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"@dmc1778,\r\nI was able to reproduce the issue on colab using TF v2.12, 2.13, tf-nightly. Please find the attached [gist](https://colab.research.google.com/gist/tilakrayal/3ce29b1cd34526d06a3ebd1a718519c0/untitled1322.ipynb). \r\n\r\nSince Segmentation faults can be considered as potential security vulnerabilities please read [SECURITY.md](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) and report them through proper channel as mentioned in SECURITY.md. If you want to stick to a specific version then please follow [this](https://github.com/tensorflow/tensorflow/blob/master/README.md#patching-guidelines) and create your own patch to resolve such issues.\r\n\r\nThank you!\r\n",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61575\">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/61575\">No</a>\n",
"@tilakrayal \r\nI am not sure if the issue is reproducible is it Ok to close it here without confirming whether its resolved or ignored. Whether can you confirm whether this needs to be fixed or not ? Can we expect this fixed in up-coming versions?"
] | 2023-08-15T01:37:14 | 2023-09-20T18:17:02 | 2023-09-01T01:48:33 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to large input tensor
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.ops import gen_ctc_ops
try:
arg_0_tensor = tf.constant(-8968073515812833920, shape=[2, 2, 3], dtype=tf.float32,)
arg_0 = tf.identity(arg_0_tensor)
arg_1_tensor = tf.constant(8968073515812833920, shape=[4, 2], dtype=tf.int64,)
arg_1 = tf.identity(arg_1_tensor)
arg_2_tensor = tf.random.uniform([4], minval=-256, maxval=257, dtype=tf.int32)
arg_2 = tf.identity(arg_2_tensor)
arg_3_tensor = tf.random.uniform([2], minval=-256, maxval=257, dtype=tf.int32)
arg_3 = tf.identity(arg_3_tensor)
preprocess_collapse_repeated = False
ctc_merge_repeated = True
ignore_longer_outputs_than_inputs = False
out = gen_ctc_ops.ctc_loss(arg_0,arg_1,arg_2,arg_3,preprocess_collapse_repeated=preprocess_collapse_repeated,ctc_merge_repeated=ctc_merge_repeated,ignore_longer_outputs_than_inputs=ignore_longer_outputs_than_inputs,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-14 21:36:56.173477: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:36:56.190838: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:36:56.190979: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:36:56.191266: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-14 21:36:56.191900: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:36:56.192006: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:36:56.192110: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:36:56.252143: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:36:56.252287: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:36:56.252387: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:36:56.252470: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4025 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
2023-08-14 21:36:56.314614: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8600
2023-08-14 21:36:56.327748: F tensorflow/stream_executor/cuda/cuda_dnn.cc:804] Check failed: cudnnSetConvolutionGroupCount( handle_.get(), convolution_descriptor.group_count()) == CUDNN_STATUS_SUCCESS (3 vs. 0)
Aborted
```
```
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"Hi @dmc1778 , \r\n\r\nI have replicated the reported behaviour with Tf2.10v. However when I have tested the same code with Tf2.12v and tf-nightly code executed successfully. Please refer to attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/ce477500dc8b53b2f48572cd3edea3c1/61574_2-12v_nightly_2-10v.ipynb).\r\n\r\nSince the issue is already fixed with latest versions it's unlikely to cherry pick by us for Tf2.10v.\r\n\r\nPlease report the security related issues through proper channel as mentioned in [SECURITY.md](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md).\r\n\r\nPlease check the [instructions](https://github.com/tensorflow/tensorflow/blob/master/README.md#patching-guidelines) for patching older versions of tensorflow at individuals repo.\r\n\r\nThanks!\r\n\r\n",
"Please always check with the latest version.",
"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/61574\">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/61574\">No</a>\n"
] | 2023-08-15T01:34:29 | 2023-08-16T07:19:20 | 2023-08-16T07:19:17 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.10.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to invalid list elements
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.ops import nn_ops
try:
arg_0_tensor = tf.random.uniform([2, 4, 4, 4, 3], dtype=tf.float32)
arg_0 = tf.identity(arg_0_tensor)
arg_1_tensor = tf.random.uniform([2, 2, 2, 5, 3], dtype=tf.float32)
arg_1 = tf.identity(arg_1_tensor)
arg_2_0 = 2
arg_2_1 = 8
arg_2_2 = 8
arg_2_3 = 8
arg_2_4 = False
arg_2 = [arg_2_0,arg_2_1,arg_2_2,arg_2_3,arg_2_4,]
arg_3 = 2
out = nn_ops.conv3d_transpose_v2(arg_0,arg_1,arg_2,arg_3,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-14 21:33:38.109289: 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.
2023-08-14 21:33:38.668776: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:33:38.686385: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:33:38.686530: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:33:38.686817: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-14 21:33:38.687712: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:33:38.687825: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:33:38.687920: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:33:38.749104: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:33:38.749246: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:33:38.749344: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:33:38.749426: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 3978 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
2023-08-14 21:33:38.812494: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8600
2023-08-14 21:33:38.825580: F tensorflow/stream_executor/cuda/cuda_dnn.cc:804] Check failed: cudnnSetConvolutionGroupCount( handle_.get(), convolution_descriptor.group_count()) == CUDNN_STATUS_SUCCESS (3 vs. 0)
Aborted
```
```
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"@dmc1778 I was able to run the code successfully on colab using TF v2.11, 2.13 and tf-nightly. Please find the attached gist [here](https://colab.research.google.com/gist/sushreebarsa/407ab1c9b9756832e3f2589f08fecdec/61573.ipynb) and confirm the same?\r\nThank you!",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61573\">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/61573\">No</a>\n"
] | 2023-08-15T01:32:49 | 2023-08-18T13:47:17 | 2023-08-18T13:47:15 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.10.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to feeding invalid list element
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.ops import nn_ops
try:
arg_0_tensor = tf.random.uniform([2, 5, 6, 4, 3], dtype=tf.float32)
arg_0 = tf.identity(arg_0_tensor)
arg_1_tensor = tf.random.uniform([3, 3, 3, 2, 3], dtype=tf.float32)
arg_1 = tf.identity(arg_1_tensor)
arg_2_0 = 2
arg_2_1 = 11
arg_2_2 = 13
arg_2_3 = 9
arg_2_4 = False
arg_2 = [arg_2_0,arg_2_1,arg_2_2,arg_2_3,arg_2_4,]
strides_0 = 1
strides_1 = 2
strides_2 = 2
strides_3 = 2
strides_4 = 1
strides = [strides_0,strides_1,strides_2,strides_3,strides_4,]
padding = "VALID"
out = nn_ops.conv3d_transpose(arg_0,arg_1,arg_2,strides=strides,padding=padding,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-14 21:32:34.731642: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:32:34.748917: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:32:34.749058: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:32:34.749350: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-14 21:32:34.750631: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:32:34.750742: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:32:34.750840: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:32:34.804553: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:32:34.804756: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:32:34.804893: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 21:32:34.804979: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4009 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
2023-08-14 21:32:34.866964: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8600
2023-08-14 21:32:34.880076: F tensorflow/stream_executor/cuda/cuda_dnn.cc:804] Check failed: cudnnSetConvolutionGroupCount( handle_.get(), convolution_descriptor.group_count()) == CUDNN_STATUS_SUCCESS (3 vs. 0)
Aborted
```
```
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"Hi @dmc1778,\r\n\r\nI was able to reproduce the issue on colab using TF v2.13. Please find the attached [gist](https://colab.research.google.com/gist/Varsha-anjanappa/d8d876886edecdc29309f7465e0defa3/61572.ipynb).\r\nSince Segmentation faults can be considered as potential security vulnerabilities please read [SECURITY.md](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) and report them through proper channel as mentioned in SECURITY.md.\r\n\r\nThank you!!!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61572\">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/61572\">No</a>\n"
] | 2023-08-15T01:29:57 | 2023-09-20T18:16:54 | 2023-09-02T01:46:12 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to feeding None argument.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.framework import kernels
try:
arg_0 = None
out = kernels.get_registered_kernels_for_op(arg_0,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-14 21:28:52.279973: 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.
Segmentation fault
```
```
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"@dmc1778,\r\nI was able to reproduce the issue on colab using TF v2.12, 2.13, tf-nightly. Please find the attached [gist](https://colab.research.google.com/gist/tilakrayal/4bd91e6432216f32cfe67a1f2de921d8/untitled1323.ipynb). \r\n\r\nSince Segmentation faults can be considered as potential security vulnerabilities please read [SECURITY.md](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) and report them through proper channel as mentioned in SECURITY.md. If you want to stick to a specific version then please follow [this](https://github.com/tensorflow/tensorflow/blob/master/README.md#patching-guidelines) and create your own patch to resolve such issues.\r\nThank you!\r\n",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61571\">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/61571\">No</a>\n",
"@tilakrayal \r\nI am not sure if the issue is reproducible is it Ok to close it here without confirming whether its resolved or ignored. Whether can you confirm whether this needs to be fixed or not ? Can we expect this fixed in up-coming versions?",
"@SuryanarayanaY bug exists on 2.13.0.",
"> @SuryanarayanaY bug exists on 2.13.0.\r\n\r\nThis bug exists on 2.13.0."
] | 2023-08-15T01:27:36 | 2024-01-18T08:47:56 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to None argument
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.framework import importer
try:
arg_0 = None
arg_1 = ""
arg_2 = None
arg_3_0 = "A"
arg_3_1 = "B"
arg_3 = [arg_3_0,arg_3_1,]
arg_4 = True
out = importer._PopulateTFImportGraphDefOptions(arg_0,arg_1,arg_2,arg_3,arg_4,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-14 21:26:55.810615: 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.
Segmentation fault
```
```
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"@dmc1778 ,\r\n\r\nI have tested the code with tf-nightly(2.15.0-dev20230813) and it fails with **segmentation fault**. Attached logs below for reference.\r\n\r\n```\r\n(tf2.13) suryanarayanay@surya-ubuntu20:~$ python 61570_vm.py\r\n2023-08-16 06:32:05.242433: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9498] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2023-08-16 06:32:05.242588: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2023-08-16 06:32:05.242678: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n2023-08-16 06:32:05.260269: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-08-16 06:32:06.805199: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\nWARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/tf2.13/lib/python3.11/site-packages/tensorflow/python/ops/distributions/distribution.py:259: ReparameterizationType.__init__ (from tensorflow.python.ops.distributions.distribution) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\nWARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/tf2.13/lib/python3.11/site-packages/tensorflow/python/ops/distributions/bernoulli.py:165: RegisterKL.__init__ (from tensorflow.python.ops.distributions.kullback_leibler) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\n2.15.0-dev20230813\r\nSegmentation fault (core dumped)\r\n```\r\n\r\nSince segmentation fault is a vulnerability and it exists in tf-nightly, please report the security related issues through proper channel as mentioned in [SECURITY.md](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md).\r\n\r\nThanks!",
"Please always check with the latest version.\r\n\r\nPlease never mention security issues in public. It is not the first time this behavior occured.",
"> Please always check with the latest version.\r\n> \r\n> Please never mention security issues in public. It is not the first time this behavior occured.\r\n\r\nSegfault exists on 2.13.0"
] | 2023-08-15T01:25:50 | 2023-09-20T18:16:38 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to None argument
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.framework import importer
try:
arg_0_0 = "Placeholder:0"
arg_0_1 = "add:0"
arg_0 = [arg_0_0,arg_0_1,]
arg_1 = None
arg_2 = None
out = importer._GatherReturnElements(arg_0,arg_1,arg_2,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-14 21:23:49.892933: 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.
Segmentation fault
```
```
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} | Migrate TF Python deps from being defined as tf_http_archives in workspace2.bzl to being in requirements.in. This consolidates where Python dependencies are defined, allows them to be more easily updated and also allows any version updates to be easily transferred to requirement.txt locked versions using the Hermetic Python requirements updater. Remove any downstream references to the deleted tf_http_archive repositories and if relevant replace them with @pypi_*name*//:pkg. | {
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"@dmc1778 I was able to reproduce the issue on colab using TF v2.11, 2.13, tf-nightly. Please find the attached [gist](https://colab.research.google.com/gist/sushreebarsa/9eabdac05e8baa8fe2b0dbdd04cd5f42/61568.ipynb#scrollTo=a77bz9T0wS_w). \r\nSince Segmentation faults can be considered as potential security vulnerabilities please read [SECURITY.md](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) and report them through proper channel as mentioned in SECURITY.md. If you want to stick to a specific version then please follow [this](https://github.com/tensorflow/tensorflow/blob/master/README.md#patching-guidelines) and create your own patch to resolve such issues.",
"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/61568\">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/61568\">No</a>\n"
] | 2023-08-14T23:47:30 | 2023-09-20T18:16:26 | 2023-09-01T01:48:37 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to feeding NaN input
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
from tensorflow.python.eager import remote
try:
try:
with tf.device('/CPU'):
arg_0 = "nan"
out = remote.connect_to_remote_host(arg_0,)
except Exception as e:
print("Error:"+str(e))
try:
with tf.device('/GPU:0'):
remote.connect_to_remote_host(arg_0,)
except Exception as e:
print("Error:"+str(e))
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-14 19:46:47.563173: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:46:47.580413: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:46:47.580559: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:46:47.580847: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-14 19:46:47.582229: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:46:47.582354: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:46:47.582453: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:46:47.643352: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:46:47.643499: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:46:47.643600: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:46:47.643684: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4110 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
2023-08-14 19:46:47.645827: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:46:47.645936: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:46:47.646029: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:46:47.646141: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:46:47.646236: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:46:47.646304: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4110 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
2023-08-14 19:46:47.654175: E tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:580] INVALID_ARGUMENT: Could not interpret "nan" as a host-port pair.
E0814 19:46:47.654333018 18949 completion_queue.cc:244] assertion failed: queue.num_items() == 0
Aborted
```
```
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"Hi @dmc1778 ,\r\n\r\nI have tested the code with tf version 2.12 and tf-nightly and its working fine by raising exception. Please refer attached [gist](https://colab.research.google.com/gist/Varsha-anjanappa/529d41b5170fb6aed82e51302d02a3f1/61567.ipynb).\r\n\r\nPlease report the security related issues through proper channel as mentioned in [SECURITY.md](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md).\r\nPlease check the [instructions](https://github.com/tensorflow/tensorflow/blob/master/README.md#patching-guidelines) for patching older versions of tensorflow at individuals repo.\r\n\r\nThanks you!",
"Please always check with the latest version.",
"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/61567\">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/61567\">No</a>\n"
] | 2023-08-14T23:40:39 | 2023-08-16T07:17:47 | 2023-08-16T07:17:45 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.10.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
due to NEGATIVE LARGE TENSOR
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.ops import gen_array_ops
try:
arg_0_tensor = tf.constant(-17, shape=[1, 4, 7, 1], dtype=tf.int64,)
arg_0 = tf.identity(arg_0_tensor)
arg_1_tensor = tf.constant(-43871863081293, shape=[4, 2], dtype=tf.int32,)
arg_1 = tf.identity(arg_1_tensor)
arg_2 = "REFLECT"
out = gen_array_ops.mirror_pad_grad(arg_0,arg_1,arg_2,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-14 19:39:35.212387: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:39:35.230641: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:39:35.230799: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:39:35.231097: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-14 19:39:35.231945: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:39:35.232079: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:39:35.232187: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:39:35.287820: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:39:35.287970: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:39:35.288088: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:39:35.288181: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4039 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
2023-08-14 19:39:35.341105: F tensorflow/core/framework/tensor_shape.cc:404] Check failed: 0 <= new_num_elements (0 vs. -1)
Aborted
```
```
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"@dmc1778,\r\nI tried to execute the mentioned code with tf-nightly and it was executed without the crash. Kindly find the gist [here](https://colab.research.google.com/gist/tilakrayal/2f3936290af09aeb21035c6b0f540429/61566.ipynb). Also most of the bugs will be resolved in the latest version. 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/61566\">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/61566\">No</a>\n"
] | 2023-08-14T23:38:01 | 2023-08-18T13:49:41 | 2023-08-18T13:49:39 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.10.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to mismatch between input tensors
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
from tensorflow.python.ops import gen_array_ops
try:
try:
with tf.device('/CPU'):
arg_0_tensor = tf.random.uniform([2, 3], dtype=tf.float32)
arg_0 = tf.identity(arg_0_tensor)
arg_1_tensor = tf.random.uniform([2], dtype=tf.float32)
arg_1 = tf.identity(arg_1_tensor)
arg_2 = tf.int32
arg_3 = False
out = gen_array_ops.lower_bound(arg_0,arg_1,arg_2,arg_3,)
except Exception as e:
print("Error:"+str(e))
try:
with tf.device('/GPU:0'):
arg_0 = tf.identity(arg_0_tensor)
arg_0 = tf.cast(arg_0, tf.float32)
arg_1 = tf.identity(arg_1_tensor)
arg_1 = tf.cast(arg_1, tf.float32)
arg_2 = tf.int32
gen_array_ops.lower_bound(arg_0,arg_1,arg_2,arg_3,)
except Exception as e:
print("Error:"+str(e))
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-14 19:37:09.497421: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:37:09.514487: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:37:09.514629: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:37:09.514917: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-14 19:37:09.515817: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:37:09.515932: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:37:09.516035: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:37:09.573389: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:37:09.573531: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:37:09.573629: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:37:09.573710: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4043 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
free(): invalid pointer
Aborted
```
```
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"Hi @akhilgoe Can you please rebase your branch and resolve conflicts? Thank you!",
"Hi @gbaned...the conflicts have been resolved. Thanks!",
"Hi @akhilgoe Can you please check @d0k's comments and keep us posted ? Thank you!",
"Hi @penpornk, could you help to review/merge this PR?",
"Hi @akhilgoe Can you please rebase your branch and resolve conflicts? Thank you!",
"Hi @akhilgoe Can you please rebase your branch and resolve conflicts? Thank you!",
"I have resolved the merge conflicts.",
"Hi @akhilgoe Can you please rebase your branch and resolve the conflicts? Thank you!",
"Replaced by https://github.com/openxla/xla/pull/7485. please close this PR.",
"Closing this PR as its replacement (https://github.com/openxla/xla/pull/7485) has been merged."
] | 2023-08-14T23:28:35 | 2023-12-06T13:03:31 | 2023-12-06T13:03:31 | CONTRIBUTOR | null | false | {
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} | This PR enables OneDNN library call for the matched XLA HLO Layer Norm pattern through custom_call instruction. In particular, this PR adds:
1. oneDNN ops rewriter pass that will rewrite custom oneDNN ops like Layer Norm and Softmax. Currently, this pass is located before the BF16 all-reduce to F32 promotion.
2. Layer Norm pattern matcher as seen in some Flax models
3. oneDNN Layer Norm custom call rewrite
4. Variable number of arguments for oneDNN custom call targets (by @mdfaijul )
5. A test for XLA HLO Layer Norm pattern match and rewrite verification | {
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"Hi @dmc1778 ,\r\n\r\nI have tested the code with tf-nightly and its working fine by raising exception.Please refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/4682c5d6b95fc981565ed6f26a05d83d/61564_nightly.ipynb).\r\n\r\nSince the issue is already fixed in tf-nightly it's unlikely to cherry pick by us for Tf2.10v.\r\n\r\nPlease report the security related issues through proper channel as mentioned in [SECURITY.md](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md).\r\n\r\nPlease check the [instructions](https://github.com/tensorflow/tensorflow/blob/master/README.md#patching-guidelines) for patching older versions of tensorflow at individuals repo.\r\n\r\nThanks!",
"Please always check with the latest version.",
"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/61564\">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/61564\">No</a>\n"
] | 2023-08-14T23:16:28 | 2023-08-16T07:18:40 | 2023-08-16T07:18:38 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.10.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Due to an empty input argument
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.ops import gen_math_ops
try:
arg_0 = 2
arg_1 = 4
arg_2 = [()]
dtype = None
out = gen_math_ops.sobol_sample(arg_0,arg_1,arg_2,dtype=dtype,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-14 19:14:54.117266: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:14:54.134520: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:14:54.134663: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:14:54.135032: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-14 19:14:54.135857: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:14:54.135969: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:14:54.136093: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:14:54.189645: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:14:54.189788: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:14:54.189886: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:14:54.189967: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4373 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
2023-08-14 19:14:54.237059: F tensorflow/core/framework/tensor.cc:733] Check failed: 1 == NumElements() (1 vs. 0)Must have a one element tensor
Aborted
```
```
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"\r\n@dmc1778 I was able to reproduce the issue on colab using TF v2.11, 2.13, tf-nightly. Please find the attached [gist](https://colab.research.google.com/gist/sushreebarsa/ece164b3f6b17b41b24ff674bb62eb04/61563.ipynb\r\n). \r\nSince Segmentation faults can be considered as potential security vulnerabilities please read [SECURITY.md](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) and report them through proper channel as mentioned in SECURITY.md. If you want to stick to a specific version then please follow [this](https://github.com/tensorflow/tensorflow/blob/master/README.md#patching-guidelines) and create your own patch to resolve such issues.",
"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/61563\">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/61563\">No</a>\n"
] | 2023-08-14T23:12:57 | 2023-09-20T18:16:13 | 2023-09-01T01:48:39 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to the large integer value
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.ops import gen_list_ops
try:
element_shape_tensor = tf.random.uniform([0], minval=-256, maxval=257, dtype=tf.int32)
element_shape = tf.identity(element_shape_tensor)
num_elements = 1250999896764
element_dtype = tf.float64
out = gen_list_ops.tensor_list_reserve(element_shape=element_shape,num_elements=num_elements,element_dtype=element_dtype,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-14 19:12:02.093783: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:12:02.111206: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:12:02.111351: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:12:02.111663: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-14 19:12:02.113096: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:12:02.113270: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:12:02.113369: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:12:02.167538: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:12:02.167673: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:12:02.167772: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:12:02.167855: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4163 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
Segmentation fault
```
```
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"On 2.13.0:\r\n\r\n```\r\n2023-08-17 23:45:41.957299: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-08-17 23:45:42.468670: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-08-17 23:45:42.899351: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-17 23:45:42.923109: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1960] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\r\nSkipping registering GPU devices...\r\nFloating point exception\r\n\r\n```",
"@SuryanarayanaY any update on this?"
] | 2023-08-14T23:10:46 | 2023-09-20T18:16:01 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to Large integer value
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.ops import gen_array_ops
try:
arg_0_tensor = tf.random.uniform([3, 2, 3, 4], dtype=tf.float32)
arg_0 = tf.identity(arg_0_tensor)
arg_1 = 536870912
arg_2 = "NHWC"
out = gen_array_ops.depth_to_space(arg_0,arg_1,arg_2,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-14 19:09:51.665038: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:09:51.681990: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:09:51.682143: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:09:51.682456: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-14 19:09:51.683591: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:09:51.683711: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:09:51.683809: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:09:51.738771: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:09:51.738915: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:09:51.739018: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:09:51.739104: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4184 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
Floating point exception
```
```
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"Please always test with the latest version. Testing with versions from last year cause needless work as people now have to spend time to reproduce issues in nightly/last release.",
"@dmc1778 ,\r\nI tried to execute the mentioned code on tf-nightly and it was executed without any abort. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/bdcb22cec1e06a08832500d54c4b0e45/61561.ipynb) and most of the bugs are resolved in the latest versions. 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/61561\">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/61561\">No</a>\n"
] | 2023-08-14T23:08:43 | 2023-08-18T13:49:31 | 2023-08-18T13:49:29 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.10.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to Large List Elements
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.ops import gen_sparse_ops
try:
indices_0 = []
indices = [indices_0,]
values_0 = 0
values = [values_0,]
shape_0 = 1
shape_1 = 1
shape = [shape_0,shape_1,]
start_0 = 4611686018427387904
start_1 = -1
start = [start_0,start_1,]
size_0 = 4611686018427387904
size_1 = 4611686018427387904
size = [size_0,size_1,]
out = gen_sparse_ops.sparse_slice(indices=indices,values=values,shape=shape,start=start,size=size,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-14 19:04:18.473689: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:04:18.490933: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:04:18.491133: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:04:18.491455: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-14 19:04:18.492102: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:04:18.492212: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:04:18.492308: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:04:18.560188: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:04:18.560326: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:04:18.560424: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:04:18.560508: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4029 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
2023-08-14 19:04:18.619230: E tensorflow/stream_executor/cuda/cuda_event.cc:29] Error polling for event status: failed to query event: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered
2023-08-14 19:04:18.619470: F tensorflow/core/common_runtime/device/device_event_mgr.cc:221] Unexpected Event status: 1
Aborted
```
```
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"Hi @dmc1778 ,\r\n\r\nI have tested the code with tf-nightly and it fails with **segmentation fault**. Attached logs below for reference.\r\n\r\n```\r\n(tf2.13) suryanarayanay@surya-ubuntu20:~$ python 61560_nightly.py \r\n2023-08-16 06:45:11.299878: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9498] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2023-08-16 06:45:11.300055: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2023-08-16 06:45:11.300153: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n2023-08-16 06:45:11.309462: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-08-16 06:45:12.117648: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\nWARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/tf2.13/lib/python3.11/site-packages/tensorflow/python/ops/distributions/distribution.py:259: ReparameterizationType.__init__ (from tensorflow.python.ops.distributions.distribution) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\nWARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/tf2.13/lib/python3.11/site-packages/tensorflow/python/ops/distributions/bernoulli.py:165: RegisterKL.__init__ (from tensorflow.python.ops.distributions.kullback_leibler) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\n2.15.0-dev20230813\r\n2023-08-16 06:45:21.472975: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:21.475183: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:21.477334: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:21.479483: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:21.548649: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:21.550648: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:21.552615: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:21.554815: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:21.556892: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:21.558755: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:21.560669: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:21.562603: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:22.823519: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:22.825689: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:22.827675: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:22.829660: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:22.831850: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:22.833754: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:22.835489: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:22.837331: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:22.839313: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:22.841153: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:22.842878: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:22.844725: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:26.097031: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:26.099287: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:26.101451: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:26.103624: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:26.105789: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:26.107707: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:26.109623: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:26.111659: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:26.113723: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:26.115658: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1898] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13621 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\r\n2023-08-16 06:45:26.116055: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:26.118033: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1898] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13621 MB memory: -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\r\n2023-08-16 06:45:26.118421: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:26.120381: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1898] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 13621 MB memory: -> device: 2, name: Tesla T4, pci bus id: 0000:00:06.0, compute capability: 7.5\r\n2023-08-16 06:45:26.120728: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-16 06:45:26.122626: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1898] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 13621 MB memory: -> device: 3, name: Tesla T4, pci bus id: 0000:00:07.0, compute capability: 7.5\r\nSegmentation fault (core dumped)\r\n(tf2.13) suryanarayanay@surya-ubuntu20:~$ \r\n```\r\nSince segmentation fault is a vulnerability and it exists in tf-nightly, please report the security related issues through proper channel as mentioned in [SECURITY.md](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md).\r\n\r\nThanks!",
"Please always check with the latest version.\r\n\r\nPlease never mention security issue in public."
] | 2023-08-14T23:03:24 | 2023-09-20T18:15:50 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to Negative Float values
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
from tensorflow.python.ops import gen_math_ops
try:
try:
with tf.device('/CPU'):
arg_0_0_0 = -1.0
arg_0_0_1 = 0.0
arg_0_0_2 = 1.5
arg_0_0 = [arg_0_0_0,arg_0_0_1,arg_0_0_2,]
arg_0_1_0 = 2.0
arg_0_1_1 = 5.0
arg_0_1_2 = 15
arg_0_1 = [arg_0_1_0,arg_0_1_1,arg_0_1_2,]
arg_0 = [arg_0_0,arg_0_1,]
arg_1_0 = -1e+40
arg_1_1 = -0.0001
arg_1 = [arg_1_0,arg_1_1,]
arg_2 = 5
dtype = tf.int32
out = gen_math_ops._histogram_fixed_width(arg_0,arg_1,arg_2,dtype=dtype,)
except Exception as e:
print("Error:"+str(e))
try:
with tf.device('/GPU:0'):
arg_0_0 = [arg_0_0_0,arg_0_0_1,arg_0_0_2,]
arg_0_1 = [arg_0_1_0,arg_0_1_1,arg_0_1_2,]
arg_0 = [arg_0_0,arg_0_1,]
arg_1 = [arg_1_0,arg_1_1,]
dtype = tf.int32
gen_math_ops._histogram_fixed_width(arg_0,arg_1,arg_2,dtype=dtype,)
except Exception as e:
print("Error:"+str(e))
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-14 19:02:34.037518: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:02:34.054273: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:02:34.054440: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:02:34.054728: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-14 19:02:34.055658: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:02:34.055769: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:02:34.055866: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:02:34.125810: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:02:34.125958: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:02:34.126058: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 19:02:34.126209: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4061 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
Segmentation fault
```
```
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"Please always test with the latest version. Testing with versions from last year cause needless work as people now have to spend time to reproduce issues in nightly/last release.",
"@dmc1778 I was able to run the provided code successfully in the latest TF version 2.13 and tf-nightly. Could you please have a look at the gist [here](https://colab.research.google.com/gist/sushreebarsa/f0542920525467642890c1de0c29cedf/61559.ipynb). 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/61559\">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/61559\">No</a>\n"
] | 2023-08-14T23:00:54 | 2023-08-18T13:49:48 | 2023-08-18T13:49:46 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.10.0
### Custom code
No
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to empty input value
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.ops import math_ops
try:
arg_0 = 10
arg_1 = 50
arg_2 = [()]
dtype = None
out = math_ops.sobol_sample(arg_0,arg_1,arg_2,dtype=dtype,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-14 18:57:53.081061: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:57:53.098644: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:57:53.098788: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:57:53.099088: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-14 18:57:53.099985: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:57:53.100112: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:57:53.100209: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:57:53.168279: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:57:53.168422: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:57:53.168524: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:57:53.168607: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 3574 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
2023-08-14 18:57:53.212928: F tensorflow/core/framework/tensor.cc:733] Check failed: 1 == NumElements() (1 vs. 0)Must have a one element tensor
Aborted
```
```
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"Please always test with the latest version. Testing with versions from last year cause needless work as people now have to spend time to reproduce issues in nightly/last release.",
"> Please always test with the latest version. Testing with versions from last year cause needless work as people now have to spend time to reproduce issues in nightly/last release.\r\n\r\nOn 2.13.0:\r\n\r\n```\r\n2023-08-17 23:43:35.865928: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-08-17 23:43:36.412728: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-08-17 23:43:36.860463: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-17 23:43:36.884449: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1960] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\r\nSkipping registering GPU devices...\r\nSegmentation fault\r\n\r\n```",
"Hi @dmc1778,\r\n\r\nI was able to reproduce the issue on colab using TF v2.13. Please find the attached [gist](https://colab.research.google.com/gist/Varsha-anjanappa/29bf7e06177c9218883fce544fc2e6b9/61558.ipynb).\r\nSince Segmentation faults can be considered as potential security vulnerabilities please read [SECURITY.md](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) and report them through proper channel as mentioned in SECURITY.md.\r\n\r\nThank you!!!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61558\">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/61558\">No</a>\n"
] | 2023-08-14T22:25:16 | 2023-09-20T18:15:35 | 2023-09-02T01:46:14 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to very large integer values
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.ops import gen_data_flow_ops
try:
file_pattern = "/tmp/record_input_testzsuyf9ap/tmpsqjnp5o1/basic.*"
file_buffer_size = 1
file_parallelism = 1676240524292489355
file_shuffle_shift_ratio = 125091515651
batch_size = 1
file_random_seed = 125091515651
compression_type = "GZIP"
out = gen_data_flow_ops.record_input(file_pattern=file_pattern,file_buffer_size=file_buffer_size,file_parallelism=file_parallelism,file_shuffle_shift_ratio=file_shuffle_shift_ratio,batch_size=batch_size,file_random_seed=file_random_seed,compression_type=compression_type,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-14 18:25:00.245344: 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.
2023-08-14 18:25:01.275751: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:25:01.292863: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:25:01.293001: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:25:01.294465: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-14 18:25:01.295740: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:25:01.295849: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:25:01.295947: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:25:01.362610: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:25:01.363085: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:25:01.363185: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:980] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:25:01.363266: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4105 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
Segmentation fault
```
```
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"Please always test with the latest version. Testing with versions from last year cause needless work as people now have to spend time to reproduce issues in nightly/last release.",
"> Please always test with the latest version. Testing with versions from last year cause needless work as people now have to spend time to reproduce issues in nightly/last release.\r\n\r\nOn 2.13.0:\r\n\r\n```\r\n2023-08-17 23:42:31.011781: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-08-17 23:42:31.547301: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-08-17 23:42:31.980268: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-17 23:42:32.038742: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1960] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\r\nSkipping registering GPU devices...\r\nSegmentation fault\r\n\r\n```",
"@sachinprasadhs,\r\nI was able to reproduce the issue on tensorflow v2.12, v2.13 and tf-nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/1bd0b42b85b84ef41df918b07a21c6c1/untitled1336.ipynb)."
] | 2023-08-14T22:16:47 | 2023-09-20T18:15:23 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.13.0
### Custom code
No
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to very large input tensor
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
from tensorflow.python.ops import list_ops
try:
try:
with tf.device('/CPU'):
tensor_tensor = tf.constant(True, shape=[1610612736,36028797018963968])
tensor = tf.identity(tensor_tensor)
element_shape = None
out = list_ops.tensor_list_from_tensor(tensor=tensor,element_shape=element_shape,)
except Exception as e:
print("Error:"+str(e))
try:
with tf.device('/GPU:0'):
tensor = tf.identity(tensor_tensor)
tensor = tf.cast(tensor, tf.bool)
element_shape = None
list_ops.tensor_list_from_tensor(tensor=tensor,element_shape=element_shape,)
except Exception as e:
print("Error:"+str(e))
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-14 18:16:26.168565: 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.
2023-08-14 18:16:26.732779: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:16:26.750396: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:16:26.750545: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:16:26.750866: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-14 18:16:26.751699: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:16:26.751813: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:16:26.751913: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:16:26.812496: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:16:26.812638: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:16:26.812740: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:16:26.812825: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4042 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
Segmentation fault
```
```
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"Please always test with the latest version. Testing with versions from last year cause needless work as people now have to spend time to reproduce issues in nightly/last release.",
"Hi @dmc1778 ,\r\n\r\nThe tested function not exists in latest version(2.13v) or in tf-nightly. Please refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/229f9407a6d6e5e15dd0f54056724b63/61556.ipynb).\r\n\r\nPlease test with latest versions only as mentioned in above [comment](https://github.com/tensorflow/tensorflow/issues/61556#issuecomment-1680097995) and use proper channels for reporting vulnerabilities as mentioned in SECURITY.md. 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/61556\">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/61556\">No</a>\n"
] | 2023-08-14T22:11:39 | 2023-08-18T13:46:58 | 2023-08-18T13:46:55 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.10.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to Feeding None value
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.eager import context
try:
arg_0 = None
out = context.add_function(arg_0,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-14 18:09:38.431471: 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.
2023-08-14 18:09:39.008742: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:09:39.026368: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:09:39.026511: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:09:39.026801: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-14 18:09:39.027611: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:09:39.027718: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:09:39.027812: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:09:39.086029: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:09:39.086174: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:09:39.086268: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 18:09:39.086349: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4023 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
Segmentation fault
```
```
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"Please always test with the latest version. Testing with versions from last year cause needless work as people now have to spend time to reproduce issues in nightly/last release.",
"@dmc1778 This issue is replicating in TF v2.11, 2.13 and tf-nightly as well. Could you please have a look at [this](https://colab.research.google.com/gist/sushreebarsa/1a4ca4d9d153b95097e2ce690be8e016/untitled827.ipynb#scrollTo=O6iBEtiEXcAo) gist. \r\nSince Segmentation faults can be considered as potential security vulnerabilities please read [SECURITY.md](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) and report them through proper channel as mentioned in SECURITY.md. If you want to stick to a specific version then please follow [this](https://github.com/tensorflow/tensorflow/blob/master/README.md#patching-guidelines) and create your own patch to resolve such issues. Thank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61555\">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/61555\">No</a>\n"
] | 2023-08-14T21:19:25 | 2023-09-20T18:18:55 | 2023-09-01T01:48:41 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to negative float argument
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
from tensorflow.python.ops import gen_math_ops
try:
try:
with tf.device('/CPU'):
arg_0_0_0 = -1.0
arg_0_0_1 = 0.0
arg_0_0_2 = 1.5
arg_0_0 = [arg_0_0_0,arg_0_0_1,arg_0_0_2,]
arg_0_1_0 = 2.0
arg_0_1_1 = 5.0
arg_0_1_2 = 15
arg_0_1 = [arg_0_1_0,arg_0_1_1,arg_0_1_2,]
arg_0 = [arg_0_0,arg_0_1,]
arg_1_0 = -1.7976931348623157e+308
arg_1_1 = -1.4013e-45
arg_1 = [arg_1_0,arg_1_1,]
arg_2 = 5
dtype = tf.int32
out = gen_math_ops._histogram_fixed_width(arg_0,arg_1,arg_2,dtype=dtype,)
except Exception as e:
print("Error:"+str(e))
try:
with tf.device('/GPU:0'):
arg_0_0 = [arg_0_0_0,arg_0_0_1,arg_0_0_2,]
arg_0_1 = [arg_0_1_0,arg_0_1_1,arg_0_1_2,]
arg_0 = [arg_0_0,arg_0_1,]
arg_1 = [arg_1_0,arg_1_1,]
dtype = tf.int32
gen_math_ops._histogram_fixed_width(arg_0,arg_1,arg_2,dtype=dtype,)
except Exception as e:
print("Error:"+str(e))
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-14 17:17:24.916189: 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.
2023-08-14 17:17:25.460939: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 17:17:25.478482: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 17:17:25.478631: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 17:17:25.478922: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-14 17:17:25.479696: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 17:17:25.479802: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 17:17:25.479896: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 17:17:25.548142: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 17:17:25.548279: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 17:17:25.548375: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 17:17:25.548456: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 3932 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
Segmentation fault
(fuzzer_tf_2.11.0) n
```
```
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"Hi @dmc1778,\r\n\r\nI have tested the code with tf version 2.12 and tf-nightly and its working fine by raising exception.Please refer attached\r\n[gist](https://colab.sandbox.google.com/gist/Varsha-anjanappa/0771e994f9d2c17c501a661b9e302c74/61554.ipynb) here.\r\nSince Segmentation faults can be considered as potential security vulnerabilities please read [SECURITY.md](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) and report them through proper channel as mentioned in SECURITY.md.\r\n\r\nThank you!!",
"Please always check with the latest version.",
"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/61554\">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/61554\">No</a>\n"
] | 2023-08-14T21:16:23 | 2023-08-18T03:15:28 | 2023-08-18T03:15:25 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.11.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to large input tensor
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.ops import nn_ops
try:
arg_0_tensor = tf.random.uniform([5, 20, 20, 3], dtype=tf.float64)
arg_0 = tf.identity(arg_0_tensor)
arg_1_0 = 4.0
arg_1_1 = 1.5
arg_1_2 = 1.5
arg_1_3 = 1
arg_1 = [arg_1_0,arg_1_1,arg_1_2,arg_1_3,]
seed = 1
seed2 = 1
deterministic = True
out = nn_ops.fractional_max_pool(arg_0,arg_1,seed=seed,seed2=seed2,deterministic=deterministic,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-14 16:35:31.365070: 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.
2023-08-14 16:35:31.920926: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 16:35:31.937761: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 16:35:31.937904: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 16:35:31.938205: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-14 16:35:31.939360: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 16:35:31.939470: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 16:35:31.939568: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 16:35:32.006452: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 16:35:32.006593: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 16:35:32.006693: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-14 16:35:32.006775: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4461 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
WARNING:tensorflow:From /home/nimashiri/anaconda3/envs/fuzzer_tf_2.11.0/lib/python3.9/site-packages/tensorflow/python/util/dispatch.py:1176: fractional_max_pool (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:
`seed2` and `deterministic` args are deprecated. Use fractional_max_pool_v2.
Segmentation fault
```
```
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} | This PR enables OneDNN library call for the matched XLA HLO Layer Norm pattern through custom_call instruction. In particular, this PR adds:
1. oneDNN ops rewriter pass that will rewrite custom oneDNN ops like Layer Norm and Softmax. Currently, this pass is located before the BF16 all-reduce to F32 promotion.
2. Layer Norm pattern matcher as seen in some Flax models
3. oneDNN Layer Norm custom call rewrite
4. Variable number of arguments for oneDNN custom call targets (by @mdfaijul )
5. A test for XLA HLO Layer Norm pattern match and rewrite verification | {
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"@edwardyehuang,\r\nCould you please provide the complete standalone code to reproduce the issue and it helps us to analyse the issue in an effective way. 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.",
"> @edwardyehuang, Could you please provide the complete standalone code to reproduce the issue and it helps us to analyse the issue in an effective way. Thank you!\r\n\r\nAs I said above, as an individual user outside of Google, I cannot manually seize a TPU node, so it is impossible to provide an independent reproduction code.\r\nBy the way, I've only had this problem happen twice (I've experienced thousands of preemptions).",
"@edwardyehuang,\r\nWithout the reproducible code, it would be difficult for us to debug the issue. In order to expedite the trouble-shooting process, could you please provide a minimal code snippet. Thank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61552\">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/61552\">No</a>\n"
] | 2023-08-14T19:17:35 | 2023-09-12T01:46:54 | 2023-09-12T01:46:51 | CONTRIBUTOR | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf.2.11
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I accidentally discovered from a TPU preemption that my ckpt cannot be used to resume training (the preemption occurs during the process of saving ckpt). The error is that some weights cannot be matched. This phenomenon is happening for the first time and has never happened before.
### Standalone code to reproduce the issue
```shell
It is very hard to reproduce because it must be preemption while saving the ckpt.
```
### Relevant log output
_No response_ | {
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"Hi @naruto-raj ,\r\n\r\nThanks for reaching out. I tried building and training a CNN model with TF prebuilt binary of 2.12v with Protobuf 4.24.0 version but the code executes fine with no such segmentation problem as reported. Please refer to attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/2a5c866d4d93983427003199b4bfbb39/61551.ipynb).\r\n\r\nCould you please check and confirm with exact code snippet with which you have tested.",
"[Protobuf_issue.zip](https://github.com/tensorflow/tensorflow/files/12335011/Protobuf_issue.zip)\r\nNotebook to reproduce the issue. I was using optimise_for_inference api and it seems to cause the issue.",
"I can reproduce this issue too, using the following code (see below, it is a very simple CNN model built with tf.v1 that includes a saver and an optimizer).\r\nIf fails when `tf.compat.v1.saved_model.simple_save()` is called.\r\n\r\nIt runs fine with `protobuf==4.23.1` but fails with `protobuf==4.24.0` (Segmentation fault).\r\nIt looks like the segfault happens before writing the `.pb` file (other assets like variables seems to be written).\r\n\r\n# Code\r\n\r\nRun the code like this (with `/tmp` some place where you can write):\r\n\r\n```bash\r\npython3 bug.py /tmp/my_savedmodel\r\n```\r\n\r\n*bug.py*:\r\n\r\n```python\r\nimport sys\r\nimport os\r\nimport tensorflow.compat.v1 as tf\r\ntf.disable_v2_behavior()\r\n\r\nnclasses=6\r\n\r\ndef create_savedmodel(sess, inputs, outputs, directory):\r\n print(\"Create a SavedModel in \" + directory)\r\n graph = tf.compat.v1.get_default_graph()\r\n inputs_names = {i: graph.get_tensor_by_name(i) for i in inputs}\r\n outputs_names = {o: graph.get_tensor_by_name(o) for o in outputs}\r\n tf.compat.v1.saved_model.simple_save(sess, directory, inputs=inputs_names, outputs=outputs_names)\r\n\r\ndef my_model(x):\r\n \r\n # input patches: 16x16x4\r\n conv1 = tf.layers.conv2d(inputs=x, filters=16, kernel_size=[5,5], padding=\"valid\", activation=tf.nn.relu)\r\n pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)\r\n conv2 = tf.layers.conv2d(inputs=pool1, filters=16, kernel_size=[3,3], padding=\"valid\", activation=tf.nn.relu)\r\n pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)\r\n conv3 = tf.layers.conv2d(inputs=pool2, filters=32, kernel_size=[2,2], padding=\"valid\", activation=tf.nn.relu)\r\n \r\n # Features\r\n features = tf.reshape(conv3, shape=[-1, 32], name=\"features\")\r\n \r\n # Neurons for classes\r\n estimated = tf.layers.dense(inputs=features, units=nclasses, activation=None)\r\n estimated_label = tf.argmax(estimated, 1, name=\"prediction\")\r\n\r\n return estimated, estimated_label\r\n \r\n\"\"\" Main \"\"\"\r\nif len(sys.argv) != 2:\r\n print(\"Usage : <output directory for SavedModel>\")\r\n sys.exit(1)\r\n\r\n# Create the TensorFlow graph\r\nwith tf.Graph().as_default():\r\n \r\n # Placeholders\r\n x = tf.placeholder(tf.float32, [None, None, None, 4], name=\"x\")\r\n y = tf.placeholder(tf.int32 , [None, None, None, 1], name=\"y\")\r\n lr = tf.placeholder_with_default(tf.constant(0.0002, dtype=tf.float32, shape=[]), shape=[], name=\"lr\")\r\n \r\n # Output\r\n y_estimated, y_label = my_model(x)\r\n \r\n # Loss function\r\n cost = tf.losses.sparse_softmax_cross_entropy(labels=tf.reshape(y, [-1, 1]), logits=tf.reshape(y_estimated, [-1, nclasses]))\r\n \r\n # Optimizer\r\n optimizer = tf.train.AdamOptimizer(learning_rate=lr, name=\"optimizer\").minimize(cost)\r\n \r\n # Initializer, saver, session\r\n init = tf.global_variables_initializer()\r\n saver = tf.train.Saver( max_to_keep=20 )\r\n sess = tf.Session()\r\n sess.run(init)\r\n\r\n # Create a SavedModel\r\n # The segfault happens here \r\n create_savedmodel(sess, [\"x:0\", \"y:0\"], [\"features:0\", \"prediction:0\"], sys.argv[1])\r\n\r\n```\r\n\r\n# Output\r\n\r\n```\r\nCreate a SavedModel in /tmp/toto_model\r\nWARNING:tensorflow:From /tmp/tf_bug.py:13: simple_save (from tensorflow.python.saved_model.simple_save) is deprecated and will be removed in a future version.\r\nInstructions for updating:\r\nThis API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2.\r\nWARNING:tensorflow:From /opt/otbtf/lib/python3/dist-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:203: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\r\nInstructions for updating:\r\nThis API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2.\r\nSegmentation fault (core dumped)\r\n```\r\n\r\n# Details\r\n\r\n- Have you reproduced the bug with TensorFlow Nightly? No\r\n- Source: built from source\r\n- TensorFlow version: TF 2.12\r\n- OS platform and distribution: ubuntu:22.04\r\n- Python version: 3.10\r\n- No cuda",
"Hi @naruto-raj ,\r\n\r\nI have replicated the reported behaviour.When tested on colab with protobuf=4.24v the session getting crashed and with 4.23v there is no crash.Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/58ef76652cee800df726fb3c63c1c42d/61551_protobuf_issue.ipynb) for reference with snapshot below.\r\n\r\n<img width=\"1509\" alt=\"Screenshot 2023-08-16 at 6 03 08 PM\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/5473f9f4-3aa5-474b-92e6-107dceebf4f3\">\r\n\r\n\r\n\r\n\r\nBut `tf.compat.v1` is deprecated in current versions and hence I doubt whether it would considered for fix. Will escalate to Engg team to hear from them. Thanks!\r\n"
] | 2023-08-14T08:04:11 | 2023-08-31T07:21:21 | null | NONE | null | null | null | ### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
TF 2.12
### Custom code
No
### OS platform and distribution
Linux,Ubuntu
### Mobile device
_No response_
### Python version
3.8
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
With source build as well as pip install TF execution models fails with segfaults and the root cause seems to be because of protobuf version 4.24.0. It works fine till protobuf 4.23.4. adding a constraint in setup.py can be an immediate fix.
### Standalone code to reproduce the issue
```shell
Any CNN model execution causes the workflow to fail.
```
### Relevant log output
```shell
2023-08-14 08:01:56.274633: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:353] MLIR V1 optimization pass is not enabled
WARNING:tensorflow:From /home/anaconda3/envs/test/lib/python3.8/site-packages/tensorflow/python/tools/strip_unused_lib.py:84: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2.
scripts/benchmark_CNN.sh: line 243: 1488387 Segmentation fault
```
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"@TimLai666 While reproducing the issue, I have faced the following outcome as a warning so could you use the latest version;\r\n```\r\nWARNING:absl:Found untraced functions such as _update_step_xla while saving (showing 1 of 1). These functions will not be directly callable after loading.\r\n```\r\nPlease find the [gist ](https://colab.research.google.com/gist/sushreebarsa/3f9711adeff619850ed50605cf61ae9a/61550.ipynb)and confirm the results of TF v2.12, 2.13 and tf-nightly as well. \r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61550\">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/61550\">No</a>\n"
] | 2023-08-14T07:51:15 | 2023-09-01T01:48:59 | 2023-09-01T01:48:42 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.13.0
### Custom code
Yes
### OS platform and distribution
Windows 11 22h2
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Current behavior:
When I attempt to use tf.keras.models.load_model to load a saved model from a folder path that contains Chinese characters, TensorFlow throws a UnicodeDecodeError. This suggests that TensorFlow may not be parsing Chinese characters correctly in the folder path.
Expected behavior:
I expect tf.keras.models.load_model to be able to load the model correctly from any folder path, regardless of whether it contains Chinese characters or not. If there are limitations on the folder name, it should be clearly documented or provide a more descriptive error message.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
# Create a simple model
model = tf.keras.models.Sequential([tf.keras.layers.Dense(1, input_shape=(1,))])
model.compile(optimizer='adam', loss='mse')
model.save('模型/') # '模型' is Chinese for 'model'
# Now, try to load the saved model
loaded_model = tf.keras.models.load_model('模型/')
```
### Relevant log output
```shell
Traceback (most recent call last):
...
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xa1 in position 27: invalid start byte
```
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"The training code used comes from github(https://github.com/tensorflow/models/tree/v2.11.0)",
"From the output you have provided, it does not look like a random convergence.\r\n\r\nAlso, as per the ResNet training with vanilla settings with a batch size of 4096 and 90 epochs, the benchmark result is what you are getting.\r\nhttps://github.com/tensorflow/models/tree/master/official/vision#resnet-models-trained-with-vanilla-settings",
"I have repeated the training many times, sometimes it can converge to 0.76, sometimes it can only achieve 0.7 accuracy, I want to see if there are any good methods or tools to help me debug? Thanks.",
"It is working as expected, you can not always achieve the same amount of accuracy for each run.\r\nThe reason you are getting `0.7` sometimes is because of the learning rate reduction stepwise set in the boundary limit.\r\nAfter certain point, training will become saturated. \r\n```\r\n learning_rate:\r\n type: 'stepwise'\r\n stepwise:\r\n boundaries: [18750, 37500, 50000]\r\n values: [0.8, 0.08, 0.008, 0.0008]\r\n```\r\nFeel free to change the configuration file which you have specified during training, for example [here](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/image_classification/imagenet_resnet50_gpu.yaml). ",
"However, the settings of my training are the same every time, and the boundary is also the same value. I have trained for 90 epochs, so I feel that the accuracy of network training random convergence to 0.76 is not normal, I may now suspect that the calculation error of some kernels is a bit large。Now the network is built based on tf.keras.model and run with model.fit. Even if I use callback function or print directly, I can’t grab the output of each layer, so I would like to ask if you have any suggestions or methods? Can help me grab the forward and backward calculation output of each layer, or are there other analysis methods? Thank you. ",
"Could you please share the reproducible code to replicate the behavior and also sample data if you can share would be helpful to assist you better. Thanks!",
"I have seted the seed by tf.random.set_seed(seed)、np.random.seed(seed）、random.seed(seed)、os.environ['PYTHONHASHSEED'] = str(seed)、os.environ['TF_DETERMINISTIC_OPS'] = '1'，and cancel the random of the imageimagenet_preprocessing.py, so i can get the reproduce behavior, Now the network is built based on tf.keras.model and run with model.fit. Even if I use callback function or print directly, I can’t grab the output of each layer, so I would like to ask if you have any suggestions or methods? Can help me grab the forward and backward calculation output of each layer, or are there other analysis methods? Thank you.\r\n\r\n",
"Hi, This issue seems to be specific to models, could you please close this issue and open a new issue in the repo https://github.com/tensorflow/models/issues",
"I have already take issue for there, but have no anwser. Does TensorFlow have any precision detection tools? It can be plugged directly into network training, similar to PyTorch's Torch GradCheck.",
"Have any questions to plugged directly into network training, similar to PyTorch's Torch GradCheck."
] | 2023-08-14T03:02:13 | 2024-01-09T06:02:00 | null | NONE | null | null | null | ### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf2.11
### Custom code
Yes
### OS platform and distribution
centos7.6
### Mobile device
_No response_
### Python version
3.7、3.8、3,。9
### Bazel version
5.3.0
### GCC/compiler version
9.4.0
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Using the inagenet dataset, on the resnet50 network, based on FP16 training with the model-2.11.0 code, the network has random convergence, is this phenomenon known, is there any solution at present? Is there any good way to debug this problem? Thanks.
### Standalone code to reproduce the issue
```shell
python3 /public/home/TF_test/rocm5.4/tf2.11/models-master_fp16/official/benchmark/models/resnet_imagenet_main.py --num_gpus=4 --batch_size=512 --train_epochs=90 --use_synthetic_data=false --data_dir=/public/software/apps/DeepLearning/Data/ImageNet-tensorflow/ --enable_checkpoint_and_export --dtype=fp16 --model_dir=./Checkpoint/
```
### Relevant log output
```shell
I0717 10:42:45.093785 47025319389696 controller.py:291] eval | step: 10009 | eval time: 74.7 sec | output: {'test_accuracy': 0.08882, 'test_loss': 1.2578498}
I0717 11:19:43.333361 47025319389696 controller.py:291] eval | step: 20018 | eval time: 47.9 sec | output: {'test_accuracy': 0.18752, 'test_loss': 1.0495228}
I0717 11:56:49.300491 47025319389696 controller.py:291] eval | step: 30027 | eval time: 48.0 sec | output: {'test_accuracy': 0.2584, 'test_loss': 0.9102226}
I0717 12:33:43.809209 47025319389696 controller.py:291] eval | step: 40036 | eval time: 47.3 sec | output: {'test_accuracy': 0.33168, 'test_loss': 0.77455497}
I0717 13:10:37.399015 47025319389696 controller.py:291] eval | step: 50045 | eval time: 48.6 sec | output: {'test_accuracy': 0.32974, 'test_loss': 0.7866249}
I0717 13:47:31.637742 47025319389696 controller.py:291] eval | step: 60054 | eval time: 48.0 sec | output: {'test_accuracy': 0.32832, 'test_loss': 0.7928696}
I0717 14:24:25.244748 47025319389696 controller.py:291] eval | step: 70063 | eval time: 47.4 sec | output: {'test_accuracy': 0.36952, 'test_loss': 0.7357325}
I0717 15:01:26.831945 47025319389696 controller.py:291] eval | step: 80072 | eval time: 49.6 sec | output: {'test_accuracy': 0.33716, 'test_loss': 0.7839137}
I0717 15:39:10.716763 47025319389696 controller.py:291] eval | step: 90081 | eval time: 51.2 sec | output: {'test_accuracy': 0.32314, 'test_loss': 0.82485163}
I0717 16:17:14.804115 47025319389696 controller.py:291] eval | step: 100090 | eval time: 49.1 sec | output: {'test_accuracy': 0.36102, 'test_loss': 0.7599265}
I0717 16:54:38.272225 47025319389696 controller.py:291] eval | step: 110099 | eval time: 49.5 sec | output: {'test_accuracy': 0.42732, 'test_loss': 0.6477533}
I0717 17:32:14.305328 47025319389696 controller.py:291] eval | step: 120108 | eval time: 49.1 sec | output: {'test_accuracy': 0.3769, 'test_loss': 0.745124}
I0717 18:19:19.995545 47025319389696 controller.py:291] eval | step: 130117 | eval time: 196.2 sec | output: {'test_accuracy': 0.43222, 'test_loss': 0.63742775}
I0717 19:01:38.542571 47025319389696 controller.py:291] eval | step: 140126 | eval time: 49.1 sec | output: {'test_accuracy': 0.43536, 'test_loss': 0.63410264}
I0717 19:39:52.303068 47025319389696 controller.py:291] eval | step: 150135 | eval time: 46.7 sec | output: {'test_accuracy': 0.43782, 'test_loss': 0.6309778}
I0717 20:17:52.557711 47025319389696 controller.py:291] eval | step: 160144 | eval time: 49.5 sec | output: {'test_accuracy': 0.44036, 'test_loss': 0.6212317}
I0717 20:55:54.236690 47025319389696 controller.py:291] eval | step: 170153 | eval time: 47.7 sec | output: {'test_accuracy': 0.4211, 'test_loss': 0.6638591}
I0717 21:33:46.721763 47025319389696 controller.py:291] eval | step: 180162 | eval time: 48.7 sec | output: {'test_accuracy': 0.41044, 'test_loss': 0.6806779}
I0717 22:11:56.432586 47025319389696 controller.py:291] eval | step: 190171 | eval time: 48.9 sec | output: {'test_accuracy': 0.4227, 'test_loss': 0.6592407}
I0717 22:49:48.895009 47025319389696 controller.py:291] eval | step: 200180 | eval time: 47.1 sec | output: {'test_accuracy': 0.41614, 'test_loss': 0.6689654}
I0717 23:27:56.641305 47025319389696 controller.py:291] eval | step: 210189 | eval time: 49.0 sec | output: {'test_accuracy': 0.43086, 'test_loss': 0.6471556}
I0718 00:06:08.226011 47025319389696 controller.py:291] eval | step: 220198 | eval time: 49.0 sec | output: {'test_accuracy': 0.43288, 'test_loss': 0.651372}
I0718 00:43:44.846733 47025319389696 controller.py:291] eval | step: 230207 | eval time: 48.6 sec | output: {'test_accuracy': 0.44876, 'test_loss': 0.61514467}
I0718 01:21:01.860804 47025319389696 controller.py:291] eval | step: 240216 | eval time: 47.8 sec | output: {'test_accuracy': 0.39994, 'test_loss': 0.6896438}
I0718 01:58:26.185002 47025319389696 controller.py:291] eval | step: 250225 | eval time: 49.3 sec | output: {'test_accuracy': 0.43974, 'test_loss': 0.63887787}
I0718 02:35:51.395891 47025319389696 controller.py:291] eval | step: 260234 | eval time: 48.7 sec | output: {'test_accuracy': 0.42148, 'test_loss': 0.65387714}
I0718 03:12:58.957236 47025319389696 controller.py:291] eval | step: 270243 | eval time: 48.0 sec | output: {'test_accuracy': 0.42988, 'test_loss': 0.64366835}
I0718 03:50:05.619722 47025319389696 controller.py:291] eval | step: 280252 | eval time: 49.1 sec | output: {'test_accuracy': 0.4292, 'test_loss': 0.64544094}
I0718 04:27:19.267962 47025319389696 controller.py:291] eval | step: 290261 | eval time: 47.7 sec | output: {'test_accuracy': 0.43688, 'test_loss': 0.633886}
I0718 05:04:16.388976 47025319389696 controller.py:291] eval | step: 300270 | eval time: 47.0 sec | output: {'test_accuracy': 0.4378, 'test_loss': 0.6427796}
I0718 05:41:28.633920 47025319389696 controller.py:291] eval | step: 310279 | eval time: 47.1 sec | output: {'test_accuracy': 0.60604, 'test_loss': 0.41581097}
I0718 06:18:23.006313 47025319389696 controller.py:291] eval | step: 320288 | eval time: 47.6 sec | output: {'test_accuracy': 0.60958, 'test_loss': 0.41247183}
I0718 06:55:19.940962 47025319389696 controller.py:291] eval | step: 330297 | eval time: 47.5 sec | output: {'test_accuracy': 0.62418, 'test_loss': 0.3925642}
I0718 07:32:11.406366 47025319389696 controller.py:291] eval | step: 340306 | eval time: 49.3 sec | output: {'test_accuracy': 0.60878, 'test_loss': 0.41140762}
I0718 08:09:13.098378 47025319389696 controller.py:291] eval | step: 350315 | eval time: 46.3 sec | output: {'test_accuracy': 0.61416, 'test_loss': 0.40241298}
I0718 08:46:11.470759 47025319389696 controller.py:291] eval | step: 360324 | eval time: 48.7 sec | output: {'test_accuracy': 0.61666, 'test_loss': 0.40103617}
I0718 09:23:19.292029 47025319389696 controller.py:291] eval | step: 370333 | eval time: 47.6 sec | output: {'test_accuracy': 0.64238, 'test_loss': 0.3717267}
I0718 09:59:58.652599 47025319389696 controller.py:291] eval | step: 380342 | eval time: 46.9 sec | output: {'test_accuracy': 0.63504, 'test_loss': 0.37905875}
I0718 10:36:30.269412 47025319389696 controller.py:291] eval | step: 390351 | eval time: 46.7 sec | output: {'test_accuracy': 0.62588, 'test_loss': 0.38983408}
I0718 11:12:59.710509 47025319389696 controller.py:291] eval | step: 400360 | eval time: 47.5 sec | output: {'test_accuracy': 0.62752, 'test_loss': 0.39042756}
I0718 11:49:34.702973 47025319389696 controller.py:291] eval | step: 410369 | eval time: 47.6 sec | output: {'test_accuracy': 0.63086, 'test_loss': 0.38619643}
I0718 12:26:10.658050 47025319389696 controller.py:291] eval | step: 420378 | eval time: 46.4 sec | output: {'test_accuracy': 0.61492, 'test_loss': 0.40776753}
I0718 13:02:54.373109 47025319389696 controller.py:291] eval | step: 430387 | eval time: 47.1 sec | output: {'test_accuracy': 0.61482, 'test_loss': 0.40698445}
I0718 13:39:56.424064 47025319389696 controller.py:291] eval | step: 440396 | eval time: 46.6 sec | output: {'test_accuracy': 0.61064, 'test_loss': 0.40885204}
I0718 14:16:56.972168 47025319389696 controller.py:291] eval | step: 450405 | eval time: 47.2 sec | output: {'test_accuracy': 0.6101, 'test_loss': 0.41232613}
I0718 14:53:49.710101 47025319389696 controller.py:291] eval | step: 460414 | eval time: 50.2 sec | output: {'test_accuracy': 0.62022, 'test_loss': 0.39717302}
I0718 15:31:00.055065 47025319389696 controller.py:291] eval | step: 470423 | eval time: 47.8 sec | output: {'test_accuracy': 0.63738, 'test_loss': 0.3755849}
I0718 16:26:43.115331 47025319389696 controller.py:291] eval | step: 480432 | eval time: 48.6 sec | output: {'test_accuracy': 0.63156, 'test_loss': 0.38420147}
I0718 17:14:57.232034 47025319389696 controller.py:291] eval | step: 490441 | eval time: 47.9 sec | output: {'test_accuracy': 0.61762, 'test_loss': 0.40265194}
I0718 18:08:04.532505 47025319389696 controller.py:291] eval | step: 500450 | eval time: 47.4 sec | output: {'test_accuracy': 0.63422, 'test_loss': 0.3809521}
I0718 18:45:18.862020 47025319389696 controller.py:291] eval | step: 510459 | eval time: 50.4 sec | output: {'test_accuracy': 0.62098, 'test_loss': 0.39487627}
I0718 19:22:18.786072 47025319389696 controller.py:291] eval | step: 520468 | eval time: 47.2 sec | output: {'test_accuracy': 0.61512, 'test_loss': 0.40484685}
I0718 19:59:17.472421 47025319389696 controller.py:291] eval | step: 530477 | eval time: 47.3 sec | output: {'test_accuracy': 0.63776, 'test_loss': 0.37440324}
I0718 20:46:59.059379 47025319389696 controller.py:291] eval | step: 540486 | eval time: 203.9 sec | output: {'test_accuracy': 0.62722, 'test_loss': 0.39110208}
I0718 21:45:11.991321 47025319389696 controller.py:291] eval | step: 550495 | eval time: 47.9 sec | output: {'test_accuracy': 0.63562, 'test_loss': 0.38335004}
I0718 22:22:17.513960 47025319389696 controller.py:291] eval | step: 560504 | eval time: 46.5 sec | output: {'test_accuracy': 0.61826, 'test_loss': 0.40100682}
I0718 22:59:05.393987 47025319389696 controller.py:291] eval | step: 570513 | eval time: 46.1 sec | output: {'test_accuracy': 0.63496, 'test_loss': 0.3817627}
I0718 23:36:01.059101 47025319389696 controller.py:291] eval | step: 580522 | eval time: 47.2 sec | output: {'test_accuracy': 0.61354, 'test_loss': 0.40860355}
I0719 00:12:57.767053 47025319389696 controller.py:291] eval | step: 590531 | eval time: 48.7 sec | output: {'test_accuracy': 0.62766, 'test_loss': 0.3902482}
I0719 00:50:08.519172 47025319389696 controller.py:291] eval | step: 600540 | eval time: 47.3 sec | output: {'test_accuracy': 0.62992, 'test_loss': 0.38722458}
I0719 01:27:14.241234 47025319389696 controller.py:291] eval | step: 610549 | eval time: 49.5 sec | output: {'test_accuracy': 0.6785, 'test_loss': 0.3319877}
I0719 02:04:12.080565 47025319389696 controller.py:291] eval | step: 620558 | eval time: 47.3 sec | output: {'test_accuracy': 0.68068, 'test_loss': 0.3297141}
I0719 02:41:31.931695 47025319389696 controller.py:291] eval | step: 630567 | eval time: 47.5 sec | output: {'test_accuracy': 0.67544, 'test_loss': 0.33620217}
I0719 03:18:53.615640 47025319389696 controller.py:291] eval | step: 640576 | eval time: 47.8 sec | output: {'test_accuracy': 0.67902, 'test_loss': 0.32960042}
I0719 03:56:03.865268 47025319389696 controller.py:291] eval | step: 650585 | eval time: 47.2 sec | output: {'test_accuracy': 0.68312, 'test_loss': 0.32697994}
I0719 04:33:13.600398 47025319389696 controller.py:291] eval | step: 660594 | eval time: 49.1 sec | output: {'test_accuracy': 0.68692, 'test_loss': 0.3218242}
I0719 05:10:26.761108 47025319389696 controller.py:291] eval | step: 670603 | eval time: 47.5 sec | output: {'test_accuracy': 0.68098, 'test_loss': 0.33020604}
I0719 05:47:24.072698 47025319389696 controller.py:291] eval | step: 680612 | eval time: 47.8 sec | output: {'test_accuracy': 0.68784, 'test_loss': 0.32333767}
I0719 06:24:23.891359 47025319389696 controller.py:291] eval | step: 690621 | eval time: 47.1 sec | output: {'test_accuracy': 0.68598, 'test_loss': 0.32332215}
I0719 07:01:34.794637 47025319389696 controller.py:291] eval | step: 700630 | eval time: 48.9 sec | output: {'test_accuracy': 0.68566, 'test_loss': 0.3250208}
I0719 07:38:47.115363 47025319389696 controller.py:291] eval | step: 710639 | eval time: 47.2 sec | output: {'test_accuracy': 0.68696, 'test_loss': 0.32350788}
I0719 08:15:57.504423 47025319389696 controller.py:291] eval | step: 720648 | eval time: 52.0 sec | output: {'test_accuracy': 0.68832, 'test_loss': 0.3217886}
I0719 08:53:14.727922 47025319389696 controller.py:291] eval | step: 730657 | eval time: 49.9 sec | output: {'test_accuracy': 0.6852, 'test_loss': 0.32565758}
I0719 09:30:20.804430 47025319389696 controller.py:291] eval | step: 740666 | eval time: 49.2 sec | output: {'test_accuracy': 0.68326, 'test_loss': 0.33065847}
I0719 10:07:20.642869 47025319389696 controller.py:291] eval | step: 750675 | eval time: 48.0 sec | output: {'test_accuracy': 0.68372, 'test_loss': 0.32719776}
I0719 10:46:23.875730 47025319389696 controller.py:291] eval | step: 760684 | eval time: 47.9 sec | output: {'test_accuracy': 0.68192, 'test_loss': 0.32962936}
I0719 11:23:17.199512 47025319389696 controller.py:291] eval | step: 770693 | eval time: 47.2 sec | output: {'test_accuracy': 0.68262, 'test_loss': 0.32934195}
I0719 12:00:04.367842 47025319389696 controller.py:291] eval | step: 780702 | eval time: 46.9 sec | output: {'test_accuracy': 0.67706, 'test_loss': 0.3367395}
I0719 12:37:04.218576 47025319389696 controller.py:291] eval | step: 790711 | eval time: 49.9 sec | output: {'test_accuracy': 0.68592, 'test_loss': 0.32515442}
I0719 13:14:02.169136 47025319389696 controller.py:291] eval | step: 800720 | eval time: 49.5 sec | output: {'test_accuracy': 0.6774, 'test_loss': 0.33403492}
I0719 13:51:13.950588 47025319389696 controller.py:291] eval | step: 810729 | eval time: 48.3 sec | output: {'test_accuracy': 0.68796, 'test_loss': 0.32154444}
I0719 14:28:19.452734 47025319389696 controller.py:291] eval | step: 820738 | eval time: 47.6 sec | output: {'test_accuracy': 0.68378, 'test_loss': 0.32751894}
I0719 15:05:49.973363 47025319389696 controller.py:291] eval | step: 830747 | eval time: 47.5 sec | output: {'test_accuracy': 0.6856, 'test_loss': 0.3268724}
I0719 15:42:51.213348 47025319389696 controller.py:291] eval | step: 840756 | eval time: 46.4 sec | output: {'test_accuracy': 0.68698, 'test_loss': 0.32407942}
I0719 16:19:54.159197 47025319389696 controller.py:291] eval | step: 850765 | eval time: 48.8 sec | output: {'test_accuracy': 0.6875, 'test_loss': 0.32387343}
I0719 16:57:06.514332 47025319389696 controller.py:291] eval | step: 860774 | eval time: 48.9 sec | output: {'test_accuracy': 0.6877, 'test_loss': 0.32420784}
I0719 17:34:12.212647 47025319389696 controller.py:291] eval | step: 870783 | eval time: 48.5 sec | output: {'test_accuracy': 0.688, 'test_loss': 0.3242049}
I0719 18:11:14.854687 47025319389696 controller.py:291] eval | step: 880792 | eval time: 48.6 sec | output: {'test_accuracy': 0.6882, 'test_loss': 0.324394}
I0719 18:48:31.401537 47025319389696 controller.py:291] eval | step: 890801 | eval time: 49.7 sec | output: {'test_accuracy': 0.68516, 'test_loss': 0.32722312}
I0719 19:25:37.033689 47025319389696 controller.py:291] eval | step: 900810 | eval time: 48.7 sec | output: {'test_accuracy': 0.68694, 'test_loss': 0.32629344}
eval | step: 10009 | eval time: 74.7 sec | output: {'test_accuracy': 0.08882, 'test_loss': 1.2578498}
eval | step: 20018 | eval time: 47.9 sec | output: {'test_accuracy': 0.18752, 'test_loss': 1.0495228}
eval | step: 30027 | eval time: 48.0 sec | output: {'test_accuracy': 0.2584, 'test_loss': 0.9102226}
eval | step: 40036 | eval time: 47.3 sec | output: {'test_accuracy': 0.33168, 'test_loss': 0.77455497}
eval | step: 50045 | eval time: 48.6 sec | output: {'test_accuracy': 0.32974, 'test_loss': 0.7866249}
eval | step: 60054 | eval time: 48.0 sec | output: {'test_accuracy': 0.32832, 'test_loss': 0.7928696}
eval | step: 70063 | eval time: 47.4 sec | output: {'test_accuracy': 0.36952, 'test_loss': 0.7357325}
eval | step: 80072 | eval time: 49.6 sec | output: {'test_accuracy': 0.33716, 'test_loss': 0.7839137}
eval | step: 90081 | eval time: 51.2 sec | output: {'test_accuracy': 0.32314, 'test_loss': 0.82485163}
eval | step: 100090 | eval time: 49.1 sec | output: {'test_accuracy': 0.36102, 'test_loss': 0.7599265}
eval | step: 110099 | eval time: 49.5 sec | output: {'test_accuracy': 0.42732, 'test_loss': 0.6477533}
eval | step: 120108 | eval time: 49.1 sec | output: {'test_accuracy': 0.3769, 'test_loss': 0.745124}
eval | step: 130117 | eval time: 196.2 sec | output: {'test_accuracy': 0.43222, 'test_loss': 0.63742775}
eval | step: 140126 | eval time: 49.1 sec | output: {'test_accuracy': 0.43536, 'test_loss': 0.63410264}
eval | step: 150135 | eval time: 46.7 sec | output: {'test_accuracy': 0.43782, 'test_loss': 0.6309778}
eval | step: 160144 | eval time: 49.5 sec | output: {'test_accuracy': 0.44036, 'test_loss': 0.6212317}
eval | step: 170153 | eval time: 47.7 sec | output: {'test_accuracy': 0.4211, 'test_loss': 0.6638591}
eval | step: 180162 | eval time: 48.7 sec | output: {'test_accuracy': 0.41044, 'test_loss': 0.6806779}
eval | step: 190171 | eval time: 48.9 sec | output: {'test_accuracy': 0.4227, 'test_loss': 0.6592407}
eval | step: 200180 | eval time: 47.1 sec | output: {'test_accuracy': 0.41614, 'test_loss': 0.6689654}
eval | step: 210189 | eval time: 49.0 sec | output: {'test_accuracy': 0.43086, 'test_loss': 0.6471556}
eval | step: 220198 | eval time: 49.0 sec | output: {'test_accuracy': 0.43288, 'test_loss': 0.651372}
eval | step: 230207 | eval time: 48.6 sec | output: {'test_accuracy': 0.44876, 'test_loss': 0.61514467}
eval | step: 240216 | eval time: 47.8 sec | output: {'test_accuracy': 0.39994, 'test_loss': 0.6896438}
eval | step: 250225 | eval time: 49.3 sec | output: {'test_accuracy': 0.43974, 'test_loss': 0.63887787}
eval | step: 260234 | eval time: 48.7 sec | output: {'test_accuracy': 0.42148, 'test_loss': 0.65387714}
eval | step: 270243 | eval time: 48.0 sec | output: {'test_accuracy': 0.42988, 'test_loss': 0.64366835}
eval | step: 280252 | eval time: 49.1 sec | output: {'test_accuracy': 0.4292, 'test_loss': 0.64544094}
eval | step: 290261 | eval time: 47.7 sec | output: {'test_accuracy': 0.43688, 'test_loss': 0.633886}
eval | step: 300270 | eval time: 47.0 sec | output: {'test_accuracy': 0.4378, 'test_loss': 0.6427796}
eval | step: 310279 | eval time: 47.1 sec | output: {'test_accuracy': 0.60604, 'test_loss': 0.41581097}
eval | step: 320288 | eval time: 47.6 sec | output: {'test_accuracy': 0.60958, 'test_loss': 0.41247183}
eval | step: 330297 | eval time: 47.5 sec | output: {'test_accuracy': 0.62418, 'test_loss': 0.3925642}
eval | step: 340306 | eval time: 49.3 sec | output: {'test_accuracy': 0.60878, 'test_loss': 0.41140762}
eval | step: 350315 | eval time: 46.3 sec | output: {'test_accuracy': 0.61416, 'test_loss': 0.40241298}
eval | step: 360324 | eval time: 48.7 sec | output: {'test_accuracy': 0.61666, 'test_loss': 0.40103617}
eval | step: 370333 | eval time: 47.6 sec | output: {'test_accuracy': 0.64238, 'test_loss': 0.3717267}
eval | step: 380342 | eval time: 46.9 sec | output: {'test_accuracy': 0.63504, 'test_loss': 0.37905875}
eval | step: 390351 | eval time: 46.7 sec | output: {'test_accuracy': 0.62588, 'test_loss': 0.38983408}
eval | step: 400360 | eval time: 47.5 sec | output: {'test_accuracy': 0.62752, 'test_loss': 0.39042756}
eval | step: 410369 | eval time: 47.6 sec | output: {'test_accuracy': 0.63086, 'test_loss': 0.38619643}
eval | step: 420378 | eval time: 46.4 sec | output: {'test_accuracy': 0.61492, 'test_loss': 0.40776753}
eval | step: 430387 | eval time: 47.1 sec | output: {'test_accuracy': 0.61482, 'test_loss': 0.40698445}
eval | step: 440396 | eval time: 46.6 sec | output: {'test_accuracy': 0.61064, 'test_loss': 0.40885204}
eval | step: 450405 | eval time: 47.2 sec | output: {'test_accuracy': 0.6101, 'test_loss': 0.41232613}
eval | step: 460414 | eval time: 50.2 sec | output: {'test_accuracy': 0.62022, 'test_loss': 0.39717302}
eval | step: 470423 | eval time: 47.8 sec | output: {'test_accuracy': 0.63738, 'test_loss': 0.3755849}
eval | step: 480432 | eval time: 48.6 sec | output: {'test_accuracy': 0.63156, 'test_loss': 0.38420147}
eval | step: 490441 | eval time: 47.9 sec | output: {'test_accuracy': 0.61762, 'test_loss': 0.40265194}
eval | step: 500450 | eval time: 47.4 sec | output: {'test_accuracy': 0.63422, 'test_loss': 0.3809521}
eval | step: 510459 | eval time: 50.4 sec | output: {'test_accuracy': 0.62098, 'test_loss': 0.39487627}
eval | step: 520468 | eval time: 47.2 sec | output: {'test_accuracy': 0.61512, 'test_loss': 0.40484685}
eval | step: 530477 | eval time: 47.3 sec | output: {'test_accuracy': 0.63776, 'test_loss': 0.37440324}
eval | step: 540486 | eval time: 203.9 sec | output: {'test_accuracy': 0.62722, 'test_loss': 0.39110208}
eval | step: 550495 | eval time: 47.9 sec | output: {'test_accuracy': 0.63562, 'test_loss': 0.38335004}
eval | step: 560504 | eval time: 46.5 sec | output: {'test_accuracy': 0.61826, 'test_loss': 0.40100682}
eval | step: 570513 | eval time: 46.1 sec | output: {'test_accuracy': 0.63496, 'test_loss': 0.3817627}
eval | step: 580522 | eval time: 47.2 sec | output: {'test_accuracy': 0.61354, 'test_loss': 0.40860355}
eval | step: 590531 | eval time: 48.7 sec | output: {'test_accuracy': 0.62766, 'test_loss': 0.3902482}
eval | step: 600540 | eval time: 47.3 sec | output: {'test_accuracy': 0.62992, 'test_loss': 0.38722458}
eval | step: 610549 | eval time: 49.5 sec | output: {'test_accuracy': 0.6785, 'test_loss': 0.3319877}
eval | step: 620558 | eval time: 47.3 sec | output: {'test_accuracy': 0.68068, 'test_loss': 0.3297141}
eval | step: 630567 | eval time: 47.5 sec | output: {'test_accuracy': 0.67544, 'test_loss': 0.33620217}
eval | step: 640576 | eval time: 47.8 sec | output: {'test_accuracy': 0.67902, 'test_loss': 0.32960042}
eval | step: 650585 | eval time: 47.2 sec | output: {'test_accuracy': 0.68312, 'test_loss': 0.32697994}
eval | step: 660594 | eval time: 49.1 sec | output: {'test_accuracy': 0.68692, 'test_loss': 0.3218242}
eval | step: 670603 | eval time: 47.5 sec | output: {'test_accuracy': 0.68098, 'test_loss': 0.33020604}
eval | step: 680612 | eval time: 47.8 sec | output: {'test_accuracy': 0.68784, 'test_loss': 0.32333767}
eval | step: 690621 | eval time: 47.1 sec | output: {'test_accuracy': 0.68598, 'test_loss': 0.32332215}
eval | step: 700630 | eval time: 48.9 sec | output: {'test_accuracy': 0.68566, 'test_loss': 0.3250208}
eval | step: 710639 | eval time: 47.2 sec | output: {'test_accuracy': 0.68696, 'test_loss': 0.32350788}
eval | step: 720648 | eval time: 52.0 sec | output: {'test_accuracy': 0.68832, 'test_loss': 0.3217886}
eval | step: 730657 | eval time: 49.9 sec | output: {'test_accuracy': 0.6852, 'test_loss': 0.32565758}
eval | step: 740666 | eval time: 49.2 sec | output: {'test_accuracy': 0.68326, 'test_loss': 0.33065847}
eval | step: 750675 | eval time: 48.0 sec | output: {'test_accuracy': 0.68372, 'test_loss': 0.32719776}
eval | step: 760684 | eval time: 47.9 sec | output: {'test_accuracy': 0.68192, 'test_loss': 0.32962936}
eval | step: 770693 | eval time: 47.2 sec | output: {'test_accuracy': 0.68262, 'test_loss': 0.32934195}
eval | step: 780702 | eval time: 46.9 sec | output: {'test_accuracy': 0.67706, 'test_loss': 0.3367395}
eval | step: 790711 | eval time: 49.9 sec | output: {'test_accuracy': 0.68592, 'test_loss': 0.32515442}
eval | step: 800720 | eval time: 49.5 sec | output: {'test_accuracy': 0.6774, 'test_loss': 0.33403492}
eval | step: 810729 | eval time: 48.3 sec | output: {'test_accuracy': 0.68796, 'test_loss': 0.32154444}
eval | step: 820738 | eval time: 47.6 sec | output: {'test_accuracy': 0.68378, 'test_loss': 0.32751894}
eval | step: 830747 | eval time: 47.5 sec | output: {'test_accuracy': 0.6856, 'test_loss': 0.3268724}
eval | step: 840756 | eval time: 46.4 sec | output: {'test_accuracy': 0.68698, 'test_loss': 0.32407942}
eval | step: 850765 | eval time: 48.8 sec | output: {'test_accuracy': 0.6875, 'test_loss': 0.32387343}
eval | step: 860774 | eval time: 48.9 sec | output: {'test_accuracy': 0.6877, 'test_loss': 0.32420784}
eval | step: 870783 | eval time: 48.5 sec | output: {'test_accuracy': 0.688, 'test_loss': 0.3242049}
eval | step: 880792 | eval time: 48.6 sec | output: {'test_accuracy': 0.6882, 'test_loss': 0.324394}
eval | step: 890801 | eval time: 49.7 sec | output: {'test_accuracy': 0.68516, 'test_loss': 0.32722312}
eval | step: 900810 | eval time: 48.7 sec | output: {'test_accuracy': 0.68694, 'test_loss': 0.32629344}
```
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"Any help is appreciated \r\n\r\n@vatsalkachhia @SuryanarayanaY @tilakrayal @sun1638650145 @pjpratik ",
"found the issue\r\n\r\nit requires python 3.8",
"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/61548\">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/61548\">No</a>\n"
] | 2023-08-13T21:34:43 | 2023-08-13T23:03:11 | 2023-08-13T23:03:09 | NONE | null | null | null | I need to install TensorFlow 2.2.0
why? because this repo (https://github.com/GantMan/nsfw_model) is requesting it and now matter what I tried can't make it work with newer TensorFlows
How can I install TensorFlow 2.2.0 on Windows 10 and Python 3.10?
The error I am getting is and I am not able to fix it
```
(venv) G:\nsfw_model>python a.py
2023-08-14 00:33:39.437427: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-14 00:33:40.148302: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 21643 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3090 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6
2023-08-14 00:33:40.149891: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 9603 MB memory: -> device: 1, name: NVIDIA GeForce RTX 3060, pci bus id: 0000:05:00.0, compute capability: 8.6
Traceback (most recent call last):
File "G:\nsfw_model\a.py", line 13, in <module>
print(predict.classify(model, 'test'))
File "G:\nsfw_model\nsfw_detector\predict.py", line 67, in classify
probs = classify_nd(model, images, predict_args)
File "G:\nsfw_model\nsfw_detector\predict.py", line 77, in classify_nd
model_preds = model.predict(nd_images, **predict_args)
File "G:\nsfw_model\venv\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "G:\nsfw_model\venv\lib\site-packages\keras\engine\training.py", line 1997, in predict
raise ValueError('Unexpected result of `predict_function` '
ValueError: Unexpected result of `predict_function` (Empty batch_outputs). Please use `Model.compile(..., run_eagerly=True)`, or `tf.config.run_functions_eagerly(True)` for more information of where went wrong, or file a issue/bug to `tf.keras`.
```
predict.py
```
#! python
import argparse
import json
from os import listdir
from os.path import isfile, join, exists, isdir, abspath
import numpy as np
import tensorflow as tf
from tensorflow import keras
import tensorflow_hub as hub
IMAGE_DIM = 299 # required/default image dimensionality
def load_images(image_paths, image_size, verbose=True):
'''
Function for loading images into numpy arrays for passing to model.predict
inputs:
image_paths: list of image paths to load
image_size: size into which images should be resized
verbose: show all of the image path and sizes loaded
outputs:
loaded_images: loaded images on which keras model can run predictions
loaded_image_indexes: paths of images which the function is able to process
'''
loaded_images = []
loaded_image_paths = []
if isdir(image_paths):
parent = abspath(image_paths)
image_paths = [join(parent, f) for f in listdir(image_paths) if isfile(join(parent, f))]
elif isfile(image_paths):
image_paths = [image_paths]
for img_path in image_paths:
try:
if verbose:
print(img_path, "size:", image_size)
image = keras.preprocessing.image.load_img(img_path, target_size=image_size)
image = keras.preprocessing.image.img_to_array(image)
image /= 255
loaded_images.append(image)
loaded_image_paths.append(img_path)
except Exception as ex:
print("Image Load Failure: ", img_path, ex)
return np.asarray(loaded_images), loaded_image_paths
def load_model(model_path):
if model_path is None or not exists(model_path):
raise ValueError("saved_model_path must be the valid directory of a saved model to load.")
model = tf.keras.models.load_model(model_path, custom_objects={'KerasLayer': hub.KerasLayer},compile=False)
return model
def classify(model, input_paths, image_dim=IMAGE_DIM, predict_args={}):
"""
Classify given a model, input paths (could be single string), and image dimensionality.
Optionally, pass predict_args that will be passed to tf.keras.Model.predict().
"""
images, image_paths = load_images(input_paths, (image_dim, image_dim))
probs = classify_nd(model, images, predict_args)
return dict(zip(image_paths, probs))
def classify_nd(model, nd_images, predict_args={}):
"""
Classify given a model, image array (numpy)
Optionally, pass predict_args that will be passed to tf.keras.Model.predict().
"""
model_preds = model.predict(nd_images, **predict_args)
# preds = np.argsort(model_preds, axis = 1).tolist()
categories = ['drawings', 'hentai', 'neutral', 'porn', 'sexy']
probs = []
for i, single_preds in enumerate(model_preds):
single_probs = {}
for j, pred in enumerate(single_preds):
single_probs[categories[j]] = float(pred)
probs.append(single_probs)
return probs
def main(args=None):
parser = argparse.ArgumentParser(
description="""A script to perform NFSW classification of images""",
epilog="""
Launch with default model and a test image
python nsfw_detector/predict.py --saved_model_path mobilenet_v2_140_224 --image_source test.jpg
""", formatter_class=argparse.RawTextHelpFormatter)
submain = parser.add_argument_group('main execution and evaluation functionality')
submain.add_argument('--image_source', dest='image_source', type=str, required=True,
help='A directory of images or a single image to classify')
submain.add_argument('--saved_model_path', dest='saved_model_path', type=str, required=True,
help='The model to load')
submain.add_argument('--image_dim', dest='image_dim', type=int, default=IMAGE_DIM,
help="The square dimension of the model's input shape")
if args is not None:
config = vars(parser.parse_args(args))
else:
config = vars(parser.parse_args())
if config['image_source'] is None or not exists(config['image_source']):
raise ValueError("image_source must be a valid directory with images or a single image to classify.")
model = load_model(config['saved_model_path'])
image_preds = classify(model, config['image_source'], config['image_dim'])
print(json.dumps(image_preds, indent=2), '\n')
if __name__ == "__main__":
main()
```
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"Hi @dmc1778 ,\r\n\r\nI have replicated the issue with Tf2.13 and tf-nightly as well and got `Segmentation fault (core dumped)` as reported\r\nAttached logs below for reference.\r\n\r\n```\r\n(tf2.13) suryanarayanay@surya-ubuntu20:~$ python 61547_vm.py \r\n2023-08-14 04:44:45.094907: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-08-14 04:44:47.352606: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2.13.0\r\n2023-08-14 04:45:07.104770: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13623 MB memory: -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\r\n2023-08-14 04:45:07.105127: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 04:45:07.106654: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 13623 MB memory: -> device: 2, name: Tesla T4, pci bus id: 0000:00:06.0, compute capability: 7.5\r\n2023-08-14 04:45:07.107012: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 04:45:07.108548: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 13623 MB memory: -> device: 3, name: Tesla T4, pci bus id: 0000:00:07.0, compute capability: 7.5\r\nSegmentation fault (core dumped)\r\n(tf2.13) suryanarayanay@surya-ubuntu20:~$ \r\n\r\n\r\n(tf2.13) suryanarayanay@surya-ubuntu20:~$ python 61547_vm.py \r\n2023-08-14 04:46:35.131256: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9498] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2023-08-14 04:46:35.131417: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2023-08-14 04:46:35.131519: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n2023-08-14 04:46:35.140268: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-08-14 04:46:36.039695: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\nWARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/tf2.13/lib/python3.11/site-packages/tensorflow/python/ops/distributions/distribution.py:259: ReparameterizationType.__init__ (from tensorflow.python.ops.distributions.distribution) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\nWARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/tf2.13/lib/python3.11/site-packages/tensorflow/python/ops/distributions/bernoulli.py:165: RegisterKL.__init__ (from tensorflow.python.ops.distributions.kullback_leibler) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\n2.15.0-dev20230813\r\n2023-08-14 04:46:49.920461: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1898] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13621 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\r\n2023-08-14 04:46:49.920861: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 04:46:49.922636: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1898] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13621 MB memory: -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\r\n2023-08-14 04:46:49.922986: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 04:46:49.924632: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1898] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 13621 MB memory: -> device: 2, name: Tesla T4, pci bus id: 0000:00:06.0, compute capability: 7.5\r\n2023-08-14 04:46:49.925033: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 04:46:49.926611: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1898] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 13621 MB memory: -> device: 3, name: Tesla T4, pci bus id: 0000:00:07.0, compute capability: 7.5\r\nSegmentation fault (core dumped)\r\n(tf2.13) suryanarayanay@surya-ubuntu20:~$ \r\n```\r\n\r\nSince Segmentation faults can be considered as potential security vulnerabilities please read [SECURITY.md](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) and report them through proper channel as mentioned in SECURITY.md. \r\n\r\nThanks!\r\n"
] | 2023-08-13T06:20:08 | 2023-09-20T18:18:46 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to feeding a list with very large integer values.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.ops import gen_image_ops
try:
images_tensor = tf.saturate_cast(tf.constant(-67, shape=[0, 1, 3, 2], dtype=tf.int64,),dtype=tf.uint16)
images = tf.identity(images_tensor)
size_0 = 536870912
size_1 = 1250999896764
size = [size_0,size_1,]
align_corners = True
half_pixel_centers = False
out = gen_image_ops.resize_bicubic(images=images,size=size,align_corners=align_corners,half_pixel_centers=half_pixel_centers,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-13 02:18:02.134306: 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.
2023-08-13 02:18:03.002101: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 02:18:03.022974: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 02:18:03.023167: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 02:18:03.023525: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-13 02:18:03.024137: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 02:18:03.024303: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 02:18:03.024449: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 02:18:03.077913: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 02:18:03.078079: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 02:18:03.078189: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 02:18:03.078278: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 761 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
Segmentation fault
```
```
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"@dmc1778 I tried the issue in colab with tf-nightly version and faced the following outcome;\r\n```\r\nWARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow/python/ops/distributions/distribution.py:259: ReparameterizationType.__init__ (from tensorflow.python.ops.distributions.distribution) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\nWARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow/python/ops/distributions/bernoulli.py:165: RegisterKL.__init__ (from tensorflow.python.ops.distributions.kullback_leibler) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\n[ ]\r\n```\r\nPlease find the attached gist and confirm the same with the [nightly](https://colab.research.google.com/gist/sushreebarsa/640aa7565016b4b7b3586fa1b41693f2/untitled822.ipynb#scrollTo=H6n4y8B2qpNG) version. Thank you!\r\n",
"My session crashes on 2.13.0 colab:\r\n\r\n```\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\"[D 02:58:55.919 NotebookApp] Searching ['/root/.jupyter', '/root/.local/etc/jupyter', '/usr/etc/jupyter', '/usr/local/etc/jupyter', '/etc/jupyter'] for config files\",\"time\":\"2023-08-18T02:58:55.920Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\"[D 02:58:55.919 NotebookApp] Looking for jupyter_config in /etc/jupyter\",\"time\":\"2023-08-18T02:58:55.931Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\"[D 02:58:55.923 NotebookApp] Looking for jupyter_config in /usr/local/etc/jupyter\",\"time\":\"2023-08-18T02:58:55.931Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\"[D 02:58:55.925 NotebookApp] Looking for jupyter_config in /usr/etc/jupyter\",\"time\":\"2023-08-18T02:58:55.931Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\"[D 02:58:55.925 NotebookApp] Looking for jupyter_config in /root/.local/etc/jupyter\",\"time\":\"2023-08-18T02:58:55.933Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\"[D 02:58:55.925 NotebookApp] Looking for jupyter_config in /root/.jupyter\",\"time\":\"2023-08-18T02:58:55.933Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\"[D 02:58:55.926 NotebookApp] Looking for jupyter_notebook_config in /etc/jupyter\",\"time\":\"2023-08-18T02:58:55.933Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\"[D 02:58:55.927 NotebookApp] Loaded config file: /etc/jupyter/jupyter_notebook_config.py\",\"time\":\"2023-08-18T02:58:55.933Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\"[D 02:58:55.927 NotebookApp] Looking for jupyter_notebook_config in /usr/local/etc/jupyter\",\"time\":\"2023-08-18T02:58:55.933Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\"[D 02:58:55.928 NotebookApp] Loaded config file: 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/usr/etc/jupyter\",\"time\":\"2023-08-18T02:58:56.020Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\"[D 02:58:56.009 NotebookApp] Looking for jupyter_notebook_config in /root/.local/etc/jupyter\",\"time\":\"2023-08-18T02:58:56.021Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\"[D 02:58:56.009 NotebookApp] Looking for jupyter_notebook_config in /root/.jupyter\",\"time\":\"2023-08-18T02:58:56.021Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\"[D 02:58:56.013 NotebookApp] Loaded config file: /root/.jupyter/jupyter_notebook_config.py\",\"time\":\"2023-08-18T02:58:56.021Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\" \\t/etc/jupyter/jupyter_notebook_config.json\",\"time\":\"2023-08-18T02:58:56.443Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\" \\t/usr/local/etc/jupyter/jupyter_notebook_config.d/panel-client-jupyter.json\",\"time\":\"2023-08-18T02:58:56.445Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\" \\t/usr/local/etc/jupyter/jupyter_notebook_config.json\",\"time\":\"2023-08-18T02:58:56.445Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\" \\t/usr/etc/jupyter/jupyter_notebook_config.json\",\"time\":\"2023-08-18T02:58:56.447Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\" \\t/root/.local/etc/jupyter/jupyter_notebook_config.json\",\"time\":\"2023-08-18T02:58:56.448Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\" \\t/root/.jupyter/jupyter_notebook_config.json\",\"time\":\"2023-08-18T02:58:56.451Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":30,\"msg\":\"Writing notebook server cookie secret to /root/.local/share/jupyter/runtime/notebook_cookie_secret\",\"time\":\"2023-08-18T02:58:56.467Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":30,\"msg\":\"Authentication of /metrics is OFF, since other authentication is disabled.\",\"time\":\"2023-08-18T02:58:56.468Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":30,\"msg\":\"google.colab serverextension initialized.\",\"time\":\"2023-08-18T02:58:56.564Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\" \\t/etc/jupyter/jupyter_notebook_config.json\",\"time\":\"2023-08-18T02:58:56.665Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\" \\t/usr/local/etc/jupyter/jupyter_notebook_config.d/panel-client-jupyter.json\",\"time\":\"2023-08-18T02:58:56.666Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\" \\t/usr/local/etc/jupyter/jupyter_notebook_config.json\",\"time\":\"2023-08-18T02:58:56.667Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\" \\t/usr/etc/jupyter/jupyter_notebook_config.json\",\"time\":\"2023-08-18T02:58:56.668Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\" \\t/root/.local/etc/jupyter/jupyter_notebook_config.json\",\"time\":\"2023-08-18T02:58:56.674Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\" \\t/root/.jupyter/jupyter_notebook_config.json\",\"time\":\"2023-08-18T02:58:56.675Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":30,\"msg\":\"Writing notebook server cookie secret to /root/.local/share/jupyter/runtime/notebook_cookie_secret\",\"time\":\"2023-08-18T02:58:56.686Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":30,\"msg\":\"Authentication of /metrics is OFF, since other authentication is disabled.\",\"time\":\"2023-08-18T02:58:56.687Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":30,\"msg\":\"google.colab serverextension initialized.\",\"time\":\"2023-08-18T02:58:56.734Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":30,\"msg\":\"Serving notebooks from local directory: /\",\"time\":\"2023-08-18T02:59:00.894Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":30,\"msg\":\"Jupyter Notebook 6.5.5 is running at:\",\"time\":\"2023-08-18T02:59:00.895Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":30,\"msg\":\"http://172.28.0.2:9000/\",\"time\":\"2023-08-18T02:59:00.895Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":30,\"msg\":\"Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\",\"time\":\"2023-08-18T02:59:00.896Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":30,\"msg\":\"Serving notebooks from local directory: /\",\"time\":\"2023-08-18T02:59:00.914Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":30,\"msg\":\"Jupyter Notebook 6.5.5 is running at:\",\"time\":\"2023-08-18T02:59:00.915Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":30,\"msg\":\"http://172.28.0.12:9000/\",\"time\":\"2023-08-18T02:59:00.916Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":30,\"msg\":\"Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\",\"time\":\"2023-08-18T02:59:00.916Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":30,\"msg\":\"Kernel started: 584f5850-e9c0-4606-bb63-8877434a5eb3, name: python3\",\"time\":\"2023-08-18T02:59:22.440Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\"2023-08-18 02:59:36.976819: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\",\"time\":\"2023-08-18T02:59:36.976Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\"To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\",\"time\":\"2023-08-18T02:59:36.977Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\"2023-08-18 02:59:39.065366: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\",\"time\":\"2023-08-18T02:59:39.065Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":30,\"msg\":\"KernelRestarter: restarting kernel (1/5), keep random ports\",\"time\":\"2023-08-18T02:59:43.439Z\",\"v\":0}\r\n{\"pid\":7,\"type\":\"jupyter\",\"level\":40,\"msg\":\"WARNING:root:kernel 584f5850-e9c0-4606-bb63-8877434a5eb3 restarted\",\"time\":\"2023-08-18T02:59:43.440Z\",\"v\":0}\r\n```",
"@SuryanarayanaY bug exists on 2.13.0."
] | 2023-08-13T06:13:49 | 2023-09-19T01:58:51 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.11.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to feeding None values
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.framework import importer
try:
arg_0 = None
arg_1 = "A"
arg_2 = None
arg_3_0 = "A"
arg_3_1 = "B"
arg_3 = [arg_3_0,arg_3_1,]
arg_4 = True
out = importer._PopulateTFImportGraphDefOptions(arg_0,arg_1,arg_2,arg_3,arg_4,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-13 02:11:56.624855: 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.
Segmentation fault
```
```
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"@dmc1778 I was able to reproduce the issue on colab using TF v2.11, 2.13, tf-nightly. Please find the attached [gist](https://colab.research.google.com/gist/sushreebarsa/e6b3d4ed44288db60f61f0572f616591/61545.ipynb). \r\nSince Segmentation faults can be considered as potential security vulnerabilities please read [SECURITY.md](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) and report them through proper channel as mentioned in SECURITY.md.\r\nThank you!\r\n",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61545\">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/61545\">No</a>\n"
] | 2023-08-13T06:11:17 | 2023-08-29T01:47:22 | 2023-08-29T01:47:20 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.11.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to feeding None value
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.framework import importer
try:
arg_0_0 = "A"
arg_0 = [arg_0_0,]
arg_1 = None
arg_2 = None
out = importer._GatherReturnElements(arg_0,arg_1,arg_2,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
023-08-13 02:10:14.149106: 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.
Segmentation fault
```
```
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"@dmc1778 After executing the code for multiple times as well, I was not able to replicate the issue reported. I have faced the error instead as below;\r\n```\r\nError:{{function_node __wrapped__SparseSplit_num_split_2_device_/job:localhost/replica:0/task:0/device:CPU:0}} Expected a non-negative size, got -11 [Op:SparseSplit]\r\n```\r\nPlease find the colab gist [here](https://colab.research.google.com/gist/sushreebarsa/bc5f582908d36aa093c95890e3809e07/61544.ipynb) in the latest versions. Thank you!",
"> @dmc1778 After executing the code for multiple times as well, I was not able to replicate the issue reported. I have faced the error instead as below;\r\n> \r\n> ```\r\n> Error:{{function_node __wrapped__SparseSplit_num_split_2_device_/job:localhost/replica:0/task:0/device:CPU:0}} Expected a non-negative size, got -11 [Op:SparseSplit]\r\n> ```\r\n> \r\n> Please find the colab gist [here](https://colab.research.google.com/gist/sushreebarsa/bc5f582908d36aa093c95890e3809e07/61544.ipynb) in the latest versions. Thank you!\r\n\r\nDid you try on 2.11.0?",
"@dmc1778 Yes, the result is the same. 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.",
"Closing as resolved at head",
"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/61544\">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/61544\">No</a>\n"
] | 2023-08-13T06:08:12 | 2023-08-22T07:42:26 | 2023-08-22T07:42:24 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.11.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to zero integer argument. It would be best if you ran multiple times to see the abort.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.ops import gen_sparse_ops
try:
arg_0 = 0
arg_1_tensor = tf.random.uniform([14, 2], minval=-256, maxval=257, dtype=tf.int64)
arg_1 = tf.identity(arg_1_tensor)
arg_2_tensor = tf.random.uniform([14], minval=-256, maxval=257, dtype=tf.int64)
arg_2 = tf.identity(arg_2_tensor)
arg_3_tensor = tf.random.uniform([2], minval=-256, maxval=257, dtype=tf.int64)
arg_3 = tf.identity(arg_3_tensor)
arg_4 = 2
out = gen_sparse_ops.sparse_split(arg_0,arg_1,arg_2,arg_3,arg_4,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-13 02:06:08.283954: 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.
2023-08-13 02:06:09.159348: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 02:06:09.181201: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 02:06:09.181463: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 02:06:09.181927: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-13 02:06:09.182527: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 02:06:09.182681: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 02:06:09.182809: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 02:06:09.234678: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 02:06:09.234880: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 02:06:09.235018: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 02:06:09.235129: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 151 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
2023-08-13 02:06:09.251615: I tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:735] failed to allocate 151.69M (159055872 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2023-08-13 02:06:09.251958: I tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:735] failed to allocate 136.52M (143150336 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2023-08-13 02:06:09.273191: E tensorflow/compiler/xla/stream_executor/cuda/cuda_event.cc:29] Error polling for event status: failed to query event: CUDA_ERROR_MISALIGNED_ADDRESS: misaligned address
2023-08-13 02:06:09.273688: F tensorflow/core/common_runtime/device/device_event_mgr.cc:221] Unexpected Event status: 1
Aborted
```
```
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"Hi @dmc1778 ,\r\n\r\nThe issue seems resolved already from TF2.12 onwards. I have tested the code and its raising intended error without abort in Tf2.12 and tf-nightly as well.Please refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/8e096b58e03a15983accd675cbe9a9a2/61543_2-12_nightly_2-11.ipynb).\r\n\r\nI even tried with TF2.11v even there it's raising intended error only even multiple runs in attached colab gist. Could you please verify it again. Thanks!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"Closing as resolved at head",
"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/61543\">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/61543\">No</a>\n"
] | 2023-08-13T05:59:59 | 2023-08-22T07:42:49 | 2023-08-22T07:42:46 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.11.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to a Negative Large Integer. The behavior is bizarre. It would be best if you ran multiple times to see the Abort.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
from tensorflow.python.ops import gen_math_ops
try:
try:
with tf.device('/CPU'):
splits_tensor = tf.constant(-1000000, shape=[129, 1, 1], dtype=tf.float16,)
splits = tf.identity(splits_tensor)
values_tensor = tf.saturate_cast(tf.constant(-153, shape=[3456], dtype=tf.int64,),dtype=tf.uint64)
values = tf.identity(values_tensor)
weights_tensor = tf.saturate_cast(tf.constant(-1012756988, shape=[128, 27], dtype=tf.int64,),dtype=tf.int16)
weights = tf.identity(weights_tensor)
size = -3046875451
out = gen_math_ops.ragged_bincount(splits=splits,values=values,weights=weights,size=size,)
except Exception as e:
print("Error:"+str(e))
try:
with tf.device('/GPU:0'):
splits = tf.identity(splits_tensor)
splits = tf.cast(splits, tf.float16)
values = tf.identity(values_tensor)
values = tf.cast(values, tf.uint64)
weights = tf.identity(weights_tensor)
weights = tf.cast(weights, tf.int16)
gen_math_ops.ragged_bincount(splits=splits,values=values,weights=weights,size=size,)
except Exception as e:
print("Error:"+str(e))
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-13 01:58:36.322308: 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.
2023-08-13 01:58:37.120869: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 01:58:37.139665: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 01:58:37.139841: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 01:58:37.140154: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-13 01:58:37.140677: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 01:58:37.140794: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 01:58:37.140896: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 01:58:37.187990: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 01:58:37.188156: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 01:58:37.188267: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 01:58:37.188356: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 152 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
Error:can't convert negative int to unsigned
2023-08-13 01:58:37.207611: I tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:735] failed to allocate 152.81M (160235520 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2023-08-13 01:58:37.207886: I tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:735] failed to allocate 137.53M (144211968 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2023-08-13 01:58:37.208138: I tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:735] failed to allocate 123.78M (129790976 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2023-08-13 01:58:37.213816: F ./tensorflow/python/eager/pywrap_tensor_conversion.h:58] Check failed: !PyErr_Occurred()
Aborted
```
```
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"@dmc1778 In TF v2.13 the session is crashing and in tf-nightly the session is not getting aborted. Could you please have a look at this [gist](https://colab.research.google.com/gist/sushreebarsa/c95f2b92e141f1fd7ba231149bce4780/61542.ipynb) and confirm?\r\nThank you!",
"> @dmc1778 In TF v2.13 the session is crashing and in tf-nightly the session is not getting aborted. Could you please have a look at this [gist](https://colab.research.google.com/gist/sushreebarsa/c95f2b92e141f1fd7ba231149bce4780/61542.ipynb) and confirm? Thank you!\r\n\r\nThat is correct.",
"@dmc1778 Thank you for the confirmation. As the issue is not appearing in tf-nightly then it would be fixed in next release so could you please let us know if it is good to close the ticket?\r\nThank you!",
"> @dmc1778 Thank you for the confirmation. As the issue is not appearing in tf-nightly then it would be fixed in next release so could you please let us know if it is good to close the ticket? Thank you!\r\n\r\nI think it is safe to close this issue.",
"@dmc1778 Thank you for the confirmation.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61542\">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/61542\">No</a>\n"
] | 2023-08-13T05:22:51 | 2023-08-18T09:14:31 | 2023-08-18T09:14:28 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.11.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
NaN string argument
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
from tensorflow.python.eager import remote
try:
try:
with tf.device('/CPU'):
arg_0 = "nan"
out = remote.connect_to_remote_host(arg_0,)
except Exception as e:
print("Error:"+str(e))
try:
with tf.device('/GPU:0'):
remote.connect_to_remote_host(arg_0,)
except Exception as e:
print("Error:"+str(e))
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-13 01:22:37.499369: 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.
2023-08-13 01:22:38.459392: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 01:22:38.480510: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 01:22:38.480708: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 01:22:38.481081: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-13 01:22:38.481707: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 01:22:38.481844: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 01:22:38.481961: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 01:22:38.536637: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 01:22:38.536859: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 01:22:38.536991: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 01:22:38.537094: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1725 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
2023-08-13 01:22:38.546718: E tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:589] INVALID_ARGUMENT: Could not interpret "nan" as a host-port pair.
E0813 01:22:38.546961566 1686085 completion_queue.cc:244] assertion failed: queue.num_items() == 0
Aborted
```
```
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"@mihaimaruseac @sachinprasadhs any update on this?",
"This is just a bug, unless you further analyze this to prove a security impact"
] | 2023-08-13T04:45:46 | 2023-09-14T20:52:28 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.11.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to the large integer argument
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.ops import array_ops
try:
arg_0_tensor = tf.random.uniform([0, 2, 3, 12], dtype=tf.float32)
arg_0 = tf.identity(arg_0_tensor)
arg_1 = 536870912
out = array_ops.depth_to_space(arg_0,arg_1,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-13 00:39:16.584885: 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.
2023-08-13 00:39:17.905164: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:39:17.935652: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:39:17.935873: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:39:17.936238: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-13 00:39:17.936772: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:39:17.936896: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:39:17.937052: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:39:17.993984: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:39:17.994150: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:39:17.994266: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:39:17.994359: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 7 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
Floating point exception
```
```
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"@dmc1778 I wasn't able to replicate the issue, please find the [gist](https://colab.research.google.com/gist/sushreebarsa/5d51f0d6ce748160e74ad55f9f1790a8/61540.ipynb) here. \r\nThe result is as follows which is intended;\r\n```\r\nError:{{function_node __wrapped__DepthToSpace_device_/job:localhost/replica:0/task:0/device:CPU:0}} Dimension -2 must be >= 0 [Op:DepthToSpace] name: \r\n[ ]\r\n\r\n```\r\nCould you please cross verify this once? Since Segmentation faults can be considered as potential security vulnerabilities please read [SECURITY.md](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) and report them through proper channel as mentioned in SECURITY.md.\r\n\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61540\">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/61540\">No</a>\n"
] | 2023-08-13T04:24:35 | 2023-09-11T06:13:47 | 2023-09-01T01:48:44 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.11.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to very large integer argument
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.ops import gen_array_ops
try:
arg_0_tensor = tf.random.uniform([3, 2, 3, 4], dtype=tf.float32)
arg_0 = tf.identity(arg_0_tensor)
arg_1 = 2147483647
arg_2 = "NHWC"
out = gen_array_ops.depth_to_space(arg_0,arg_1,arg_2,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
023-08-13 00:23:53.644564: 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.
2023-08-13 00:23:54.491071: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:23:54.510564: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:23:54.510736: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:23:54.511051: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-13 00:23:54.511595: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:23:54.511717: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:23:54.511830: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:23:54.572398: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:23:54.572634: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:23:54.572791: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:23:54.572916: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 153 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
2023-08-13 00:23:54.594062: I tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:735] failed to allocate 153.88M (161349632 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2023-08-13 00:23:54.594484: I tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:735] failed to allocate 138.49M (145214720 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2023-08-13 00:23:54.600623: F tensorflow/core/framework/tensor_shape.cc:201] Non-OK-status: InitDims(dim_sizes) status: INVALID_ARGUMENT: Expected a non-negative size, got -2
Aborted
```
```
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"Hi @dmc1778 ,\r\n\r\nI tried to replicate the reported behaviour but for me it's working fine and code execution is success.Its working fine with Tf2.11 and tf-nightly as well. Ran the code multiple times and no error. Please refer to attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/839aab2129cda05a0fa1cf8f55cea59e/61539_2-11_nightly.ipynb).\r\n\r\nCould you please cross verify again. Thanks!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"Closing as resolved at head",
"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/61539\">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/61539\">No</a>\n"
] | 2023-08-13T04:15:00 | 2023-08-22T07:42:38 | 2023-08-22T07:42:35 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.11.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to input tensor with zero shape
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.ops import gen_nn_ops
try:
input_sizes_0 = 2
input_sizes_1 = 8
input_sizes_2 = 8
input_sizes_3 = 8
input_sizes_4 = 5
input_sizes = [input_sizes_0,input_sizes_1,input_sizes_2,input_sizes_3,input_sizes_4,]
filter_tensor = tf.random.uniform([0, 1, 2, 5, 3], dtype=tf.float32)
filter = tf.identity(filter_tensor)
out_backprop_tensor = tf.random.uniform([2, 4, 4, 4, 3], dtype=tf.float32)
out_backprop = tf.identity(out_backprop_tensor)
strides_0 = 1
strides_1 = 2
strides_2 = 2
strides_3 = 2
strides_4 = 1
strides = [strides_0,strides_1,strides_2,strides_3,strides_4,]
padding = "SAME"
data_format = "NDHWC"
dilations_0 = 1
dilations_1 = 1
dilations_2 = 1
dilations_3 = 1
dilations_4 = 1
dilations = [dilations_0,dilations_1,dilations_2,dilations_3,dilations_4,]
out = gen_nn_ops.conv3d_backprop_input_v2(input_sizes=input_sizes,filter=filter,out_backprop=out_backprop,strides=strides,padding=padding,data_format=data_format,dilations=dilations,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-13 00:13:03.668988: 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.
2023-08-13 00:13:04.462547: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:13:04.483261: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:13:04.483427: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:13:04.483905: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-13 00:13:04.484753: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:13:04.484944: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:13:04.485057: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:13:04.585176: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:13:04.585346: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:13:04.585468: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:13:04.585565: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 744 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
2023-08-13 00:13:04.615688: F ./tensorflow/core/util/gpu_launch_config.h:129] Check failed: work_element_count > 0 (0 vs. 0)
Aborted
```
```
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"@SuryanarayanaY I was able to replicate the issue on colab, could you please have a look at this [gist](https://colab.research.google.com/gist/sushreebarsa/d3670f02ad6199f9d77d97834909dc5a/61538.ipynb). Thank you!",
"@dmc1778 ,\r\n\r\nI am able to replicate the reported behaviour with tf-nightly.\r\n\r\n```\r\n(tf2.13) suryanarayanay@surya-ubuntu20:~$ python 61538_nightly_gpu.py \r\n2023-08-14 12:58:34.899604: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9498] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2023-08-14 12:58:34.899770: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2023-08-14 12:58:34.899866: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n2023-08-14 12:58:34.908645: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-08-14 12:58:35.727028: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\nWARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/tf2.13/lib/python3.11/site-packages/tensorflow/python/ops/distributions/distribution.py:259: ReparameterizationType.__init__ (from tensorflow.python.ops.distributions.distribution) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\nWARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/tf2.13/lib/python3.11/site-packages/tensorflow/python/ops/distributions/bernoulli.py:165: RegisterKL.__init__ (from tensorflow.python.ops.distributions.kullback_leibler) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\n2.15.0-dev20230813\r\n2023-08-14 12:58:44.885421: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:44.887650: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:44.889692: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:44.891934: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:44.935350: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:44.937477: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:44.939647: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:44.941635: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:44.943520: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:44.945386: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:44.947283: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:44.949323: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:46.373274: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:46.375551: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:46.377681: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:46.379825: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:46.381762: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:46.383614: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:46.385423: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:46.387350: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:46.389056: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:46.390970: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:46.392864: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:46.394804: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:50.295197: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:50.298640: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:50.300830: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:50.303021: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:50.306589: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:50.312381: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:50.314450: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:50.316508: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:50.319334: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:50.325042: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1898] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13621 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\r\n2023-08-14 12:58:50.330942: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:50.332832: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1898] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13621 MB memory: -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\r\n2023-08-14 12:58:50.333182: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:50.335091: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1898] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 13621 MB memory: -> device: 2, name: Tesla T4, pci bus id: 0000:00:06.0, compute capability: 7.5\r\n2023-08-14 12:58:50.335462: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 12:58:50.337286: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1898] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 13621 MB memory: -> device: 3, name: Tesla T4, pci bus id: 0000:00:07.0, compute capability: 7.5\r\nSegmentation fault (core dumped)\r\n(tf2.13) suryanarayanay@surya-ubuntu20:~$ \r\n```\r\n\r\nSince Segmentation faults can be considered as potential security vulnerabilities please read [SECURITY.md](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) and report them through proper channel as mentioned in SECURITY.md.\r\n\r\nThanks!"
] | 2023-08-13T04:11:20 | 2023-09-20T18:19:09 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Due to very large input tensor.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
from tensorflow.python.autograph.operators import data_structures
try:
try:
with tf.device('/CPU'):
arg_0_tensor = tf.constant(False, shape=[1610637938,36028797018963968])
arg_0 = tf.identity(arg_0_tensor)
out = data_structures.tf_tensor_list_new(arg_0,)
except Exception as e:
print("Error:"+str(e))
try:
with tf.device('/GPU:0'):
arg_0 = tf.identity(arg_0_tensor)
arg_0 = tf.cast(arg_0, tf.bool)
data_structures.tf_tensor_list_new(arg_0,)
except Exception as e:
print("Error:"+str(e))
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-13 00:10:56.144934: 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.
2023-08-13 00:10:57.038182: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:10:57.059056: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:10:57.059246: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:10:57.059594: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-13 00:10:57.060159: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:10:57.060289: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:10:57.060399: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:10:57.112692: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:10:57.112870: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:10:57.112993: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-13 00:10:57.113089: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 751 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
Segmentation fault
```
```
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"@dmc1778 I was able to replicate this issue on colab, please find the [gist](https://colab.research.google.com/gist/sushreebarsa/bf40e78a80ac6c76f2c10caeaa83837e/61537.ipynb) here. Could you report this issue through the proper channel as mentioned in [SECURITY.md](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md).\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61537\">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/61537\">No</a>\n"
] | 2023-08-13T03:08:38 | 2023-09-20T18:19:18 | 2023-09-01T01:48:46 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
When num_elements is very large
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.ops import list_ops
try:
element_shape_0 = 1
element_shape = [element_shape_0,]
num_elements = 1250999896764
element_dtype = tf.float32
out = list_ops.tensor_list_reserve(element_shape=element_shape,num_elements=num_elements,element_dtype=element_dtype,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-12 23:05:56.608429: 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.
2023-08-12 23:05:57.434965: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 23:05:57.454466: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 23:05:57.454644: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 23:05:57.454958: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-12 23:05:57.455499: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 23:05:57.455620: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 23:05:57.455724: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 23:05:57.504359: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 23:05:57.504534: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 23:05:57.504649: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 23:05:57.504741: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 151 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
2023-08-12 23:05:57.518166: I tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:735] failed to allocate 151.44M (158793728 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
Segmentation fault
```
```
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https://api.github.com/repos/tensorflow/tensorflow/issues/61536 | https://api.github.com/repos/tensorflow/tensorflow | https://api.github.com/repos/tensorflow/tensorflow/issues/61536/labels{/name} | https://api.github.com/repos/tensorflow/tensorflow/issues/61536/comments | https://api.github.com/repos/tensorflow/tensorflow/issues/61536/events | https://github.com/tensorflow/tensorflow/issues/61536 | 1,848,374,147 | I_kwDOArmXAs5uK_OD | 61,536 | Abort when running tensorflow.python.ops.linalg_ops.self_adjoint_eig | {
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"@dmc1778 I was able to run the code successfully using TF v2.11, 2.13 and tf-nightly as well. Could you please have a look at this [gist](https://colab.research.google.com/gist/sushreebarsa/1b475de59ee1a050f03d406e1c0c2525/61536.ipynb) and confirm the same?\r\nThank you! ",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"Closing as resolved at head",
"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/61536\">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/61536\">No</a>\n"
] | 2023-08-13T03:05:27 | 2023-08-22T07:42:12 | 2023-08-22T07:42:09 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.11.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
This behavior is very strange and should not throw OOM error.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.ops import linalg_ops
try:
arg_0_tensor = tf.random.uniform([1, 1], dtype=tf.float32)
arg_0 = tf.identity(arg_0_tensor)
out = linalg_ops.self_adjoint_eigvals(arg_0,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-12 23:02:34.613725: 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.
2023-08-12 23:02:35.612147: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 23:02:35.634199: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 23:02:35.634612: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 23:02:35.635038: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-12 23:02:35.635637: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 23:02:35.635829: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 23:02:35.635948: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 23:02:35.639564: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:370] A non-primary context 0x5a7ae50 for device 0 exists before initializing the StreamExecutor. The primary context is now 0x7ffd00000000. We haven't verified StreamExecutor works with that.
2023-08-12 23:02:35.639662: F tensorflow/tsl/platform/statusor.cc:33] Attempting to fetch value instead of handling error INTERNAL: failed initializing StreamExecutor for CUDA device ordinal 0: INTERNAL: failed call to cuDevicePrimaryCtxRetain: CUDA_ERROR_OUT_OF_MEMORY: out of memory; total memory reported: 6216417280
Aborted
```
```
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"Hi @dmc1778 ,\r\n\r\nThe issue seems fixed already in tf-nightly(2.15.0-dev20230813).\r\n\r\nI ran the code multiple runs and every time it is raising intended error without any check fail. Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/9627a70359bd02f60d0d7c3be03e88f1/61535_nightly.ipynb#scrollTo=drACW0d11VbT) for reference. Could you please check and confirm with tf-nightly as well. \r\n\r\nThanks!",
"@dmc1778 ,\r\n\r\nChecked the code on a GPU-VM also with tf-nightly and every run was success without any check fail. Please refer attached logs.\r\n\r\n[61535_gpu_logs.txt](https://github.com/tensorflow/tensorflow/files/12332350/61535_gpu_logs.txt)\r\n",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"Closing as resolved at head",
"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/61535\">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/61535\">No</a>\n"
] | 2023-08-12T20:10:54 | 2023-08-22T07:42:06 | 2023-08-22T07:42:04 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.11.0
### Custom code
No
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
```
The following input combination causes check failure
```
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.ops import nn_ops
try:
arg_0_tensor = tf.random.uniform([2, 5, 6, 4, 3], dtype=tf.float32)
arg_0 = tf.identity(arg_0_tensor)
arg_1_tensor = tf.random.uniform([3, 3, 3, 2, 3], dtype=tf.float32)
arg_1 = tf.identity(arg_1_tensor)
arg_2_0 = 2
arg_2_1 = 11
arg_2_2 = 13
arg_2_3 = 9
arg_2_4 = False
arg_2 = [arg_2_0,arg_2_1,arg_2_2,arg_2_3,arg_2_4,]
arg_3_0 = 1
arg_3_1 = 2
arg_3_2 = 2
arg_3_3 = 2
arg_3_4 = 1
arg_3 = [arg_3_0,arg_3_1,arg_3_2,arg_3_3,arg_3_4,]
padding = "VALID"
data_format = "NDHWC"
dilations = None
out = nn_ops.conv3d_transpose_v2(arg_0,arg_1,arg_2,arg_3,padding=padding,data_format=data_format,dilations=dilations,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-06 06:58:26.108204: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-06 06:58:26.851251: 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; LD_LIBRARY_PATH: /home/nimashiri/anaconda3/envs/fuzzer_tf_2.11.0/lib/:/home/nimashiri/anaconda3/envs/fuzzer_tf_2.11.0/lib/python3.9/site-packages/nvidia/cudnn/lib:
2023-08-06 06:58:26.851492: 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; LD_LIBRARY_PATH: /home/nimashiri/anaconda3/envs/fuzzer_tf_2.11.0/lib/:/home/nimashiri/anaconda3/envs/fuzzer_tf_2.11.0/lib/python3.9/site-packages/nvidia/cudnn/lib:
2023-08-06 06:58:26.851500: 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.
2023-08-06 06:58:27.585126: 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; LD_LIBRARY_PATH: /home/nimashiri/anaconda3/envs/fuzzer_tf_2.11.0/lib/:/home/nimashiri/anaconda3/envs/fuzzer_tf_2.11.0/lib/python3.9/site-packages/nvidia/cudnn/lib:
2023-08-06 06:58:27.585372: 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; LD_LIBRARY_PATH: /home/nimashiri/anaconda3/envs/fuzzer_tf_2.11.0/lib/:/home/nimashiri/anaconda3/envs/fuzzer_tf_2.11.0/lib/python3.9/site-packages/nvidia/cudnn/lib:
2023-08-06 06:58:27.585380: 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.
2023-08-06 06:59:12.781705: F tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:876] Check failed: cudnnSetConvolutionGroupCount( handle_.get(), convolution_descriptor.group_count()) == CUDNN_STATUS_SUCCESS (3 vs. 0)
```
```
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"@dmc1778 I was able to run the code successfully using TF v2.11, 2.13 and tf-nightly. Could you please have a look at this [gist](https://colab.research.google.com/gist/sushreebarsa/48f980b8e884f3fdc57d982f337f5438/61534.ipynb). 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.",
"Closing as resolved at head",
"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/61534\">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/61534\">No</a>\n"
] | 2023-08-12T19:32:57 | 2023-08-22T07:41:55 | 2023-08-22T07:41:52 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.11.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Probably due to the large input tensor
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.ops import nn_ops
try:
arg_0_tensor = tf.constant(-1048576, shape=[2, 6, 4, 3], dtype=tf.float16,)
arg_0 = tf.identity(arg_0_tensor)
arg_1_tensor = tf.constant(-1250999896764, shape=[0, 3, 2, 3], dtype=tf.float16,)
arg_1 = tf.identity(arg_1_tensor)
arg_2_0 = 2
arg_2_1 = 12
arg_2_2 = 8
arg_2_3 = 2
arg_2 = [arg_2_0,arg_2_1,arg_2_2,arg_2_3,]
strides_0 = 1
strides_1 = 2
strides_2 = 2
strides_3 = 1
strides = [strides_0,strides_1,strides_2,strides_3,]
padding = "SAME"
out = nn_ops.conv2d_transpose(arg_0,arg_1,arg_2,strides=strides,padding=padding,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-12 15:32:22.021693: 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.
2023-08-12 15:32:22.879671: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 15:32:22.899716: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 15:32:22.899926: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 15:32:22.900241: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-12 15:32:22.900789: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 15:32:22.900913: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 15:32:22.901022: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 15:32:22.952156: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 15:32:22.952340: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 15:32:22.952462: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 15:32:22.952557: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
2023-08-12 15:32:22.974261: F ./tensorflow/core/util/gpu_launch_config.h:129] Check failed: work_element_count > 0 (0 vs. 0)
Aborted
```
```
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"@dmc1778 I was able to replicate this issue on colab, please find the attached [gist](https://colab.research.google.com/gist/sushreebarsa/c69fc9c0cde97f7378518fc5ee8fe8ab/61533.ipynb) here. \r\nSince Segmentation faults can be considered as potential security vulnerabilities please read [SECURITY.md](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) and report them through proper channel as mentioned in SECURITY.md.\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61533\">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/61533\">No</a>\n"
] | 2023-08-12T19:28:30 | 2023-09-20T18:19:27 | 2023-08-29T01:47:22 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Probably due to invalid string argument.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
from tensorflow.python.eager import context
try:
try:
with tf.device('/CPU'):
arg_0 = "/job:remote_device/replica:0/task:1"
out = context.check_alive(arg_0,)
except Exception as e:
print("Error:"+str(e))
try:
with tf.device('/GPU:0'):
context.check_alive(arg_0,)
except Exception as e:
print("Error:"+str(e))
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
/cudnn/lib:
2023-08-12 15:27:22.605920: 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.
2023-08-12 15:27:23.511442: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 15:27:23.533020: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 15:27:23.533197: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 15:27:23.533535: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-12 15:27:23.534136: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 15:27:23.534277: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 15:27:23.534432: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 15:27:23.605501: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 15:27:23.605704: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 15:27:23.605831: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 15:27:23.605930: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 77 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
Segmentation fault
```
```
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"@dmc1778 I was able to replicate the issue on colab using TF v2.11, 2.13 and tf-nightly, please find the error log as below;\r\n```\r\n\r\n\r\nAug 14, 2023, 8:24:58 PM | WARNING | WARNING:root:kernel e2934124-9e47-46b5-b3bd-6324982ca4e7 restarted\r\n-- | -- | --\r\nAug 14, 2023, 8:24:58 PM | INFO | KernelRestarter: restarting kernel (1/5), keep random ports\r\nAug 14, 2023, 8:24:56 PM | WARNING | 2023-08-14 14:54:56.547597: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\r\nAug 14, 2023, 8:24:56 PM | WARNING | 2023-08-14 14:54:56.547547: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\r\nAug 14, 2023, 8:24:54 PM | WARNING | 2023-08-14 14:54:54.417141: 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\nAug 14, 2023, 8:24:54 PM | WARNING | 2023-08-14 14:54:54.417106: 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; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\r\nAug 14, 2023, 8:24:54 PM | WARNING | 2023-08-14 14:54:54.416387: 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\n\r\n\r\n```\r\nSince Segmentation faults can be considered as potential security vulnerabilities please read [SECURITY.md](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) and report them through proper channel as mentioned in SECURITY.md.\r\nPlease find the attached [gist](https://colab.research.google.com/gist/sushreebarsa/0793ad74444028815dd05eb0b3929aba/61532.ipynb) here. Thank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61532\">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/61532\">No</a>\n"
] | 2023-08-12T18:32:22 | 2023-09-20T18:19:35 | 2023-08-29T01:47:24 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Probably due to the NONE argument
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.eager import context
try:
arg_0 = None
out = context.add_function(arg_0,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-12 14:32:03.270708: 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.
2023-08-12 14:32:04.485549: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 14:32:04.505262: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 14:32:04.505426: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 14:32:04.505739: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-12 14:32:04.506268: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 14:32:04.506389: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 14:32:04.506492: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 14:32:04.556530: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 14:32:04.556701: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 14:32:04.556817: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 14:32:04.556911: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 739 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
Segmentation fault
```
```
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"@dmc1778 ,\r\n\r\nI have replicated the behaviour in tf-nightly and got segmentation fault. Attaching logs below for reference.\r\n\r\n```\r\n(tf2.13) suryanarayanay@surya-ubuntu20:~$ python 61531_nightly.py \r\n2023-08-14 07:07:51.716502: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9498] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2023-08-14 07:07:51.716678: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2023-08-14 07:07:51.716781: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n2023-08-14 07:07:51.725885: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-08-14 07:07:52.645760: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\nWARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/tf2.13/lib/python3.11/site-packages/tensorflow/python/ops/distributions/distribution.py:259: ReparameterizationType.__init__ (from tensorflow.python.ops.distributions.distribution) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\nWARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/tf2.13/lib/python3.11/site-packages/tensorflow/python/ops/distributions/bernoulli.py:165: RegisterKL.__init__ (from tensorflow.python.ops.distributions.kullback_leibler) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\n2.15.0-dev20230813\r\nSegmentation fault (core dumped)\r\n```\r\n\r\nSince Segmentation faults can be considered as potential security vulnerabilities please read [SECURITY.md](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) and report them through proper channel as mentioned in SECURITY.md.\r\n\r\nThanks!",
"@SuryanarayanaY bug exists on 2.13.0"
] | 2023-08-12T17:41:26 | 2023-09-19T01:59:46 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.11.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
nvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0
### GPU model and memory
_No response_
### Current behavior?
Probably due to feeding None argument
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.framework import kernels
try:
arg_0 = None
out = kernels.get_registered_kernels_for_op(arg_0,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-12 13:41:10.388491: 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.
Segmentation fault
```
```
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"@SuryanarayanaY I was able to replicate the issue reported here in [this](https://colab.research.google.com/gist/sushreebarsa/60141b6c105e378fec90b70be9cca633/untitled823.ipynb#scrollTo=l9Ywbr3SL-Hn) gist. Thank you! ",
"> @SuryanarayanaY I was able to replicate the issue reported here in [this](https://colab.research.google.com/gist/sushreebarsa/60141b6c105e378fec90b70be9cca633/untitled823.ipynb#scrollTo=l9Ywbr3SL-Hn) gist. Thank you!\r\n\r\nNo worries. A quick question. Do you still accept issues for v2.10.0?",
"@dmc1778 ,\r\n\r\nReported behaviour is replicated with tf-nightly.\r\n\r\n```\r\n(tf2.13) suryanarayanay@surya-ubuntu20:~$ python 61530_nightly_gpu.py \r\n2023-08-14 13:10:04.426433: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9498] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2023-08-14 13:10:04.426603: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2023-08-14 13:10:04.426695: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n2023-08-14 13:10:04.435483: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-08-14 13:10:05.457204: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\nWARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/tf2.13/lib/python3.11/site-packages/tensorflow/python/ops/distributions/distribution.py:259: ReparameterizationType.__init__ (from tensorflow.python.ops.distributions.distribution) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\nWARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/tf2.13/lib/python3.11/site-packages/tensorflow/python/ops/distributions/bernoulli.py:165: RegisterKL.__init__ (from tensorflow.python.ops.distributions.kullback_leibler) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\n2.15.0-dev20230813\r\n2023-08-14 13:10:14.923089: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:14.925285: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:14.927493: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:14.929514: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:15.013437: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:15.015470: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:15.017432: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:15.019435: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:15.021710: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:15.023581: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:15.025587: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:15.027413: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:16.293444: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:16.295481: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:16.297415: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:16.299343: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:16.301327: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:16.303011: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:16.304768: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:16.306565: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:16.308453: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:16.310164: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:16.311861: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:16.313625: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:18.779116: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:18.781443: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:18.783849: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:18.785923: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:18.787990: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:18.789881: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:18.791925: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:18.793745: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:18.795609: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:18.797549: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1898] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13621 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\r\n2023-08-14 13:10:18.797966: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:18.800003: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1898] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13621 MB memory: -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\r\n2023-08-14 13:10:18.800400: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:18.802178: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1898] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 13621 MB memory: -> device: 2, name: Tesla T4, pci bus id: 0000:00:06.0, compute capability: 7.5\r\n2023-08-14 13:10:18.802512: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-14 13:10:18.804347: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1898] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 13621 MB memory: -> device: 3, name: Tesla T4, pci bus id: 0000:00:07.0, compute capability: 7.5\r\n2023-08-14 13:10:19.172628: F tensorflow/core/framework/tensor.cc:780] Check failed: 1 == NumElements() (1 vs. 0)Must have a one element tensor\r\nAborted (core dumped)\r\n(tf2.13) suryanarayanay@surya-ubuntu20:~$ \r\n```\r\nYou can still report this issue through proper channel as mentioned in [SECURITY.md](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md).",
"> > @SuryanarayanaY I was able to replicate the issue reported here in [this](https://colab.research.google.com/gist/sushreebarsa/60141b6c105e378fec90b70be9cca633/untitled823.ipynb#scrollTo=l9Ywbr3SL-Hn) gist. Thank you!\r\n> \r\n> No worries. A quick question. Do you still accept issues for v2.10.0?\r\n\r\nIf the issue already fixed in latest versions (last release or tf-nightly) then it's most unlikely to cherry pick for previous version unless it is very critical issue. The call will be taken by Engg team.",
"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/61530\">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/61530\">No</a>\n",
"The issue resolved already.Tested with tf-nightly and attaching [gist](https://colab.research.google.com/gist/SuryanarayanaY/094c5bba489a0c7f924bb4b829fdf4a4/61530_nightly.ipynb) for reference. Thanks!"
] | 2023-08-12T16:55:59 | 2024-02-27T16:38:04 | 2024-02-23T00:51:49 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
22.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Probably due to the feeding zero or empty input argument.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.ops import array_ops
try:
arg_0_tensor = tf.random.uniform([2, 3, 4, 5], dtype=tf.float32)
arg_0 = tf.identity(arg_0_tensor)
arg_1 = 0
arg_2 = [()]
range_given = True
round_mode = "HALF_UP"
axis = None
out = array_ops.quantize_and_dequantize_v2(arg_0,arg_1,arg_2,range_given=range_given,round_mode=round_mode,axis=axis,)
except Exception as e:
print("Error:"+str(e))
```
```
### Relevant log output
```shell
2023-08-12 12:56:52.291182: 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.
2023-08-12 12:56:53.170157: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 12:56:53.190481: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 12:56:53.190660: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 12:56:53.190982: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-12 12:56:53.191527: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 12:56:53.191651: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 12:56:53.191767: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 12:56:53.240721: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 12:56:53.240904: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 12:56:53.241022: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-08-12 12:56:53.241116: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 172 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
2023-08-12 12:56:53.255445: I tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:735] failed to allocate 172.69M (181075968 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2023-08-12 12:56:53.255723: I tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:735] failed to allocate 155.42M (162968576 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2023-08-12 12:56:53.263318: F tensorflow/core/framework/tensor.cc:734] Check failed: 1 == NumElements() (1 vs. 0)Must have a one element tensor
Aborted
```
```
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"@bfs18 I was able ti run the code successfully using colab, could you please have a look at this [gist](https://colab.research.google.com/gist/sushreebarsa/f3a5afe835c60b19c7ae22bbe4da783b/61529.ipynb#scrollTo=XJRwhkCxST9R) and confirm the outcome?\r\nThank you!",
"> \r\n\r\nHi @sushreebarsa , the result is the same as mine. The problem is 128 cannot be represented by an 8-bit signed number."
] | 2023-08-12T09:04:02 | 2023-08-29T20:54:15 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.13
### Custom code
Yes
### OS platform and distribution
ubuntu 22.04
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I implemented `fake_quant_with_min_max_args` in numpy according to the source code to get the quantized values. However the quantized range is `-2**num_bits + 1` to `2 ** num_bits` and the quantized weights cannot be represented by `num_bits` int. What should I do if I have to use signed type to represent the weights?
### Standalone code to reproduce the issue
```shell
def fake_quant_with_min_max_args(inputs, min=-0.99, max=0.99, num_bits=8, narrow_range=False):
assert min < 0 < max
quant_min = 1 if narrow_range else 0
quant_max = (1 << num_bits) - 1
quant_min_float = np.float32(quant_min)
quant_max_float = np.float32(quant_max)
scale = np.float32(max - min) / (quant_max_float - quant_min_float)
inv_scale = (quant_max_float - quant_min_float) / np.float32(max - min)
zero_point_from_min = quant_min - min / scale
if zero_point_from_min < quant_min:
nudged_zero_point = quant_min
elif zero_point_from_min > quant_max:
nudged_zero_point = quant_max
else:
nudged_zero_point = np.round(zero_point_from_min)
nudged_min = (quant_min_float - nudged_zero_point) * scale
nudged_max = (quant_max_float - nudged_zero_point) * scale
quant_zero = np.floor(-nudged_min * inv_scale + 0.5)
# print(quant_min, quant_max, nudged_zero_point)
# print(nudged_min, nudged_max, scale, inv_scale, quant_zero)
clamped = np.clip(inputs, nudged_min, nudged_max)
clamp_shifted = clamped - nudged_min
# quant = np.clip(np.floor(clamp_shifted * inv_scale - quant_zero + 0.5),
# quant_min - 2 ** (num_bits - 1), quant_max - 2 ** (num_bits - 1))
quant = np.floor(clamp_shifted * inv_scale - quant_zero + 0.5)
dequant = quant * scale
return quant, dequant
if __name__ == '__main__':
import numpy as np
import tensorflow as tf
np.random.seed(12345)
data = np.random.uniform(-1, 1, (2,))
data = np.r_[1, -1, data]
d = fake_quant_with_min_max_args(data, -1, 1, 8, False)
print(d[0])
print(d[1])
print(tf.quantization.fake_quant_with_min_max_args(data, -1, 1, 8, False))
```
```
### Relevant log output
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"I believe you are fundamentally misunderstanding the original issue in #61420. They are not looking for an example network, and I doubt using python async mechanisms will address the issue. It has to do with the internal workings of `tensorflow.experimental.dlpack.to_dlpack` and `tensorflow.experimental.dlpack.from_dlpack` functions, which are currently synchronous operations in the TensorFlow execution graph, but the user would like an asynchronous interface."
] | 2023-08-11T19:34:30 | 2023-08-16T18:02:36 | 2023-08-16T18:02:33 | NONE | null | false | {
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} | This pull request addresses the issue #61504 and adds the following functions:
Asynchronous processing: The code uses multiprocessing to asynchronously process the input tensors. This allows the code to process multiple tensors in parallel, which can improve the performance of the code.
Error handling: The code handles errors that may occur during the processing of the input tensors. This includes errors such as invalid input tensors, network failures, and out of memory errors.
Integration with other components: The code can be integrated with other components, such as data loading libraries and model inference libraries. This allows the code to be used in a wider variety of applications.
Variable number of networks and CPUs: The code can be used to process a variable number of networks and CPUs. This allows the code to be scaled up or down to meet the needs of the application. | {
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"Hi @weihanmines Can you please rebase your branch and resolve conflicts? Thank you!",
"> Hi @weihanmines Can you please rebase your branch and resolve conflicts? Thank you!\r\n\r\nyes, I will.",
"> Hi @weihanmines Can you please rebase your branch and resolve conflicts? Thank you!\r\n\r\nrebased."
] | 2023-08-11T19:28:17 | 2023-09-27T10:19:48 | 2023-09-27T10:19:47 | CONTRIBUTOR | null | false | {
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"@ndeepesh,\r\nCould you please provide the complete standalone code to reproduce the issue and it helps us to analyse the issue in an effective way. 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.",
"Hi @tilakrayal \r\nAny model on GPU with above code should be good for reproducing the issue. Is there a sampling parameter that can be introduced so that not all events are captured?",
"@ndeepesh,\r\nWithout the reproducible code, it would be difficult for us to debug the issue. In order to expedite the trouble-shooting process, could you please provide a minimal code snippet you are using. Thank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61526\">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/61526\">No</a>\n"
] | 2023-08-11T18:32:31 | 2023-09-13T01:47:37 | 2023-09-13T01:47:35 | NONE | null | null | null | ### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
TF 2.11, TF 2.4
### Custom code
No
### OS platform and distribution
Red Had
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.2
### GPU model and memory
_No response_
### Current behavior?
Running TensorFlow profiler for longer than 10 second period results into OOM error, crashes the tf inference process and the profiler returns DEADLINE_EXCEEDED. Is there anyway to limit the sampling rate or way to reduce the amount of information being collected to avoid crashing the process?
Here is the code that I run:
tensorflow_profiler.experimental.client("grpc://localhost:3222", "profiles", 30000)
### Standalone code to reproduce the issue
```shell
tensorflow_profiler.experimental.client("grpc://localhost:3222", "profiles", 30000)
```
### Relevant log output
```shell
DEADLINE_EXCEEDED
```
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"Hi @cpodczerwinski ,\r\n\r\nCould you please submit minimal reproducible code snippet here. Thanks!\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/61525\">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/61525\">No</a>\n"
] | 2023-08-11T17:32:02 | 2023-08-29T01:47:31 | 2023-08-29T01:47:25 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.13
### Custom code
No
### OS platform and distribution
Windows 11
### Mobile device
_No response_
### Python version
3.11.3
### Bazel version
N/A
### GCC/compiler version
N/A
### CUDA/cuDNN version
Not using GPU
### GPU model and memory
N/A
### Current behavior?
I have been receiving this message when trying to save a variety of tensorflow models since version 2.11. I have reported it before.
It appears to be produced by the lack of an ability by tensorfow to serialize the model representations it maintains in memory.
TypeError: Unable to serialize 64.0 to JSON. Unrecognized type <class 'tensorflow.python.framework.ops.EagerTensor'>.
See the debugger output below for details
Can I change anything in my models to dodge this logic?
Is there another model saving function I can try?
### Standalone code to reproduce the issue
```shell
Please email me for code.
```
### Relevant log output
```shell
Traceback (most recent call last):
File "D:\Craig\Python\Projects\CraigsPackages\koopman_operator_autoencoder_unit_tests.py", line 132, in <module>
tf.keras.saving.save_model(model=autoencoder, filepath='autoencoder_model', save_format="tf")
File "C:\Users\Craig\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\saving\saving_api.py", line 149, in save_model
return legacy_sm_saving_lib.save_model(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Craig\AppData\Local\Programs\Python\Python311\Lib\site-packages\keras\src\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\Craig\AppData\Local\Programs\Python\Python311\Lib\json\encoder.py", line 200, in encode
chunks = self.iterencode(o, _one_shot=True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Craig\AppData\Local\Programs\Python\Python311\Lib\json\encoder.py", line 258, in iterencode
return _iterencode(o, 0)
^^^^^^^^^^^^^^^^^
TypeError: Unable to serialize 64.0 to JSON. Unrecognized type <class 'tensorflow.python.framework.ops.EagerTensor'>.
```
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"@mattbarrett98 I tried to replicate the issue and faced the output as follows;\r\nFirstly when calling numpy();\r\n```\r\n1.753767936\r\n2.954133504\r\n```\r\nSecondly when calling the numpy() on a model variable;\r\n```\r\n2.958012416\r\n2.958012416\r\n```\r\nPlease find the attached [gist](https://colab.research.google.com/gist/sushreebarsa/2c96698cf2c90df5e93fd33cf69bfb69/61524.ipynb) and confirm the results. Thank you!",
"yea these are the correct results, though I think you said it the wrong way round- firstly it's calling `numpy()` on a model variable which is the one we see an increase in memory for. Second is `numpy()` on a 'normal' tensor, and there is no copies made in this case",
"Hi Matt, I think this is working as expected. The copy comes from the call that converts the Tensor to a NP array. See this discussion from a few years ago: https://github.com/tensorflow/tensorflow/issues/33254",
"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/61524\">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/61524\">No</a>\n"
] | 2023-08-11T15:09:17 | 2023-08-30T10:10:40 | 2023-08-30T10:10:38 | NONE | null | null | null | ### Issue type
Performance
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.12.0
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When calling `numpy()` here no copies are made:
```python
import os, psutil
import tensorflow as tf
process = psutil.Process(os.getpid())
x = tf.random.normal((1, 300000))
print(process.memory_info().rss / 1e9). # 0.48
npy = x.numpy()
print(process.memory_info().rss / 1e9). # 0.48
```
However in the below example the increase in memory implies a copy is being made when calling `numpy()` on a model variable, is there any reason for this?
### Standalone code to reproduce the issue
```shell
import os, psutil
import tensorflow as tf
class TestKerasLinear(tf.keras.Model):
def __init__(self, out_size):
super(TestKerasLinear, self).__init__()
self._linear = tf.keras.layers.Dense(out_size)
def build(self, input_shape):
super(TestKerasLinear, self).build(input_shape)
def call(self, x):
return self._linear(x)
tf_module = TestKerasLinear(1000)
tf_module.build((None, 300000))
process = psutil.Process(os.getpid())
print(process.memory_info().rss / 1e9). # 1.68
npy = tf_module.variables[0].numpy()
print(process.memory_info().rss / 1e9) # 2.88
```
### Relevant log output
_No response_ | {
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https://api.github.com/repos/tensorflow/tensorflow/issues/61523 | https://api.github.com/repos/tensorflow/tensorflow | https://api.github.com/repos/tensorflow/tensorflow/issues/61523/labels{/name} | https://api.github.com/repos/tensorflow/tensorflow/issues/61523/comments | https://api.github.com/repos/tensorflow/tensorflow/issues/61523/events | https://github.com/tensorflow/tensorflow/issues/61523 | 1,846,883,757 | I_kwDOArmXAs5uFTWt | 61,523 | When converting tensorflow model to tflite model, is there any way to fix the output order during inference using tflite as Facing an issue of output order of tflite inference on meraki custom cv | {
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"@RohanEmpire Please have a look at the [model conversion overview](https://www.tensorflow.org/lite/models/convert) of TFlite to know how to convert TF models to TFlite ones. This [guide](https://www.tensorflow.org/lite/inference_with_metadata/lite_support) also describes well on the processing of inputs and output data of Tflite model. Thank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61523\">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/61523\">No</a>\n"
] | 2023-08-11T14:02:25 | 2023-08-29T01:47:34 | 2023-08-29T01:47:27 | NONE | null | null | null | I took a pretrained model (SSD MobileNet 320x320) for object detection from the TensorFlow Zoo and configured/tuned it according to my data. I trained a TensorFlow model which detects 2 labels.
I have used the latest checkpoint to save the model, then froze it, and finally performed TF Lite conversion. I did this because I need to upload the TF Lite model only to a Cisco camera.
I'm facing an issue with the output order during TF Lite inference, as the output arrays get jumbled /rearranged. I need help on how to convert the TensorFlow model to TF Lite efficiently. My TensorFlow version is 2.10 | {
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"The Py+CPP Test Suite failure cannot be related to this PR."
] | 2023-08-11T10:36:53 | 2023-08-15T08:32:29 | 2023-08-14T19:56:38 | CONTRIBUTOR | null | false | {
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"Hi @kuangzy2011 \r\n\r\nThe tflite-runtime has a fraction the size of the full tensorflow package and includes the bare minimum code required to run inferences. \r\n\r\nTo resolve the custom operators, we need to define own custom implementation of an unsupported TensorFlow operator in TensorFlow Lite.\r\n\r\nWe need to [create and register the operator](https://www.tensorflow.org/lite/guide/ops_custom#create_and_register_the_operator) so that the TensorFlow Lite runtime knows how to map your operator and parameters in your graph to executable C/C++ code.\r\n\r\nPlease refer to this [documentation](https://www.tensorflow.org/lite/guide/ops_custom#example_custom_atan_operator) on examples for resolving custom ops.\r\n\r\nThanks.",
"REGISTER_OP(\"FarthestPointSample\")\r\n .Attr(\"npoint: int\")\r\n .Input(\"inp: float32\")\r\n .Output(\"out: int32\")\r\n\r\nc->GetAttr(\"npoint\", &npoint)\r\n\r\n\r\nThis custom OP is in tensorflow/core/kernels, it has Attr, but how to get this attr in tensorflow/lite/kernels OP?",
"Hi @kuangzy2011 \r\n\r\nCan you provide more details about your issue?\r\n\r\nWe have to create a custom `FarthestPointSample.cc` in tensorflow/lite/kernels and register in \r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/kernels/custom_ops_register.h\r\n\r\nPlease check `Atan2` op for reference asn provided in the documentation.\r\nhttps://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/kernels/atan2_custom.cc\r\n\r\nThanks.",
"@pjpratik \r\n In pointnet++ project, compiles following custom OP to a share library, and load it with tf.load_op_library to train the model.\r\nAfter the training is done, convert this model to tflite with \"converter.allow_custom_ops = True\".\r\n When tried to do inference with tflite-runtime, it raised error \"RuntimeError: Encountered unresolved custom op: FarthestPointSample.\"\r\n I noticed the OP in tensorflow/core/kernels is different with that in tensorflow/lite/kernels, so I am trying to convert these OPs to fit tensorflow/lite/kernels. There is Attr in following OP, but not found any related information in tensorflow/lite/kernels OP.\r\n\r\n\r\n Source target:\r\nhttps://github.com/charlesq34/pointnet2/blob/master/tf_ops/sampling/tf_sampling.cpp\r\nhttps://github.com/charlesq34/pointnet2/blob/master/tf_ops/sampling/tf_sampling_g.cu\r\n```\r\n/* Furthest point sampling\r\n * Original author: Haoqiang Fan\r\n * Modified by Charles R. Qi\r\n * All Rights Reserved. 2017.\r\n */\r\n#include \"tensorflow/core/framework/op.h\"\r\n#include \"tensorflow/core/framework/op_kernel.h\"\r\n#include \"tensorflow/core/framework/shape_inference.h\"\r\n#include \"tensorflow/core/framework/common_shape_fns.h\"\r\n#include <cuda_runtime.h>\r\n\r\nusing namespace tensorflow;\r\n\r\nREGISTER_OP(\"ProbSample\")\r\n .Input(\"inp: float32\")\r\n .Input(\"inpr: float32\")\r\n .Output(\"out: int32\")\r\n .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {\r\n ::tensorflow::shape_inference::ShapeHandle dims1; // batch_size * ncategory\r\n TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &dims1));\r\n ::tensorflow::shape_inference::ShapeHandle dims2; // batch_size * npoints\r\n TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 2, &dims2));\r\n // batch_size * npoints\r\n ::tensorflow::shape_inference::ShapeHandle output = c->MakeShape({c->Dim(dims2, 0), c->Dim(dims2, 1)});\r\n c->set_output(0, output);\r\n return OkStatus();\r\n });\r\nREGISTER_OP(\"FarthestPointSample\")\r\n .Attr(\"npoint: int\") >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>Attr\r\n .Input(\"inp: float32\")\r\n .Output(\"out: int32\")\r\n .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {\r\n ::tensorflow::shape_inference::ShapeHandle dims1; // batch_size * npoint * 3\r\n TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 3, &dims1));\r\n int npoint;\r\n TF_RETURN_IF_ERROR(c->GetAttr(\"npoint\", &npoint));\r\n ::tensorflow::shape_inference::ShapeHandle output = c->MakeShape({c->Dim(dims1, 0), npoint});\r\n c->set_output(0, output);\r\n return OkStatus();\r\n });\r\nREGISTER_OP(\"GatherPoint\")\r\n .Input(\"inp: float32\")\r\n .Input(\"idx: int32\")\r\n .Output(\"out: float32\")\r\n .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {\r\n ::tensorflow::shape_inference::ShapeHandle dims1; // batch_size * ndataset * 3\r\n TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 3, &dims1));\r\n ::tensorflow::shape_inference::ShapeHandle dims2; // batch_size * npoints\r\n TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 2, &dims2));\r\n // batch_size * npoints * 3\r\n ::tensorflow::shape_inference::ShapeHandle output = c->MakeShape({c->Dim(dims1, 0), c->Dim(dims2, 1), c->Dim(dims1, 2)});\r\n c->set_output(0, output);\r\n return OkStatus();\r\n });\r\nREGISTER_OP(\"GatherPointGrad\")\r\n .Input(\"inp: float32\")\r\n .Input(\"idx: int32\")\r\n .Input(\"out_g: float32\")\r\n .Output(\"inp_g: float32\")\r\n .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {\r\n c->set_output(0, c->input(0));\r\n return OkStatus();\r\n });\r\n\r\nvoid probsampleLauncher(int b,int n,int m,const float * inp_p,const float * inp_r,float * temp,int * out);\r\nclass ProbSampleGpuOp: public OpKernel{\r\n public:\r\n explicit ProbSampleGpuOp(OpKernelConstruction* context):OpKernel(context){}\r\n void Compute(OpKernelContext * context)override{\r\n const Tensor& inp_tensor=context->input(0);\r\n const Tensor& inpr_tensor=context->input(1);\r\n auto inp_flat=inp_tensor.flat<float>();\r\n auto inpr_flat=inpr_tensor.flat<float>();\r\n const float * inp=&(inp_flat(0));\r\n const float * inpr=&(inpr_flat(0));\r\n OP_REQUIRES(context,inp_tensor.dims()==2,errors::InvalidArgument(\"ProbSample expects (batch_size,num_choices) inp shape\"));\r\n int b=inp_tensor.shape().dim_size(0);\r\n int n=inp_tensor.shape().dim_size(1);\r\n OP_REQUIRES(context,inpr_tensor.dims()==2 && inpr_tensor.shape().dim_size(0)==b,errors::InvalidArgument(\"ProbSample expects (batch_size,num_points) inpr shape\"));\r\n int m=inpr_tensor.shape().dim_size(1);\r\n Tensor * out_tensor=NULL;\r\n OP_REQUIRES_OK(context,context->allocate_output(0,TensorShape{b,m},&out_tensor));\r\n auto out_flat=out_tensor->flat<int>();\r\n int * out=&(out_flat(0));\r\n Tensor temp_tensor;\r\n OP_REQUIRES_OK(context,context->allocate_temp(DataTypeToEnum<float>::value,TensorShape{b,n},&temp_tensor));\r\n auto temp_flat=temp_tensor.flat<float>();\r\n float * temp=&(temp_flat(0));\r\n probsampleLauncher(b,n,m,inp,inpr,temp,out);\r\n }\r\n};\r\nREGISTER_KERNEL_BUILDER(Name(\"ProbSample\").Device(DEVICE_GPU), ProbSampleGpuOp);\r\n\r\nvoid farthestpointsamplingLauncher(int b,int n,int m,const float * inp,float * temp,int * out);\r\nclass FarthestPointSampleGpuOp: public OpKernel{\r\n public:\r\n explicit FarthestPointSampleGpuOp(OpKernelConstruction* context):OpKernel(context) {\r\n### >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>GetAttr\r\n OP_REQUIRES_OK(context, context->GetAttr(\"npoint\", &npoint_)); \r\n OP_REQUIRES(context, npoint_ > 0, errors::InvalidArgument(\"FarthestPointSample expects positive npoint\"));\r\n }\r\n void Compute(OpKernelContext * context)override{\r\n int m = npoint_;\r\n\r\n const Tensor& inp_tensor=context->input(0);\r\n OP_REQUIRES(context,inp_tensor.dims()==3 && inp_tensor.shape().dim_size(2)==3,errors::InvalidArgument(\"FarthestPointSample expects (batch_size,num_points,3) inp shape\"));\r\n int b=inp_tensor.shape().dim_size(0);\r\n int n=inp_tensor.shape().dim_size(1);\r\n auto inp_flat=inp_tensor.flat<float>();\r\n const float * inp=&(inp_flat(0));\r\n Tensor * out_tensor;\r\n OP_REQUIRES_OK(context,context->allocate_output(0,TensorShape{b,m},&out_tensor));\r\n auto out_flat=out_tensor->flat<int>();\r\n int * out=&(out_flat(0));\r\n Tensor temp_tensor;\r\n OP_REQUIRES_OK(context,context->allocate_temp(DataTypeToEnum<float>::value,TensorShape{32,n},&temp_tensor));\r\n auto temp_flat=temp_tensor.flat<float>();\r\n float * temp=&(temp_flat(0));\r\n farthestpointsamplingLauncher(b,n,m,inp,temp,out);\r\n }\r\n private:\r\n int npoint_;\r\n};\r\nREGISTER_KERNEL_BUILDER(Name(\"FarthestPointSample\").Device(DEVICE_GPU),FarthestPointSampleGpuOp);\r\n\r\nvoid gatherpointLauncher(int b,int n,int m,const float * inp,const int * idx,float * out);\r\nclass GatherPointGpuOp: public OpKernel{\r\n public:\r\n explicit GatherPointGpuOp(OpKernelConstruction * context):OpKernel(context){}\r\n void Compute(OpKernelContext * context)override{\r\n const Tensor& inp_tensor=context->input(0);\r\n OP_REQUIRES(context,inp_tensor.dims()==3 && inp_tensor.shape().dim_size(2)==3,errors::InvalidArgument(\"GatherPoint expects (batch_size,num_points,3) inp shape\"));\r\n int b=inp_tensor.shape().dim_size(0);\r\n int n=inp_tensor.shape().dim_size(1);\r\n const Tensor& idx_tensor=context->input(1);\r\n OP_REQUIRES(context,idx_tensor.dims()==2 && idx_tensor.shape().dim_size(0)==b,errors::InvalidArgument(\"GatherPoint expects (batch_size,num_result) idx shape\"));\r\n int m=idx_tensor.shape().dim_size(1);\r\n auto inp_flat=inp_tensor.flat<float>();\r\n const float * inp=&(inp_flat(0));\r\n auto idx_flat=idx_tensor.flat<int>();\r\n const int * idx=&(idx_flat(0));\r\n Tensor * out_tensor=NULL;\r\n OP_REQUIRES_OK(context,context->allocate_output(0,TensorShape{b,m,3},&out_tensor));\r\n auto out_flat=out_tensor->flat<float>();\r\n float * out=&(out_flat(0));\r\n gatherpointLauncher(b,n,m,inp,idx,out);\r\n }\r\n};\r\nREGISTER_KERNEL_BUILDER(Name(\"GatherPoint\").Device(DEVICE_GPU),GatherPointGpuOp);\r\n\r\nvoid scatteraddpointLauncher(int b,int n,int m,const float * out_g,const int * idx,float * inp_g);\r\nclass GatherPointGradGpuOp: public OpKernel{\r\n public:\r\n explicit GatherPointGradGpuOp(OpKernelConstruction * context):OpKernel(context){}\r\n void Compute(OpKernelContext * context)override{\r\n const Tensor& inp_tensor=context->input(0);\r\n OP_REQUIRES(context,inp_tensor.dims()==3 && inp_tensor.shape().dim_size(2)==3,errors::InvalidArgument(\"GatherPointGradGpuOp expects (batch_size,num_points,3) inp\"));\r\n int b=inp_tensor.shape().dim_size(0);\r\n int n=inp_tensor.shape().dim_size(1);\r\n const Tensor& idx_tensor=context->input(1);\r\n OP_REQUIRES(context,idx_tensor.dims()==2 && idx_tensor.shape().dim_size(0)==b,errors::InvalidArgument(\"GatherPointGradGpuOp expects (batch_size,num_result) idx shape\"));\r\n int m=idx_tensor.shape().dim_size(1);\r\n auto inp_flat=inp_tensor.flat<float>();\r\n const float * inp=&(inp_flat(0));\r\n auto idx_flat=idx_tensor.flat<int>();\r\n const int * idx=&(idx_flat(0));\r\n const Tensor& out_g_tensor=context->input(2);\r\n OP_REQUIRES(context,out_g_tensor.dims()==3 && out_g_tensor.shape().dim_size(0)==b && out_g_tensor.shape().dim_size(1)==m && out_g_tensor.shape().dim_size(2)==3,errors::InvalidArgument(\"GatherPointGradGpuOp expects (batch_size,num_result,3) out_g shape\"));\r\n auto out_g_flat=out_g_tensor.flat<float>();\r\n const float * out_g=&(out_g_flat(0));\r\n Tensor * inp_g_tensor=NULL;\r\n OP_REQUIRES_OK(context,context->allocate_output(0,TensorShape{b,n,3},&inp_g_tensor));\r\n auto inp_g_flat=inp_g_tensor->flat<float>();\r\n float * inp_g=&(inp_g_flat(0));\r\n cudaMemset(inp_g,0,b*n*3*4);\r\n scatteraddpointLauncher(b,n,m,out_g,idx,inp_g);\r\n }\r\n};\r\nREGISTER_KERNEL_BUILDER(Name(\"GatherPointGrad\").Device(DEVICE_GPU),GatherPointGradGpuOp);\r\n```",
"Hi @kuangzy2011 \r\n\r\nThanks for the information.\r\n\r\nTensorflow and TFLite APIs for creating op kernels are, at the moment, not identical. Check [tf-shim](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/kernels/shim) which creates a shim over the custom op APIs of TF and TFLite which might help in your case.\r\n\r\nThanks.\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/61521\">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/61521\">No</a>\n",
"@pjpratik ,\r\n\r\n I added shim APIs, but how to compile them into the library.\r\n I rebuilt tflite_runtime and it raised the same errors.\r\n\r\n Here is the change.\r\n\r\nhttps://github.com/kuangzy2011/tensorflow/blob/main/tensorflow/lite/kernels/shim/test_op/sampling_op.h\r\nhttps://github.com/kuangzy2011/tensorflow/blob/main/tensorflow/lite/kernels/shim/test_op/sampling_tflite_op.cc\r\nhttps://github.com/kuangzy2011/tensorflow/blob/main/tensorflow/lite/kernels/shim/test_op/sampling_tflite_op.h\r\n\r\nhttps://github.com/kuangzy2011/tensorflow/blob/98a7af9c8383a1e1fde154700bcc66493277298e/tensorflow/lite/kernels/shim/test_op/BUILD#L156",
"Hi @kuangzy2011 \r\n\r\nCould you please the share the error log and steps you have followed in order to better understand and reproduce the issue?\r\n\r\nThanks.",
"> Hi @kuangzy2011\r\n> \r\n> Could you please the share the error log and steps you have followed in order to better understand and reproduce the issue?\r\n> \r\n> Thanks.\r\n\r\n@pjpratik ,\r\n\r\n It works now. Need to add test_op to lite/kernels/BUILD\r\n Thanks.\r\n",
"@kuangzy2011 \r\n\r\nGlad it works. Please feel free to close the issue since it is resolved.\r\n\r\nThanks.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61521\">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/61521\">No</a>\n",
"If I understand this correctly - you would have to build `tflite-runtime` with the custom operator in order to use a tflite model that requires that custom operator? There's no way provide access to the custom operator via a shared library object that is provided at runtime?\r\n\r\nAlthough - it does seem like there's some mechanism via `tflite_runtime.interpreter.InterpreterWithCustomOps`; but this isn't part of the public API."
] | 2023-08-11T09:46:28 | 2023-10-27T08:40:10 | 2023-09-12T13:29:47 | NONE | null | null | null | **System information**
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 20.04
- TensorFlow installed from (source or binary): source
- TensorFlow version (or github SHA if from source): 2.13.0
**Provide the text output from tflite_convert**
I did some test with pointnet++(https://github.com/charlesq34/pointnet2), and tried to inference with tf.lite or tflite-runtime, but both of them show the error message below:
```
Traceback (most recent call last):
File "test.py", line 13, in <module>
predict = PointNetPredict('/kaggle/input/model-sign/model_sign.tflite')
File "/kaggle/working/pointnet3c1/models/pointnet_predict.py", line 27, in __init__
self.interpreter = self.init_model()
File "/kaggle/working/pointnet3c1/models/pointnet_predict.py", line 39, in init_model
interpreter.allocate_tensors()
File "/opt/conda/lib/python3.7/site-packages/tensorflow/lite/python/interpreter.py", line 513, in allocate_tensors
return self._interpreter.AllocateTensors()
RuntimeError: Encountered unresolved custom op: FarthestPointSample.
See instructions: https://www.tensorflow.org/lite/guide/ops_custom Node number 0 (FarthestPointSample) failed to prepare.Encountered unresolved custom op: FarthestPointSample.
See instructions: https://www.tensorflow.org/lite/guide/ops_custom Node number 0 (FarthestPointSample) failed to prepare.
```
I noticed there were some custom ops(tf_ops) in project pointnet2, but how to convert these ops to tflite-runtime operators?
```
# Copy and paste here
```
**Standalone code to reproduce the issue**
Provide a reproducible test case that is the bare minimum necessary to generate
the problem. If possible, please share a link to Colab/Jupyter/any notebook.
Also, please include a link to a GraphDef or the model if possible.
**Any other info / logs**
Include any logs or source code that would be helpful to diagnose the problem.
If including tracebacks, please include the full traceback. Large logs and files
should be attached.
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"@JinJiefeng,\r\nI tried to execute the mentioned code on latest tensorflow v2.15 and observed that the output for both [GPU](https://colab.research.google.com/gist/tilakrayal/a56345f3301d4a0b4c357c38e21c6e67/untitled1872.ipynb) and [CPU](https://colab.research.google.com/gist/tilakrayal/6953a05fcd5d76f12a689787d29152d4/untitled1873.ipynb) are not much difference for **tf.math.floormod** float and int. Kindly find the gist of it here. Thank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61520\">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/61520\">No</a>\n"
] | 2023-08-11T07:59:42 | 2024-05-11T05:50:33 | 2024-05-11T01:48:15 | NONE | null | null | null | ### Issue type
Performance
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
v2.13.0-rc2-7-g1cb1a030a62 2.13.0
### Custom code
No
### OS platform and distribution
20.04.1-Ubuntu
### Mobile device
_No response_
### Python version
3.8.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
CUDA Version: 11.6
### GPU model and memory
Tesla V100
### Current behavior?
Dear Tensorflow support,
I found that the tf.math.floormod function is very slow for the tensors of float dtype, even if the size of the tensor is very small, in my case, only 6. The platform is a powerful server with GPU acceleration, but the performance is much slower than an old laptop.
Output on server, 20.04.1-Ubuntu, GPU accelerated, TensorFlow version 2.13.0
> tf.math.floormod float cost: 1.0610415935516357 seconds
> tf.math.floormod int cost: 0.005458354949951172 seconds
Output on laptop, Windows 10, cpu-only, TensorFlow version 2.10.0 :
> tf.math.floormod float cost: 0.002992391586303711 seconds
> tf.math.floormod int cost: 0.006021261215209961 seconds
Could you please check this issue?
Thanks!
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import time
PI = 3.141592653589793
random_tensor = tf.random.uniform(shape=(1, 6), minval=-PI*2, maxval=PI*2, dtype=tf.float32)
start_time = time.time()
random_tensor = tf.math.floormod(random_tensor+PI, 2*PI)-PI
print(f"tf.math.floormod float cost: {time.time() - start_time} seconds")
random_tensor = tf.random.uniform(shape=(1, 6), minval=-200, maxval=200, dtype=tf.int32)
start_time = time.time()
random_tensor = tf.math.floormod(random_tensor+100, 2*100)-100
print(f"tf.math.floormod int cost: {time.time() - start_time} seconds")
```
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"You need to have \"apply_class_balancing= True\" if you want to see the effects of alpha.\r\n\r\nInstead of :\r\ntf.keras.losses.BinaryFocalCrossentropy(gamma=gamma, alpha=0.25, from_logits=True)\r\n\r\nTry:\r\ntf.keras.losses.BinaryFocalCrossentropy(gamma=gamma, alpha=0.25, from_logits=True, , apply_class_balancing= True)\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/61519\">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/61519\">No</a>\n"
] | 2023-08-11T06:17:10 | 2023-08-11T22:53:28 | 2023-08-11T22:53:26 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
tf 2.13
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`tf.keras.losses.BinaryFocalCrossentropy` computes the loss without using the `alpha`.
```
import tensorflow as tf
y_true_list = [0, 1, 0, 0]
logits_list = [-1.6, 0.51, 2.94, -1.8]
gamma = 2
focal_func1 = tf.keras.losses.BinaryFocalCrossentropy(gamma=gamma, alpha=0.25, from_logits=True)
focal_loss1 = focal_func1(y_true_list, logits_list)
focal_func2 = tf.keras.losses.BinaryFocalCrossentropy(gamma=gamma, alpha=10, from_logits=True)
focal_loss2 = focal_func2(y_true_list, logits_list)
focal_func3 = tf.keras.losses.BinaryFocalCrossentropy(gamma=gamma, alpha=100, from_logits=True)
focal_loss3 = focal_func3(y_true_list, logits_list)
print(focal_loss1)
print(focal_loss2)
print(focal_loss3)
```
The results are:
```
tf.Tensor(0.6932789, shape=(), dtype=float32)
tf.Tensor(0.6932789, shape=(), dtype=float32)
tf.Tensor(0.6932789, shape=(), dtype=float32)
```
`focal_loss1` in the codes should be `0.51168`
### Standalone code to reproduce the issue
```shell
In above.
```
### Relevant log output
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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/61518\">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/61518\">No</a>\n",
"Added:\r\n\r\[email protected]\r\ndef example(self, value):\r\n self.inputs, self.labels = value\r\n\r\nso the @property of example looks like:\r\n\r\n@property\r\ndef example(self):\r\n \"\"\"Get and cache an example batch of `inputs, labels` for plotting.\"\"\"\r\n result = getattr(self, '_example', None)\r\n if result is None:\r\n # No example batch was found, so get one from the `.train` dataset\r\n result = next(iter(self.train))\r\n # And cache it for next time\r\n self._example = result\r\n return result\r\n\r\[email protected]\r\ndef example(self, value):\r\n self.inputs, self.labels = value\r\n"
] | 2023-08-10T23:41:58 | 2023-08-11T02:53:03 | 2023-08-11T00:18:58 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.8
### Custom code
No
### OS platform and distribution
Windows
### Mobile device
na
### Python version
3.8
### Bazel version
na
### GCC/compiler version
?
### CUDA/cuDNN version
?
### GPU model and memory
colab notebook
### Current behavior?
I'm running this tensorflow example:
https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/time_series.ipynb
I wanted to add some additional models at the end and tried to create new data windows as done above. I noticed the example already given, and my new code requires the following line to be run before the @property for "example" is created:
" w2.example = example_inputs, example_labels "
else you get a vague error "AttributeError: can't set attribute " but it looks like this has a setter?
This line is found under "3. Plot" and if moved to a later section after the
@property
def example
under section 4 this error occurs.
### Standalone code to reproduce the issue
```shell
Move:
w2.example = example_inputs, example_labels
to under section 4 which should be ok. Can't set error occurs.
```
### Relevant log output
```shell
AttributeError: can't set attribute
```
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"Hi @Ferev Can you please review this PR ? Thank you!",
"Hi @Ferev Can you please review this PR ? Thank you!",
"Hi @Ferev Can you please review this PR ? Thank you!",
"Hi @Ferev Can you please review this PR ? Thank you!",
"Hi @Ferev Can you please review this PR ? Thank you!",
"Hi @Ferev Can you please review this PR ? Thank you!",
"Hi @Ferev Can you please review this PR ? Thank you!",
"Hi @LakshmiKalaKadali Can you please resolve conflicts? Thank you!",
"This PR is out of date and no longer necessary, link is no longer broken: https://www.tensorflow.org/lite/microcontrollers/build_convert, so I am closing."
] | 2023-08-10T17:02:53 | 2024-01-05T22:47:03 | 2024-01-05T22:46:33 | CONTRIBUTOR | null | false | {
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} | The link to the colab notebook about training is broken and it is now updated with `train.py` in the hello world example.
Thanks. | {
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"@marcmk6,\r\n\r\nGroups windows of elements by key and reduces them.\r\n\r\nThis transformation maps each consecutive element in a dataset to a key using `key_func` and groups the elements by key. It then applies reduce_func to at most `window_size_func(key)` elements matching the same key. All except the final window for each key will contain **window_size_func(key)** elements; the final window may be smaller.\r\n\r\nYou may provide either a constant **window_size** or a window size determined by the key through **window_size_func**\r\n\r\nhttps://www.tensorflow.org/api_docs/python/tf/data/Dataset#group_by_window\r\n\r\nThank you!",
"Hi @tilakrayal,\r\n\r\nI've already read the documentation before making this post and I know what this function is used for. \r\nThe thing that seems problematic to me is it uses the **values** of the key to sort the outputs, in an ascending order. In the first example I provided, the key equals the element. You can see in the output that although the elements are grouped together, the groups are also sorted by the values of their respective keys. If I switch to use the negative value of the element as key, the output order is reversed. \r\n\r\nIn my opinion the output order should not be related to the values of the key. This binding cancels all the prior randomness of elements and this sorting behavior is not documented. I think the most natural implementation is that the output ordering being identical to the ordering before grouping. \r\nFor instance, let's say the elements before grouping is `[8, 7, 7, 6, 3, 2, 8, 3, 8, 4, 9, 4, 6, 8, 6, 5, 5, 9, 7, 7, 9, 2, 7, 5, 9, 7, 9, 1, 6, 9, 9, 8, 5, 6, 3, 7, 8, 9, 8, 6, 4, 5, 4, 9, 8]`. The output in this case should be `[8, 8, 8, 8, 8, 8, 8, 8, 7, 7, 7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 6, 3, 3, 3, 2, 2, 4, 4, 4, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 5, 5, 5, 5, 5, 1]`.\r\n\r\nThanks",
"@marcmk6,\r\n**group_by_window** does not iterate through the entire dataset before outputing something. It iterates through enough of the dataset for one of the windows to reach window_size. \r\n\r\nAlos group_by_window doesn't explicitly sort the elements within each group based on their keys. It groups the elements based on the key and then applies the reduce function to the elements within each group.\r\n\r\nIf you need the elements within each group to be sorted based on their keys, you might need to perform additional sorting after applying **group_by_window**. This could be done by using the **[tf.data.Dataset.map](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#map)** transformation along with a custom sorting function. Thank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61515\">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/61515\">No</a>\n"
] | 2023-08-10T15:24:14 | 2023-12-09T01:48:31 | 2023-12-09T01:48:28 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf-nightly 2.15.0.dev20230810
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`tf.data.Dataset.group_by_window` uses the values of key for ordering outputs, which is neither documented nor desired.
This behavior cancels all the prior randomness and links groups/outputs order with the order of the key.
My expectation is that the key is used to group elements only and does nothing else.
### Standalone code to reproduce the issue
```python
import tensorflow as tf
datasets = tf.data.Dataset.from_tensor_slices([
tf.data.Dataset.from_tensors(i).repeat(i)
for i in range(1, 10)
])
datasets = datasets.interleave(
map_func=lambda x: x,
cycle_length=1,
block_length=1,
)
shuffled_ds = datasets.shuffle(100)
print('Shuffled dataset:', [e.numpy() for e in shuffled_ds])
ds = (
shuffled_ds
.group_by_window(
key_func=lambda x: tf.cast(x, tf.int64),
reduce_func=lambda key, dataset: dataset,
window_size=10,
)
)
ds2 = (
shuffled_ds
.group_by_window(
key_func=lambda x: -tf.cast(x, tf.int64),
reduce_func=lambda key, dataset: dataset,
window_size=10,
)
)
print('Output re-ordered by `group_by_window` using key')
print([e.numpy() for e in ds])
print('-'*50)
print([e.numpy() for e in ds2])
```
### Relevant log output
```python
Shuffled dataset: [8, 7, 7, 6, 3, 2, 8, 3, 8, 4, 9, 4, 6, 8, 6, 5, 5, 9, 7, 7, 9, 2, 7, 5, 9, 7, 9, 1, 6, 9, 9, 8, 5, 6, 3, 7, 8, 9, 8, 6, 4, 5, 4, 9, 8]
Output re-ordered by `group_by_window` using key
[1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9]
--------------------------------------------------
[9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 8, 8, 8, 8, 8, 7, 7, 7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 4, 4, 4, 4, 3, 3, 3, 2, 2, 1]
```
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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/61514/checks?check_run_id=15786390192) 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 @MarkDaoust Can you please review this PR ? Thank you!"
] | 2023-08-10T14:11:37 | 2023-08-24T15:52:00 | 2023-08-24T15:51:57 | NONE | null | false | {
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"Hi @Chris0215 \r\n\r\nThe examples covering other on-device training use cases is on roadmap.\r\n\r\nOn Android, TensorFlow Lite on-device training can be performed using either Java or C++ APIs. The train function can be invoked using TensorFlow Lite’s [runSignature](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/examples/on_device_training/overview.ipynb#scrollTo=jBYb5JEnPeOR) method by specifying the name of the signature (‘train’) and Similarly, we can invoke inference using the model’s ‘infer’ signature.\r\n\r\nPlease refer this [blog](https://blog.tensorflow.org/2021/11/on-device-training-in-tensorflow-lite.html) on OD training for insight on implementation.\r\n\r\nThanks.",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further."
] | 2023-08-10T07:53:12 | 2023-08-29T01:47:29 | 2023-08-29T01:47:29 | NONE | null | null | null | **System information**
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): web
- TensorFlow installed from (source or binary): colab
- TensorFlow version (or github SHA if from source):colab
Hi I’m new to tensorflow and I’m trying to make LSTM or GRU model to be enable to re-train on-device(Android) with tabular data (mostly customer interaction).
I’m referencing these examples
[On-Device Training 1](https://www.tensorflow.org/lite/examples/on_device_training/overview)
This is an example of a CNN, but not able to understand how can I enable on-device training for lstm or gru model.
Are there any examples for reference? Thanks | {
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"@rahul-fnu \r\nIn order to expedite the trouble-shooting process, please provide a code snippet to reproduce the issue reported here. Thank you!",
"@sushreebarsa This is exactly what I run after importing tensorflow: `tensorflow_profiler.experimental.client(\"grpc://localhost:3222\", \"profiles\", 30000)`",
"Reduce Input Size",
"Is there a way to configure the profiler? Either by reducing the amount of information being collected or by reducing the sampling rate?",
"@rahul-fnu Please refer to [this](https://www.tensorflow.org/tensorboard/tensorboard_profiling_keras) guide to know the way to configure the TF profiler ? Please make sure to use the latest TF version and TF profiler. \r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"@sushreebarsa @Divin-18 \r\nThis profiler is used for debugging production issues. We cant reduce the input size. Is there a sampling parameter which we can use so that not all events are captured?",
"because of tracing and profiling, the amount of data accumulated is proportionate to the time of profile.\r\nI would suggest that you profile a small period of time, and check if kernel or host side info is more verbose, this is really more work load specific properties.\r\nif host side info is verbose and not very useful, you can specify host_trace_level = 0 (I am vague on if this is available on 2.11)",
"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/61512\">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/61512\">No</a>\n"
] | 2023-08-10T04:03:47 | 2023-09-15T01:47:43 | 2023-09-15T01:47:41 | NONE | null | null | null | ### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.11.0.5
### Custom code
No
### OS platform and distribution
Linux CentOS 7.9.2009
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Running TensorFlow profiler for longer than 10 second period crashes the inference process because of OOM error and the profiler returns DEADLINE_EXCEEDED. Is there anyway to limit the sampling rate or way to reduce the amount of information being collected to avoid crashing the process?
### Standalone code to reproduce the issue
```shell
`tensorflow_profiler.experimental.client("grpc://localhost:3222", "profiles", 30000)`
```
### Relevant log output
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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/61511/checks?check_run_id=15760454282) 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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"+@yehudaorel, @agramesh1 ",
"@zhezherun thank you for raising this issue, I was able to reproduce the issue using tensorflow==2.13.0 binary release:\r\n\r\n**TF_ENABLE_ONEDNN_OPTS=1**\r\n```\r\nonednn_verbose,info,oneDNN v2.7.3 (commit N/A)\r\nonednn_verbose,info,cpu,runtime:threadpool,nthr:224\r\nonednn_verbose,info,cpu,isa:Intel AVX-512 with float16, Intel DL Boost and bfloat16 support and Intel AMX with bfloat16 and 8-bit integer support\r\nonednn_verbose,info,gpu,runtime:none\r\nonednn_verbose,info,prim_template:operation,engine,primitive,implementation,prop_kind,memory_descriptors,attributes,auxiliary,problem_desc,exec_time\r\nonednn_verbose,exec,cpu,matmul,brg:avx512_core,undef,src_f32::blocked:acb:f0 wei_f32::blocked:abc:f0 dst_f32::blocked:abc:f0,attr-scratchpad:user ,,3x3x5000:3x5000x3:3x3x3,2.125\r\n[[[2.3496000e+04 4.5627100e+02 9.7381091e+02]\r\n [4.5627100e+02 2.3422373e+04 4.2656726e+02]\r\n [9.7381091e+02 4.2656726e+02 2.3594883e+04]]\r\n\r\n [[4.5893926e-41 0.0000000e+00 0.0000000e+00]\r\n [1.4012985e-45 1.7743547e+28 0.0000000e+00]\r\n [0.0000000e+00 2.2420775e-43 0.0000000e+00]]\r\n\r\n [[1.1210388e-43 0.0000000e+00 1.1527105e-35]\r\n [0.0000000e+00 7.0064923e-45 0.0000000e+00]\r\n [6.7421017e+22 1.6815582e-43 0.0000000e+00]]]\r\n```\r\n\r\n**TF_ENABLE_ONEDNN_OPTS=0** \r\n```\r\n[[[5000. 128.78204 131.61632 ]\r\n [ 128.78204 4929.8867 -54.454735]\r\n [ 131.61632 -54.454735 4999.6367 ]]\r\n\r\n [[5000. 128.78204 131.61632 ]\r\n [ 128.78204 4929.8867 -54.454735]\r\n [ 131.61632 -54.454735 4999.6367 ]]\r\n\r\n [[5000. 128.78204 131.61632 ]\r\n [ 128.78204 4929.8867 -54.454735]\r\n [ 131.61632 -54.454735 4999.6367 ]]]\r\n```\r\n\r\n@agramesh1 @vpirogov I also found that this issue only happens when **length is greater than 2681**\r\n\r\nCurrently building tf from source to see if issue still there in latest oneDNN release.\r\n\r\n\r\n\r\n",
"I can reproduce this with oneDNN tests. Looks like oneDNN v2.7.3 is affected, but not the current version, oneDNN v3.2.\r\n\r\nOutput with `ONEDNN_VERBOSE=1`:\r\n```\r\n$ ONEDNN_VERBOSE=1 python3 ./test.py\r\n2023-08-09 10:54:14.079700: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n2023-08-09 10:54:14.081993: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\r\n2023-08-09 10:54:14.131952: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\r\n2023-08-09 10:54:14.132449: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX512F AVX512_VNNI AVX512_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-08-09 10:54:16.185646: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\nonednn_verbose,info,oneDNN v2.7.3 (commit N/A)\r\nonednn_verbose,info,cpu,runtime:threadpool,nthr:72\r\nonednn_verbose,info,cpu,isa:Intel AVX-512 with Intel DL Boost and bfloat16 support\r\nonednn_verbose,info,gpu,runtime:none\r\nonednn_verbose,info,prim_template:operation,engine,primitive,implementation,prop_kind,memory_descriptors,attributes,auxiliary,problem_desc,exec_time\r\nonednn_verbose,exec,cpu,matmul,brg:avx512_core,undef,src_f32::blocked:acb:f0 wei_f32::blocked:abc:f0 dst_f32::blocked:abc:f0,attr-scratchpad:user ,,3x3x5000:3x5000x3:3x3x3,0.442139\r\n[[[ 1.1928000e+04 4.0859866e+02 3.7975815e+02]\r\n [ 4.0859866e+02 1.1693020e+04 -9.1308746e+00]\r\n [ 3.7975815e+02 -9.1308746e+00 1.1971855e+04]]\r\n\r\n [[ 0.0000000e+00 0.0000000e+00 0.0000000e+00]\r\n [ 0.0000000e+00 1.8600073e+02 0.0000000e+00]\r\n [ 0.0000000e+00 1.1210388e-44 0.0000000e+00]]\r\n\r\n [[ 4.5191123e-06 2.0196074e-40 1.1210388e-44]\r\n [ 0.0000000e+00 3.0517854e-36 0.0000000e+00]\r\n [ 0.0000000e+00 0.0000000e+00 3.0522912e-36]]]\r\n```\r\n\r\nBenchdnn results:\r\n```\r\n$ DNNL_VERBOSE=1 ./benchdnn --matmul --stag=acb 3x3x5000:3x5000x3:3x3x3\r\nonednn_verbose,info,oneDNN v2.7.3 (commit 7710bdc92064a08b985c5cbdb09de773b19cba1f)\r\nonednn_verbose,info,cpu,runtime:OpenMP,nthr:72\r\nonednn_verbose,info,cpu,isa:Intel AVX-512 with Intel DL Boost and bfloat16 support\r\nonednn_verbose,info,gpu,runtime:none\r\nonednn_verbose,info,prim_template:operation,engine,primitive,implementation,prop_kind,memory_descriptors,attributes,auxiliary,problem_desc,exec_time\r\nonednn_verbose,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:abc:f0 dst_f32::blocked:acb:f0,,,3x3x5000,3.79419\r\nonednn_verbose,exec,cpu,reorder,simple:any,undef,src_f32::blocked:abc:f0 dst_f32::blocked:abc:f0,,,3x5000x3,0.0388184\r\nonednn_verbose,exec,cpu,matmul,brg:avx512_core,undef,src_f32::blocked:acb:f0 wei_f32::blocked:abc:f0 dst_f32::blocked:abc:f0,,,3x3x5000:3x5000x3:3x3x3,0.166016\r\nonednn_verbose,exec,cpu,reorder,simple:any,undef,src_f32::blocked:abc:f0 dst_f32::blocked:abc:f0,,,3x3x3,0.0319824\r\nonednn_verbose,exec,cpu,reorder,simple:any,undef,src_f32::blocked:abc:f0 dst_f32::blocked:abc:f0,,,3x3x3,0.0361328\r\n[ 0][DST][0:0:0] exp_f32: -35516 exp: -35516 got: -539159 diff: 503643 rdiff: 14.1807\r\n[ 1][DST][0:0:1] exp_f32: 114098 exp: 114098 got: -19606 diff: 133704 rdiff: 1.17183\r\n[ 2][DST][0:0:2] exp_f32: 929 exp: 929 got: 22304 diff: 21375 rdiff: 23.0086\r\n[ 3][DST][0:1:0] exp_f32: 68428 exp: 68428 got: -518338 diff: 586766 rdiff: 8.57494\r\n[ 4][DST][0:1:1] exp_f32: 51054 exp: 51054 got: -275533 diff: 326587 rdiff: 6.39689\r\n[ 5][DST][0:1:2] exp_f32: 58651 exp: 58651 got: 154408 diff: 95757 rdiff: 1.63266\r\n[ 6][DST][0:2:0] exp_f32: -137905 exp: -137905 got: 221970 diff: 359875 rdiff: 2.60959\r\n[ 7][DST][0:2:1] exp_f32: -73033 exp: -73033 got: 346390 diff: 419423 rdiff: 5.74292\r\n[ 8][DST][0:2:2] exp_f32: 287192 exp: 287192 got: 45300 diff: 241892 rdiff:0.842266\r\n0:FAILED (errors:27 total:27) __REPRO: --matmul --stag=acb 3x3x5000:3x5000x3:3x3x3\r\ntests:1 passed:0 skipped:0 mistrusted:0 unimplemented:0 invalid_arguments:0 failed:1 listed:0\r\ntotal compute_ref: sum(s):0.00\r\n```",
"The fix is now available in [oneDNN v2.7.5](https://github.com/oneapi-src/oneDNN/releases/tag/v2.7.5). Current version, [oneDNN v3.2.1](https://github.com/oneapi-src/oneDNN/releases/tag/v3.2.1) is not affected.",
"> The fix is now available in [oneDNN v2.7.5](https://github.com/oneapi-src/oneDNN/releases/tag/v2.7.5). Current version, [oneDNN v3.2.1](https://github.com/oneapi-src/oneDNN/releases/tag/v3.2.1) is not affected.\r\n\r\nThanks a lot for a quick fix! Is it possible to include it in 2.13.1?",
"hi @zhezherun there is no plan for updating oneDNN to 2.7.5 in TF 2.13.1. We are expecting to update oneDNN with this fix in the upcoming release of TF 2.14.",
"> hi @zhezherun there is no plan for updating oneDNN to 2.7.5 in TF 2.13.1. We are expecting to update oneDNN with this fix in the upcoming release of TF 2.14.\r\n\r\nI see, unfortunately this means that TF 2.13 is unusable for us :(",
"@zhezherun are you able to build TF by yourself? You can patch TF 2.13 to use oneDNN 2.7.5.",
"I also observed similar computing bugs in the following APIs in some older versions of tensorflow.\r\nUsers should be cautious when using them on CPU from tensorflow 2.9.0 (v2.9.0-rc2-42-g8a20d54a3c1) to 2.13.0 (v2.13.0-rc2-7-g1cb1a030a62).\r\n\r\n- `tf.linalg.matmul`, `(tf.matmul)`, `tf.compat.v1.linalg.matmul`, `tf.compat.v1.matmul`\r\n\r\n<details>\r\n <summary>Code to reproduce the issue in <code>tf.linalg.matmul</code> APIs in older versions</summary>\r\n\r\n```python\r\nimport tensorflow as tf\r\nprint(tf.version.GIT_VERSION, tf.version.VERSION, flush=True)\r\nprint(tf.config.list_physical_devices(), flush=True)\r\n\r\n\r\ntf.keras.utils.set_random_seed(1)\r\nlength = 5000\r\nx = tf.concat([tf.ones([length, 1]), tf.random.normal([length, 2])], axis=1)\r\nx = tf.tile(x[None, ...], [3, 1, 1])\r\nxx = tf.linalg.matmul(x, x, transpose_a=True)\r\n# xx = tf.matmul(x, x, transpose_a=True)\r\n# xx = tf.compat.v1.linalg.matmul(x, x, transpose_a=True)\r\n# xx = tf.compat.v1.matmul(x, x, transpose_a=True)\r\nprint(f\"{xx.numpy()}\", flush=True)\r\n```\r\n\r\nThe GPU provides the following outputs:\r\n\r\n```text\r\nv2.13.0-rc2-7-g1cb1a030a62 2.13.0\r\n[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\n[[[5000. 128.77832 131.63278 ]\r\n [ 128.77832 4929.636 -54.427303]\r\n [ 131.63278 -54.427303 4999.2427 ]]\r\n\r\n [[5000. 128.77832 131.63278 ]\r\n [ 128.77832 4929.636 -54.427303]\r\n [ 131.63278 -54.427303 4999.2427 ]]\r\n\r\n [[5000. 128.77832 131.63278 ]\r\n [ 128.77832 4929.636 -54.427303]\r\n [ 131.63278 -54.427303 4999.2427 ]]]\r\n```\r\n\r\nWhile the CPU provides the following outputs, which are inconsistent with the GPU outputs:\r\n\r\n```text\r\nv2.13.0-rc2-7-g1cb1a030a62 2.13.0\r\n[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]\r\n[[[ 2.2984000e+04 5.9729730e+02 9.9359949e+02]\r\n [ 5.9729730e+02 2.2604811e+04 5.0240503e+02]\r\n [ 1.1394688e+03 4.4723795e+02 2.4646848e+04]]\r\n\r\n [[ 6.7804801e-10 1.7862294e+31 1.6027861e-10]\r\n [ 1.8617160e+25 1.8639604e+02 6.7787642e-10]\r\n [-5.4521859e-04 0.0000000e+00 0.0000000e+00]]\r\n\r\n [[ 5.6192068e-43 0.0000000e+00 -1.4373869e+22]\r\n [ 4.5728573e-41 -1.4373869e+22 4.5728573e-41]\r\n [ 0.0000000e+00 0.0000000e+00 0.0000000e+00]]]\r\n```\r\n</details>\r\n\r\nIt seems to be fixed by pull request #61595, and tensorflow 2.14.0-rc1 (v2.14.0-rc0-34-gdd01672d9a9) and nightly (2.15.0-dev20230906) are not affected.\r\n\r\nBesides, I also found a slight numerical precision difference between CPU and GPU, which is acceptable in many cases but should be noted.\r\n\r\n<details>\r\n <summary>Show outputs of the slight numerical precision difference between CPU and GPU</summary>\r\n\r\nThe GPU outputs:\r\n\r\n```text\r\nv2.14.0-rc0-34-gdd01672d9a9 2.14.0-rc1\r\n[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\n[[[5000. 128.77832 131.63278 ]\r\n [ 128.77832 4929.636 -54.427303]\r\n [ 131.63278 -54.427303 4999.2427 ]]\r\n\r\n [[5000. 128.77832 131.63278 ]\r\n [ 128.77832 4929.636 -54.427303]\r\n [ 131.63278 -54.427303 4999.2427 ]]\r\n\r\n [[5000. 128.77832 131.63278 ]\r\n [ 128.77832 4929.636 -54.427303]\r\n [ 131.63278 -54.427303 4999.2427 ]]]\r\n```\r\n\r\nThe CPU outputs:\r\n\r\n```text\r\nv2.14.0-rc0-34-gdd01672d9a9 2.14.0-rc1\r\n[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]\r\n[[[5000. 128.78206 131.61627]\r\n [ 128.78206 4929.8867 -54.45468]\r\n [ 131.61627 -54.45468 4999.636 ]]\r\n\r\n [[5000. 128.78206 131.61627]\r\n [ 128.78206 4929.8867 -54.45468]\r\n [ 131.61627 -54.45468 4999.636 ]]\r\n\r\n [[5000. 128.78206 131.61627]\r\n [ 128.78206 4929.8867 -54.45468]\r\n [ 131.61627 -54.45468 4999.636 ]]]\r\n```\r\n</details>",
"@zhezherun,\r\nThe related PR #61595 has been merged and also tried to execute the above mentioned code on tensorflow v2.14 & it was executed with the result expected. Kindly find the gist of it here [CPU](https://colab.research.google.com/gist/tilakrayal/a03a8c725734519e5f76d62adc75bde3/untitled1540.ipynb) and [GPU](https://colab.research.google.com/gist/tilakrayal/7aff4ed034c0770efe11611a3a9d9402/untitled1541.ipynb).\r\n\r\n```\r\n[[[5000. 128.78201 131.6163 ]\r\n [ 128.78201 4929.8857 -54.454735]\r\n [ 131.6163 -54.454735 4999.6367 ]]\r\n\r\n [[5000. 128.78201 131.6163 ]\r\n [ 128.78201 4929.8857 -54.454735]\r\n [ 131.6163 -54.454735 4999.6367 ]]\r\n\r\n [[5000. 128.78201 131.6163 ]\r\n [ 128.78201 4929.8857 -54.454735]\r\n [ 131.6163 -54.454735 4999.6367 ]]]\r\n```\r\n\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"Thanks @tilakrayal, I confirm that 2.14 is working",
"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/61510\">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/61510\">No</a>\n"
] | 2023-08-09T11:02:05 | 2023-12-12T18:14:04 | 2023-12-12T18:14:01 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.13.0
### Custom code
No
### OS platform and distribution
Linux
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When running on a CPU with avx512_vnni instructions (e.g. Xeon Platinum 8260), tf.matmul in 2.13 gives a completely wrong result that also changes from run to run. Other functions, e.g. tf.einsum, are affected too. A reproducer is included below. 2.12 is working correctly.
I believe this is due to a bug in oneDNN, since running with TF_ENABLE_ONEDNN_OPTS=0 restores the correct behaviour.
Could you please try building TF against the latest oneDNN to see if this bug is already fixed there, and either upgrade oneDNN or revert back to the version used in 2.12? If this bug is still present in latest oneDNN, could you also forward this issue to them so that they can fix it? tf.matmul is such a fundamental part of TensorFlow, so it would be great to have a fix for this as soon as possible (and perhaps add further tests to prevent this kind of bug from reoccurring?)
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
tf.keras.utils.set_random_seed(1)
length = 5000
x = tf.concat([tf.ones([length, 1]), tf.random.normal([length, 2])], axis=1)
x = tf.tile(x[None, ...], [3, 1, 1])
xx = tf.matmul(x, x, transpose_a=True)
# xx = tf.einsum("ijk,ijm->ikm", x, x) # Also doesn't work
print(f"{xx.numpy()}")
```
### Relevant log output
```shell
[[[ 2.0936000e+04 4.1334631e+02 8.8164221e+02]
[ 4.1334631e+02 2.0951623e+04 5.5098944e+02]
[ 8.1284619e+02 4.7815466e+02 1.9981070e+04]]
[[ 0.0000000e+00 6.8663625e-44 0.0000000e+00]
[ 2.7628342e-35 0.0000000e+00 2.6752920e-35]
[ 0.0000000e+00 2.3822074e-44 0.0000000e+00]]
[[-7.9164143e+31 8.9978802e+02 1.7936620e-43]
[ 0.0000000e+00 1.1210388e-43 0.0000000e+00]
[ 2.7767702e-35 0.0000000e+00 7.0064923e-45]]]
```
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"Hi @lidamsoukaina ,\r\n\r\nAt present all the TF Ops are not implemented to workable on TFlite. Hence you are getting this error. ",
"@lidamsoukaina ,\r\n\r\nPlease find the list of Ops supported with TFlite [here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/builtin_ops.h).",
"@SuryanarayanaY I am not facing this problem while converting to tflite. In fact the conversion to tflite is well done.\r\nI have this issue with the conversion to coreml.",
"Hi @lidamsoukaina, this appears to be an issue with coreml? It does not appear to touch TFLite code, I think you may have better luck with their code base?",
"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/61509\">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/61509\">No</a>\n"
] | 2023-08-09T08:30:33 | 2023-09-09T01:57:27 | 2023-09-09T01:57:08 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I am facing a NotImplementedError while trying to convert [MoViNets](https://github.com/tensorflow/models/tree/master/official/projects/movinet) model to coreml.
The model is saved in [SavedModel](https://www.tensorflow.org/guide/saved_model) format and I am using a tensorflow version equal to **2.13.0** and a version of [Core ML Tools](https://coremltools.readme.io/docs/what-are-coreml-tools) equal to **6.3.0** and a **3.10.6** python.
I don't understand the origin of **StatefulPartitionedCall** operation and its meaning, any idea what's could be going on?
### Here are the steps to reproduce the error:
1. Download a savedModel from the following [link](https://tfhub.dev/tensorflow/movinet/a0/base/kinetics-600/classification/3).
2. Convert the model using:
```shell
import coremltools as ct
coreml_model = ct.convert(saved_dir, convert_to="mlprogram")
```
where *saved_dir* is the path to the downloaded model.
### Relevant log output
```shell
NotImplementedError: Conversion for TF op 'StatefulPartitionedCall' not implemented.
name: "StatefulPartitionedCall"
op: "StatefulPartitionedCall"
input: "image"
input: "unknown"
input: "unknown_0"
input: "unknown_1"
input: "unknown_2"
input: "unknown_3"
input: "unknown_4"
input: "unknown_5"
input: "unknown_6"
input: "unknown_7"
input: "unknown_8"
input: "unknown_9"
input: "unknown_10"
input: "unknown_11"
input: "unknown_12"
input: "unknown_13"
input: "unknown_14"
input: "unknown_15"
...
input: "unknown_597"
input: "unknown_598"
input: "unknown_599"
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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/61508\">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/61508\">No</a>\n"
] | 2023-08-09T08:25:13 | 2023-08-09T08:28:06 | 2023-08-09T08:27:58 | NONE | null | null | null | null | {
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"Hi @mirial65 ,\r\n\r\nI am able to execute the code on google colab with tensorflow version 2.12. Each epocs executes multiple batch size. It is recommended to upgrade your tf version. Please find the [gist](https://colab.research.google.com/gist/Varsha-anjanappa/49522b4014ac944bf7ed72691850e5cd/61507.ipynb).\r\n\r\nThank you.\r\n\r\n\r\n\r\n",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61507\">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/61507\">No</a>\n"
] | 2023-08-09T07:58:04 | 2023-08-25T01:47:44 | 2023-08-25T01:47:42 | NONE | null | null | null | ### Issue type
Others
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.3.0
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
3.8
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
T4
### Current behavior?
After adding @tf.function, I found that each epoch only executes one batch_size, and this does not happen when @tf.function are removed
### Standalone code to reproduce the issue
```shell
from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, MaxPool2D,GlobalAveragePooling2D, Flatten, Dense, Dropout
from tensorflow.keras import Model, Sequential
from tensorflow.keras.regularizers import L2
class ResNetBlock(Model):
def __init__(self, filters=64, strides=1):
super(ResNetBlock, self).__init__()
self.strides = strides
self.c1 = Conv2D(filters=filters, kernel_size=(3, 3), strides=strides, padding='same')
self.b1 = BatchNormalization()
self.a1 = Activation('relu')
self.c2 = Conv2D(filters=filters, kernel_size=(3, 3), strides=1, padding='same')
self.b2 = BatchNormalization()
if(strides > 1):
self.c3 = Conv2D(filters=filters, kernel_size=(3, 3), strides=strides, padding='same')
self.b3 = BatchNormalization()
self.a2 = Activation('relu')
def call(self, inputs):
short_x = inputs
x = self.c1(inputs)
x = self.b1(x)
x = self.a1(x)
x = self.c2(x)
y = self.b2(x)
if(self.strides > 1):
short_x = self.c3(short_x)
short_x = self.b3(short_x)
return self.a2(short_x + y)
class ResNet(Model):
def __init__(self, model_lst, cur_filters = 64):
super(ResNet, self).__init__()
self.c1 = Conv2D(filters=cur_filters, kernel_size=(7, 7), strides=2, padding='same')
self.b1 = BatchNormalization()
self.a1 = Activation('relu')
self.p1 = MaxPool2D((2, 2), 2)
self.blocks = Sequential()
for (i, lst) in enumerate(model_lst):
for ids in range(lst):
if(i != 0 and ids == 0):
block = ResNetBlock(cur_filters, strides=2)
else:
block = ResNetBlock(cur_filters, strides=1)
self.blocks.add(block)
cur_filters *= 2
self.g1 = GlobalAveragePooling2D()
self.d1 = Dense(10, activation='softmax', kernel_regularizer=L2())
def call(self, inputs):
x = self.c1(inputs)
x = self.b1(x)
x = self.a1(x)
x = self.p1(x)
x = self.blocks(x)
x = self.g1(x)
y = self.d1(x)
return y
# ---------------------------------------------
# ResNet18
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
# import matplotlib
# matplotlib.rcParams['font.family']=['SimHei', 'Arial']
from tensorflow.keras import *
from tensorflow.keras.layers import Conv2D, Dense, BatchNormalization, Activation, MaxPool2D,GlobalAveragePooling2D
from tensorflow.keras.models import Sequential
from tensorflow.keras.losses import SparseCategoricalCrossentropy
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.metrics import Mean,SparseCategoricalAccuracy
from tensorflow.keras.datasets.fashion_mnist import load_data
batch_size = 64
epochs = 20
validation_freq = 2
(x_train, y_train), (x_test, y_test) = load_data()
x_train, x_test = x_train/255., x_test/255.
x_train = np.expand_dims(x_train, -1).astype(np.float32)
x_test = np.expand_dims(x_test, -1).astype(np.float32)
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(len(x_train)).batch(batch_size)
test_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test)).shuffle(len(x_test)).batch(batch_size)
model = ResNet([2, 2, 2, 2])
losses = SparseCategoricalCrossentropy(from_logits=False)
optimizer = Adam()
train_metrics_loss = Mean()
train_metrics_accuracy = SparseCategoricalAccuracy()
test_metrics_loss = Mean()
test_metrics_accuracy = SparseCategoricalAccuracy()
train_losses = []
train_accuracy = []
test_losses = []
test_accuracy = []
@tf.function
def train_step(model, input_images, y_real):
with tf.GradientTape() as tape:
y_pred = model(input_images, training=True)
loss = losses(y_real, y_pred)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
train_metrics_loss.update_state(loss)
train_metrics_accuracy.update_state(y_real, y_pred)
@tf.function
def test_step(model, input_images, y_real):
with tf.GradientTape() as tape:
y_pred = model(input_images, training=False)
loss = losses(y_real, y_pred)
test_metrics_loss.update_state(loss)
test_metrics_accuracy.update_state(y_real, y_pred)
for epoch in range(epochs):
train_metrics_loss.reset_states()
train_metrics_accuracy.reset_states()
test_metrics_accuracy.reset_states()
test_metrics_loss.reset_states()
for x_batch, y_batch in train_dataset:
train_step(model, x_batch, y_batch)
train_losses.append(train_metrics_loss.result())
train_accuracy.append(train_metrics_accuracy.result())
print(f"epoch={epoch}, train_loss={train_metrics_loss.result()}, train_accuracy={train_metrics_accuracy.result()}")
if(epoch % validation_freq == 0):
for test_x_batch, test_y_batch in test_dataset:
test_step(model, test_x_batch, test_y_batch)
test_losses.append(test_metrics_loss.result())
test_accuracy.append(test_metrics_accuracy.result())
print(f"epoch={epoch}, test_loss={test_metrics_loss.result()}, test_accuracy={test_metrics_accuracy.result()}")
plt.figure(figsize=(8, 5))
plt.subplot(1, 2, 1)
plt.title('损失值变化图')
plt.plot(test_losses, 'g-', label="Test_Loss")
plt.plot(train_losses, 'r-', label="Train_Loss")
plt.legend()
plt.subplot(1, 2, 2)
plt.title("准确率变化图")
plt.plot(train_accuracy, 'r-', label="Train_Accuracy")
plt.plot(test_accuracy, 'g-', label="Test_Accuracy")
plt.legend()
plt.show()
```
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"What is difference between TF 2 and 1.15?",
"@NeighborhoodCoding The source code I want to test is written in tensorflow 1.15.\r\nThere was many syntactic change in TF2 compared to TF1.15",
"@KeondoPark,\r\nTensorFlow 1.x is not actively supported. Could you please update TensorFlow to the latest stable version v2.13.0 and check if you are facing the same issue. \r\nAlso in the document which you were referring to where it was mentioned **#r1.9, r1.10** as the example. \r\nhttps://github.com/tensorflow/build/tree/master/raspberry_pi_builds#download-the-tensorflow-source-code\r\n\r\n`git checkout <branch_name> # r1.9, r1.10, etc.`\r\n\r\nThank you!",
"@tilakrayal \r\nHi, I would like to test another person's code written in tf1.15.\r\nI tried another version as you recommended(1.10, 1.9), but same error occurred.",
"@KeondoPark,\r\nAs suggested tensorFlow 1.x is not actively supported. Could you please update TensorFlow to the latest stable version v2.13.0 and check if you are facing the same issue. We are requesting the community to go with the 2.x rather than 1.x where most of the bugs were resolved in latest versions.\r\n\r\nhttps://www.tensorflow.org/guide/migrate\r\nThank you!\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/61506\">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/61506\">No</a>\n"
] | 2023-08-09T01:19:25 | 2023-08-26T01:45:54 | 2023-08-26T01:45:52 | NONE | null | null | null | ### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 1.15
### Custom code
No
### OS platform and distribution
Linux and Ubuntu 18.05
### Mobile device
target platform: Raspberry pi 4
### Python version
3.7
### Bazel version
na
### GCC/compiler version
na
### CUDA/cuDNN version
na
### GPU model and memory
na
### Current behavior?
Hi
I am trying to install tensorflow 1.15 on Raspberry pi and found this page:
https://github.com/tensorflow/build/tree/master/raspberry_pi_builds
From my understanding, I can do this at another platform (GPU server, ubuntu 18.05 installed).
I followed the instruction in the page, but came across the following error message:
From my guess, it looks like pip is not recognized in Docker.
Could you tell me how to resolve this issue??
### Standalone code to reproduce the issue
```shell
# I followed the instruction here: https://github.com/tensorflow/build/tree/master/raspberry_pi_builds
# And use this command to build
tensorflow/tools/ci_build/ci_build.sh PI-PYTHON37 \
tensorflow/tools/ci_build/pi/build_raspberry_pi.sh
```
### Relevant log output
```shell
CI_DOCKER_BUILD_EXTRA_PARAMS:
CI_DOCKER_EXTRA_PARAMS:
COMMAND: tensorflow/tools/ci_build/pi/build_raspberry_pi.sh
CI_COMMAND_PREFIX: ./tensorflow/tools/ci_build/builds/with_the_same_user ./tensorflow/tools/ci_build/builds/configured pi-python37
CONTAINER_TYPE: pi-python37
BUILD_TAG: tf_ci
(docker container name will be tf_ci.pi-python37)
Building container (tf_ci.pi-python37)...
[+] Building 2.9s (10/17)
=> [internal] load build definition from Dockerfile.pi-python37 0.0s
=> => transferring dockerfile: 866B 0.0s
=> [internal] load .dockerignore 0.0s
=> => transferring context: 2B 0.0s
=> [internal] load metadata for docker.io/library/ubuntu:16.04 1.4s
=> [internal] load build context 0.0s
=> => transferring context: 22.45kB 0.0s
=> [ 1/13] FROM docker.io/library/ubuntu:16.04@sha256:1f1a2d56de1d604801a9671f301190704c25d604a416f59e03c04f5c6ffee0d6 0.0s
=> CACHED [ 2/13] COPY install/*.sh /install/ 0.0s
=> CACHED [ 3/13] RUN /install/install_bootstrap_deb_packages.sh 0.0s
=> CACHED [ 4/13] RUN add-apt-repository -y ppa:openjdk-r/ppa && add-apt-repository -y ppa:george-edison55/cmake-3.x 0.0s
=> CACHED [ 5/13] RUN /install/install_deb_packages.sh 0.0s
=> ERROR [ 6/13] RUN /install/install_pip_packages.sh 1.4s
------
> [ 6/13] RUN /install/install_pip_packages.sh:
#0 0.789 Searching for pip
#0 0.789 Reading https://pypi.python.org/simple/pip/
#0 0.867 Couldn't find index page for 'pip' (maybe misspelled?)
#0 0.867 Scanning index of all packages (this may take a while)
#0 0.867 Reading https://pypi.python.org/simple/
#0 0.936 No local packages or download links found for pip
#0 0.937 error: Could not find suitable distribution for Requirement.parse('pip')
------
Dockerfile.pi-python37:11
--------------------
9 | add-apt-repository -y ppa:george-edison55/cmake-3.x
10 | RUN /install/install_deb_packages.sh
11 | >>> RUN /install/install_pip_packages.sh
12 | RUN /install/install_bazel.sh
13 | RUN /install/install_proto3.sh
--------------------
ERROR: failed to solve: process "/bin/sh -c /install/install_pip_packages.sh" did not complete successfully: exit code: 1
ERROR: docker build failed. Dockerfile is at /home/keondopark/tensorflow/tensorflow/tools/ci_build/Dockerfile.pi-python37
```
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"This pull request fixes #61420 ",
"@cantonios I have addressed all the issues and now i believe asynchronous function should work as expected\r\nI have also updated the files to be displayed in the correct format and added functions to implement asynchronous function without dependencies on external libraries like asynchio also some other extra features to handle the number of networks.\r\n\r\nBut when i was trying to push all my new changes i got confused as i am new to open source and accidently closed the issue because i deleted the previous branch so can i open a new pull request with all the new changes or is there any way to reopen and import the changes in this one?",
"If your new branch has the same name and you push it to your PR repo, you might be able to reopen this. Otherwise, just create a new PR.",
"@cantonios can you please review my code and suggest more changes before i create a new pull request \r\nasy.py file\r\n```python\r\nimport multiprocessing\r\nfrom functools import lru_cache\r\nimport traceback\r\n\r\n\r\ndef pre_process_network(input_tensor):\r\n \"\"\"Pre-process the input tensor.\"\"\"\r\n w1 = tf.Variable(tf.random.normal([10000, 10000]))\r\n w2 = tf.Variable(tf.random.normal([10000, 10000]))\r\n w3 = tf.Variable(tf.random.normal([10000, 10000]))\r\n x = tf.matmul(input_tensor, w1)\r\n x = tf.matmul(x, w2)\r\n x = tf.matmul(x, w3)\r\n return x\r\n\r\n\r\ndef mid_process_network_async(input_tensor):\r\n \"\"\"Process the input tensor asynchronously.\"\"\"\r\n w1 = tf.Variable(tf.random.normal([10000, 100]))\r\n w2 = tf.Variable(tf.random.normal([100, 100]))\r\n w3 = tf.Variable(tf.random.normal([100, 10000]))\r\n input_tensor = tf.compat.v1.convert_to_tensor(input_tensor)\r\n x = tf.matmul(input_tensor, w1)\r\n x = tf.matmul(x, w2)\r\n x = tf.matmul(x, w3)\r\n return x\r\n\r\n\r\n@lru_cache(maxsize=None) # Cache all results\r\nasync def cached_mid_process_network_async(input_tensor):\r\n \"\"\"Asynchronously process the input tensor and cache the result.\"\"\"\r\n return await mid_process_network_async(input_tensor)\r\n\r\n\r\ndef post_process_network(input_tensor):\r\n \"\"\"Post-process the input tensor.\"\"\"\r\n w1 = tf.Variable(tf.random.normal([10000, 10000]))\r\n w2 = tf.Variable(tf.random.normal([10000, 10000]))\r\n w3 = tf.Variable(tf.random.normal([10000, 10000]))\r\n x = tf.matmul(input_tensor, w1)\r\n x = tf.matmul(x, w2)\r\n x = tf.matmul(x, w3)\r\n return x\r\n\r\n\r\ndef process(input_tensor):\r\n \"\"\"Process a single input tensor.\"\"\"\r\n pre_output = pre_process_network(input_tensor)\r\n try:\r\n mid_output = cached_mid_process_network_async(pre_output)\r\n post_output = post_process_network(mid_output)\r\n except Exception:\r\n print(\"An error occurred:\")\r\n print(traceback.format_exc())\r\n return None\r\n\r\n return post_output\r\n\r\n\r\ndef main(number_of_networks, number_of_cpus):\r\n \"\"\"Main function.\"\"\"\r\n input_tensors = [tf.ones([1, 10000]) for _ in range(number_of_networks)]\r\n\r\n pool = multiprocessing.Pool(processes=number_of_cpus)\r\n\r\n results = pool.map(process, input_tensors)\r\n\r\n pool.close()\r\n pool.join()\r\n\r\n for result in results:\r\n if result is not None:\r\n print(result)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n \"\"\"Entry point.\"\"\"\r\n number_of_networks = int(input(\"Enter the number of asynchronous networks: \"))\r\n number_of_cpus = int(input(\"Enter the number of CPUs: \"))\r\n main(number_of_networks, number_of_cpus)\r\n\r\n``` \r\n\r\nasy_test.py file\r\n```python\r\nimport unittest\r\n\r\n\r\nimport asy\r\n\r\n\r\nclass TestAsync(unittest.TestCase):\r\n\r\n def test_main(self):\r\n number_of_networks = 10\r\n number_of_cpus = 4\r\n results = asy.main(number_of_networks, number_of_cpus)\r\n self.assertEqual(len(results), number_of_networks)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n unittest.main()\r\n\r\n``` "
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} | The pre_process_network function takes an input tensor and performs 3 matmuls on it. The output of the function is a tensor of the same shape as the input tensor.
The mid_process_network_async function takes an input tensor and performs 3 matmuls on it. The output of the function is a tensor of the same shape as the input tensor. This function also adds 100 to the input tensor before it is passed to the post-processing network.
The post_process_network function takes an input tensor and performs 3 matmuls on it. The output of the function is a tensor of the same shape as the input tensor.
The main function creates an input tensor, calls the pre-processing network, the mid-process network, and the post-processing network 10 times, and prints the final result.
The tf.function API is used to create asynchronous functions. This can make it easier to write and debug asynchronous code, and it can also improve the performance of the code.
The tf.data API is used to create pipelines of asynchronous operations. This can help to improve the performance of asynchronous computations.
The tf.distribute API is used to distribute asynchronous computations across multiple devices. This can also help to improve the performance of asynchronous computations. | {
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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/61503/checks?check_run_id=15728974098) 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 @matthew-olson-intel Can you please sign CLA. Thank you!",
"@gbaned Done!"
] | 2023-08-08T22:18:09 | 2023-08-10T05:58:54 | 2023-08-10T05:58:53 | CONTRIBUTOR | null | false | {
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} | This is a PR which fixes the TensorFlow build when using Clang 16; this file previously used `uint64_t` without including the proper header.
#61289 | {
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"@penpork Thank you very much.\r\n\r\n"
] | 2023-08-08T21:54:47 | 2023-08-21T15:44:50 | 2023-08-11T17:40:03 | CONTRIBUTOR | null | false | {
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Changes include fixing potential nullptr dereference checks, out-of-bounds memory accesses, removing unused variables etc. | {
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"Hi @Tai78641 Can you please resolve conflicts? Thank you!",
"rebased and resolved merge conflicts",
"Hi @jpienaar, Can you please review this PR ? Thank you!",
"Hi @jpienaar, Can you please review this PR ? Thank you!",
"Hi @Tai78641 Can you please resolve conflicts? Thank you!",
"Hi @jpienaar Can you please review this PR ? Thank you!",
"Hi @jpienaar Can you please review this PR ? Thank you!",
"Hi @jpienaar Can you please review this PR ? Thank you!",
"Hi @jpienaar / @rsuderman Can you please review this PR ? Thank you!",
"Hi @jpienaar / @rsuderman Can you please review this PR ? Thank you!",
"Hi @jpienaar / @rsuderman Can you please review this PR ? Thank you!",
"Hi @jpienaar / @rsuderman Can you please review this PR ? Thank you!",
"Hi @jpienaar / @rsuderman Can you please review this PR ? Thank you!",
"FYI, the corresponding llvm PR, https://github.com/llvm/llvm-project/pull/71084, has been merged.",
"Hi @Tai78641 Can you please resolve conflicts? Thank you!",
"resolved conflicts, and turned out the code changes have been merged with a slight casting diff.\r\nforce pushed, and PR is automatically closed because there is no code diff anymore in the PR"
] | 2023-08-08T20:53:10 | 2024-01-17T16:57:12 | 2024-01-17T16:53:52 | CONTRIBUTOR | null | false | {
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} | Fixes required by Rescale shift attr data type changing to DenseI8ArrayAttr (from DenseI32ArrayAttr)
this needs to be coordinated with https://reviews.llvm.org/D157439 which changes tosa dialect's shift attribute types
Change-Id: I6f27d32b60847a296994687d8b6807065958f218 | {
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"Hi @Tai78641 Can you please resolve conflicts? Thank you!"
] | 2023-08-08T20:41:38 | 2023-08-18T16:08:02 | 2023-08-18T16:07:25 | CONTRIBUTOR | null | false | {
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} | Fix issues due to TOSA axis attribute type changing from I64Attr to I32Attr to match the TOSA spec.
this needs to be coordinated with https://reviews.llvm.org/D157424 which changes tosa dialect axis attribute to I32Attr
Change-Id: If8bfc4c4af30712226b36f79dcd600e47d0f168d | {
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"@mihaimaruseac could you please approve?",
"Is this a cherrypick from master?\r\n"
] | 2023-08-08T19:47:38 | 2023-08-08T20:10:00 | 2023-08-08T20:10:00 | NONE | null | false | {
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} | The requirements.in file was updated in https://github.com/tensorflow/tensorflow/commit/4a43c56418ce2edf6c7f7b61e1f1061a02005e05 but the updater wasn't run. | {
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} | The build tests and similar have an empty language tag and so will not run when test_lang_filters has a positive language choice, so change it to use a negative choice to exclude the python based tests that are tested in a different script.
Also remove use of python venv that is no longer useful. | {
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"Please also update the test in https://github.com/tensorflow/tensorflow/blob/88e5914db537d91e779b6e6a56c593146c441bc3/tensorflow/compiler/mlir/lite/tests/end2end/unroll_batch_matmul.pbtxt\r\n\r\nthanks.",
"@sirakiin thanks for taking a look. I updated the mentioned test and rebased the PR onto the latest master.",
"Hi @sirakiin Could you have a look at this PR? Thank you!",
"Hi @sirakiin Could you have a look at this PR? Thank you!",
"Hi @lgeiger Can you please resolve conflicts? Thank you!",
"Hi @lgeiger Can you please resolve conflicts? Thank you!",
"Hi @lgeiger Can you please resolve conflicts? Thank you!",
"> Hi @lgeiger Can you please resolve conflicts? Thank you!\r\n\r\nrebased",
"Hi @haozha111 Can you please review this PR ? Thank you!",
"> Hi @haozha111 Can you please review this PR ? Thank you!\r\n\r\nDeferring to Luke who is working on TFL converter.",
"Hi @LukeBoyer Can you please review this PR ? Thank you!",
"Hi @LukeBoyer Can you please review this PR ? Thank you!",
"Hi @LukeBoyer Can you please review this PR ? Thank you!",
"Hi @LukeBoyer Can you please review this PR ? Thank you!",
"Hi @LukeBoyer Can you please review this PR ? Thank you!",
"Hi @LukeBoyer Can you please review this PR ? Thank you!",
"Hi @LukeBoyer Can you please review this PR ? Thank you!",
"Hi @lgeiger Can you please resolve conflicts? Thank you!",
"Hi @lgeiger Can you please resolve conflicts? Thank you!",
"Hi @lgeiger Can you please resolve conflicts? Thank you!",
"Hi @lgeiger Can you please resolve conflicts? Thank you!",
"Hi @lgeiger Can you please resolve conflicts? Thank you!\r\n\r\n"
] | 2023-08-08T15:25:53 | 2024-06-05T08:22:10 | null | CONTRIBUTOR | null | false | {
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} | This changes the unrolling of `tf.BatchMatMul` in the TFLite converter to use a `tf.Unpack` operation instead of a `tf.Split` operation followed by multiple `tf.Reshape` operations which simplifies the resulting graph.
The difference can be observed with the following code:
```python
import tensorflow as tf
query = tf.keras.Input(shape=(15, 64), name="query", batch_size=1)
key = tf.keras.Input(shape=(15, 64), name="key", batch_size=1)
value = tf.keras.Input(shape=(15, 64), name="value", batch_size=1)
attention_layer = tf.keras.layers.MultiHeadAttention(num_heads=8, key_dim=8)
model = tf.keras.Model(
[query, key, value], attention_layer(query=query, key=key, value=value)
)
model.save("saved_model")
converter = tf.lite.TFLiteConverter.from_saved_model("saved_model")
converter.unfold_batchmatmul = True
converter.optimizations = [tf.lite.Optimize.DEFAULT]
flatbuffer = converter.convert()
open("saved_model_unfolded.tflite", "wb").write(flatbuffer)
```
Previously this would produce the following graph which includes quite a lot of unnecessary reshape ops:
<img width="850" alt="Screenshot 2023-08-08 at 16 13 12" src="https://github.com/tensorflow/tensorflow/assets/13285808/e10cd9b1-5d1b-47e7-a2fe-ea4df784eb33">
With this change the resulting graph doesn't include these reshape ops:
<img width="746" alt="Screenshot 2023-08-08 at 17 21 16" src="https://github.com/tensorflow/tensorflow/assets/13285808/f89614e2-0e5d-4e92-b72b-a7a1fed5a87d">
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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/61496\">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/61496\">No</a>\n"
] | 2023-08-08T01:42:25 | 2023-08-08T02:34:37 | 2023-08-08T02:34:35 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.8
### Custom code
Yes
### OS platform and distribution
linux centos
### Mobile device
_No response_
### Python version
3.8
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
My model can train normally, but there was an error when inference after the training was completed.The model structure code is as follows:
conv_1 = Conv2D(32,(1,5),(1,1),name='mode0_conv_1',padding='same')(input_1)
bn_1 = BatchNormalizetion(name='mode0_bn_1')(conv_1)
out_1 = PReLU(shared_axes=[1,2])(bn_1)
out_2 = tf.reshape(........)(out_1)
dp_1 = LSTM(......)(out_2)
dp_o1 = Dense(32,)(dp_1)
dp_o2 = PReLU(shared_axes=[1,2])(dp_o1)
ls_o1 = LSTM(................)(dp_o2)
dp_o3 = Dense(2,)(ls_o1)
The error is as follows:
tensorflow.pyrhon.framework.errors_impl.InvalidArgumentError:Graph execution error:
....
Node:'model/model0_bn_1/FuseBatchNormV3'
scale must have the same number of elements as the channels of x, got 32 and 2
[[{node model/model0_bn_1/FuseBatchNormV3}]] [op:__inference_predict_function_3355]
May I ask what caused this and if it can be resolved?
### Standalone code to reproduce the issue
```shell
No
```
### Relevant log output
_No response_ | {
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"Hi @pcoramasionwu, thanks for reporting the issue.\r\n\r\nI'm not quite sure what you mean by \"if I provide tensorflowlite.dll.if.lib and tensorflowlite_flex.dll.if.lib as Linker dependencies\", where are these files coming from? and how did you provide them as Linker dependencies? If you are using command prompt/power shell, please include exact commands. Similarly, what are the exact steps you use to produce the tensorflowlite.dll and tensorflowlite_flex.dll files? Thanks for any additional information you can provide, that will help us resolve your issue faster.",
"Hi @pkgoogle \r\n\r\n**tensorflowlite.dll.if.lib** and **tensorflowlite.dll** are generated by using the following command at the root of the local tensorflow repo on a windows machine:\r\n\r\n`bazel build --explain=file_tflite.txt --verbose_explanations --verbose_failures --subcommands //tensorflow/lite:tensorflowlite`\r\n\r\n**tensorflowlite_flex.dll.if.lib** and **tensorflowlite_flex.dll** are produced by the follow command: \r\n\r\n`bazel build --explain=file_flex.txt --verbose_explanations --verbose_failures --subcommands tensorflow/lite/delegates/flex:tensorflowlite_flex`\r\n\r\nthe .libs are added as linker dependencies within visual studio (https://learn.microsoft.com/en-us/cpp/build/reference/dot-lib-files-as-linker-input?view=msvc-170#to-add-lib-files-as-linker-input-in-the-development-environment)",
"Hi @nutsiepully, can you please take a look? Thanks."
] | 2023-08-07T23:58:50 | 2023-08-10T20:38:06 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.11
### Custom code
No
### OS platform and distribution
Windows 10
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
5.3
### GCC/compiler version
MSVC 16 (2019)
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When running an application that uses a model that uses select ops, if I provide tensorflowlite.dll.if.lib and tensorflowlite_flex.dll.if.lib as Linker dependencies and place both tensorflowlite.dll and tensorflowlite_flex.dll in the application folder, the flex delegate is not loaded before inference.
When invoke is called on the interpreter we get the error message:
ERROR: Select TensorFlow op(s), included in the given model, is(are) not supported by this interpreter. Make sure you apply/link the Flex delegate before inference.
### Standalone code to reproduce the issue
```shell
The issue is reproducible using the tensorflow test code:
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/model_flex_test.cc
---------------
The problem is the flex delegate is not automatically applied successfully here https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/core/interpreter_builder.cc#L179
This is because nullptr is being passed to getProcAddress() rather than a handle to tensorflowlite_flex.dll here https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/shared_library.h#L42
---------------
When I modify the tensorflow sources to pass a handle to tensorflowlite_flex.dll, things work as expected
```
### Relevant log output
```shell
ERROR: Select TensorFlow op(s), included in the given model, is(are) not supported by this interpreter. Make sure you apply/link the Flex delegate before inference.
```
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"rebased and resolved merge conflicts",
"Hi @rsuderman Can you please review this PR ? Thank you!",
"Hi @jpienaar, Can you please review this PR ? Thank you!",
"Hi @jpienaar, Can you please review this PR ? Thank you!",
"Hi @jpienaar, Can you please review this PR ? Thank you!",
"Hi @rdzhabarov, Can you please review this PR ? Thank you!",
"@NatashaKnk would you review this?\r\nthe corresponding llvm PR (https://github.com/llvm/llvm-project/pull/80047) has been approved ",
"Hi @Tai78641 Can you please resolve conflicts? Thank you!",
"> Hi @Tai78641 Can you please resolve conflicts? Thank you!\r\n\r\nrebased and resolved conflicts",
"Hi @NatashaKnk Can you please review this PR ? Thank you!",
"Hi @Tai78641 Can you please resolve conflicts? Thank you!"
] | 2023-08-07T21:23:14 | 2024-05-07T17:49:50 | 2024-05-07T17:49:09 | CONTRIBUTOR | null | false | {
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} | Change all references to Tosa Div Op to Tosa IntDiv Op.
This is due to Tosa renaming Div Op to IntDiv in order to align with TOSA spec
This needs to be coordinated with llvm PR: https://github.com/llvm/llvm-project/pull/80047
Change-Id: Iee5aace5c66b1cc2e2008005766ace446ad3e215 | {
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"Hi @rsuderman Can you please review this PR ? Thank you!",
"Thank you for the review @jpienaar, I have posted replies to your comments.",
"Hi @jpienaar, Can you please review this PR ? Thank you!",
"Hi @jpienaar, could you review this PR please? Thanks!",
"Hi @jpienaar, Can you please review this PR ? Thank you!",
"Hi @jpienaar, Can you please review this PR ? Thank you!",
"Hi @jpienaar, could you review this PR please? Thanks!",
"Hi @rdzhabarov, can you review this PR please? Thanks!",
"Hi @jpienaar, Can you please review this PR ? Thank you!",
"Hey @jpienaar, could you review this PR please? Many thanks!",
"Hey @jpienaar, could you review this PR please? Thanks in advance!",
"Hi @tom-arm Can you please resolve conflicts? Thank you!",
"This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.",
"Hi @gbaned, the conflict is fixed now. It would be good to get this reviewed if possible",
"Hi @jpienaar, Can you please review this PR ? Thank you!",
"Hi @jpienaar, Can you please review this PR ? Thank you!"
] | 2023-08-07T15:38:04 | 2024-06-07T16:10:57 | null | CONTRIBUTOR | null | false | {
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"@SuryanarayanaY I was able to replicate this issue on colab, please find the [gist](https://colab.research.google.com/gist/sushreebarsa/9f28b8e2cbd814bbf962b191bfe58cf5/61492.ipynb#scrollTo=ah8YR0tL_DdN) here. Thank you!",
"Hi @christian-steinmeyer ,\r\n\r\nI have checked the code with keras_core which is now a multi backend support library. In `keras_core` there is no reported behaviour. Please refer to attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/0451166bfed11cb4020bb43e323a3b6c/61492_keras-core.ipynb).\r\n\r\n`keras_core` subsequently will release as `Keras3` and tf.keras module will become legacy code. Thanks!",
"LGTM! Thanks!",
"Hi @christian-steinmeyer ,\r\n\r\nCan we mark this issue as closed ? Please feel free to close the issue if resolved for you.\r\n\r\nThanks!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"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/61492\">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/61492\">No</a>\n"
] | 2023-08-07T15:13:10 | 2023-12-23T10:46:24 | 2023-12-23T10:46:21 | CONTRIBUTOR | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
3.10.6
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
While the below code works, when using the sequential model, it doesn't for the functional one. With the functional model, the [while loop in `reconstruct_from_config()`](https://github.com/tensorflow/tensorflow/blob/ae5e557a555e2aa5f236af6cfdb83dc568ea38e1/tensorflow/python/keras/engine/functional.py#L779) loops indefinitely.
I don't fully understand the code there, but my hunch is that either I'm doing wrong on the usage side, or there might be a bug close to the `node_count_by_layer`, which is set to the return value of `should_skip_first_node` although that seems like a different concept.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
def replace_conv2d_with_sub_model(layer: tf.keras.layers.Layer) -> tf.keras.layers.Layer:
if isinstance(layer, tf.keras.layers.Conv2D):
inputs = layer.input
x = layer(inputs)
y = tf.keras.layers.Lambda(lambda t: t * 2)(x)
outputs = tf.keras.layers.Add()([x, y])
layer = tf.keras.models.Model(inputs=inputs, outputs=outputs, name=layer.name)
return layer
def get_sequential_model() -> tf.keras.Model:
return tf.keras.Sequential(
[
tf.keras.layers.InputLayer(input_shape=(28, 28, 1)),
tf.keras.layers.Conv2D(32, 3, activation='relu'),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(10, activation='softmax'),
]
)
def get_functional_model() -> tf.keras.Model:
inputs = tf.keras.Input(shape=(28, 28, 1))
x = tf.keras.layers.Conv2D(32, 3, activation='relu')(inputs)
x = tf.keras.layers.Flatten()(x)
outputs = tf.keras.layers.Dense(10, activation='softmax')(x)
return tf.keras.Model(inputs=inputs, outputs=outputs)
if __name__ == '__main__':
model = get_functional_model() # get_sequential_model() works
model.compile()
sample_input = np.random.uniform(size=(1, 28, 28, 1))
o1 = model.predict(sample_input)
new_model = tf.keras.models.clone_model(
model, input_tensors=model.inputs, clone_function=replace_conv2d_with_sub_model
) # <-- results in an infinite loop
o2 = new_model.predict(sample_input)
```
### Relevant log output
_No response_ | {
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"Thanks, I've added notes to Release.md.",
"I've also rebased and bumped the compat horizon.",
"@cantonios are there still some internal tests failing?"
] | 2023-08-07T12:31:45 | 2023-09-11T23:28:08 | 2023-09-11T23:28:07 | CONTRIBUTOR | null | false | {
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} | Sequel to https://github.com/tensorflow/tensorflow/pull/61294
(This is a rebased version of the approved https://github.com/benbarsdell/tensorflow/pull/2)
cc @bfontain @nluehr @pjannaty | {
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"@tellts,\r\nCould you please provide the tensorflow version you are trying and also this issue doesn't look like the issue from the tensorflow side. \r\n\r\nAlso this kind of error emerges from your python interpreter. So the best solution is to set your python interpreter in your project env directory or set the interpreters virtual env properly using your IDE's interpreter configuration. 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.",
"I've tried different versions of Tensorflow, but now I've uninstalled them all. I also tried with a freshly installed Python so that there are no conflicts with other applications. I also tried also the latest version which is Paycharm. I mean all this in relation to the version with CPU.",
"@tellts,\r\nWithout the reproducible code, it would be difficult for us to debug the issue. In order to expedite the trouble-shooting process, could you please provide a minimal code snippet and the TensorFlow version you are using.\r\n\r\nAlso please try to set your python interpreter in your project env directory or set the interpreters virtual env properly using your IDE's interpreter configuration. Thank you!\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.",
"Closing this as stale. Please reopen if this is still a valid request. 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/61490\">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/61490\">No</a>\n"
] | 2023-08-07T11:39:20 | 2023-12-29T07:39:09 | 2023-12-29T07:39:05 | NONE | null | null | null | Windows 10 Russian locale (utf-8) Microsoft Windows [Version 10.0.19045.3208]. Python 3.10.8 (i tried with other versions too). Tensorflow-cpu (I do not see the version in Paycharm, I installed it today. And before, without Paycharm, I tried several other versions and got the same error.).
Windows PowerShell
Copyright (C) Microsoft Corporation. All rights reserved.
Try the new cross-platform PowerShell https://aka.ms/pscore6
PS C:\Users\admin\PycharmProjects\pythonProject3> python
Python 3.10.8 (tags/v3.10.8:aaaf517, Oct 11 2022, 16:50:30) [MSC v.1933 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Python3108\lib\site-packages\tensorflow\__init__.py", line 38, in <module>
from tensorflow.python.tools import module_util as _module_util
File "C:\Python3108\lib\site-packages\tensorflow\python\__init__.py", line 37, in <module>
from tensorflow.python.eager import context
File "C:\Python3108\lib\site-packages\tensorflow\python\eager\context.py", line 29, in <module>
from tensorflow.core.framework import function_pb2
File "C:\Python3108\lib\site-packages\tensorflow\core\framework\function_pb2.py", line 14, in <module>
from tensorflow.core.framework import attr_value_pb2 as tensorflow_dot_core_dot_framework_dot_attr__value__pb2
File "C:\Python3108\lib\site-packages\tensorflow\core\framework\attr_value_pb2.py", line 14, in <module>
from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
ValueError: source code string cannot contain null bytes
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"This appears to only be a problem with gcc/ld builds. When using clang/lld the build succeeds.",
"Hi @cheshire Can you please review this PR ? Thank you!",
"TF has a canonical build configuration, and OSS CI is passing, why should this change be required?",
"@cheshire It fails when building with gcc toolchain. AFAIK the policy is to accept patches that are for gcc fixes.",
"Then there should be a GCC CI?",
"@cheshire Please see the [SIG BUILD notes](http://bit.ly/tf-sig-build-notes) from 2nd May 2023, with the comment about \"we'll accept PRs to fix gcc!\"",
"@cheshire Indeed, this is a special case. Although TF's official builds now use and are gated by clang, we (the TF DevInfra team) decided to keep accepting changes that help make TF able to build with GCC, since a large portion of TF's community builders still use gcc for their platforms. We currently leave verification up to them. ",
"This sounds like a difference with `-Was-needed`, not actually due to clang/gcc. I wouldnt be surprised if you added as-needed on clang it'd fail as well. Maybe `:stream_executor_pimpl` needs to have `alwayslink=True` set?\r\n",
"Hi @elfringham Can you please resolve conflicts? Thank you!",
"Due to the delays in getting this merged, it is now too late as the code has been moved to a different repo."
] | 2023-08-07T11:24:48 | 2024-02-29T11:08:47 | 2023-09-08T13:43:26 | CONTRIBUTOR | null | false | {
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"https://github.com/tensorflow/tensorflow/blob/2bade54ecda9cf8fe3c66487931ebda0babc9d10/.bazelrc#L111\r\n\r\nMaybe adding `build --host_features=-force_no_whole_archive` after this line is enough?",
"interested to hear if that fixes ^, otherwise it's likely another `--features` flag that needs a new `--host_features` equivalent",
"@meteorcloudy Could you please keep this issue assigned to you (if you're working on it) or assign it to the person you expect to look into it? When it's unassigned the gTech team assigns it to new owners and this causes confusion and spamming.\r\n@sachinprasadhs FYI",
"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/61488\">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/61488\">No</a>\n"
] | 2023-08-07T09:06:18 | 2023-08-17T12:42:25 | null | MEMBER | null | null | null | https://buildkite.com/bazel/bazel-at-head-plus-disabled/builds/1741#01899b1f-bbb1-4dc9-8ffa-1b1b129810b3
```
(06:36:48) ERROR: /var/lib/buildkite-agent/builds/bk-docker-bl1p/bazel-downstream-projects/tensorflow/tensorflow/lite/experimental/microfrontend/BUILD:97:21: Linking tensorflow/lite/experimental/microfrontend/gen_audio_microfrontend_op_py_wrappers_cc [for tool] failed: (Exit 1): clang failed: error executing CppLink command (from target //tensorflow/lite/experimental/microfrontend:gen_audio_microfrontend_op_py_wrappers_cc)
(cd /var/lib/buildkite-agent/.cache/bazel/_bazel_buildkite-agent/c3b80eb6321395e802c419deaac81a18/execroot/org_tensorflow && \
exec env - \
PATH=/var/lib/buildkite-agent/.cache/bazelisk/local/-tmp-tmpxmujvc4j-bazel/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin \
PWD=/proc/self/cwd \
ZERO_AR_DATE=1 \
/usr/lib/llvm-16/bin/clang @bazel-out/k8-opt-exec-ST-a0a42e9628a1/bin/tensorflow/lite/experimental/microfrontend/gen_audio_microfrontend_op_py_wrappers_cc-2.params)
# Configuration: 58da8197376b354f6825ccc0ccc40424332586b6eb574b2ca1de78a5bc0d109a
# Execution platform: @local_execution_config_platform//:platform
ld.lld: error: undefined symbol: tsl::Flag::Flag(char const*, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char>>*, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char>> const&, bool*)
>>> referenced by python_op_gen_main.cc
>>> bazel-out/k8-opt-exec-ST-a0a42e9628a1/bin/tensorflow/python/framework/_objs/python_op_gen_main/python_op_gen_main.o:(main)
>>> referenced by python_op_gen_main.cc
>>> bazel-out/k8-opt-exec-ST-a0a42e9628a1/bin/tensorflow/python/framework/_objs/python_op_gen_main/python_op_gen_main.o:(main)
>>> referenced by python_op_gen_main.cc
>>> bazel-out/k8-opt-exec-ST-a0a42e9628a1/bin/tensorflow/python/framework/_objs/python_op_gen_main/python_op_gen_main.o:(main)
>>> referenced 4 more times
ld.lld: error: undefined symbol: tsl::Flags::Usage(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char>> const&, std::vector<tsl::Flag, std::allocator<tsl::Flag>> const&)
>>> referenced by python_op_gen_main.cc
>>> bazel-out/k8-opt-exec-ST-a0a42e9628a1/bin/tensorflow/python/framework/_objs/python_op_gen_main/python_op_gen_main.o:(main)
ld.lld: error: undefined symbol: tsl::Flags::Parse(int*, char**, std::vector<tsl::Flag, std::allocator<tsl::Flag>> const&)
>>> referenced by python_op_gen_main.cc
>>> bazel-out/k8-opt-exec-ST-a0a42e9628a1/bin/tensorflow/python/framework/_objs/python_op_gen_main/python_op_gen_main.o:(main)
clang: error: linker command failed with exit code 1 (use -v to see invocation)
Target //tensorflow/tools/pip_package:build_pip_package failed to build
```
[A bisect](https://buildkite.com/bazel/culprit-finder/builds/6232#0189ba86-a70a-4cc4-af12-1d689bb7e617) shows the breaking change is: https://github.com/bazelbuild/bazel/pull/17498
@keith Can you give some guidance on how to adapt for this breaking change?
/cc @learning-to-play
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"This is not a broken link. The purpose of these g3doc files are to help build up our website. Check out https://www.tensorflow.org/lite/guide/ops_compatibility & you'll find the link is working just fine."
] | 2023-08-07T09:00:08 | 2023-12-27T23:00:45 | 2023-08-09T17:07:28 | CONTRIBUTOR | null | false | {
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"@mengran1234 As per the documentation [here ](https://www.tensorflow.org/lite/performance/coreml_delegate)in coreml_delegate, the ResizeBilinear is a supported ops. Could you please let us know if this is what you are looking for? \r\nThank you!",
"> @mengran1234 As per the documentation [here ](https://www.tensorflow.org/lite/performance/coreml_delegate)in coreml_delegate, the ResizeBilinear is a supported ops. Could you please let us know if this is what you are looking for? Thank you!\r\n\r\nyes\r\nbut the documentation is very simple。\r\n\r\nthis ResizeBilinear layer is not supported in coreml, only if \"half_pixel_centers =false\", the coreml can support it. \r\n",
"align_corners = false , half_pixel_centers = true \r\nthis configuration parameter is common， how coreml can support it ?",
"Hi @mengran1234, the current version is https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/delegates/coreml/coreml_delegate.mm, which seems different than your code snippet. Can you try with the latest release and let us know if it resolves your issue? Also I am having trouble understanding your issue. Does your code work? Does it work but your performance doesn't match what you expected? If so, please tell us how. Thanks for any additional information you can provide.",
"My code can run normally.\r\nBut performance doesn't match what i expected because of ResizeBilinear layer.\r\nthe code is:\r\n // For most ops, only version 1 is supported.\r\n if (registration->version > 1) {\r\n switch (registration->builtin_code) {\r\n case kTfLiteBuiltinDepthwiseConv2d:\r\n if (registration->version > 2) return false;\r\n break;\r\n // FullyConnected without bias is supported starting from version 6.\r\n case kTfLiteBuiltinFullyConnected:\r\n if (registration->version > 6) return false;\r\n break;\r\n default:\r\n return false;\r\n }\r\n }\r\nIf ResizeBilinear (align_corners = false , half_pixel_centers = true) ,\r\nabove code will run to \"\r\n default:\r\n return false;\r\n\"\r\nso coreml do not support this layer (meet the conditions registration->version > 1)\r\n\r\n\r\n\r\n",
"Ok thanks for the information, we'll consider this a feature request then, @yishuangP can you please take a look?\r\n",
"do you know \r\n// For most ops, only version 1 is supported.\r\nif (registration->version > 1) {\r\n\r\nabove where is the value of registration->version from? how it defined "
] | 2023-08-07T03:15:33 | 2023-08-14T15:07:18 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.10 or 2.11
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
no
### GCC/compiler version
no
### CUDA/cuDNN version
no
### GPU model and memory
111
### Current behavior?
i run coreml delegate on iphone 12. The tflite model has ResizeBilinear layer ( align_coreners == false , half_pixel_center ==true),
but I find the tflite code following (coreml_delegate.mm):
// For most ops, only version 1 is supported.
if (registration->version > 1) {
return false;
}
ResizeBilinear layer is not supported by ANE because of version >1.
But if ResizeBilinear layer ( align_coreners == false , half_pixel_center ==false) , it is supported by ANE engine.So the performance will be improved
### Standalone code to reproduce the issue
```shell
my question is ,
ResizeBilinear layer ( align_coreners == false , half_pixel_center ==true) can be supported by ANE?
this format is common during training model
thank you!
```
### Relevant log output
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"I ran it again and got this `In file included from /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/build/xnnpack/src/amalgam/gen/fma3.c:7:\r\n/usr/include/math.h:37:10: fatal error: /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/build/xnnpack/src/bits/types.h: Cannot allocate memory\r\n 37 | #include <bits/types.h>`",
"Hi @Luca-Stefanescu \r\n\r\nHave you tried [these](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/examples/minimal#tensorflow-lite-c-minimal-example) steps for minimal example and hitting the errors?\r\n\r\nIn the minimal_build directory, after build we need to run the executable by \r\n\r\n`./minimal <path/to/tflite/model>`\r\n\r\nThanks.",
"> Hi @Luca-Stefanescu\r\n> \r\n> Have you tried [these](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/examples/minimal#tensorflow-lite-c-minimal-example) steps for minimal example and hitting the errors?\r\n> \r\n> In the minimal_build directory, after build we need to run the executable by\r\n> \r\n> `./minimal <path/to/tflite/model>`\r\n> \r\n> Thanks.\r\n\r\nYep I followed those instructions and got \"failed to allocate memory\" when compiling. It didn't complete compilation",
"@pkgoogle I was able to reproduce this issue on both master and 2.13 branches. Please find this [gist](https://colab.research.google.com/gist/pjpratik/0bc677213e4e457cb44a7622c5bbe89d/61485.ipynb).\r\n\r\nCould you please look into this issue?\r\n\r\nThanks.",
"Hi @Luca-Stefanescu, thanks for reporting the issue.\r\n\r\nYou don't need to run ./configure, that is mainly for building with bazel. Where did you make your build directory? it should be a sibling of your tensorflow_src directory. Can you confirm you are following the directions here? https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/examples/minimal Are you in the build directory when you run\r\n```sh\r\ncmake ../tensorflow_src/tensorflow/lite/examples/minimal\r\n```\r\n?\r\n\r\nAlso please let us know the errors you are getting when you say \"I get quite a lot of errors\" (like the first page if possible)\r\n\r\nIt's possible the commit you are cloning is unstable, you can also try switching to the nightly or r2.14 (or earlier) branch.",
"> Hi @Luca-Stefanescu, thanks for reporting the issue.\r\n> \r\n> You don't need to run ./configure, that is mainly for building with bazel. Where did you make your build directory? it should be a sibling of your tensorflow_src directory. Can you confirm you are following the directions here? https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/examples/minimal Are you in the build directory when you run\r\n> \r\n> ```shell\r\n> cmake ../tensorflow_src/tensorflow/lite/examples/minimal\r\n> ```\r\n> \r\n> ?\r\n> \r\n> Also please let us know the errors you are getting when you say \"I get quite a lot of errors\" (like the first page if possible)\r\n> \r\n> It's possible the commit you are cloning is unstable, you can also try switching to the nightly or r2.14 (or earlier) branch.\r\n\r\nBuild directory is a sibling of tensorflow_src/ so I go into build directory and if I do `ls ../tensorflow_src/tensorflow/lite/examples/minimal` it shows the CMakeLists.txt file and the other files in the minimal/ folder. And then I run cmake from the build directory. \r\n\r\nThis is the first page with fatal error. I am getting high memory usage by WSL, more than 11GB during this compilation.\r\n\r\n```[ 92%] Building CXX object tensorflow-lite/CMakeFiles/tensorflow-lite.dir/kernels/internal/utils/sparsity_format_converter.cc.o\r\nIn file included from /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/tensorflow_src/tensorflow/lite/core/async/c/async_signature_runner.cc:17:\r\n/mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/tensorflow_src/tensorflow/lite/c/c_api_internal.h:31:10: fatal error: /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/build/cpuinfo/tensorflow/lite/profiling/telemetry/c/profiler.h: Cannot allocate memory\r\n 31 | #include \"tensorflow/lite/profiling/telemetry/c/profiler.h\"\r\n | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\ncompilation terminated.\r\nIn file included from /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/tensorflow_src/third_party/eigen3/Eigen/Core:1,\r\n from /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/tensorflow_src/tensorflow/lite/kernels/dequantize.h:20,\r\n from /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/tensorflow_src/tensorflow/lite/kernels/numeric_verify.cc:26:\r\n/mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/build/eigen/Eigen/Core:50:10: fatal error: /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/build/xnnpack/include/complex: Cannot allocate memory\r\n 50 | #include <complex>\r\n | ^~~~~~~~~\r\nIn file included from /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/tensorflow_src/tensorflow/lite/kernels/internal/optimized/optimized_ops.h:41,\r\n from /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/tensorflow_src/tensorflow/lite/kernels/detection_postprocess.cc:27:\r\n/mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/tensorflow_src/third_party/eigen3/Eigen/Core:1:10: fatal error: /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/build/xnnpack/include/Eigen/Core: Cannot allocate memory\r\n 1 | #include \"Eigen/Core\"\r\n | ^~~~~~~~~~~~\r\ncompilation terminated.\r\ncompilation terminated.\r\nmake[2]: *** [tensorflow-lite/CMakeFiles/tensorflow-lite.dir/build.make:412: tensorflow-lite/CMakeFiles/tensorflow-lite.dir/core/async/c/async_signature_runner.cc.o] Error 1\r\nmake[2]: *** Waiting for unfinished jobs....\r\nmake[2]: *** [tensorflow-lite/CMakeFiles/tensorflow-lite.dir/build.make:1434: tensorflow-lite/CMakeFiles/tensorflow-lite.dir/kernels/detection_postprocess.cc.o] Error 1\r\nmake[2]: *** [tensorflow-lite/CMakeFiles/tensorflow-lite.dir/build.make:2008: tensorflow-lite/CMakeFiles/tensorflow-lite.dir/kernels/numeric_verify.cc.o] Error 1\r\nIn file included from /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/tensorflow_src/third_party/eigen3/Eigen/Core:1,\r\n from /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/tensorflow_src/tensorflow/lite/kernels/internal/optimized/optimized_ops.h:41,\r\n from /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/tensorflow_src/tensorflow/lite/kernels/gru_cell.cc:20:\r\n/mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/build/eigen/Eigen/Core:93:10: fatal error: /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/build/flatbuffers/include/climits: Input/output error\r\ncompilation terminated.\r\nIn file included from /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/tensorflow_src/tensorflow/lite/c/c_api_internal.h:28,\r\n from /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/tensorflow_src/tensorflow/lite/c/c_api_for_testing.cc:17:\r\n/mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/tensorflow_src/tensorflow/lite/core/interpreter.h:58:10: fatal error: /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/tensorflow_src/tensorflow/lite/stderr_reporter.h: Input/output error\r\n 58 | #include \"tensorflow/lite/stderr_reporter.h\"\r\n | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\nIn file included from /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/tensorflow_src/third_party/eigen3/Eigen/Core:1,\r\n from /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/tensorflow_src/tensorflow/lite/kernels/internal/reference/reference_ops.h:29,\r\n from /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/tensorflow_src/tensorflow/lite/kernels/logical.cc:19:\r\n/mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/build/eigen/Eigen/Core:287:10: fatal error: /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/build/eigen/Eigen/src/Core/ArithmeticSequence.h: Input/output error\r\ncompilation terminated.\r\nIn file included from /usr/lib/gcc/x86_64-linux-gnu/9/include/xmmintrin.h:34,\r\n from /usr/lib/gcc/x86_64-linux-gnu/9/include/emmintrin.h:31,\r\n from /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/build/eigen/Eigen/src/Core/util/ConfigureVectorization.h:337,\r\n from /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/build/eigen/Eigen/Core:22,\r\n from /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/tensorflow_src/third_party/eigen3/Eigen/Core:1,\r\n from /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/tensorflow_src/tensorflow/lite/kernels/internal/optimized/optimized_ops.h:41,\r\n from /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/tensorflow_src/tensorflow/lite/kernels/sub.cc:29:\r\n/usr/lib/gcc/x86_64-linux-gnu/9/include/mm_malloc.h:27:10: fatal error: /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/build/FP16-source/include/stdlib.h: Cannot allocate memory\r\n 27 | #include <stdlib.h>\r\n | ^~~~~~~~~~\r\n```\r\n\r\n\r\n\r\nI am using the master branch, that's the default after git cloning. I'll try the nightly branch now.",
"I got the same issue in the nightly branch, cannot allocate memory but it continues compilation. This is after I introduced a memory limit in my .wslconfig file to 1GB: \r\n\r\n```\r\n[ 30%] Built target absl_base\r\nIn file included from /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/build/xnnpack/src/xnnpack/assembler.h:8,\r\n from /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/build/xnnpack/src/xnnpack/aarch32-assembler.h:12,\r\n from /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/build/xnnpack/src/jit/aarch32-assembler.cc:9:\r\n/mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/build/xnnpack/src/xnnpack/memory.h:12:10: fatal error: /mnt/c/Users/Lucas/Documents/WWP/ML/edge_inference/build/xnnpack/include/xnnpack/common.h: Cannot allocate memory\r\n 12 | #include <xnnpack/common.h>\r\n | ^~~~~~~~~~~~~~~~~~\r\ncompilation terminated.\r\n[ 30%] Built target mutex\r\n```\r\n\r\nI tried it in my docker container\r\n\r\n```\r\n33.21 [ 92%] Building CXX object tensorflow-lite/CMakeFiles/tensorflow-lite.dir/kernels/internal/utils/sparsity_format_converter.cc.o\r\n33.21 [ 92%] Building CXX object tensorflow-lite/CMakeFiles/tensorflow-lite.dir/schema/schema_utils.cc.o\r\n124.2 tensorflow-lite/CMakeFiles/tensorflow-lite.dir/build.make:2007: recipe for target 'tensorflow-lite/CMakeFiles/tensorflow-lite.dir/kernels/numeric_verify.cc.o' failed \r\n275.8 c++: internal compiler error: Killed (program cc1plus)\r\n275.8 Please submit a full bug report,\r\n275.8 with preprocessed source if appropriate.\r\n275.8 See <file:///usr/share/doc/gcc-7/README.Bugs> for instructions.\r\n275.8 make[2]: *** [tensorflow-lite/CMakeFiles/tensorflow-lite.dir/kernels/numeric_verify.cc.o] Error 4\r\n302.1 make[2]: *** Waiting for unfinished jobs....\r\n302.1 c++: internal compiler error: Killed (program cc1plus)\r\n302.1 Please submit a full bug report,\r\n302.1 with preprocessed source if appropriate.\r\n302.1 See <file:///usr/share/doc/gcc-7/README.Bugs> for instructions.\r\n```\r\n\r\nI am not sure how to access that file for instructions, is it in the docker container I think. One thing to note is that the minimal.cc code is not complete but I still expect it to compile or at least compile until getting to the incomplete `Interpreter->Invoke()` line and then fail with the appropriate error.",
"Hi @Luca-Stefanescu, it looks like you cloned everything under /mnt/c which is technically in the in the Windows FS. It's possible the code can't allocate memory because it's coming from Windows in a sense, can you redo everything completely within the WSL FS? i.e. follow the directions from the home directory let's say or make a git folder that you can do everything in as long as it's not a subtree of /mnt/c.\r\n\r\nLet me know how that goes.\r\n\r\nAlso, can you test with this limit removed? \r\n\r\n>This is after I introduced a memory limit in my .wslconfig file to 1GB:",
"> Hi @Luca-Stefanescu, it looks like you cloned everything under /mnt/c which is technically in the in the Windows FS. It's possible the code can't allocate memory because it's coming from Windows in a sense, can you redo everything completely within the WSL FS? i.e. follow the directions from the home directory let's say or make a git folder that you can do everything in as long as it's not a subtree of /mnt/c.\r\n> \r\n> Let me know how that goes.\r\n> \r\n> Also, can you test with this limit removed?\r\n> \r\n> > This is after I introduced a memory limit in my .wslconfig file to 1GB:\r\n\r\nYep I can try but I have tried it in the official tensorflow docker image and in another image that my company uses and I get the same issues. Although sometimes it's input/output error rather than failed to allocate memory. And it's always issues with many various include statements in the tensorflow source code.\r\n",
"I tried it in the wsl file system with the memory limit removed:\r\n```\r\n[ 92%] Building CXX object tensorflow-lite/CMakeFiles/tensorflow-lite.dir/schema/schema_utils.cc.o\r\nc++: fatal error: Killed signal terminated program cc1plus\r\ncompilation terminated.\r\nmake[2]: *** [tensorflow-lite/CMakeFiles/tensorflow-lite.dir/build.make:2960: tensorflow-lite/CMakeFiles/tensorflow-lite.dir/optional_debug_tools.cc.o] Error 1\r\nmake[2]: *** Waiting for unfinished jobs....\r\n```",
"Hi @Luca-Stefanescu, I am unable to replicate locally, my cmake version is 3.25.1 but I doubt that's the different factor. I am not using WSL, so it's likely an interaction with your OS and WSL. How much free memory do you have on your hard drive? Is your OS installed on your hard drive or do you have multiple hard drives / partitions? What does that distribution look like? Which hard drive (if multiple) does WSL use? Is there some WSL restriction imposed by your OS? Can you check what configurations your WSL is under? How much RAM do you have? Can you check Task manager while the build is running and see how much Memory WSL is using? Does it reach your RAM limit? Does it ever reach your pagefile (i.e. is the program swapping out memory from RAM to disk)?\r\n\r\nApologies for the question storm but the more information we have the better chance I have of helping you more immediately.",
"> Hi @Luca-Stefanescu, I am unable to replicate locally, my cmake version is 3.25.1 but I doubt that's the different factor. I am not using WSL, so it's likely an interaction with your OS and WSL. How much free memory do you have on your hard drive? Is your OS installed on your hard drive or do you have multiple hard drives / partitions? What does that distribution look like? Which hard drive (if multiple) does WSL use? Is there some WSL restriction imposed by your OS? Can you check what configurations your WSL is under? How much RAM do you have? Can you check Task manager while the build is running and see how much Memory WSL is using? Does it reach your RAM limit? Does it ever reach your pagefile (i.e. is the program swapping out memory from RAM to disk)?\r\n> \r\n> Apologies for the question storm but the more information we have the better chance I have of helping you more immediately.\r\n\r\nHard drive has hundreds of GB free. I have standard C and D partitions (D is a different hard drive that I bought) but OS and WSL are both on C. Not sure about WSL restrictions, I'll do some research. I am not sure how to check what configurations wsl is under, can you be more specific? I have 16GB RAM, looking at task manager, WSL will typically use as much RAM as I allow it to. I use a .wslconfig file to set this limit but other than that it is empty. WSL can easily reach the ram limit if I allow it to, I did try with the limit set to 16GB and it will exhaust all memory.\r\n\r\nJust had another go now, I set the RAM limit to 16GB and tried `cmake --build . -j` and looking at the performance monitor it does show ~10% of the page file being used. I will have a look for WSL restrictions and configurations and get back to you. \r\n\r\nI am in wsl file system rather than /mnt/c and somehow it has just finished compiling. Last time I tried it failed so I am not sure what I am doing differently... I did remove the RAM limit and see that the page file was being used. It is still at more than 5% now after compilation is finished, but task manager shows 50% of RAM being used and vmmemWSL is using 1.7GB. I will try it on my company docker image and see if we can move forward with the project, thanks for assistance.",
"Np, @Luca-Stefanescu sounds like Schrödinger's memory allocation failure :). If I had to guess it sounds like it is reaching capacity in RAM and starts to try to use the pagefile but Windows doesn't like that and limits its RAM usage sometimes in which case the allocation request gets denied. I'm guessing more RAM would help but 16GB should be enough. I'm wondering if there is a way to not restrict WSL RAM/pagefile Usage. I'm thinking reducing simultaneous programs that are using other RAM or unleashing WSL is probably your best way to consistently compile.",
"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/61485\">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/61485\">No</a>\n",
"Any update with this issue? I'm having the same problem.\r\n\r\nI am building Tensorflow Lite with cmake following the instruction given on the minimal example. I am building in a docker container with ubuntu using WSL 2 with docker desktop. The build seems to work until 91%. Then it will start to allocate all the memory (16gb of ram + 8gb of swap) until it fails to allocate throwing an allocation error or sometimes an input/output error.\r\nI think that this error message could be helpful:\r\n```\r\nIn file included from /workspaces/tfl-dev-env/tie/../tensorflow_src/tensorflow/lite/kernels/internal/runtime_shape.h:22,\r\n from /workspaces/tfl-dev-env/tie/../tensorflow_src/tensorflow/lite/kernels/internal/types.h:24,\r\n from /workspaces/tfl-dev-env/tie/../tensorflow_src/tensorflow/lite/kernels/internal/tensor_ctypes.h:22,\r\n from /workspaces/tfl-dev-env/tensorflow_src/tensorflow/lite/kernels/embedding_lookup_sparse.cc:72:\r\n/usr/include/c++/13/memory:81:12: fatal error: /workspaces/tfl-dev-env/tie/../tensorflow_src/bits/shared_ptr_atomic.h: Cannot allocate memory\r\n 81 | # include <bits/shared_ptr_atomic.h>\r\n | ^~~~~~~~~~~~~~~~~~~~~~~~~~\r\ncompilation terminated.\r\n```\r\n\r\nHowever trying to follow the same steps on a ubuntu VM using VMWare (and with less memory) seems to work.",
"> Any update with this issue? I'm having the same problem.\r\n> \r\n> I am building Tensorflow Lite with cmake following the instruction given on the minimal example. I am building in a docker container with ubuntu using WSL 2 with docker desktop. The build seems to work until 91%. Then it will start to allocate all the memory (16gb of ram + 8gb of swap) until it fails to allocate throwing an allocation error or sometimes an input/output error. I think that this error message could be helpful:\r\n> \r\n> ```\r\n> In file included from /workspaces/tfl-dev-env/tie/../tensorflow_src/tensorflow/lite/kernels/internal/runtime_shape.h:22,\r\n> from /workspaces/tfl-dev-env/tie/../tensorflow_src/tensorflow/lite/kernels/internal/types.h:24,\r\n> from /workspaces/tfl-dev-env/tie/../tensorflow_src/tensorflow/lite/kernels/internal/tensor_ctypes.h:22,\r\n> from /workspaces/tfl-dev-env/tensorflow_src/tensorflow/lite/kernels/embedding_lookup_sparse.cc:72:\r\n> /usr/include/c++/13/memory:81:12: fatal error: /workspaces/tfl-dev-env/tie/../tensorflow_src/bits/shared_ptr_atomic.h: Cannot allocate memory\r\n> 81 | # include <bits/shared_ptr_atomic.h>\r\n> | ^~~~~~~~~~~~~~~~~~~~~~~~~~\r\n> compilation terminated.\r\n> ```\r\n> \r\n> However trying to follow the same steps on a ubuntu VM using VMWare (and with less memory) seems to work.\r\n\r\nthere was some way to disable memory usage limits which allowed it to work by using more memory in the pagefile (SWAP momey) but utlimately i just found a workaround using the keras2c github repo which converts tensorflow model into a C file.",
"> there was some way to disable memory usage limits which allowed it to work by using more memory in the pagefile (SWAP momey)\r\n\r\nSo I just need to give WSL more memory? I thought 16+8gb would be enough.\r\n\r\n> but utlimately i just found a workaround using the keras2c github repo which converts tensorflow model into a C file.\r\n\r\nCan you give me a link or more infos? I am building a tensorflow lite C++ app with cmake, I don't know if this could be a suitable solution.\r\n\r\nCan you re-open the issue so that maybe @pjpratik or @pkgoogle can help me finding a solution?\r\n\r\nThanks for the reply.",
"> > there was some way to disable memory usage limits which allowed it to work by using more memory in the pagefile (SWAP momey)\r\n> \r\n> So I just need to give WSL more memory? I thought 16+8gb would be enough.\r\n> \r\n> > but utlimately i just found a workaround using the keras2c github repo which converts tensorflow model into a C file.\r\n> \r\n> Can you give me a link or more infos? I am building a tensorflow lite C++ app with cmake, I don't know if this could be a suitable solution.\r\n> \r\n> Can you re-open the issue so that maybe @pjpratik or @pkgoogle can help me finding a solution?\r\n> \r\n> Thanks for the reply.\r\n\r\nhttps://github.com/f0uriest/keras2c this is the github repo. I would reopen this issue but I don't know how.",
"Ok, thanks. Maybe it cannot be re-opened. If they don't reply I'll open a new issue. Thanks for your help btw"
] | 2023-08-06T18:24:47 | 2023-09-19T13:35:22 | 2023-08-25T01:47:44 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
cloned the master branch
### Custom code
Yes
### OS platform and distribution
windows 11 wsl 2 with Ubuntu 20.04.6 LTS
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
6.3.1
### GCC/compiler version
9.4.0
### CUDA/cuDNN version
Cuda compilation tools, release 10.1, V10.1.243
### GPU model and memory
rtx 2060 6GB dedicated
### Current behavior?
I am using cmake 3.22.2
I cloned tensorflow into a directory tensorflow_src, then I ran ./configure and set ROCm and CUDA support to none, because I am building tflite. Then I made and moved into a build directory. I run `cmake ../tensorflow_src/tensorflow/lite` and `cmake --build . -j` I get quite a lot of errors, the first is "failed to allocate memory". But there's not enough space to paste the entire log here.
I also tried `cmake ../tensorflow_src/tensorflow/lite/examples/minimal` and `cmake --build . -j` and the error I get is that
I am trying to instantiate the tflite interpreter object in the easiest possible way.
### Standalone code to reproduce the issue
I also tried bazel build in the tensorflow_sec directory, which seems like it executed fine. then I used `g++ -std=c++17 inference.cpp model.cc -Ltensorflow_src/bazel-bin/tensorflow/lite -ltensorflowlite -o inference -Itensorflow_src/tensorflow/lite`
```shell
In file included from inference.cpp:6:
tensorflow_src/tensorflow/lite/interpreter.h:21:10: fatal error: tensorflow/lite/core/interpreter.h: No such file or directory
21 | #include "tensorflow/lite/core/interpreter.h"
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
compilation terminated.
```
I am following exactly the instructions on tensorflolw website to set up tflite for C++, it seems like there is missing information
### Relevant log output
_No response_ | {
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"I think a reason why this was left out is probably explained here:\r\nhttps://stats.stackexchange.com/questions/271701/why-is-step-function-not-used-in-activation-functions-in-machine-learning",
"Hi, needed help creating a PR with the upstream repo from my fork - not sure which branch to create the PR to. Any help/ resources would be appreciated",
"@mahimairaja,\r\nThank you for the request. Could you please elaborate about your Feature. Also, please specify the Use Cases for this feature. Thank you!\r\n\r\n@AnanayVikramGupta,\r\nCould you please have a look at this **CONTRIBUTING.md** file for the process of contribution in the tensorflow repository.\r\nhttps://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md\r\n\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further."
] | 2023-08-05T11:48:09 | 2023-08-22T01:47:18 | 2023-08-22T01:47:18 | NONE | null | null | null | Some of the implementations like Single Layer Perceptron needs discrete outputs like 0 or 1. Adding this could make the model building ease. | {
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"@gzmkl please review.",
"@penpornk I have reviewed the code change which is fine. Thanks! --GZ",
"Hi @penpornk Can you please review this PR ? Thank you!",
"Hi @penpornk Can you please review this PR ? Thank you!",
"Hi @penpornk Can you please review this PR ? Thank you!",
"Hi @penpornk Can you please review this PR ? Thank you!",
"Hi @penpornk Can you please review this PR ? Thank you!",
"Hi @penpornk Can you please review this PR ? Thank you!"
] | 2023-08-05T01:01:43 | 2024-02-15T19:38:01 | 2024-02-15T19:37:58 | CONTRIBUTOR | null | false | {
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"Hi @API92 ,\r\n\r\nThanks for reporting the issue. I have replicated the reported error with intel-tensorflow and attached the [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/54451b7df0eb418cb83e2f2739101cf0/61482_mkl_maxpool_cache.ipynb) here.\r\n\r\nI have cross checked the same code with TF built binaries and its working fine.Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/a922b3444b47697fca239c2d5b541d7d/61482_tfbuilt-gpu.ipynb) for reference.\r\n\r\nThis confirms the issue is specific to intel-tensorflow. Hence tagging @TensorFlow-MKL team for comments.\r\n\r\nThanks for the PR !",
"@API92 \r\nWe will check and feedback soon!",
"@API92 \r\nThis issue appears in TF 2.13 only. \r\nPlease use TF .2.12.\r\n\r\nWe will report to dev team to fix it as soon.\r\n\r\nThank you!",
"The fix is in https://github.com/tensorflow/tensorflow/pull/62819, and it should be in next TF release version.\r\n"
] | 2023-08-04T22:24:18 | 2024-01-26T18:24:12 | null | CONTRIBUTOR | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.13
### Custom code
No
### OS platform and distribution
Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
There is an incorrect output of tf.nn.max_pool2d when calling with the input of the same batch size, height, width and depth first in NCHW, then in NHWC format with MKL support. \
Output of tf.nn.max_pool2d on the same input (except data format) must be the same (except data format).
### Standalone code to reproduce the issue
Colab to reproduce https://colab.research.google.com/drive/1Vuo7txDPDq-YXAuCFQcPNowHgOwbT7Oi?usp=sharing
### Relevant log output
_No response_ | {
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"@ghashi Does the issue replicate in TF v2.13 as well? Please let us know. 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.",
"I tried to use tensorflow 2.13, but it was not possible.\r\n\r\nAfter installing tensorflow 2.13 (it needs python3.8), I installed tflite-model-maker using: `pip install tflite-model-maker`\r\nThe installation finished but tflite-model-maker downggrades tensorflow to 2.9. \r\n\r\nObs: I am almost sure it is a problem on the pod TensorFlowLiteTaskAudio because it works on Android and it also works on iOS if you use TensorFlowLite (instead of TensorFlowLiteTaskAudio) \r\nTo use TensorFlowLite you can follow this example: https://github.com/tensorflow/examples/tree/master/lite/examples/sound_classification/ios\r\n",
"@ghashi Thank you for the update!\r\n@pjpratik Could you please have a look at this?\r\nThank you!",
"Hi @ghashi, I am having trouble reproducing your issue. I cloned the github repo and opened the \"iOS\" folder in xcode, then tried to run it on an emulator. It is saying \"unable to open configuration settings file\" I'm not sure if this project was exported properly or perhaps I missed a step. Do you have any idea what may be the issue there? Alternatively, if there is a way to reduce your code to just reproduce the offending part? (Such as just loading the model and running it on a sample audio file in the App's init function), a toy project.\r\n<img width=\"1246\" alt=\"image\" src=\"https://github.com/tensorflow/tensorflow/assets/132095473/8ef5e716-c6c4-4e9d-90d3-54cfc7b1010f\">\r\n",
"Hi @pkgoogle , thanks for your quick response!\r\n\r\nMy code is almost identical to this one https://github.com/tensorflow/examples/tree/master/lite/examples/sound_classification/ios\r\nThe difference is that it also runs a model created using \"TensorFlow Lite Model Maker\"\r\n\r\n> Do you have any idea what may be the issue there? A\r\n\r\nFrom your error message, it seems that you need to run `pod install`\r\n\r\n--\r\n\r\nPlease try [these instructions ](https://github.com/tensorflow/examples/tree/master/lite/examples/sound_classification/ios#build-and-run): \r\n\r\n1. Install the pod to generate the workspace file: `cd tensor-flow-lite-task-audio-poc/ios && pod install`\r\n Note: If you have installed this pod before and that command doesn't work, try `pod update`.\r\n At the end of this step you should have a directory called `AudioClassification.xcworkspace`.\r\n2. Open the project in Xcode with the following command: `open AudioClassification.xcworkspace`\r\n\r\nThis launches Xcode and opens the `AudioClassification` project.\r\n\r\n1. Select the `SoundClassification` project in the left hand navigation to open the project configuration. In the **Signing** section of the **General** tab, select your development team from the dropdown.\r\n2. In order to build the project, you must modify the **Bundle Identifier** in the **Identity** section so that it is unique across all Xcode projects. To create a unique identifier, try adding your initials and a number to the end of the string.\r\n3. Build and run the app in Xcode.\r\n\r\n",
"Hi @ghashi, I was able to use your code and run it on emulator, thanks for your help.\r\n\r\nHow are you expecting results.classifications[1] to be used? The code seems to be expecting to just use results.classifications[0], which it seems to be able to be use. I guess I'm unsure what you are expecting there as it seems the program doesn't use it.",
"> How are you expecting results.classifications[1] to be used?\r\n\r\nI want to print them on the screen, but they are always empty on iOS (on Android it works)\r\n\r\n> The code seems to be expecting to just use results.classifications[0], which it seems to be able to be use\r\n\r\nIn fact it is not exactly right. It is supposed to return 521 results, but it only returns 5 results on iOS (on Android it works)\r\n\r\n--\r\n\r\nThere are some screenshots here showing this behavior: https://github.com/tensorflow/tflite-support/issues/933#issue-1825138115 ",
"Hi @ghashi, I understand that the results are different but do we know where these 521 results are used or how they are supposed to be used? if used at all? If it's never used, perhaps it was never intended to be consistent. Do you have a use case that requires these results?",
"I understand your point. Let me try to clarify it.\r\n\r\nas described here https://www.tensorflow.org/lite/models/modify/model_maker/audio_classification#testing_the_model_optional\r\n\r\n> The model you've just trained has 2 outputs: The original YAMNet's output and the one you've just trained. This is important because the real world environment is more complicated than just bird sounds. You can use the YAMNet's output to filter out non relevant audio, for example, on the birds use case, if YAMNet is not classifying Birds or Animals, this might show that the output from your model might have an irrelevant classification.\r\n\r\nIn my case, I want to use the 521 results (= `results.classifications[0]` ; = YAMNet's raw output) to do exactly what's described in the text: \"You can use the YAMNet's output to filter out non relevant audio...\"\r\n\r\nBut more important than that, I need `results.classifications[1]` because that's the classification of my custom model built using [TensorFlow Lite Model Maker](https://www.tensorflow.org/lite/models/modify/model_maker) and right now it is always empty. \r\n\r\nDo you know why `results.classifications[1]` is always empty?",
"Hi @ghashi, thanks for the additional input. We just wanted to be able to prioritize things effectively, Thanks for your help!\r\n\r\nHi, @yishuangP, can you please take a look?"
] | 2023-08-04T19:42:31 | 2023-08-23T23:25:45 | null | NONE | null | null | null | ### 1. System information
#### model generation
- OS Platform and Distribution: macOS 12.6.5 (21G531)
- Python 3.7
- Numpy Version: 1.21.6
- TensorFlow Version: 2.9.3
- Model Maker Version: 0.4.2 (and 0.3.4)
- portaudio: stable 19.7.0
#### model execution
- pod TensorFlowLiteTaskAudio: 0.4.3
- iPhone 8 - iOS 16.5.1
### 2. What's happening
I am encountering an issue while working with TensorFlowLite TaskAudio. Despite my attempts to seek assistance through various channels, I haven't received a response yet. I am reaching out to you here as well.
#### Summary:
I have created a model using TensorFlow Lite Model Maker, and it performs as expected on Android. However, when running the same model on iOS using TensorFlowLiteTaskAudio version 0.4.3 (and nightly), the output is consistently empty. This issue prevents the accurate classification of audio data, as the expected output is missing.
#### Steps to Reproduce:
1. Clone the sample repository: [GitHub Repository](https://github.com/ghashi/tensor-flow-lite-task-audio-poc)
2. Run the iOS application and provide input audio data for classification using the model created with "TensorFlow Lite Model Maker".
3. Observe that the output results.classifications[1].categories are empty, unlike the expected output.
#### Expected Behavior:
The output of the TensorFlow Lite model should contain meaningful results in the results.classifications[1].categories field, similar to the behavior observed on Android.
#### Additional Information:
I have opened an issue on the tflite-support repository (which has more info regarding this issue) [GitHub Issue #933](https://github.com/tensorflow/tflite-support/issues/933), but now I see that this repo has more issues related to "TensorFlow Lite Model Maker"
I have also posted a topic on the TensorFlow forum: [Forum Topic](https://discuss.tensorflow.org/t/possble-bug-model-created-with-tensorflow-lite-model-maker-model-always-returns-empty-output-on-ios-with-tensorflowlitetaskaudio-0-4-3/18622)
The problem seems to be specific to the TensorFlowLiteTaskAudio on iOS, as the same model works as expected on Android.
I kindly request your assistance in investigating and resolving this issue.
Thank you for your time and consideration. | {
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"Hi @dre-hh ,\r\n\r\nAs you can see this [issue](https://github.com/python-poetry/poetry/issues/8271). Yes tensorflow's metadata does not express tensorflow-macos dependency. As a workaround you can explicitly add tensorflow-macos which solves the issue - \r\npoetry add tensorflow-macos==2.13.0 \r\n\r\nThank you!!\r\n",
"Thx for reply. Unfortunately this is not a good workaround. People nowadays develop on arm macs and deploy on x86. That means , one cannot produce a lockfile for the whole team to install same package versions cross platform . It is a problem which is hard to solve in python but poetry addresses quite well.\r\n\r\nThere is also another problem:\r\n\r\nIf you check the Metadata inside the pip site_packages. On arm linux tensorflow \r\nalso installs a different package ( tensorflow aws). This dependency is also not expressed on pypa json Metadata . I suppose poetry is only using that, and will install wrong packages on linux arm as well.\r\n\r\nCould you please publish consistent Metadata to pypa in same way it is expressed in Metadata file when the package is downloaded ",
"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.",
"@Varsha-anjanappa any update on the idea adding same dependencies to pypi metadata as in the pip package?\r\nIs this maybe tracked on another issue?\r\nIs there a reason which prevents tensorflow team from doing this?\r\nWithout this metadata , the user of the package has to figure out himself which platform specific packages to install\r\n",
"@kulinseth , Is it something to do with Tensorflow MacOS release?",
"@sachinprasadhs, no it is the tensorflow package itself.\r\n```\r\ncd python-3.11/lib/python3.11/site-packages/tensorflow-2.13.0.dist-info\r\ncat METADATA\r\n\r\n=>\r\nDescription-Content-Type: text/markdown\r\nRequires-Dist: tensorflow-macos (==2.13.0) ; platform_system == \"Darwin\" and platform_machine == \"arm64\"\r\nRequires-Dist: tensorflow-cpu-aws (==2.13.0) ; platform_system == \"Linux\" and (platform_machine == \"arm64\" or platform_machine == \"aarch64\")\r\nRequires-Dist: tensorflow-intel (==2.13.0) ; platform_system == \"Windows\"\r\n\r\n```\r\n\r\nThis metadata was not published to pypi \r\n\r\n```\r\n$ curl -s https://pypi.org/pypi/tensorflow/2.13.0/json | jq '.info.requires_dist'\r\n[\r\n \"absl-py (>=1.0.0)\",\r\n \"astunparse (>=1.6.0)\",\r\n \"flatbuffers (>=23.1.21)\",\r\n \"gast (<=0.4.0,>=0.2.1)\",\r\n \"google-pasta (>=0.1.1)\",\r\n \"h5py (>=2.9.0)\",\r\n \"libclang (>=13.0.0)\",\r\n \"numpy (<=1.24.3,>=1.22)\",\r\n \"opt-einsum (>=2.3.2)\",\r\n \"packaging\",\r\n \"protobuf (!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3)\",\r\n \"setuptools\",\r\n \"six (>=1.12.0)\",\r\n \"termcolor (>=1.1.0)\",\r\n \"typing-extensions (<4.6.0,>=3.6.6)\",\r\n \"wrapt (>=1.11.0)\",\r\n \"grpcio (<2.0,>=1.24.3)\",\r\n \"tensorboard (<2.14,>=2.13)\",\r\n \"tensorflow-estimator (<2.14,>=2.13.0)\",\r\n \"keras (<2.14,>=2.13.1)\",\r\n \"tensorflow-io-gcs-filesystem (>=0.23.1) ; platform_machine != \\\"arm64\\\" or platform_system != \\\"Darwin\\\"\"\r\n]\r\n```\r\n\r\nPlease republish a new version of the package with consitent medata as pip downloads into `tensorflow-2.13.0.dist-info/METADATA` file\r\n"
] | 2023-08-04T15:23:34 | 2023-08-25T08:13:24 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.13.0
### Custom code
No
### OS platform and distribution
macOS-13.3-arm64-arm-64bit
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
After installing tensorflow 2.13.0 with poetry it cannot be imported
### Standalone code to reproduce the issue
See https://github.com/python-poetry/poetry/issues/8271
According to @dimbleby, the issue happens due to missing wheel metadata.
> please encourage the tensorflow folk to publish consistent metadata in all of their wheels, with platform-specific variations described by markers
Tensorflow folks, please publish consistent metadata ;)
```shell
poetry add tensorflow==2.13.0
poetry shell
python3
import tensorflow
```
### Relevant log output
```shell
ModuleNotFoundError: No module named 'tensorflow
```
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"Hi @penpornk, @tilakrayal , @sachinprasadhs, @TensorFlow-MKL, @huiyan2021,\r\nCan i get any suggestions/help/answer regarding the question?",
"@vineel96 \r\nIn TF1.x, model.fit() always run in graph model, while in TF2.x eager mode is enabled by default. You can disable eager mode by **tf.compat.v1.disable_eager_execution()**. How did you train the model? Could you share the code and the whole log? Thanks!\r\n\r\n",
"Hi @huiyan2021,\r\n\r\n**My Doubt:** If we concentrate only on the execution of OP kernels (MKL and non MKL) in the log file **after fit() function** of model, For **non MKL kernel OP's** (AddV2,Mul) there is eager execution & graph execution and for **MKL OP Kernel's** (_MKLMatMul) I observe only graph execution & no eager execution. Question is how decision is made to which kernel OP's are executed in eager mode & graph mode.\r\n**Other observation:** For transpose operation, I observe two kernels for it: normal Transpose OP and _MKLTranspose OP, where normal Transpose OP executes in eager mode and _MKLTranspose OP executes in graph mode. So either of the one OP should be present, why there is two kernel OP's are present? \r\n\r\n**Train Code:**\r\nSet: export TF_CPP_MAX_VLOG_LEVEL=2\r\n export ONEDNN_VERBOSE=1\r\n```\r\nfrom transformers import TFBertForSequenceClassification\r\nfrom transformers import BertTokenizer, glue_convert_examples_to_features\r\nimport tensorflow as tf\r\nimport tensorflow_datasets as tfds\r\nimport datetime\r\n\r\nlogdir = \"logs/fit/\"\r\ntf.debugging.experimental.enable_dump_debug_info(logdir, tensor_debug_mode=\"FULL_HEALTH\", circular_buffer_size=-1)\r\n\r\ntf.config.threading.set_inter_op_parallelism_threads(1)\r\ntf.config.threading.set_intra_op_parallelism_threads(1)\r\n\r\nmodel = TFBertForSequenceClassification.from_pretrained('bert-base-uncased')\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\ndata, info = tfds.load('glue/mrpc',with_info=True)\r\ntrain_dataset = glue_convert_examples_to_features(data['train'], tokenizer, max_length=128, task='mrpc')\r\n#print(info)\r\ntrain_dataset = train_dataset.shuffle(100).batch(1).repeat(1) # batch 32\r\n \r\n\r\noptimizer = tf.keras.optimizers.Adam(learning_rate=3e-5)\r\nloss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\r\nmodel.compile(optimizer=optimizer, loss=loss)\r\n\r\nlog_dir = logdir + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\r\ntensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1,profile_batch=1)\r\n\r\n\r\nmodel.fit(train_dataset, epochs=1, steps_per_epoch=1,callbacks=[tensorboard_callback]) \r\n```\r\nI attach two log files:\r\n**_bert_pretrain.txt:_** This logs are only from bert model object creation and weight loading line only.\r\n**_bert_recent.txt:_** This logs are from model creation and model fitting , full model fitting for 1 epoch & 1 steps per epoch\r\n\r\n**_bert_pretrain.txt:_** https://drive.google.com/file/d/1TDHrKr6ccCOEmQNVPny6B1Vm7P2NrIBz/view?usp=drive_link\r\n**_bert_recent.txt:_** :https://drive.google.com/file/d/1WJyP7r8B4C8wZmyumw1kJhiPDHncvmAb/view?usp=drive_link \r\n\r\n**Observation:**\r\n1. From **_bert_pretrain.txt_**, I see both MKL and non MKL kernel OP's executing **only** in **eager mode.**\r\n2. From **_bert_recent.txt_** , If we filter out logs corresponding to starting from model.fit() function, I observe only graph execution for MKL kernel OP's and both eager and graph execution for non MKL kernel OP's.\r\nWhy two modes of execution (eager and graph) for non MKL kernel OP's ? \r\n\r\n",
"Hi @sachinprasadhs, @vineel96\r\n\r\nIt is better that someone from Google answers this question. Model could have a mixed execution between eager and graph. TF decides which ops are part of the graph so it runs in graph mode, and which runs in eager mode. Intel code does not control that decision, we replace an op by _Mkl* only after it was decided by TF to run eagerly or in graph mode. Also, not all ops are supported by Mkl (oneDNN) in both eager and graph mode. For example, Transpose op is only supported by Mkl in graph mode not in eager mode. So if it runs in eager mode, it will be non-mkl Transpose op.",
"Hi @huiyan2021,\r\nThanks for the answer.\r\nCan we know the reason for why a MKL op is choosen to run in graph mode only or eager mode only or in both eager and graph mode? If MKL op is supported in both eager and graph modes, which mode is selected to run the MKL op finally? Is there any file location where this decision is made?\r\n\r\n**My understanding:** \r\n_**(Pls refer below figure)**_\r\nThere are two phases: 1.) graph optimization and 2.) graph execution. In graph optimization, there are two modes: eager execution and MKL layout rewrite propagation. TF OP to MKL OP conversion takes place if MKL support is there for that, if there is no equivalent MKL support for the op then it optimizes in eager mode. In graph execution, the actual execution of MKL and non MKL ops takes place. \r\n\r\n\r\n**Is my understanding correct?**\r\n\r\nAlso tagging google members @sachinprasadhs @penpornk @TensorFlow-MKL again, to get answers through TF side.",
"Hello @sachinprasadhs , @penpornk, @TensorFlow-MKL \r\nCan I get any suggestions/help/answer regarding the question?"
] | 2023-08-04T12:25:08 | 2023-08-16T04:47:42 | null | NONE | null | null | null | I ran HuggingFace BERT model which uses tensorflow 2.13v with oneDNN support on intel machine and recorded its execution logs by setting TF_CPP_MAX_VLOG_LEVEL=2 & ONEDNN_VERBOSE=1 in file.
**Observation :** I have observing logs that are produced after model creation and its weight loading. Since model.fit() always run in graph model, all tensorflow kernel OPs (onednn's mkl kernel op and non-mkl kernel ops) should run in graph mode. But i observe only for non-mkl kernel ops (like ADDV2, Mul) are executing in eager mode followed by graph mode. I dont see any mkl kernel ops(like _MklMatMul) running in eager mode.
**Questions:** I want to know the reason and file where decision making is made for which op there should be eager mode. Since model.fit() runs in graph mode, why I am seeing eager mode execution for all non-mkl ops?
Sample Logs for model.fit() for ADDV2 kernel op:
```
2023-07-31 03:48:44.632289: I tensorflow/core/common_runtime/eager/execute.cc:1678] Executing op AddV2 in device /job:localhost/replica:0/task:0/device:CPU:0 --> executing addv2 eagerly After some other logs in between, I see below log:
2023-07-31 03:50:01.968512: I tensorflow/core/common_runtime/executor.cc:841] Process node: 8127 step -4458402160563696089 {{node tf_bert_for_sequence_classification/bert/encoder/layer_._0/output/LayerNorm/batchnorm/add_1}} = AddV2[T=DT_FLOAT, _XlaHasReferenceVars=false, device="/job:localhost/replica:0/task:0/device:CPU:0"](tf_bert_for_sequence_classification/bert/encoder/layer.0/output/LayerNorm/batchnorm/mul_1, tf_bert_for_sequence_classification/bert/encoder/layer._0/output/LayerNorm/batchnorm/sub) device:
```
/job:localhost/replica:0/task:0/device:CPU:0 --> executing addv2 in graph mode i assume
**Expected to happen:** All kerenl ops should execute in graph mode.
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