operator.h:18,\r\n from external/XNNPACK/src/subgraph/global-average-pooling.c:13:\r\nexternal/XNNPACK/src/xnnpack/math.h: In function 'min':\r\nexternal/XNNPACK/src/xnnpack/math.h:35:3: internal compiler error: Illegal instruction\r\n 35 | return XNN_UNPREDICTABLE(b < a) ? b : a;\r\n | ^~~~~~\r\n0x7ffb93661d5f ???\r\n\t./signal/../sysdeps/unix/sysv/linux/x86_64/sigaction.c:0\r\n0x7ffb9364cd09 __libc_start_main\r\n\t../csu/libc-start.c:308\r\nPlease submit a full bug report,\r\nwith preprocessed source if appropriate.\r\nPlease include the complete backtrace with any bug report.\r\nSee <https://bugs.linaro.org/> for instructions.\r\nTarget //tensorflow/lite:libtensorflowlite.so failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nINFO: Elapsed time: 88.774s, Critical Path: 26.47s\r\nINFO: 210 processes: 177 internal, 33 local.\r\nFAILED: Build did NOT complete successfully\r\n\r\n```", "@turgut-baba Thanks for the update.\r\n\r\n@sachinprasadhs Could you please look into this. Thank you.", "@pjpratik On a side note, I've found some pre-compiled versions of the library on the internet from 2021, it runs just fine on it's own but when I use it inside my project, which uses threading on android, it gives out an error. (I've set `setnumThreads` to 2.). I assume this issue is addressed in the newer releases but since I can't compile it on my own I'm stuck. Also: If I compile the .so files with NDK 19, can I use that in my NDK 25 > project?", "> C/C++: ld: error: undefined symbol: tflite::impl::Interpreter::Invoke()\r\n\r\nThat symbol is supposed to be defined in the file tensorflow/lite/core/interpreter.cc.\r\n\r\nThere have been a few changes related to this recently, with the implementation of this method recently moving\r\n(a) from the \"tflite\" namespace to the \"tflite::impl\" namespace and\r\n(b) from tensorflow/lite/interpreter.cc to tensorflow/lite/core/interpreter.cc\r\n\r\nI suspect the problem may be related to one of those, probably (b).\r\n", "@fergushenderson Do you have any suggestions I can try in the meantime? Which branch do you suggest I use?", "This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n", "Hi, Could you please try to build against the latest stable branch `2.11` and let us know if it is working for you.\r\n`2.12` is not yet released, till `2.12` is available you could build it against the last stable branch which is `2.11`. Thanks!", "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/59692\">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/59692\">No</a>\n", "@sachinprasadhs the issue was not fixed since then it seems\r\nthis commit https://github.com/tensorflow/tensorflow/commit/734de9e767fdd81d67bd47a14a1281b9736c8544#diff-7456c1d9f62f2c8c05621072dba61fc0db513a09331f60f9601d2e610c114dda updated the ARM toolchain to 11.3 Rel1 - however, according to \r\nhttps://bugs.linaro.org/show_bug.cgi?id=5825#c34\r\n```\r\nThe issues was only fixed in the baremetal builds with 11.3 Rel1. Unfortunately the aarch64-none-linux-gnu and arm-none-linux-gnueabihf targets are still affected by the same underlying issue in that release.\r\n```\r\nso I'm assuming it is still broken on latest master - but it is definitely broken in 2.12rc0, which is what I'm trying to build.\r\n\r\nI'm trying swapping the toolchain to arm-gnu-toolchain-12.3.rel1-aarch64-arm-none-linux-gnueabihf, the build seems to be progressing as of now.\r\n\r\nIf it builds and runs, would you like me to make a PR? Do you have a CI test for this in place?", "You can go ahead and make the changes." ]

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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/59691/checks?check_run_id=11352869680) 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.", "@Layconnn Can you please sign CLA. Thank you!", "Hi @Layconnn I can see no changes in file. Hence closing the PR. Thanks you!", "Yeah, this seems spam" ]

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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/59689/checks?check_run_id=11343709078) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request." ]

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[ "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/59688/checks?check_run_id=11342188340) 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 @Horlaboi Can you please sign CLA. Thank you!" ]

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[ "@matthiaskramm for visibility (can't seem to assign you)", "Thanks for the detailed bug report! I'll take a look. (Give me a few days, I'm in the middle of another thing.)", "I can't find a pass that would already do this (take the exported_names and create a wrapper function for each). The one that comes closest is `tensorflow/compiler/mlir/tfrt/transforms/lower_saved_model.cc`, but it duplicates the function(s) in question which I don't think we should.\r\n\r\nWill write a new pass and hook up to `tf-lower-to-mlprogram-and-hlo`.", "This should be fixed now. (In tf-nightly)", "Hi, @dellis23 \r\n\r\nApologize for the delayed response and I was able to replicate the same issue with `tf-nightly` and I have added [gist-file](https://colab.research.google.com/gist/gaikwadrahul8/d4697e78fc93c5b2440e6898cfb00a03/-59685.ipynb) for your reference and May I know have we taken care of your issue in `tf-nightly` please ? Thank you!\r\n\r\nI have added details about `simple-model.mlir` below:\r\n\r\n```\r\nmodule {\r\n func.func @train(%arg0: tensor<f32>) -> tensor<f32> {\r\n %0 = call @__inference_train_3160_internal(%arg0) : (tensor<f32>) -> tensor<f32>\r\n return %0 : tensor<f32>\r\n }\r\n ml_program.global public mutable @vars.__sm_node1__w(dense<1.200000e+01> : tensor<f32>) : tensor<f32>\r\n ml_program.global public mutable @vars.__sm_node2__b(dense<7.000000e+00> : tensor<f32>) : tensor<f32>\r\n func.func private @__inference_train_3160_internal(%arg0: tensor<f32>) -> tensor<f32> {\r\n %0 = ml_program.global_load @vars.__sm_node1__w : tensor<f32>\r\n %1 = stablehlo.add %0, %arg0 : tensor<f32>\r\n ml_program.global_store @vars.__sm_node1__w = %1 : tensor<f32>\r\n %2 = ml_program.global_load @vars.__sm_node1__w : tensor<f32>\r\n %3 = ml_program.global_load @vars.__sm_node2__b : tensor<f32>\r\n %4 = stablehlo.add %3, %arg0 : tensor<f32>\r\n ml_program.global_store @vars.__sm_node2__b = %4 : tensor<f32>\r\n return %2 : tensor<f32>\r\n }\r\n}\r\n```", "@gaikwadrahul8 No worries! Yes, this seems to be fixed in the latest nightly. Thanks.", "Hi, @dellis23 \r\n\r\nThank you for the confirmation and Could you please confirm if this issue is resolved for you ? Please feel free to close the issue if it is resolved ? Thank you!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59685\">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/59685\">No</a>\n" ]

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[ "Hi @codingzebra333 !\r\n I tried to replicate the issue on Colab using TF v2.11 but facing a different error. Could you please provide dependencies to reproduce the issue and find the gist [here](https://colab.research.google.com/gist/tiruk007/b23c955476d80be16eed766de5ba2c8f/untitled122.ipynb) for reference.\r\n\r\nThank you!\r\n", "Hey, \r\nThank you for fast response! [ Here ](https://colab.research.google.com/drive/14zMCm0XaszVrUoWd9bMvBjSXSdPvSWuw?userstoinvite=tirumaleshk%40google.com&actionButton=1#scrollTo=t-P2l2a-9AOm) is a colab file.\r\nI also tested .h5 file and .tflite on a different dataset (400 images) and compared the results:\r\n- .tflite vs .h5 (images that are in a good quality)\r\nMAD: 1.8\r\nRMSE: 1.9\r\n- tflite vs .h5 (same images but in a bad quality)\r\nMAD: 0.3\r\nRMSE: 0.4\r\nTflite model evaluates good images and bad images pretty much in the same way. \r\n", "@pjpratik \r\n \r\nI was able to reproduce the issue on Colab using TFv2.11. please find the gist [here](https://colab.research.google.com/gist/tiruk007/bcd8c781bbe3a2ab376fdb90cd342d8d/untitled132.ipynb) for reference.\r\n\r\nThank you!", "@codingzebra333 Sorry for the delayed response. \r\n\r\nI did try to reproduce the .csv file but facing some error while creating the model. Please find the [gist](https://colab.research.google.com/gist/pjpratik/1db27191830e15d4540b2984484b0983/59684.ipynb). \r\n\r\nThe conversion seems to be fine in both the cases. Also, the error difference from what is [reported](https://github.com/tensorflow/tensorflow/issues/59684#issuecomment-1431121144), specifically for good quality images seems to be on bit higher side.\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.\r\n\r\n", "Hi, thank you for your response! \r\nIf the conversion is fine in both cases, how come the model is unusable because of the terrible accuracy? Post training quantization is supposed to reduce the model accuracy by a little - not make it unusable. Is it because of the model architecture or is there any other possible explanation to this?\r\nAlso, what other solutions would you suggest without having to re-train all models? \r\nRegarding the errors, you just need to change from \"from backbone.resnet50 import ResNet50\" to \"from triq.src.backbone.resnet50 import ResNet50\" - it can't find a model because the path is incorrect.\r\nThank you in advance!", "Hi @codingzebra333 \r\n\r\nI have tested the both tflite model and .h5 model on the sample data provided. The results are\r\n```\r\nFor 2.9 MOS\r\ntflite = 2.8592536100477446\r\n.h5= 2.906168824993074\r\n\r\nFor 2.865 MOS\r\ntflite=2.880292271496728\r\n.h5= 2.742017399519682\r\n```\r\nWhat I see is that tflite results are not bad compared to the MOS infact tflite results are closer than .h5 in second case.\r\nPlease find the gist [here](https://colab.research.google.com/gist/pjpratik/934d2e4f087fd8ac484d3faac3c72b1f/59684.ipynb). I feel it is acceptable but depends on the use case.\r\n\r\nCan you please let us know your observation regarding the same?\r\n\r\n", "Hey, as I mentioned before, I tested it out on a different dataset with 400 images - good and bad quality. I provided the MAD and RMSE scores before. Tflite model evaluates good images and bad images pretty much in the same way. \r\n[Here](https://drive.google.com/drive/folders/1YcCSJgG6R4wRJ5qeU3gUoUd2296GghoL?usp=sharing) is a dataset available for comparison. First MOS score in image name is .h5 result and second one is .tflite result. \r\n\r\n", "Hey, are there any updates regarding this? ", "Hey, is there anything new regarding this?", "Hi, is there anyone who can help me with this? ", "Hi @codingzebra333, can you please share an up to date colab which reproduces the results, including the dataset (or where I can get the dataset) mentioned here, please also include the unconverted and converted model:\r\n\r\n>I also tested .h5 file and .tflite on a different dataset (400 images) and compared the results:\r\n\r\n>.tflite vs .h5 (images that are in a good quality)\r\n>MAD: 1.8\r\n>RMSE: 1.9\r\n>tflite vs .h5 (same images but in a bad quality)\r\n>MAD: 0.3\r\n>RMSE: 0.4\r\n>Tflite model evaluates good images and bad images pretty much in the same way.\r\n\r\nThanks for helping us refresh this issue.", "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/59684\">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/59684\">No</a>\n" ]

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[ "Hi @krittapat-canik Can you please check @cantonios's comments and keep us posted ? Thank you!", "Hi @krittapat-canik Can you please check @cantonios's comments and keep us posted ? 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.", "Hi @krittapat-canik Any update on this PR? Please. Thank you!", "No response from author, and I doubt this will actually save anything significant in practice." ]

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[ "Hi @mikolajpabiszczak Can you please resolve conflicts? Thank you!", "From what I see the Dockerfiles used to build official images were move to [tensorflow/build](https://github.com/tensorflow/build) rendering this PR as out-of-place. Closing." ]

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[ "@penpornk @cantonios " ]

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[ "@angerson", "Introduced by https://github.com/tensorflow/tensorflow/commit/397570c85afa4dacd0a10869a1463bac8872bfd7", "Hi, @elfringham \r\n\r\nThank you for noticing the issue and I see you've submitted PR [#59681](https://github.com/tensorflow/tensorflow/pull/59681) for this issue so this issue will be taken care once PR got merged after following the PR process. I really appreciate your efforts and time. Thank you!", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59680\">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/59680\">No</a>\n" ]

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[ "@PPParticle Thanks for reporting the issue.\r\n\r\nSorry for the delayed response.\r\n\r\nAs per the documentation, [prune_low_magnitude](https://www.tensorflow.org/model_optimization/api_docs/python/tfmot/sparsity/keras/prune_low_magnitude) expects pruning schedule, but a dictionary of pruning schedule is given which can be resolved by either directly passing pruing schedule or getting pruning schedule from dictionary. Also, the callbacks are not called during pruned `model.fit`. \r\n\r\nPlease find the working [gist](https://colab.research.google.com/gist/pjpratik/3b25bca188e4b09526addf1418c197b0/59679.ipynb) and let us know if it helps.\r\n\r\nThe weights are not loaded as they are not provided.\r\n\r\nThanks!", "> \r\nThanks so much for your reply which has solved my problems !! However, I haven't figure out the reason why there are still so many non-trainable parameters after `model.fit()` with `model_for_pruning.summary()`. Maybe I should look up TF guidebook! All in all, thank you so much again !!", "@PPParticle Please feel free to close the issue if it is resolved. 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/59679\">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/59679\">No</a>\n" ]

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[ "Note, the experimental API is not required to reproduce the bug. Just use `force_generator=True` instead.\r\n\r\n```py\r\nimport tensorflow as tf\r\n\r\ntf.keras.mixed_precision.set_global_policy('mixed_float16')\r\n\r\ndropout = tf.keras.layers.Dropout(rate=0.5, force_generator=True)\r\n\r\[email protected](jit_compile=True)\r\ndef run(x):\r\n return dropout(x, training=True)\r\n\r\nprint(run(tf.constant([0.1, 0.2], dtype=tf.dtypes.float32)))\r\n```", "Hi, @AndreasMadsen \r\n\r\nApologize for the delay and I was able to replicate the same issue with `tf-nightly-2.13.0-dev20230216` and `Tensorflow==2.11`, for your reference I have added [gist-file](https://colab.research.google.com/gist/gaikwadrahul8/039b9344253d44c324ee07ccceffb2cf/-59678-tf-nightly.ipynb) but when I tried the same code with Pre Release `tensorflow 2.12.0rc0` and it seems like working fine as expected, for your reference I have added [gist-file-tensorflow-2.12.0rc0\r\n](https://colab.research.google.com/gist/gaikwadrahul8/2448d5ff89748bbbf87f8911ca8ce38f/-59678-tensorflow-2-12-0rc0.ipynb) so Could you please try with `tensorflow 2.12.0rc0` from your end and please let us know whether is it working fine or not ?\r\n\r\nIf issue still persists please let us know ? or Could you please confirm if this issue is resolved for you ? Please feel free to close the issue if it is resolved ? Thank you!", "@gaikwadrahul8 Hi, I can confirm that `2.12.0rc0` works using the docker image on a V100 GPU. Actually running `2.12.0rc0` on our typical system will require some more work as the CUDA and CuDNN have now changed. I have issued an update request to fully check this.\r\n\r\n\r\nAlso, before closing this issue, I would like clarification as to how this works on `2.12.0.rc0` but not the nightly version. The changelog does not appear to mention that float16 support was added to XLA StatelessUniform. \r\n\r\nFinally, I suggest checking that there is no issue `mixed_bfloat16` too.", "This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n", "@google-ml-butler The stalling is not my responsibility and I don't see a reason why this issue should be closed. Maybe you can remove the `stat:awaiting response` label to avoid confusion.", "Hi, @AndreasMadsen \r\n\r\nI'm really sorry, The **google-ml-butler** bot is not working as expected and our team is working on it, I'll remove **stalled** and **stat:awaiting response** labels manually now. Thank you!", "Hi, @AndreasMadsen \r\n\r\nI was able to replicate the same issue with `tf-nightly` and `tensorflow==2.12.0rc0` and It seems like, it's working as expected and in new release of `TF2.12 `this issue will be resolved, for your reference I have added [gist-file ](https://colab.research.google.com/gist/gaikwadrahul8/696769f161928a97c4272729e86e84fa/-59678-test.ipynb). Thank you!", "@gaikwadrahul8 Sorry, you were able to replicate the issue `tf-nightly` and `tensorflow==2.12.0rc0`? How is that \"working as expected\"?\r\n\r\nThe desired result is that you can **not** replicate the issue on `tf-nightly` and `2.12rcX`. Maybe that is what you meant.\r\n\r\nGreat to hear that it will be fixed on 2.12. I'm just confused about the first comment.", "Hi, @AndreasMadsen \r\n\r\nApologize for the delay and I have tested the same code with `tf-nightly `and `tensorflow==2.12.0rc0` and It seems like it's working as expected(without any erros) for your reference here is [gist-file](https://colab.research.google.com/gist/gaikwadrahul8/696769f161928a97c4272729e86e84fa/-59678-test.ipynb) so you can use either `tf-nightly` or `tensorflow==2.12.0rc0` at the moment and we don't support all the `ops` with XLA at the moment, our developer team is working on it and it's in progress so your issue will be fixed in upcoming stable version `TF2.12 `. \r\n\r\nIf issue still persists with `tf-nightly` or `tensorflow==2.12.0rc0` please let us know or Could you please confirm if this issue is resolved for you ? Please feel free to close the issue if it is resolved ? Thank you!", "Thanks for the help, I truly appreciate it.", "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/59678\">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/59678\">No</a>\n" ]

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[ "@kasrash \r\nI tried to replicate the issue on Colab using TF v2.11 but facing a different problem. Please find the gist [here](https://colab.research.google.com/gist/tiruk007/b415002491a70d80b4181d00aeef3369/untitled123.ipynb) for reference.\r\nCould you please provide all dependencies to replicate the issue reported here ?\r\n\r\nThank you!", "> @kasrash I tried to replicate the issue on Colab using TF v2.11 but facing a different problem. Please find the gist [here](https://colab.research.google.com/gist/tiruk007/b415002491a70d80b4181d00aeef3369/untitled123.ipynb) for reference. Could you please provide all dependencies to replicate the issue reported here ?\r\n> \r\n> Thank you!\r\n\r\nHi Tiruk007,\r\n\r\nThank you for checking this. I created a gist at [here](https://colab.research.google.com/gist/kasrash/5ad0b2e8df48b288c82d98be7b59803b/tferrorreplicate.ipynb), and you can download the required data [here](https://drive.google.com/drive/folders/1IG5q0U1xCqVjpC_35F4j0uER5rackoNK?usp=share_link). However, I had to reduce the model size so that it won't run out of Ram. As you can see the issue is not created in the Colab. I am not sure if some of the outputs are suppressed there or there is an issue with my TF installation. I would be grateful to know your thoughts.\r\n", "@kasrash \r\nSorry for the late reply. I tried to execute the given code on a Linux machine using 2.11 where I got 6.47 seconds for each step. Please find the [screenshot](https://user-images.githubusercontent.com/111861663/219692401-c6e1b83e-b0c4-4a9c-a845-7af47412d5f0.png) for reference.Could you please make sure to check the tested build configuration as mentioned [here](https://www.tensorflow.org/install/source#tested_build_configurations) and use the latest TF version.\r\n\r\nThank you !\r\n\r\n", "> @kasrash Sorry for the late reply. I tried to execute the given code on a Linux machine using 2.11 where I got 6.47 seconds for each step. Please find the [screenshot](https://user-images.githubusercontent.com/111861663/219692401-c6e1b83e-b0c4-4a9c-a845-7af47412d5f0.png) for reference.Could you please make sure to check the tested build configuration as mentioned [here](https://www.tensorflow.org/install/source#tested_build_configurations) and use the latest TF version.\r\n> \r\n> Thank you !\r\n\r\n@tiruk007 hi\r\n\r\nThank you so much for testing this. I see that your not getting the same error as I am getting. I will check my installation, but I installed TF 2.11 using pip and the installation guide on the website. Nevertheless, don't you thin that 6.47 s for this toy example is too high? Since this is almost the same logic as model.fit() and that takes around ms to complete each step. Is there any way to speed up this process?", "Hello,\r\nJust an update on this, I tried the code and a MNIST toy model on several machines (Windows and Ubuntu) with different versions of TF (2.9 and 2.11) all give the same error.\r\n\r\nChanging the activation from tanh to sigmoid or relu solves the issue, and the performance is blazing fast. Of course CuDNN will not be utilized.\r\n\r\nHope it helps in solving this issue and I would be grateful to know your inputs.", "Also, [here](https://colab.research.google.com/gist/kasrash/ba04bd4f82e3de6423d67f828083f03b/untitled2.ipynb) is a gist of toy MNIST model. If you go to Runtime->Runtime log you can see the error.", "Hi, @kasrash \r\n\r\nI have tried to execute the same code from gist-file of toy MNIST model with `tanh` activation function and it's working as expected here is [gist-file](https://colab.research.google.com/gist/gaikwadrahul8/09be3a67beeb4957871cefb232efb964/-59677-test.ipynb) and even I checked the runtime error log in that all I'm getting warning messages as far I know we can ignore those warnings messages unless and until the code is running as expected and giving the correct result. You can refer [stack overflow answers ](https://stackoverflow.com/questions/35911252/disable-tensorflow-debugging-information)which also supports \r\n\r\nIf you're looking to disable `absl INFO and WARNING log messages `please use below code snippet:\r\n\r\n```\r\nimport absl.logging\r\nabsl.logging.set_verbosity(absl.logging.ERROR)\r\n\r\n```\r\nHere is screenshot of Google Colab runtime error log :\r\n\r\n\r\n\r\n\r\nIf I've missed something here please let me know ? Just to confirm, May I know your initial issue slow performance with tf.fuction got resolved or not or do you still need help with that issue please ? Thank you!\r\n", "Hi @gaikwadrahul8,\r\n\r\nThat is true that it is a warning, but when I searched for this message on Google, I found several bug reports here dating back to 2019 and 2020, which TF team confirmed that it was actually a bug. Also, with \"tanh\" activation on my main code, the TF GradiantTape Jacobian (decorated with tf.function) calculation takes ~13 s, but when changing it to relu or sigmoid activations, it only takes ~0.9 s when decorated with tf.function and ~15 s without tf.function. So, I am guessing definitely something (maybe odd )is happening when tanh activation is used as it should be faster since it use CuDNN kernel.\r\n\r\nI would be grateful to know your thoughts on this, and many thanks for your time.", "Hi, @kasrash \r\n\r\nThank you for your detailed observations and if possible could you please help us with issues number which we considered warnings as bug back to 2019 and 2020 ?\r\n\r\nIf you're using `tf.function`,the behavior of `tf.function `when `AutoGraph `is used by default `AutoGraph==True `and `@tf.function` decorated function behaves as we expect if we use a `tf.Tensor `as input type (please use `tf.Tensor `everywhere!) and if the input is a Python native type creates a new graph. You can refer this [stack overflow answer](https://stackoverflow.com/questions/55711115/tensorflow-2-0-model-using-tf-function-very-slow-and-is-recompiling-every-time-t) and also refer these useful article links [Ref-1](https://pgaleone.eu/tensorflow/tf.function/2019/04/03/dissecting-tf-function-part-2/#using-a-python-native-type), [Ref-2](https://pgaleone.eu/tensorflow/tf.function/2019/05/10/dissecting-tf-function-part-3/), I hope it will help you to resolve your issue. Thank you!\r\n\r\n", "> Hi, @kasrash\r\n> \r\n> Thank you for your detailed observations and if possible could you please help us with issues number which we considered warnings as bug back to 2019 and 2020 ?\r\n> \r\n> If you're using `tf.function`,the behavior of `tf.function `when `AutoGraph `is used by default `AutoGraph==True `and `@tf.function` decorated function behaves as we expect if we use a `tf.Tensor `as input type (please use `tf.Tensor `everywhere!) and if the input is a Python native type creates a new graph. You can refer this [stack overflow answer](https://stackoverflow.com/questions/55711115/tensorflow-2-0-model-using-tf-function-very-slow-and-is-recompiling-every-time-t) and also refer these useful article links [Ref-1](https://pgaleone.eu/tensorflow/tf.function/2019/04/03/dissecting-tf-function-part-2/#using-a-python-native-type), [Ref-2](https://pgaleone.eu/tensorflow/tf.function/2019/05/10/dissecting-tf-function-part-3/), I hope it will help you to resolve your issue. Thank you!\r\n\r\nHi @gaikwadrahul8 \r\n\r\nPlease see issues [#35928](https://github.com/tensorflow/tensorflow/issues/35928), [#41129](https://github.com/tensorflow/tensorflow/issues/41129), and [#41949](https://github.com/tensorflow/tensorflow/issues/41949). There was also a PR ([here](https://github.com/tensorflow/tensorflow/commit/3a03164a24ea7dbfe221e84ca62e2a39b745c379), which not sure if it is relevant.\r\n\r\nThank you for the suggestion. But, the input to the function decorated with @tf.function is a Tensor. Also, it seems odd to me that this is happening only when using tanh activation (hence, CuDNNLSTM), but not with other activations (i.e., not CuDNNLSTMs).", "Hi, @kasrash\r\n\r\nThank you for sharing those similar issues, if possible could you please help me with your complete code snippet in Google Colab notebook or as gist-file with all imports so I'll test it from my end because I tested above toy `MNIST` model it's working fine with `tanh` activation functions ? also please help me with Tensorlflow, Python versions ? I hope you're using `cudatoolkit=11.2` and `cudnn=8.1.0 `which is tested configurations as per our [official documentation ](https://www.tensorflow.org/install/source#gpu). \r\n\r\nYou can also refer our official documentation for [Speeding-up your training step with tf.function](https://www.tensorflow.org/guide/keras/writing_a_training_loop_from_scratch#speeding-up_your_training_step_with_tffunction) and You can also try [tf.function(jit_compile=True)](https://www.tensorflow.org/api_docs/python/tf/function) for a more significant performance boost, especially if your code is heavy on TensorFlow control flow and uses many small tensors. Learn more in the _Explicit compilation with [tf.function(jitcompile=True)](https://www.tensorflow.org/api_docs/python/tf/function) section of the [XLA overview](https://www.tensorflow.org/xla). Thank you!\r\n\r\n", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59677\">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/59677\">No</a>\n" ]

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[ "@myplc,\r\nCould you please post this issue in [tensorflow/tfjs](https://github.com/tensorflow/tfjs/issues) repo to get the right help there?\r\nThanks!", "This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n", "Closing as stale. Please reopen if you'd like to work on this further.\n", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59676\">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/59676\">No</a>\n" ]

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[ "v0.8 is not available at https://github.com/NVIDIA/cudnn-frontend at the main branch. I cannot proceed until it has been updated.", "> v0.8 is not available at https://github.com/NVIDIA/cudnn-frontend at the main branch. I cannot proceed until it has been updated.\r\n\r\nThanks for the comment. Yes, we are working on releasing it soon. And this PR will be updated accordingly. Will ping you after that.", "There was an update ~1.5 days ago. Is that the final update to v0.8? Or are more changes expected?", "@pgpetrak Thanks for the checkup. I have pushed a new commit to use the official v0.8 release and updated the PR description to reflect this. PTAL.", "Basically, there are two changes of this PR: (1) update the download url and (2) update the patch file.\r\n\r\nThen, I checked the above automatically created PR https://github.com/openxla/xla/pull/1589, and saw one test \"XLA Linux GPU\" fails.\r\n\r\nIt seems that the above PR only fetches the change (2) and I don't know how to make it to apply the change (1) as well (I guess it uses a different way to download the zip rather than `workspace2.bzl`.).\r\n\r\n@reedwm Can you help or redirect?", "> Basically, there are two changes of this PR: (1) update the download url and (2) update the patch file.\r\n> \r\n> Then, I checked the above automatically created PR [openxla/xla#1589](https://github.com/openxla/xla/pull/1589), and saw one test \"XLA Linux GPU\" fails.\r\n> \r\n> It seems that the above PR only fetches the change (2) and I don't know how to make it to apply the change (1) as well (I guess it uses a different way to download the zip rather than `workspace2.bzl`.).\r\n> \r\n> @reedwm Can you help or redirect?\r\n\r\n@jakeharmon8 do you know how to fix this? I think this PR also needs to update the [`workspace2.bzl` in the openxla/xla repo](https://github.com/openxla/xla/blob/main/workspace2.bzl), in addition to the `workspace2.bzl` that it already updates in the TF repo. But a PR can only update one repo at once. When merging internally, should we update both `workspace2.bzl`s in the same CL?\r\n", "Is there an ETA on the merge for this PR. https://github.com/tensorflow/tensorflow/pull/60002 build is dependent on this PR being merged.", "@pgpetrak I guess you're still the reviewer for this? I'm going to remove myself as reviewer if you can take care of this.", "@reedwm Yes, you're correct. Tensorflow isn't using the OpenXLA version of XLA (yet! we should have that working soon). Because of that, we have to change both workspace files at the same time, which can only be done with an internal CL." ]

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[ "Thanks @tpopp for your review." ]

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[ "This change (from compare+select operations, to a min/max operation) can improve implementation speed and size on some hardware platforms." ]

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[ "In this gist, I have removed the dependency on `transformers` and a large amount of irrelevant code:\r\nV1: https://gist.github.com/AndreasMadsen/dc5785b5a55bf740c555b2fb5cdab1db (~600 LOC)\r\nV2: https://gist.github.com/AndreasMadsen/5fdaa8431929e25cf3a990f234f88a8c (update, more code removed. ~450 LOC)\r\nV3: https://gist.github.com/AndreasMadsen/2590423c055bc47a932a449a5161bac7 (update, more code removed. 209 LOC)\r\n\r\nI have tried to reduce the code further, but this causes the bug to disappear.", "@pjpratik I think you should add the `comp:xla` label too. As this bug only occurs when XLA is enabled.", "There is no failure with the reproduction script on `2.12.0rc0`. However, given how difficult this bug is to reproduce (e.g. changing just the seed of the shuffle function will make it disappear). I can't tell if this solves the issue consistently.", "The error appears to be specific to V100, as it does not reproduce on T4, A100, and P100 GPUs. I have however reproduced it on two different systems running V100. It is possible this is related to the specific memory size of the GPU.", "Hi, @AndreasMadsen \r\n\r\nApologize for the delay and Thank you for your detailed observations and efforts to find out the root cause for this issue and We'll try it from our end and will check whether this issue is specific to V100 GPU or not and we'll update you soon here. Thank you for noticing this issue. Thank you!", "@gaikwadrahul8 Thanks for looking into this. It did take me several days to make this error reproducible. So I hope it will help you. I suspect it's a rather complex integration bug, as removing pretty much any line in the latest (V3) script cause the bug to disappear. ", "Hi, @AndreasMadsen \r\n\r\nApologize for the delay and I was able to replicate the issue on `Tesla V100 GPU` and I was getting the exact same error message which you mentioned in your error log output above with `tensorflow/tensorflow:latest-gpu` docker image which is using `Tensorflow==2.11`, I have added screenshot for your reference below and even tested the same code on Google Colab and it's working fine as expected here is [gist-file](https://colab.research.google.com/gist/gaikwadrahul8/caf4e40e4ef5c295f9c0b1defcacc63a/-59671.ipynb), I did not run for complete epoch because it was taking more time but code was running as expected but as you confirmed it's working fine on all other GPU's so this issue seems like specific to `Tesla V100 GPU` at the moment. Thank you!\r\n\r\nWhen I tried the same code with `tensorflow/tensorflow:2.12.0rc0-gpu` and `tensorflow/tensorflow:nightly-gpu` on `Tesla V100 GPU` it's working as expected, I have added screenshot below\r\n\r\nHere is screenshot for code execution with `tensorflow/tensorflow:latest-gpu` docker image\r\n\r\n\r\n\r\nHere is screenshot for code execution on Google-Colab :\r\n\r\n\r\n\r\nHere is screenshot for code execution with `tensorflow/tensorflow:2.12.0rc0-gpu` docker image:\r\n\r\n\r\n\r\nHere is screenshot for code execution with `tensorflow/tensorflow:nightly-gpu` docker image:\r\n\r\n\r\n\r\n\r\n", "Hi @gaikwadrahul8, you didn't ask a question, so I'm not sure what you want me to respond to.\r\n\r\nI agree with your findings. Given how sensitive the bug is in terms of reproduction, I would not trust that it is something specific to V100 TF 2.11. I hope you will be able to debug this further and get to the resolve bug.", "Note, that this kind of bug has been reported quite a few times before, but I think most issues were not able to provide a reproduction script. So I hope you will find this script quite useful.\r\n\r\nISSUE: https://github.com/tensorflow/tensorflow/issues/50735\r\n\r\nI remember there were more closed issues, when I was researching solutions.\r\n\r\n", "Hi. @AndreasMadsen \r\n\r\nThank you for mentioning the similar issue, It seems like we have to dig more into this issue to find out the root cause and we'll update you soon and I was able to replicate the exact same issue with error log with `tensorflow/tensorflow:latest-gpu ` docker image that's the reason I did not ask any questions to you if we need any input or further details from your end certainly we will ask you. Thank you for noticing this issue, I really appreciate your efforts and time. Thank you!", "@gaikwadrahul8 Thanks for letting me know. I'm was just confused about the `stat:awaiting response` label.", "Hi, @sachinprasadhs \r\n\r\nCould you please look into this issue ? Thank you!", "Hi @sachinprasadhs, did you manage to investigate this? ", "Hi @AndreasMadsen , Apologies for the delayed response, based on the issue thread so far, it is difficult to point to an area why this is happening.\r\nBut you could try below steps and see if that works.\r\n1. Try reinstalling the CUDA and cuDNN dependency, in some cases it could be due to improper installation of these dependencies.\r\n2. Try setting the environmental variable `TF_FORCE_GPU_ALLOW_GROWTH `to `true`\r\n\r\nIf the above steps did not work could you please run the model with the env var `CUDA_LAUNCH_BLOCKING` set to `1` and attach the full logs?", "@sachinprasadhs Thanks for your response. Please note that I spent several days reducing this to a script that reproduces the issue. I think you can benifit tremendously from using that. Furthermore, @gaikwadrahul8 confirms that the issue could be reproduced with a V100 GPU.\r\n\r\n1. Try reinstalling the CUDA and cuDNN dependency, in some cases it could be due to improper installation of these dependencies.\r\n\r\nLet's not go down that route, since @gaikwadrahul8 was able to reproduce the issue on Google's infrastructure.\r\n\r\n2. Try setting the environmental variable `TF_FORCE_GPU_ALLOW_GROWTH` to `true`.\r\n\r\nSame issue. Expand the `<details>` to see the log.\r\n\r\n<details>\r\n<summary><code>TF_FORCE_GPU_ALLOW_GROWTH=true python main.py</code></summary>\r\n\r\n```\r\n2023-03-15 11:55:41.321502: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-03-15 11:55:41.504643: I tensorflow/core/util/port.cc:104] 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\n2023-03-15 11:55:42.479709: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\r\n2023-03-15 11:55:42.480077: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\r\n2023-03-15 11:55:42.480113: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\r\n2023-03-15 11:55:43.729040: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-03-15 11:55:44.593353: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\r\n2023-03-15 11:55:44.593541: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 30971 MB memory: -> device: 0, name: Tesla V100-SXM2-32GB, pci bus id: 0000:18:00.0, compute capability: 7.0\r\n2023-03-15 11:56:14.842711: I tensorflow/compiler/xla/service/service.cc:173] XLA service 0x20192630 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\r\n2023-03-15 11:56:14.842838: I tensorflow/compiler/xla/service/service.cc:181] StreamExecutor device (0): Tesla V100-SXM2-32GB, Compute Capability 7.0\r\n2023-03-15 11:56:15.808448: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\r\n2023-03-15 11:56:15.816882: W tensorflow/compiler/tf2xla/kernels/random_ops.cc:57] Warning: Using tf.random.uniform with XLA compilation will ignore seeds; consider using tf.random.stateless_uniform instead if reproducible behavior is desired. tf_roberta_for_sequence_classification/layer_._0/self/dropout/dropout/random_uniform/RandomUniform\r\n2023-03-15 11:57:26.919108: I tensorflow/compiler/xla/stream_executor/gpu/asm_compiler.cc:325] ptxas warning : Registers are spilled to local memory in function 'sort_9'\r\nptxas warning : Registers are spilled to local memory in function 'fusion_1107'\r\n\r\n2023-03-15 11:57:27.290497: I tensorflow/compiler/jit/xla_compilation_cache.cc:477] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\r\n2023-03-15 11:57:27.774582: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:1159] failed to enqueue async memcpy from device to host: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered; host dst: 0x2b4b7e184eeb; GPU src: 0x2b4e0836e500; size: 1=0x1\r\n2023-03-15 11:57:27.774751: I tensorflow/compiler/xla/stream_executor/stream.cc:2485] INTERNAL: Unknown error\r\n2023-03-15 11:57:27.774869: I tensorflow/compiler/xla/stream_executor/stream.cc:2489] [stream=0x1c1751e0,impl=0x5e7c020] INTERNAL: stream did not block host until done; was already in an error state\r\nTraceback (most recent call last):\r\n File \"/scratch/anmadc/workspace/economical-roar/main.py\", line 209, in <module>\r\n model.fit(dataset_train_batched, verbose=1, epochs=1)\r\n File \"/localscratch/anmadc.62577500.0/env/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 70, in error_handler\r\n raise e.with_traceback(filtered_tb) from None\r\n File \"/localscratch/anmadc.62577500.0/env/lib/python3.10/site-packages/tensorflow/python/eager/execute.py\", line 52, in quick_execute\r\n tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\r\ntensorflow.python.framework.errors_impl.InternalError: Graph execution error:\r\n\r\nDetected at node 'StatefulPartitionedCall' defined at (most recent call last):\r\n File \"/scratch/anmadc/workspace/economical-roar/main.py\", line 209, in <module>\r\n model.fit(dataset_train_batched, verbose=1, epochs=1)\r\n File \"/localscratch/anmadc.62577500.0/env/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n File \"/localscratch/anmadc.62577500.0/env/lib/python3.10/site-packages/keras/engine/training.py\", line 1650, in fit\r\n tmp_logs = self.train_function(iterator)\r\n File \"/localscratch/anmadc.62577500.0/env/lib/python3.10/site-packages/keras/engine/training.py\", line 1249, in train_function\r\n return step_function(self, iterator)\r\n File \"/localscratch/anmadc.62577500.0/env/lib/python3.10/site-packages/keras/engine/training.py\", line 1233, in step_function\r\n outputs = model.distribute_strategy.run(run_step, args=(data,))\r\nNode: 'StatefulPartitionedCall'\r\nFailed to retrieve branch_index value on stream 0x1c1751e0: stream did not block host until done; was already in an error state.\r\n\t [[{{node StatefulPartitionedCall}}]] [Op:__inference_train_function_37296]\r\n```\r\n</details>\r\n\r\n\r\n<details>\r\n<summary><code>CUDA_LAUNCH_BLOCKING=1 TF_FORCE_GPU_ALLOW_GROWTH=true python main.py</code></summary>\r\n\r\n```\r\n2023-03-15 11:57:59.121539: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-03-15 11:57:59.307027: I tensorflow/core/util/port.cc:104] 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\n2023-03-15 11:58:00.281216: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\r\n2023-03-15 11:58:00.281601: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\r\n2023-03-15 11:58:00.281646: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\r\n2023-03-15 11:58:01.543891: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-03-15 11:58:02.412447: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\r\n2023-03-15 11:58:02.412647: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 30971 MB memory: -> device: 0, name: Tesla V100-SXM2-32GB, pci bus id: 0000:18:00.0, compute capability: 7.0\r\n2023-03-15 11:58:32.523032: I tensorflow/compiler/xla/service/service.cc:173] XLA service 0x1fb485b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\r\n2023-03-15 11:58:32.523516: I tensorflow/compiler/xla/service/service.cc:181] StreamExecutor device (0): Tesla V100-SXM2-32GB, Compute Capability 7.0\r\n2023-03-15 11:58:33.492464: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\r\n2023-03-15 11:58:33.500937: W tensorflow/compiler/tf2xla/kernels/random_ops.cc:57] Warning: Using tf.random.uniform with XLA compilation will ignore seeds; consider using tf.random.stateless_uniform instead if reproducible behavior is desired. tf_roberta_for_sequence_classification/layer_._0/self/dropout/dropout/random_uniform/RandomUniform\r\n2023-03-15 11:59:44.579959: I tensorflow/compiler/xla/stream_executor/gpu/asm_compiler.cc:325] ptxas warning : Registers are spilled to local memory in function 'sort_9'\r\nptxas warning : Registers are spilled to local memory in function 'fusion_1107'\r\n\r\n2023-03-15 11:59:44.945238: I tensorflow/compiler/jit/xla_compilation_cache.cc:477] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\r\nTraceback (most recent call last):\r\n File \"/scratch/anmadc/workspace/economical-roar/main.py\", line 209, in <module>\r\n model.fit(dataset_train_batched, verbose=1, epochs=1)\r\n File \"/localscratch/anmadc.62577500.0/env/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 70, in error_handler\r\n raise e.with_traceback(filtered_tb) from None\r\n File \"/localscratch/anmadc.62577500.0/env/lib/python3.10/site-packages/tensorflow/python/eager/execute.py\", line 52, in quick_execute\r\n tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\r\ntensorflow.python.framework.errors_impl.InternalError: Graph execution error:\r\n\r\nDetected at node 'StatefulPartitionedCall' defined at (most recent call last):\r\n File \"/scratch/anmadc/workspace/economical-roar/main.py\", line 209, in <module>\r\n model.fit(dataset_train_batched, verbose=1, epochs=1)\r\n File \"/localscratch/anmadc.62577500.0/env/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\r\n return fn(*args, **kwargs)\r\n File \"/localscratch/anmadc.62577500.0/env/lib/python3.10/site-packages/keras/engine/training.py\", line 1650, in fit\r\n tmp_logs = self.train_function(iterator)\r\n File \"/localscratch/anmadc.62577500.0/env/lib/python3.10/site-packages/keras/engine/training.py\", line 1249, in train_function\r\n return step_function(self, iterator)\r\n File \"/localscratch/anmadc.62577500.0/env/lib/python3.10/site-packages/keras/engine/training.py\", line 1233, in step_function\r\n outputs = model.distribute_strategy.run(run_step, args=(data,))\r\nNode: 'StatefulPartitionedCall'\r\nFailed to launch CUDA kernel: fusion_1428 with block dimensions: 128x1x1 and grid dimensions: 1280x1x1: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered\r\n\t [[{{node StatefulPartitionedCall}}]] [Op:__inference_train_function_37296]\r\n```\r\n</details>", "Thank you @AndreasMadsen for the short script to reproduce and for removing the `transformers` dependency to keep the example simpler.\r\n\r\nI can reproduce the issue outside docker with CUDA 11.2.0 using the pre-built TF 2.11 pip package. However, the issue disappears when I use CUDA 11.8.0 using the same pip package (the pip package is built with CUDA 11.2 but applications built for one version of CUDA also work for future minor versions of CUDA). So I'm guessing this was a bug with CUDA 11.2 that was fixed somewhere between CUDA 11.2.1 and 11.8.0. Since the TF nightly pip packages are now built with CUDA 11.8, I think this issue is fixed so I'm closing this issue.\r\n\r\nIt's possible this is a race condition in TF instead of a bug in CUDA, and that updating the CUDA version changes some timings which causes the bug not to occur. But I think this is unlikely, so unless we later see CUDA_ERROR_ILLEGAL_ADDRESS errors in other models, we should consider this issue fixed.\r\n\r\n", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59671\">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/59671\">No</a>\n" ]

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[ "any one could help on this?\r\n", "Hi, @ysong2123 \r\n\r\nApologize for the delay and it seems like the problem is due to SSL certificate on your machine and at the moment I'm providing some useful links which will help you to solve your SSL certificate problem [Ref-1](https://docs.mend.io/en-US/bundle/wsk/page/how_to_resolve_error_message__pkix_path_building_failed__sun_security_provider_certpath_suncertpathbuilderexception__unable_to_find_valid_certification_path_to_requested_target_.html), [Ref-2](https://stackoverflow.com/questions/21076179/pkix-path-building-failed-and-unable-to-find-valid-certification-path-to-requ#:~:text=28-,Mac,-The%20accepted%20answer), [Ref-3](https://stackoverflow.com/questions/6908948/java-sun-security-provider-certpath-suncertpathbuilderexception-unable-to-find#:~:text=And%20if%20you%20are%20here%20in%202022%20and%20are%20on%20mac%20follow%20this) so please try to follow workaround from those references and please let me know is it resolving your issue?\r\n\r\nJust to confirm are you looking to install `Tensorflow 2.11` build from source or you just want to install Bazel on Macos ? I hope you're following this [official documentation](https://www.tensorflow.org/install/source#macos) to install Tensorflow build from source\r\n\r\nI'll also try to install `Tensorflow 2.11` build from source on `MacOS` once you confirm that you're looking to install `Tensorflow 2.11 `build from source so please confirm it ? Thank you!", "Hi Gaikwadrahul8,\r\nYes, i strictly followed the steps in the official doc, after git clone tensorflow, then cd to root directory, then try:\r\n'bazel build //tensorflow/tools/pip_package:build_pip_package' (all prerequisites have been installed in advance), finally i got the certificate issue above.\r\nBTW, the Ref-1,2,3 you shared above are talking about java home, may i know where's the java home for bazel build? i already tried to export certificate and added into my local macOS java home keystore, but i still got the same ssl exception.", "Hi, @ysong2123 \r\n\r\nThank you for the details and 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\nIf you have already done above steps and installed Bazel correctly then please execute below steps to see the Bazel or Bazelisk location and if possible please help me with those details \r\n\r\n```\r\n1. whereis bazel\r\n2. whereis bazelisk\r\n```\r\n\r\nI'll try to install Tensorflow on my MacOS from my end till then by following our official documentation and I'll update the same here. Thank you!", "ysong@LM-SHB-40523659 tensorflow % whereis bazel\r\nbazel: /opt/homebrew/bin/bazel\r\nysong@LM-SHB-40523659 tensorflow % whereis bazelisk\r\nbazelisk: /opt/homebrew/bin/bazelisk\r\nysong@LM-423659 tensorflow % \r\nysong@LM-423659 tensorflow % bazel version\r\nBazelisk version: development\r\nAnother command (pid=24928) is running. Waiting for it to complete on the server (server_pid=24932)...\r\nBuild label: 5.3.0\r\nBuild target: bazel-out/darwin_arm64-opt/bin/src/main/java/com/google/devtools/build/lib/bazel/BazelServer_deploy.jar\r\nBuild time: Tue Aug 23 01:07:52 2022 (1661216872)\r\nBuild timestamp: 1661216872\r\nBuild timestamp as int: 1661216872\r\n\r\nMy new finding:\r\nafter run cmd in tensorflow root directory: \r\n'bazel build //tensorflow/tools/pip_package:build_pip_package', \r\nThen i found there's a jdk env auto-generated under path:\r\n/private/var/tmp/_bazel_ysong2/install/3c4a7a3c33b4c067b191587b066e5f1b/embedded_tools/jdk/\r\n\r\nthis JDK is isolated with my local JDK env. so i wrote a simple java demo tried to make a java ssl call to get tensorflow dependencies. If i use my local JDK(openjdk8 or openjdk11) i can get those dependencies with no any exception. \r\nBut when i pointed to /private/var/tmp/_bazel_ysong2/install/3c4a7a3c33b4c067b191587b066e5f1b/embedded_tools/jdk/, \r\ni got the same ssl issue. \r\n\r\nBesides, i tried to copy my local jdk to /private/var/tmp/_bazel_ysong2/install/3c4a7a3c33b4c067b191587b066e5f1b/embedded_tools/jdk/, then i got Fatal error：\r\nFATAL: corrupt installation: file '/var/tmp/_bazel_ysong2/install/3c4a7a3c33b4c067b191587b066e5f1b/embedded_tools/jdk/ASSEMBLY_EXCEPTION' is missing or modified. Please remove '/var/tmp/_bazel_ysong2/install/3c4a7a3c33b4c067b191587b066e5f1b' and try again.\r\n\r\nIs there any way to adopt my local jdk for bazel build?", "i solved this problem using :\r\nhttps://bazel.build/install/compile-source#bootstrap-unix. \r\nI commented 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 using this to build tensorflow, no any exception now\r\n", "Hi, @ysong2123 \r\n\r\nGood to hear that you're able to resolve your issue and even I was able to install `Tensorflow` build from source with `Bazelisk` approach and it seems like in your case there was some conflict between local JDK and Bazel embedded JDK and it got resolve by this command `env EXTRA_BAZEL_ARGS=\"--tool_java_runtime_version=local_jdk\" bash ./compile.sh`\r\n\r\nI have added screenshot below for your reference with `Bazelisk` Approach:\r\n\r\n\r\n\r\nCould you please confirm if this issue is resolved for you ? Please feel free to close the issue if it is resolved ? if you need any further assistance please let us know? Thank you!\r\n\r\n", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59669\">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/59669\">No</a>\n" ]

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[ "I've tried building with `bazel build -c opt //tensorflow/lite:libtensorflowlite.so --fat_apk_cpu=arm64-v8a` but now it says `<path to .so> is not compatible with aarch64linux` . I couldn't build with `bazel build -c opt //tensorflow/lite:libtensorflowlite.so --cpu=arm64-v8a` because I got `cc_toolchain_suite rule @local_config_cc//:toolchain: cc_toolchain_suite '@local_config_cc//:toolchain' does not contain a toolchain for cpu 'arm64-v8a'` error.", "Hi @turgut-baba, Please find the latest official pre release of TF v2.12 [here](https://github.com/tensorflow/tensorflow/tree/v2.12.0-rc0) and confirm if you are trying with the same source. Thank you! ", "I've deleted all the files, git clone again, checkout to the branch you linked, did everything the same, same result.", "Thank you for the update @turgut-baba. It seems like we have to dig more into this issue, we will update soon here. Thanks! ", "Also, I've opened another issue tackling more stuff I've tried: https://github.com/tensorflow/tensorflow/issues/59692", "Hi, @pjpratik \r\n\r\nCould you please look into this issue ?", "Hi @turgut-baba, sorry for delayed response.\r\n\r\nAs suggested [here](https://github.com/tensorflow/tensorflow/issues/59692#issuecomment-1451090438), can you please build on latest stable branch 2.11 and let us know if the issue still persists.\r\n\r\nThanks.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59666\">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/59666\">No</a>\n" ]

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[ "@albertvillanova Thanks for reporting the issue.\r\n\r\nI have tried installing `tensorflow==2.3.0` and `tensorboard==2.12` and was able to import tensorflow without any error.\r\n\r\nPlease find the gist [here](https://colab.research.google.com/gist/pjpratik/9d7f805f3e64536f1531cd732c4b3980/59665.ipynb). Could you please verify and confirm the same. Thanks!", "Hi @pjpratik, thanks for your answer and for having tried to reproduce the bug.\r\n\r\nPlease note that I get the error when trying to install tensorflow-2.3.0 in a completely empty Python environment.\r\n\r\nYou are not able to reproduce the bug because in the Colab Python environment, you have `protobuf-3.19.6` already installed from the beginning, which fulfills:\r\n- protobuf>=3.9.2 from tensorflow\r\n- protobuf>=3.19.6 from tensorboard\r\n\r\nand hence it is not updated.\r\n\r\nHowever, if you start with a completely empty Python environment, the requirements above resolve to installing `protobuf-4.21.12` and then you get the bug.\r\n\r\nAlso note that there is no problem with `protobuf-3.20.3`.", "These are the dependency packages installed in an empty Python environment, when installing `tensorflow-2.3.0`:\r\n```\r\nSuccessfully installed MarkupSafe-2.1.2 absl-py-1.4.0 astunparse-1.6.3 cachetools-5.3.0 charset-normalizer-3.0.1 gast-0.3.3 google-auth-2.16.0 google-auth-oauthlib-0.4.6 google-pasta-0.2.0 grpcio-1.51.1 h5py-2.10.0 idna-3.4 importlib-metadata-6.0.0 keras-preprocessing-1.1.2 markdown-3.4.1 numpy-1.18.5 oauthlib-3.2.2 opt-einsum-3.3.0 protobuf-4.21.12 pyasn1-0.4.8 pyasn1-modules-0.2.8 requests-2.28.2 requests-oauthlib-1.3.1 rsa-4.9 scipy-1.4.1 six-1.16.0 tensorboard-2.12.0 tensorboard-data-server-0.7.0 tensorboard-plugin-wit-1.8.1 tensorflow-2.3.0 tensorflow-estimator-2.3.0 termcolor-2.2.0 urllib3-1.26.14 werkzeug-2.2.2 wrapt-1.14.1 zipp-3.13.0\r\n```", "@albertvillanova Thanks for the clarification.\r\n\r\nI was able to reproduce this issue after installing protobuf-4.21.12. Please find the gist [here](https://colab.research.google.com/gist/pjpratik/32e420ffd7e93c16b29cd0c4f6069d66/59665.ipynb).\r\n\r\n@SuryanarayanaY Could you please look into this issue. Thanks!", "Also note that there was no issue before the `tensorboard-2.12.0` release, because in that case pip resolved to `protobuf-3.20.3`:\r\n```\r\npip install \"tensorflow==2.3.0\" \"tensorboard<2.12.0\"\r\n```\r\n```\r\nSuccessfully installed MarkupSafe-2.1.2 absl-py-1.4.0 astunparse-1.6.3 cachetools-5.3.0 charset-normalizer-3.0.1 gast-0.3.3 google-auth-2.16.0 google-auth-oauthlib-0.4.6 google-pasta-0.2.0 grpcio-1.51.1 h5py-2.10.0 idna-3.4 importlib-metadata-6.0.0 keras-preprocessing-1.1.2 markdown-3.4.1 numpy-1.18.5 oauthlib-3.2.2 opt-einsum-3.3.0 protobuf-3.20.3 pyasn1-0.4.8 pyasn1-modules-0.2.8 requests-2.28.2 requests-oauthlib-1.3.1 rsa-4.9 scipy-1.4.1 six-1.16.0 tensorboard-2.11.2 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 tensorflow-2.3.0 tensorflow-estimator-2.3.0 termcolor-2.2.0 urllib3-1.26.14 werkzeug-2.2.2 wrapt-1.14.1 zipp-3.13.0\r\n```", "Hi, Thanks for opening the issue. \r\nWe have upgraded the protobuf dependency to 3.21.9 (4.21.9) for python from Tensorflow version 2.12, which was long awaited ask.\r\nFor Tensorflow and Tensorboard 2.12 version, the protobuf minimum version requirement is `'protobuf>=3.20.3,<5.0.0dev,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5'`.\r\nFor any older Tensorflow version and new Tensorboard version the only version which acts as bridge between both is 3.20.3, which is working fine in your case.\r\nOnce the Tensorflow 2.12 is released, you can use Tensorboard 2.12 without any package conflict. ", "This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n", "Thanks @sachinprasadhs.", "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/59665\">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/59665\">No</a>\n" ]

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[]

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[ "Hi, @aim2002 \r\n\r\nI'm not sure which instructions are you following but I'm able to install Tensorflow on `Apple M1 Pro` and it should work on `Mac M2` also so you can install Tensorlflow by using one of the `Conda`, `Miniconda` or `Miniforge` approach so I followed [Get started with tensorflow-metal](https://developer.apple.com/metal/tensorflow-plugin/) with `Miniconda3` instructions so could you please try with `arm64 : Apple silicon`\r\n\r\nWhile following the instructions and after clicking on [Download Conda environment ](https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh) `Miniconda3-latest-MacOSX-arm64.sh` file will be downloaded and replace that `.sh` file with `miniconda.sh` in command `bash ~/miniconda.sh -b -p $HOME/miniconda `as shown below in first step 1. Set up and please follow all steps from `2. Install base TensorFlow` as it is :\r\n\r\n```\r\nbash ~/Miniconda3-latest-MacOSX-arm64.sh -b -p $HOME/miniconda\r\nsource ~/miniconda/bin/activate\r\nconda install -c apple tensorflow-deps\r\n```\r\nYou'll have to use this command `source ~/miniconda/bin/activate` whenever you want to use/activate `Miniconda` environment \r\n\r\nIf you're looking to go with `Miniforge` approach then please refer this [Article](https://caffeinedev.medium.com/how-to-install-tensorflow-on-m1-mac-8e9b91d93706) and for [Miniconda-Article](https://medium.com/mlearning-ai/install-tensorflow-on-mac-m1-m2-with-gpu-support-c404c6cfb580) and also please refer [Releases-Table](https://developer.apple.com/metal/tensorflow-plugin/#:~:text=Releases-,tensorflow%2Dmacos,Distributed%20training,-Troubleshooting)\r\n\r\nI have added screenshot for your reference below, \r\n\r\n\r\n\r\nIf issue still persists, please let us know? or Could you please confirm if this issue is resolved for you ? Please feel free to close the issue if it is resolved ? Thank you!\r\n", "This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n", "Closing as stale. Please reopen if you'd like to work on this further.\n", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59663\">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/59663\">No</a>\n", "Mac mini M2. Used pip install tensorflow. \r\nAs soon as I import tensorflow in my python script I am getting:\r\n`zsh: illegal hardware instruction`\r\n" ]

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[ "All the details are in the colab file, and I would like to know if this situation is a bug or an internal optimization of the converter. If it is an optimization, can I turn it off because I only want the model structure for later use and not for inference speed. Thank you for your help.", "@Sun2018421 \r\nI was able to reproduce the issue on colab using TF v2.11. Please find the gist [here](https://colab.research.google.com/gist/tiruk007/92b83900215f08fef3ea53a1dc9e5dfe/tflite.ipynb) for reference. It seems like we have to dig more into this issue, we will update soon here.\r\n\r\nThank you!", "Okay, Thank you", "@Sun2018421 Sorry for the delayed response.\r\n\r\nThe tflite converter may flatten/prune the graph during conversion process for better optimization. If the model structure needs to be retained, the converter can be used with just using select ops. Please refer the [gist](https://colab.research.google.com/gist/pjpratik/c0d1aaddce410eca58a3b5cdd35cd926/59662.ipynb).\r\n\r\nAlso, we can use [TFLite Model Anylzer](https://www.tensorflow.org/lite/guide/model_analyzer) to analyze tflite model structure and [Quantization Debugger](https://www.tensorflow.org/lite/performance/quantization_debugger) to assist the quantization debugging process. \r\n\r\nThanks!", "Thank you very much, I will go read and understand it.", "As you mentioned, \"the converter can be used with just using select ops\". Do you have any related information about the converter? Thank you. The docs that I know are https://www.tensorflow.org/lite/convert?hl=zh-cn.", "@Sun2018421 As per the select ops [documentation](https://www.tensorflow.org/lite/guide/ops_select), the select ops can be used for the ops which are not present in TFLite builtins. But using select ops might increase Tensorflow Lite binary size. \r\n\r\nI have observed that when just select ops is used, the concat operator is indeed taking four different inputs but not in the case when tflite builtins are added when observed in the [gist](https://colab.research.google.com/gist/pjpratik/c0d1aaddce410eca58a3b5cdd35cd926/59662.ipynb).\r\n\r\n\r\n\r\n\r\nMay I know the use case of requiring model structure but not for inference speed? Thanks.", "Thank you very much for your help. I will go and take a look, as my current work focuses on operator scheduling and intermediate tensor memory, as mentioned in the [paper](https://arxiv.org/abs/2003.02369) Therefore, I would prefer to have a clear network structure for analysis. I am not an experienced researcher, so my naive questions may have caused you some trouble. Thanks again for your help.", "Sorry for the feedback for so long, when i check the network structure in [gist](https://colab.research.google.com/gist/pjpratik/c0d1aaddce410eca58a3b5cdd35cd926/59662.ipynb#scrollTo=DsRBbVLeL_9x), I find it still has 1 repeated input, Op#68 FlexConcatV2(T#216, T#217, T#218, T#217, T#2[1]) -> [T#219]. I am trying select ops or tflite builtins , but i am working on some errors when converting. Thanks very much.", "Hi @Sun2018421, Thanks for the information.\r\n\r\n@sachinprasadhs Could you please look into this issue. Thanks!", "Hi, The code which you have provided is quite large to debug. \r\nCould you please provide simple reproducible example and attach the Gist. Thanks!", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59662\">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/59662\">No</a>\n" ]

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[ "Please squash the commits", "Thanks @mihaimaruseac PR has been updated with commits squashed. Please take a look." ]

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[ "Thanks @cantonios . The PR has been updated. \r\n\r\nIn the past I did notice that internal tests may have different error codes. Not sure the change will cover all scenarios in internal tests. Please let me know if there are additional failures in internal tests.", "\r\nHere are the internal errors, @yongtang can you please verify ? Thank you!\r\n\r\nDuring handling of the above exception, another exception occurred:\r\nTraceback (most recent call last):\r\n File \"/tensorflow/python/framework/test_util.py\", line 1526, in decorated\r\n run_eagerly(self, **kwargs)\r\n File \"/tensorflow/python/framework/test_util.py\", line 1510, in run_eagerly\r\n f(self, *args, **kwargs)\r\n File \"/tensorflow/python/ops/ragged/ragged_tensor_test.py\", line 2019, in testToVariantInvalidInputs\r\n self.assertRaisesRegex(\r\nAssertionError: \"must be less than 0, got 0|Shape must be at least rank 1 but is rank 0\" does not match \"{{function_node __wrapped__RaggedTensorToVariant_RAGGED_RANK_1_device_/job:localhost/replica:0/task:0/device:CPU:0}} Requires the first element of nested_row_splits[0] to be 0 but is 150 [Op:RaggedTensorToVariant]\"\r\n", "There might be some skew in Graph mode (shape inference error) vs eager mode (kernel check) error messages. Maybe look at the shape inference for this particular op?", "Hi @yongtang Any update on this PR? Please. 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 @alankelly Can you please review this PR ? Thank you!", "Hi @alankelly Can you please review this PR ? Thank you!", "Hi @alankelly Can you please review this PR ? Thank you!", "Hi @alankelly Can you please review this PR ? Thank you!" ]

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[ "@mihaimaruseac @cantonios PR has been updated with commits merged. Please take a look." ]

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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/59657/checks?check_run_id=11283700124) 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 @dsr-lab 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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[ "Also, please squash all commits, we don't need 3 commits for a check and a test.", "Thanks @cantonios. The PR has been updated with `with self.session():` removed. Let me know if this works (I see most of the other tests seems to have this)", "Thanks @mihaimaruseac The PR has been updated with commits merged. Please take a look.\r\n", "@mihaimaruseac (In github there is an option in settings which allows the \"squash merge\" that can do the squash merge automatically when merge. This allows adjustments of try/revert tests in different sections while also keep the final commit history linear/clean.\r\n\r\nUnderstand tensorflow has an internal merge system, though maybe something similar to \"squash merge\" can be considered as well?\r\n\r\n_Below is a screenshot of the config in some other repos:_\r\n\r\n-------\r\n\r\n<img width=\"814\" alt=\"screenshot\" src=\"https://user-images.githubusercontent.com/6932348/218561562-4d8f417e-ae25-4f17-8f8b-37379863c978.png\">\r\n\r\n", "That would be ideal, but unfortunately it requires significant changes in Copybara, so instead we're suggesting squashes on PRs if we can. This is useful for vuln fixes as it is easier to cherrypick a squashed commit than 3 commits on a fix." ]

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[ "Hi @alex19999, \r\nIn order to expedite the trouble-shooting process, kindly share complete reproducible code to replicate the issue reported here. Thank you!", "This issue has been automatically marked as stale because it has no recent activity. It will be closed if no further activity occurs. Thank you.\n", "Closing as stale. Please reopen if you'd like to work on this further.\n", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/59655\">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/59655\">No</a>\n" ]