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Any _working_ alternative to tf?
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[
"Universal Sentence Encoder 4: Complex model, especially for multi-epoch processing.\r\nTry to use the lighter \"Universal Sentence Encoder Lite\" (https://www.tensorflow.org/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder_lite) for potential speed improvements.\r\n\r\nBatch Processing: Instead of loading the entire file at once, process sentences in batches to reduce memory usage.\r\nbatch_size = 32 \r\nembeddings = []\r\nfor i in range(0, len(lines), batch_size):\r\n batch_embeddings = use(lines[i:i+batch_size])\r\n embeddings.extend(batch_embeddings)\r\n\r\nRemove the redundant with open(...) block as you're already reading the file using tf.io.read_file.",
"Hi @deadjdona ,\r\n\r\nYou are using __call__ method on the model with epochs as one argument.Here `epochs` is not an accepted argument. Also the input i.e `l` should be a Vector i.e list. Could you please verify your input. Please refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/228197d31975ba304a7b51bd6067e73c/63090.ipynb) for a demo.",
"Also please ensure latest version of TF with its compatible TF-Hub version.",
"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/63090\">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/63090\">No</a>\n",
"> Hi @deadjdona ,\r\n> \r\n> You are using **call** method on the model with epochs as one argument.Here `epochs` is not an accepted argument. Also the input i.e `l` should be a Vector i.e list. Could you please verify your input. Please refer attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/228197d31975ba304a7b51bd6067e73c/63090.ipynb) for a demo.\r\n\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\n[<ipython-input-15-0874f4832b42>](https://localhost:8080/#) in <cell line: 1>()\r\n----> 1 train_embeddings = use(l, epochs=2)\r\n 2 train_embeddings\r\n\r\n2 frames\r\n[/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/function_type_utils.py](https://localhost:8080/#) in bind_function_inputs(args, kwargs, function_type, default_values)\r\n 444 )\r\n 445 except Exception as e:\r\n--> 446 raise TypeError(\r\n 447 f\"Binding inputs to tf.function failed due to `{e}`. \"\r\n 448 f\"Received args: {args} and kwargs: {sanitized_kwargs} for signature:\"\r\n\r\nTypeError: Binding inputs to tf.function failed due to `got an unexpected keyword argument 'epochs'`. Received args: (<tf.Tensor: shape=(), dtype=string, numpy=b'asdfbbbbsffnnc'>,) and kwargs: {'epochs': 2} for signature: (inputs: TensorSpec(shape=<unknown>, dtype=tf.string, name=None))."
] | 2024-02-29T12:43:55 | 2024-04-14T17:43:50 | 2024-03-23T01:46:35 |
NONE
| null | null | null |
### Issue type
Performance
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2
### Custom code
No
### OS platform and distribution
Win 11
### Mobile device
_No response_
### Python version
3.11.8
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
expected it to actualy works **_🙄_**
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import tensorflow_hub as hub
module_url = "https://tfhub.dev/google/universal-sentence-encoder/4"
use = hub.load(module_url)
l = tf.io.read_file('yt2.txt')
# open and read yt2.txt file
with open("yt2.txt", "r") as f:
contents = f.read()
lines = contents.splitlines()
train_embeddings = use(l, epochs=2)
```
### Relevant log output
```shell
12666m 00s
```
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PR_kwDOArmXAs5oRIS_
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Fix checkfail of tf.raw_ops.AvgPool with negative kernel size in Debug builds
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[
"Hi @SuryanarayanaY Can you please check @mihaimaruseac's comments ? 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 @SuryanarayanaY Any update on this PR? Please. Thank you!",
"Hi @SuryanarayanaY Any update on this PR? Please. Thank you!",
"This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This PR was closed because it has been inactive for 14 days since being marked as stale. Please reopen if you'd like to work on this further."
] | 2024-02-29T09:09:37 | 2024-05-25T14:10:31 | 2024-05-25T01:48:45 |
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Currently shape inference step of `tf.raw_ops.AvgPool` allows negative kernel size.But in debug build it aborts with checkfail as there is DCHECK which works only in debug build. Replaced DCHECK with general conditional check.
Ref issue #63034
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Fix check fail in SparseApplyAdaDelta Op
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C++ API `SparseApplyAdadelta` segfaults due to lack of input shape check.
Error [location](https://github.com/tensorflow/tensorflow/blob/f647f0cf7c36c7bb7ec531f11df1a028e343749a/tensorflow/core/ops/training_ops.cc#L63):
` TF_RETURN_IF_ERROR(c->Merge(c->Dim(indices, 0), c->Dim(grad, 0), &unused));
`
At `c->Dim(grad, 0)`, it reads 0th dim without checking rank of grad. Therefore when a scalar(0-rank) is given for arg grad it crashes.
The same things happen for other SparseApply* APIs(SparseApplyAdagrad, SparseApplyAdagradDA, SparseApplyFtrl, SparseApplyFtrlV2, SparseApplyMomentum, SparseApplyProximalAdagrad, SparseApplyProximalGradientDescent) as reported by the user in #62978.
Since all the above APIs calls HandleGradAndIndicesInputs , hence fixing it there might resolve the issue with all the above APIs.
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cannot install tensorflow 2.14 through pip
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[
"Given that the error message suggests version 2.16.0rc0 is available, it seems you're trying to install TensorFlow in a state where version 2.14 does not exist, but newer versions do",
"@nilesh-sengupta,\r\nI tried to install the tensorflow 2.14.0 using pip install and I was able to install without any issues. I suspect you missed following the installation with compatible tested build configurations which are mentioned in the official document.\r\nAlso please check the python version you are using.\r\nhttps://www.tensorflow.org/install/source#linux\r\n\r\n\r\n\r\n\r\nThank you!",
"seems like using the latest Miniconda3 version base env caused the issue since it was using python3.12 using miniconda3 for 3.11 and v24.1.2 resolved the issue, 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/63087\">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/63087\">No</a>\n"
] | 2024-02-29T05:53:28 | 2024-02-29T07:55:12 | 2024-02-29T07:55:09 |
NONE
| null | null | null |
while running the command pip install tensorflow==2.14 in debian 11 system running into the following error:
ERROR: Could not find a version that satisfies the requirement tensorflow==2.14 (from versions: 2.16.0rc0)
ERROR: No matching distribution found for tensorflow==2.14
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Fix overflow problem in SparseFillEmptyRows Op
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The C++ API SparseFillEmptyRows lacks validation of negative values passed to dense_shape argument.For example if we pass some large -ve value like` -1l `(long int) then it will be passed as it is and due to integer overflow at the below line the value is converted to extremely large number and it ends up with `std::length_error` causing abortion(core dumped). https://github.com/tensorflow/tensorflow/blob/9da417e3f63215efe995b83e7b9f9b34115a424e/tensorflow/core/kernels/fill_empty_rows_functor.h#L114
Fixes #63066
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ValueError: Only instances of `keras.Layer` can be added to a Sequential mode
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[
"Hi @zzj0402 ,\r\n\r\nThe code executes fine on Colab as per [gist](https://colab.research.google.com/gist/SuryanarayanaY/8a70d4b8f7772d1da6878a0e9c85507e/63085.ipynb). This moght be related to Kaggle environment which might have incompatible TF and tensorflow_hub version. You may check the Tensorflow and tensorflow_hub in kaggle environment and try to install those works in locally. Neverthless it's not problem with Tensorflow or Keras.\r\n\r\nThanks!",
"i got the same issue also with Kaggle's environment, with @SuryanarayanaY conclusion i got the solution by just \r\n`pip install tensorflow` \r\nagain and found out that the keras library reinstalls to latest version ",
"```\r\n!pip install tensorflow\r\n```\r\nfixes the problem in Kaggle.",
"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/63085\">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/63085\">No</a>\n",
"i had the same issue, tried all the things explained here but still having the same issue. i am not using kaggle i am coding on jupyter notebook. please help",
"@Kun7l I am also having same issue. I am using jupyter notebook as well with PyCharm. I have tried many things but cannot fix this error. I believe it might be a package incompatibility error. However, I am using most up to date versions. ",
"im also having this using conda, tried conda install again tensorflow and it no compatible with tensorflow_hub that i installed before, please help",
"Has someone resolved this for jupyter notebook / conda enviroment? I am also having a similar problem (although with tensorflow probability)",
"I got the same error when running the official notebook, https://www.tensorflow.org/tutorials/interpretability/integrated_gradients, the environment I am using is tensorflow==2.16 and tensorflow-hub==0.16.\r\nBut if I roll back to tensorflow 2.14 and tensorflow-hub 0.15, there is no such error.",
"I've tried to use some previous versions of tensorflow, but on MacOS there was another bug that was't allowing me to use GPU acceleration. So I've solved the problem using the Model Subclassing API, here the example from my code:\r\n\r\n class MyModel(keras.Model):\r\n def __init__(self):\r\n super(MyModel, self).__init__()\r\n self.embedding_layer = embed\r\n self.dense_layer1 = layers.Dense(256, activation='relu')\r\n self.dense_layer2 = layers.Dense(128, activation='relu')\r\n self.dense_layer3 = layers.Dense(64, activation='relu')\r\n self.output_layer = layers.Dense(13, activation='softmax')\r\n\r\n def call(self, inputs):\r\n x = self.embedding_layer(inputs)\r\n x = self.dense_layer1(x)\r\n x = self.dense_layer2(x)\r\n x = self.dense_layer3(x)\r\n return self.output_layer(x)\r\n",
"do anyone know how to resolve this error, i have been trying and reinstalling the tensorflow to its latest but still its gets the error "
] | 2024-02-29T03:27:03 | 2024-05-27T14:02:03 | 2024-03-01T21:36:49 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
v2.15.0-rc1-8-g6887368d6d4 2.15.0
### Custom code
No
### OS platform and distribution
Kaggle
### 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 trying to build a sequential model with TFhub model USE in Kaggle, the notebook throws an error complaining that the hub model is not a valid class instance. At the same time, the same code works fine locally.
### Standalone code to reproduce the issue
```shell
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
from tensorflow.keras.optimizers import Adam, SGD
from tensorflow.keras.layers import Dense, Input, BatchNormalization, Dropout, Concatenate
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.callbacks import ModelCheckpoint
module_url = 'https://tfhub.dev/google/universal-sentence-encoder-large/5'
embed = hub.KerasLayer(module_url, trainable=True, name='USE_embedding')
def build_model(embed):
model = Sequential([
Input(shape=[], dtype=tf.string),
embed,
Dense(1, activation='sigmoid')
])
model.compile(loss='mean_squared_error',
optimizer=tf.keras.optimizers.Adam(
learning_rate=0.0001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-07,
amsgrad=False,
name='Adam'
), metrics=['accuracy'])
return model
```
```
### Relevant log output
```shell
ValueError Traceback (most recent call last)
Cell In[9], line 1
----> 1 model=build_model(embed)
2 model.fit(descriptions, labels, epochs=4)
3 model.save('quality/use.keras')
Cell In[8], line 12, in build_model(embed)
10 def build_model(embed):
---> 12 model = Sequential([
13 Input(shape=[], dtype=tf.string),
14 embed,
15 Dense(1, activation='sigmoid')
16 ])
17 model.compile(loss='mean_squared_error',
18 optimizer=tf.keras.optimizers.Adam(
19 learning_rate=0.0001,
(...)
24 name='Adam'
25 ), metrics=['accuracy'])
26 return model
File /opt/conda/lib/python3.10/site-packages/keras/src/models/sequential.py:70, in Sequential.__init__(self, layers, trainable, name)
68 if layers:
69 for layer in layers:
---> 70 self.add(layer, rebuild=False)
71 self._maybe_rebuild()
File /opt/conda/lib/python3.10/site-packages/keras/src/models/sequential.py:92, in Sequential.add(self, layer, rebuild)
90 layer = origin_layer
91 if not isinstance(layer, Layer):
---> 92 raise ValueError(
93 "Only instances of `keras.Layer` can be "
94 f"added to a Sequential model. Received: {layer} "
95 f"(of type {type(layer)})"
96 )
97 if not self._is_layer_name_unique(layer):
98 raise ValueError(
99 "All layers added to a Sequential model "
100 f"should have unique names. Name '{layer.name}' is already "
101 "the name of a layer in this model. Update the `name` argument "
102 "to pass a unique name."
103 )
ValueError: Only instances of `keras.Layer` can be added to a Sequential model. Received: <tensorflow_hub.keras_layer.KerasLayer object at 0x793cedbef220> (of type <class 'tensorflow_hub.keras_layer.KerasLayer'>)
```
```
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PR_kwDOArmXAs5oOhST
| 63,084 |
r2.16 cherry-pick: 5e39a976964 "Update grpc_tpu_worker.py file"
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[] | 2024-02-28T22:32:58 | 2024-02-29T18:50:53 | 2024-02-29T18:50:53 |
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Refer to the original commit: https://github.com/tensorflow/tensorflow/commit/5e39a976964e0b5bb12d6dd0bfa43350e39e7557
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PR_kwDOArmXAs5oM34l
| 63,083 |
Prevent out of scope access when clearing an object
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Releasing an object and then passing the original pointer to Py_CLEAR results in an out of scope access which is undefined behaviour. So instead call Py_CLEAR on the object before calling its release function.
This behaviour was seen with the address sanitizer.
Fixes: https://github.com/tensorflow/tensorflow/issues/63012
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DLL load failed while importing _pywrap_tensorflow_internal: A dynamic link library (DLL) initialization routine failed.
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[
"@Icacoding1 The error originates from tensorflow.python.pywrap_tensorflow.py, indicating an issue loading the native TensorFlow runtime, which is a Dynamic Link Library (DLL) on Windows. Please download and install the appropriate Microsoft Visual C++ Redistributable from https://learn.microsoft.com/en-us/cpp/windows/latest-supported-vc-redist?view=msvc-170\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/63082\">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/63082\">No</a>\n"
] | 2024-02-28T16:41:49 | 2024-03-15T01:47:17 | 2024-03-15T01:47:14 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.10.0
### Custom code
Yes
### OS platform and distribution
Windows 11
### Mobile device
Android
### Python version
3.9.18
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
the tensorflow is already installed but when I am going to import it there is an error
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
```
### Relevant log output
```shell
ImportError Traceback (most recent call last)
File ~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\pywrap_tensorflow.py:62
61 try:
---> 62 from tensorflow.python._pywrap_tensorflow_internal import *
63 # This try catch logic is because there is no bazel equivalent for py_extension.
64 # Externally in opensource we must enable exceptions to load the shared object
65 # by exposing the PyInit symbols with pybind. This error will only be
66 # caught internally or if someone changes the name of the target _pywrap_tensorflow_internal.
67
68 # This logic is used in other internal projects using py_extension.
ImportError: DLL load failed while importing _pywrap_tensorflow_internal: A dynamic link library (DLL) initialization routine failed.
During handling of the above exception, another exception occurred:
ImportError Traceback (most recent call last)
Cell In[2], line 1
----> 1 import tensorflow as tf
File ~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\__init__.py:37
34 import sys as _sys
35 import typing as _typing
---> 37 from tensorflow.python.tools import module_util as _module_util
38 from tensorflow.python.util.lazy_loader import LazyLoader as _LazyLoader
40 # Make sure code inside the TensorFlow codebase can use tf2.enabled() at import.
File ~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\__init__.py:36
27 import traceback
29 # We aim to keep this file minimal and ideally remove completely.
30 # If you are adding a new file with @tf_export decorators,
31 # import it in modules_with_exports.py instead.
32
33 # go/tf-wildcard-import
34 # pylint: disable=wildcard-import,g-bad-import-order,g-import-not-at-top
---> 36 from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
37 from tensorflow.python.eager import context
39 # pylint: enable=wildcard-import
40
41 # Bring in subpackages.
File ~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\pywrap_tensorflow.py:77
75 sys.setdlopenflags(_default_dlopen_flags)
76 except ImportError:
---> 77 raise ImportError(
78 f'{traceback.format_exc()}'
79 f'\n\nFailed to load the native TensorFlow runtime.\n'
80 f'See https://www.tensorflow.org/install/errors '
81 f'for some common causes and solutions.\n'
82 f'If you need help, create an issue '
83 f'at https://github.com/tensorflow/tensorflow/issues '
84 f'and include the entire stack trace above this error message.')
ImportError: Traceback (most recent call last):
File "C:\Users\Angel\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 62, in <module>
from tensorflow.python._pywrap_tensorflow_internal import *
ImportError: DLL load failed while importing _pywrap_tensorflow_internal: A dynamic link library (DLL) initialization routine failed.
Failed to load the native TensorFlow runtime.
```
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[
"Hi @Milehigh-wrld,\r\n\r\n\r\nAs per your query even though the conversion is successful, but model produces wrong results and less due to insufficient data, architectural flaws, in correct hyperparameter tuning. The model produces correct results and is slower than expected: This is right, even though the model produced correct results, based on the target hardware the speed might differ. Based on the hardware different optimization techniques help to improve speed.\r\n \r\nIn your use case, the tflite model conversion is successful and accuracy also as expected. Refer the [gist](https://colab.sandbox.google.com/gist/LakshmiKalaKadali/efcebb0f6170263c3c05a8305db4c21e/tflite_63081_accuracy_speed.ipynb) for accuracy and size. Still if any specific query regarding the model, please feel to elaborate.\r\n\r\nThe issue is the same as [62833]([url](https://github.com/tensorflow/tensorflow/issues/62833)). \r\n\r\nThank You\r\n\r\n",
"Closing as it is only the template"
] | 2024-02-28T15:24:29 | 2024-03-05T14:43:57 | 2024-03-05T14:43:46 |
NONE
|
spam
| null | null |
### 1. System information
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- TensorFlow installation (pip package or built from source):
- TensorFlow library (version, if pip package or github SHA, if built from source):
### 2. Code
Provide code to help us reproduce your issues using one of the following options:
#### Option A: Reference colab notebooks
1) Reference [TensorFlow Model Colab](https://colab.research.google.com/gist/ymodak/e96a4270b953201d5362c61c1e8b78aa/tensorflow-datasets.ipynb?authuser=1): Demonstrate how to build your TF model.
2) Reference [TensorFlow Lite Model Colab](https://colab.research.google.com/gist/ymodak/0dfeb28255e189c5c48d9093f296e9a8/tensorflow-lite-debugger-colab.ipynb): Demonstrate how to convert your TF model to a TF Lite model (with quantization, if used) and run TFLite Inference (if possible).
```
(You can paste links or attach files by dragging & dropping them below)
- Provide links to your updated versions of the above two colab notebooks.
- Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model.
```
#### Option B: Paste your code here or provide a link to a custom end-to-end colab
```
(You can paste links or attach files by dragging & dropping them below)
- Include code to invoke the TFLite Converter Python API and the errors.
- Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model.
```
### 3. Failure after conversion
If the conversion is successful, but the generated model is wrong, then state what is wrong:
- Model produces wrong results and/or has lesser accuracy.
- Model produces correct results, but it is slower than expected.
### 4. (optional) RNN conversion support
If converting TF RNN to TFLite fused RNN ops, please prefix [RNN] in the title.
### 5. (optional) 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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Failing to build version 2.14.0 with numa
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[
"@fgava90,\r\nFrom the information provided, I can see that you are trying to install the tensorflow v2.14 with python 3.8.7 and gcc 11.3.0 which are incompatible. Tensorflow v2.14 is compatible with python v3.9-3.11 and compiler Clang 16.0.0.\r\n\r\nCould you please try to follow the tested build configurations from the official document for the smooth installation of tensorflow.\r\nhttps://www.tensorflow.org/install/source#tested_build_configurations\r\n\r\nThank you!",
"Sorry, my bad. It wasn't python 3.8.7 but python 3.11.6. \r\nCompiler was gcc 11.3.0",
"This command:\r\n```bazel --nohome_rc --nosystem_rc --output_user_root=/tmp/spackd8v53qm6 build --color=no --jobs=16 --config=opt --verbose_failures --config=dynamic_kernels --config=cuda --config=noaws --config=nogcp --config=nohdfs --config=numa --config=v2 //tensorflow/tools/pip_package:build_pip_package```\r\nproduces:\r\n```ERROR: Config value 'numa' is not defined in any .rc file```\r\n\r\nThis command:\r\n```bazel --nohome_rc --nosystem_rc --output_user_root=/tmp/spackd8v53qm6 build --color=no --jobs=16 --config=opt --verbose_failures --config=dynamic_kernels --config=cuda --config=noaws --config=nogcp --config=nohdfs --config=v2 //tensorflow/tools/pip_package:build_pip_package```\r\n(which is the same without `--config=numa`) passes the configuration stage.\r\n\r\nTrying to grep for `numa` in the sources directory yelds:\r\n```console\r\n$ grep numa *\r\nconfigure.py: config_info_line('numa', 'Build with NUMA support.')\r\nRELEASE.md: * Removing the `experimental_numa_aware` option from `tf.data.Options`.\r\n$ grep numa .bazel*\r\n```\r\n\r\nUp to version 2.13.1, this was present in `.bazelrc` and is no longer present in 2.14\r\n```\r\n# Options extracted from configure script\r\nbuild:numa --define=with_numa_support=true\r\n```\r\n\r\nDoes this means that `--config=numa` is no longer supported from version 2.14 and that the following line should be removed from `configure.py`?\r\n```python\r\n config_info_line('numa', 'Build with NUMA support.')\r\n```\r\n(it's still present in 2.15 but same stuff missing in `.bazelrc`)\r\n\r\nI thinks all this is independent from python or compiler type/version as we are still in bazel configuration and haven't started building anything yet.\r\n\r\n",
"@fgava90,\r\nApologies for the delay. Could you please try to install the tensorflow with the latest version 2.16.1(~2.16.0) from the below commit https://github.com/tensorflow/tensorflow/commit/5bc9d26649cca274750ad3625bd93422617eed4b and let us know if you are facing the same issue. Most of the bugs are resolved in the latest version. Thank you!",
"@tilakrayal \r\nI will try to do it ASAP, but the incongruency between `.bazerc` and `configure.py` is still there (also in `master`) \r\n\r\nI guess we need to understand which one of these possibility is correct:\r\n\r\n- the `numa` option is now active by default and doesn't need to be passed by configure arguments, so the line in `configure.py` is superflous\r\n- the `numa` option has been deprecated and can no longer be used, so the line in `configure.py` is wrong and should be removed\r\n- nothing changed WRT the `numa` option, so there is a missing line in `.bazelrc` \r\n\r\nAs you can imagine, I do not have the necessary knowledge of the code to answer this",
"@fgava90,\r\nCould you please provide the update if you were tried installing the tensorflow latest stable v2.16 and facing the same error/issue. Thank you!",
"Hello @tilakrayal .\r\nSorry, i have been busy and couldn't work on this for a bit. \r\nI have just tested the commit you suggested and the issue persists. ",
"I have tried as an experiment to pass `--config=xxx` and i get the same error as with `--config=numa`:\r\n```\r\nbazel' '--nohome_rc' '--nosystem_rc' '--output_user_root=/tmp/spack1nfqzayk' 'build' '--color=no' '--jobs=16' '--config=opt' '--verbose_failures' '--config=dynamic_kernels' '--config=cuda' '--config=noaws' '--config=nogcp' '--config=nohdfs' '--config=xxx' '--config=v2' '//tensorflow/tools/pip_package:build_pip_package'\r\n...\r\nStarting local Bazel server and connecting to it...\r\nERROR: Config value 'xxx' is not defined in any .rc file\r\n```\r\n\r\nSo either `--config=numa` have been removed at some point, or there's something missing in the .bazelrc file (see my comments above)",
"@kanglant Tagging you here because i found this commit which removes the numa config:\r\nhttps://github.com/tensorflow/tensorflow/commit/ffd06c0782a31766b15b6730aac48d124cdf4a11",
"@fgava90,\r\nCould you please check with the latest build or the tf-nightly build, and let us know if you are facing the same build error. As per the above mentioned commit it might be resolved in the latest versions. Thank you!",
"The above mentioned commit is the cause of the issue, not the fix. ",
"//cc @learning-to-play ",
"Have you tried updating to TF 2.16 as suggested [above](https://github.com/tensorflow/tensorflow/issues/63080#issuecomment-2006533553)? Unfortunately TF 2.14 isn't supported any more. FYI, new TF 2.17 release and patch TF 2.16.2 release are scheduled for early July.",
"Hi @learning-to-play ,\r\n\r\nYes, i had tried also 2.16. \r\n\r\nThe issue is caused by this commit https://github.com/tensorflow/tensorflow/commit/ffd06c0782a31766b15b6730aac48d124cdf4a11 which removes the numa config (and many others) from the bazelrc.\r\nIf you diff the bazelrc from that commit onwards you'll see it has never been put back.\r\n\r\nNow it would be interesting to understand why numa (and the other options) has been removed from the bazelrc.\r\nIs it because it is always implicitly active? Is it automatically detected? Was the corresponding feature removed completely? \r\nAs it is still present in the configure.py, making the two files incoherent with each other, this is pretty confusing and the comment of the mentioned commit does not explain why they are deemed no longer relevant.",
"Hi @fgava90 , \r\n\r\nApologies for the delayed response. We removed the NUMA configuration flag because it was enabled by default in TensorFlow's BUILD (and later refactored to tsl/BUILD in this [commit](https://github.com/tensorflow/tensorflow/commit/dc2bfae69be58eea63927ffa5900d70b349d1638)), and we didn't find any instances where the flag was explicitly used. That's why we removed it, and we apologize for not updating the configure.py script to reflect this change. The feature is still supported.\r\n\r\nIf you'd like to enable NUMA support explicitly, you can add `--define=with_numa_support=true` to your bazel build command or include it in an [user-specified RC file](https://bazel.build/run/bazelrc#bazelrc-file-locations). If you've previously run configure.py, it should have generated a .tf_configure.bazelrc file that's imported into TensorFlow's .bazelrc; you can add the configuration there as well.\r\n\r\nIf you think the `numa` configuration flag should be added back to TensorFlow's .bazelrc file, could you please let us know why?",
"@kanglant if I understand correctly, for TF 2.13 and older, the correct flag to control NUMA support was:\r\n```\r\n--config=numa\r\n```\r\nand for TF 2.14+ it is now:\r\n```\r\n--define=with_numa_support=true\r\n```\r\nIs that correct? If so, we'll update our [Spack](https://spack.io) package recipe for TF.",
"Yes. The `--config=numa` flag was used as a convenient shorthand for grouping options related to NUMA support, initially containing just the `--define=with_numa_support=true` option. If we anticipate adding more NUMA-specific options in the future, it would make sense to assess the potential impact on users and determine if reintroducing the `--config=numa` flag would improve the overall experience for a substantial number of users."
] | 2024-02-28T13:11:30 | 2024-06-06T18:44:33 | null |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.14.0
### Custom code
No
### OS platform and distribution
linux rocky8
### Mobile device
_No response_
### Python version
3.8.7
### Bazel version
6.1.0
### GCC/compiler version
gcc 11.3.0
### CUDA/cuDNN version
cuda 11.8.0
### GPU model and memory
_No response_
### Current behavior?
tensorflow v2.14.0 seems to fail configuring when adding --config=numa
```
bazel' '--nohome_rc' '--nosystem_rc' '--output_user_root=/tmp/spackweoy5szc' 'build' '--color=no' '--jobs=10' '--config=opt' '--verbose_failures' '--config=dynamic_kernels' '--config=cuda' '--config=noaws' '--config=nogcp' '--config=nohdfs' '--config=numa' '--config=v2' '//tensorflow/tools/pip_package:build_pip_package'
...
ERROR: Config value 'numa' is not defined in any .rc file
```
### Standalone code to reproduce the issue
```shell
bazel' '--nohome_rc' '--nosystem_rc' '--output_user_root=/tmp/spackweoy5szc' 'build' '--color=no' '--jobs=10' '--config=opt' '--verbose_failures' '--config=dynamic_kernels' '--config=cuda' '--config=noaws' '--config=nogcp' '--config=nohdfs' '--config=numa' '--config=v2' '//tensorflow/tools/pip_package:build_pip_package'
```
### Relevant log output
_No response_
|
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I_kwDOArmXAs6Aq1wR
| 63,079 |
Is there a way to sync the journal of data service with checkpoint?
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[] | 2024-02-28T11:13:27 | 2024-03-01T21:28:43 | null |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.6
### Custom code
No
### 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?
In my app, I set fault_tolerant_mode True with data service, and save_checkpoint_steps is 5000 in tf.train.MonitoredTrainingSession.
For example, the app is killed when the global step is 9500, and then I resume the app with checkpoint from the step 5000, but the data service will continue consume the data from the step 9500. It skip about 4500 steps data.
Is there a way to sync the journal of data service with checkpoint, or make the skip less?
### Standalone code to reproduce the issue
```shell
# dispatcher server snippet
dispatcher_server = tf.data.experimental.service.DispatchServer(
tf.data.experimental.service.DispatcherConfig(
fault_tolerant_mode=True,
work_dir=...
))
# train worker
dataset = dataset.apply(tf.data.experimental.service.distribute(service=dispatcher_service,...)
with tf.train.MonitoredTrainingSession(checkpoint_dir=checkpoint_dir,
save_checkpoint_steps=5000, ...) as mon_sess:
```
### Relevant log output
_No response_
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I_kwDOArmXAs6AqWME
| 63,078 |
ModuleNotFoundError: No module named 'tensorflow.python'
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[
"@ACE07-Sev Please ensure that you're using the Python environment where TensorFlow is installed. \r\nKindly run `python --version ` to check. If you haven't installed TensorFlow yet, use the following command in your terminal:\r\n```\r\npip install tensorflow\r\n\r\n```\r\nCould you try to use the latest TF version, please use the following;\r\n```\r\nimport tensorflow as tf\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/63078\">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/63078\">No</a>\n"
] | 2024-02-28T10:10:12 | 2024-03-14T01:46:47 | 2024-03-14T01:46:44 |
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 10
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I am trying to import tensorflow but get the error I mentioned.
### Standalone code to reproduce the issue
```shell
Just import tensorflow as tf.
```
### Relevant log output
```shell
ModuleNotFoundError Traceback (most recent call last)
Cell In[3], line 1
----> 1 import tensorflow as tf
3 # Load the Fashion MNIST dataset
4 (train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.fashion_mnist.load_data()
File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\tensorflow\__init__.py:37
34 import site as _site
35 import sys as _sys
---> 37 from tensorflow.python.tools import module_util as _module_util
38 from tensorflow.python.util.lazy_loader import KerasLazyLoader as _KerasLazyLoader
40 # Make sure code inside the TensorFlow codebase can use tf2.enabled() at import.
ModuleNotFoundError: No module named 'tensorflow.python'
```
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PR_kwDOArmXAs5oIrIJ
| 63,077 |
Fix checkfail in Dilation2DBackpropInput & Dilation2DBackpropFilter
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`tf.raw_ops.Dilation2DBackpropInput` aborts at `out_backprop.tensor<T, 4>()`, if `out_backprop` rank!=4, due to lack of rank check. Same things happen with tf.raw_ops.Dilation2DBackpropFilter.
Hence adding rank check of `out_backprop` in `ParseSizes` function that should work for both APIs.
Reference issue #62951 .
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I_kwDOArmXAs6AopuY
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Conv2D backprop APIs can lead to assertion failure at graph optimization step
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[
"Hi @Sehun0819 ,\r\n\r\nI can see missing validation check for strides in the API Conv2DBackpropFilter.\r\n\r\nFor other APIs DepthwiseConv2DNativeBackpropFilter & DepthwiseConv2DNativeBackpropInput there is check for `strides.size() `and it should raise error if `strides.size() !=4` .\r\nhttps://github.com/tensorflow/tensorflow/blob/6fd130f8ad839257a274212fbd935b35f2625584/tensorflow/core/kernels/depthwise_conv_grad_op.cc#L559\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/6fd130f8ad839257a274212fbd935b35f2625584/tensorflow/core/kernels/depthwise_conv_grad_op.cc#L1070\r\n\r\nCould you please confirm whether the behaviour happens with same code above where `strides.size!=4`",
"For Conv2DBackpropInput it should check strides.size here.\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/6fd130f8ad839257a274212fbd935b35f2625584/tensorflow/core/kernels/conv_grad_input_ops.h#L294",
"Hi @Sehun0819 , The proposed PR got merged.You can you test with nightly and confirm if this is still an issue?",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63076\">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/63076\">No</a>\n"
] | 2024-02-28T05:19:29 | 2024-03-23T01:46:41 | 2024-03-23T01:46:36 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.17
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04 LTS
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
6.5.0
### GCC/compiler version
clang 16
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Conv2D backprop APIs can lead to assertion failure at graph optimization step.
[Error location](https://github.com/tensorflow/tensorflow/blob/955c46c2d5e5af67ccd9e692c518fed559c1d326/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L982C34-L982C44):
```C++
std::vector<int64_t> strides = GetStrides(op_info);
```
Here It tries to get strides from input. But because there is no guard that checks size of `strides`, it ends up with assertion failure at [`GetStrides`](https://github.com/tensorflow/tensorflow/blob/955c46c2d5e5af67ccd9e692c518fed559c1d326/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L192-L193):
```C++
DCHECK(strides.size() == 4)
<< "Attr strides is not a length-4 vector: " << op_info.DebugString();
```
APIs which have same problem:
`tf.raw_ops.Conv2DBackpropFilter`
`tf.raw_ops.Conv2DBackpropInput`
`tf.raw_ops.DepthwiseConv2DNativeBackpropFilter`
`tf.raw_ops.DepthwiseConv2DNativeBackpropInput`
### Standalone code to reproduce the issue
```Python
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
x = tf.raw_ops.Conv2DBackpropFilter(
input=tf.random.normal([1,1,1,1]),
filter_sizes=[1,1,1,1],
out_backprop=tf.random.normal([1,1,1,1]),
strides=[1,1,1,1,1],
padding="VALID",
use_cudnn_on_gpu=True,
explicit_paddings=[],
data_format='NHWC',
dilations=[1, 1, 1, 1],
name=None
)
sess = tf.compat.v1.Session()
sess.run(x)
```
### Relevant log output
Note that assertion failure is observable in debug build only.
Release build:
```shell
2024-02-28 14:02:25.871735: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled
Traceback (most recent call last):
File "/home/loft/anaconda3/envs/tf-latest-release/lib/python3.11/site-packages/tensorflow/python/client/session.py", line 1402, in _do_call
return fn(*args)
^^^^^^^^^
File "/home/loft/anaconda3/envs/tf-latest-release/lib/python3.11/site-packages/tensorflow/python/client/session.py", line 1385, in _run_fn
return self._call_tf_sessionrun(options, feed_dict, fetch_list,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/loft/anaconda3/envs/tf-latest-release/lib/python3.11/site-packages/tensorflow/python/client/session.py", line 1478, in _call_tf_sessionrun
return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
tensorflow.python.framework.errors_impl.InvalidArgumentError: Sliding window strides field must specify 4 dimensions
[[{{node Conv2DBackpropFilter}}]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/loft/Conv2DBackpropFilter.py", line 19, in <module>
sess.run(x)
File "/home/loft/anaconda3/envs/tf-latest-release/lib/python3.11/site-packages/tensorflow/python/client/session.py", line 972, in run
result = self._run(None, fetches, feed_dict, options_ptr,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/loft/anaconda3/envs/tf-latest-release/lib/python3.11/site-packages/tensorflow/python/client/session.py", line 1215, in _run
results = self._do_run(handle, final_targets, final_fetches,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/loft/anaconda3/envs/tf-latest-release/lib/python3.11/site-packages/tensorflow/python/client/session.py", line 1395, in _do_run
return self._do_call(_run_fn, feeds, fetches, targets, options,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/loft/anaconda3/envs/tf-latest-release/lib/python3.11/site-packages/tensorflow/python/client/session.py", line 1421, in _do_call
raise type(e)(node_def, op, message) # pylint: disable=no-value-for-parameter
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:
Detected at node 'Conv2DBackpropFilter' defined at (most recent call last):
File "/home/loft/Conv2DBackpropFilter.py", line 5, in <module>
Node: 'Conv2DBackpropFilter'
Sliding window strides field must specify 4 dimensions
[[{{node Conv2DBackpropFilter}}]]
Original stack trace for 'Conv2DBackpropFilter':
File "/home/loft/Conv2DBackpropFilter.py", line 5, in <module>
File "/home/loft/anaconda3/envs/tf-latest-release/lib/python3.11/site-packages/tensorflow/python/util/tf_export.py", line 377, in wrapper
File "/home/loft/anaconda3/envs/tf-latest-release/lib/python3.11/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 1536, in conv2d_backprop_filter
File "/home/loft/anaconda3/envs/tf-latest-release/lib/python3.11/site-packages/tensorflow/python/framework/op_def_library.py", line 796, in _apply_op_helper
File "/home/loft/anaconda3/envs/tf-latest-release/lib/python3.11/site-packages/tensorflow/python/framework/ops.py", line 2682, in _create_op_internal
File "/home/loft/anaconda3/envs/tf-latest-release/lib/python3.11/site-packages/tensorflow/python/framework/ops.py", line 1177, in from_node_def
```
Debug build:
```shell
2024-02-28 14:03:56.399860: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
F0000 00:00:1709096636.447526 4015264 op_level_cost_estimator.cc:192] Check failed: strides.size() == 4 Attr strides is not a length-4 vector: op: "Conv2DBackpropFilter"
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device {
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vendor: "AuthenticAMD"
model: "241"
frequency: 2250
num_cores: 256
environment {
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value: "SSE, SSE2, SSE3"
}
environment {
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l1_cache_size: 32768
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*** Check failure stack trace: ***
@ 0x7fefbb0d5467 absl::lts_20230802::log_internal::LogMessage::PrepareToDie()
@ 0x7fefbb0d4ecf absl::lts_20230802::log_internal::LogMessage::SendToLog()
@ 0x7fefbb0d442d absl::lts_20230802::log_internal::LogMessage::Flush()
@ 0x7fefbb0d5605 absl::lts_20230802::log_internal::LogMessageFatal::~LogMessageFatal()
@ 0x7fefbad34a2e tensorflow::grappler::(anonymous namespace)::GetStrides()
@ 0x7fefbad33112 tensorflow::grappler::OpLevelCostEstimator::ConvolutionDimensionsFromInputs()
@ 0x7fefbad3a99d tensorflow::grappler::OpLevelCostEstimator::CountConv2DBackpropFilterOperations()
@ 0x7fefbad28588 tensorflow::grappler::OpLevelCostEstimator::PredictConv2DBackpropFilter()
@ 0x7fefbad3e095 tensorflow::grappler::OpLevelCostEstimator::OpLevelCostEstimator()::$_0::operator()()::{lambda()#1}::operator()()
@ 0x7fefbad3e003 std::__invoke_impl<>()
@ 0x7fefbad3dfa0 std::__invoke_r<>()
@ 0x7fefbad3de60 std::_Function_handler<>::_M_invoke()
@ 0x7fefbad40338 std::function<>::operator()()
@ 0x7fefbad2eae0 tensorflow::grappler::OpLevelCostEstimator::PredictNodeCosts()
@ 0x7fefbad2e37a tensorflow::grappler::OpLevelCostEstimator::PredictCosts()
@ 0x7fefbad1b8a5 tensorflow::grappler::AnalyticalCostEstimator::PredictCosts()
@ 0x7fefbad17b3b tensorflow::grappler::VirtualCluster::Run()
@ 0x7fefbac23b72 tensorflow::grappler::GraphMemory::InferStatically()
@ 0x7fefbac0604d tensorflow::grappler::(anonymous namespace)::IdentifySwappingCandidates()
@ 0x7fefbabf9cc5 tensorflow::grappler::(anonymous namespace)::SwappingPass()
@ 0x7fefbabf5546 tensorflow::grappler::MemoryOptimizer::Optimize()
@ 0x7fefbaa4cf4f tensorflow::grappler::MetaOptimizer::RunOptimizer()
@ 0x7fefbaa4bcc2 tensorflow::grappler::MetaOptimizer::OptimizeGraph()
@ 0x7fefbaa4db9c tensorflow::grappler::MetaOptimizer::OptimizeGraph()
@ 0x7fefbaa4ea00 tensorflow::grappler::MetaOptimizer::OptimizeConsumeItem()
@ 0x7fefbaa50cef tensorflow::grappler::RunMetaOptimizer()
@ 0x7fefba760621 tensorflow::GraphExecutionState::OptimizeGraph()
@ 0x7fefba75c50d tensorflow::GraphExecutionState::BuildGraph()
@ 0x7fefb0c7c0dc tensorflow::DirectSession::CreateGraphs()
@ 0x7fefb0c7ad04 tensorflow::DirectSession::CreateExecutors()
@ 0x7fefb0c78082 tensorflow::DirectSession::GetOrCreateExecutors()
@ 0x7fefb0c769ab tensorflow::DirectSession::Run()
@ 0x7fefb0c7663e tensorflow::DirectSession::Run()
@ 0x7fefb4f92467 tensorflow::SessionRef::Run()
@ 0x7fef94109085 TF_Run_Helper()
@ 0x7fef941129c9 TF_SessionRun
@ 0x7fefb4f830da tensorflow::TF_SessionRun_wrapper_helper()
@ 0x7fefb4f8354b tensorflow::TF_SessionRun_wrapper()
@ 0x7fef6965c9c3 pybind11_init__pywrap_tf_session()::$_46::operator()()
@ 0x7fef6965c726 pybind11::detail::argument_loader<>::call_impl<>()
@ 0x7fef6965c63c pybind11::detail::argument_loader<>::call<>()
@ 0x7fef6965c53b pybind11::cpp_function::initialize<>()::{lambda()#1}::operator()()
@ 0x7fef6965c439 pybind11::cpp_function::initialize<>()::{lambda()#1}::__invoke()
@ 0x7fef69685777 pybind11::cpp_function::dispatcher()
@ 0x528187 cfunction_call
Aborted (core dumped)
```
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PR_kwDOArmXAs5oHXf-
| 63,075 |
Change visibility status for schema_conversion_utils to allow compila…
|
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[
"@majiddadashi @gbaned it looks like http://ml-ci.amd.com:21096/job/tensorflow/job/github-prs-upstream-master/job/AMD-ROCm-Community-CI-Build/job/PR-63075/1/display/redirect fail is unrelated?\r\nDo you have suggestions to get this PR approved?",
"> @majiddadashi @gbaned it looks like http://ml-ci.amd.com:21096/job/tensorflow/job/github-prs-upstream-master/job/AMD-ROCm-Community-CI-Build/job/PR-63075/1/display/redirect fail is unrelated? Do you have suggestions to get this PR approved?\r\n\r\nA similar situation appeared in [a recent PR](https://github.com/tensorflow/tensorflow/pull/63049) (unrelated failure for AMD-ROCm), but was pushed through. @mihaimaruseac was instrumental to have this approved...",
"@mihaimaruseac look like merging is still blocked, could you advice?\r\n",
"Sorry, this was stuck on internal review (see the image above https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md#contributor-license-agreements for the process).\r\n\r\nThere is one internal comment that was not transferred here:\r\n\r\n> the visibility of this should not be change, rather the header should be included in the target tflite_internal_cc_3p_api_deps_src\",",
"Thanks, @mihaimaruseac. @Namburger, can you please provide more insight/suggestions on how to implement the requested change? Is this on the TF or on the libcoral side?\r\n",
"@mihaimaruseac, I manually changed the definition of `tflite_internal_cc_3p_api_deps_src` in `tensorflow/lite/schema/BUILD` as suggested:\r\n\r\n```\r\nfilegroup(\r\n name = \"tflite_internal_cc_3p_api_deps_src\",\r\n srcs = [\r\n \":schema_fbs_srcs\",\r\n \":schema_utils.cc\",\r\n \":schema_utils.h\",\r\n \":schema_conversion_utils.cc\",\r\n \":schema_conversion_utils.h\",\r\n \r\n ],\r\n visibility = [\r\n \"//tensorflow/lite:__pkg__\",\r\n ],\r\n)\r\n```\r\n\r\nYet compilation of libcoral stops with the same error. ",
"@feranick can you post the log failures?",
"> filegroup(\r\n> name = \"tflite_internal_cc_3p_api_deps_src\",\r\n> srcs = [\r\n> \":schema_fbs_srcs\",\r\n> \":schema_utils.cc\",\r\n> \":schema_utils.h\",\r\n> \":schema_conversion_utils.cc\",\r\n> \":schema_conversion_utils.h\",\r\n> \r\n> ],\r\n> visibility = [\r\n> \"//tensorflow/lite:__pkg__\",\r\n> ],\r\n> )\r\n\r\nHere it is, both the procedure to reproduce and the log.\r\n\r\n[procedure.txt](https://github.com/tensorflow/tensorflow/files/14488001/procedure.txt)\r\n[log.txt](https://github.com/tensorflow/tensorflow/files/14488003/log.txt)\r\n\r\nThis is the identical error and log you would get from an untouched TF repo.\r\n",
"I am going to close this as it is resolved internally with commit https://github.com/tensorflow/tensorflow/commit/79ecb3f8bb6bd73f0115fa9a97b630a6f745a426"
] | 2024-02-28T01:14:06 | 2024-03-05T20:18:01 | 2024-03-05T20:17:58 |
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…tion of ext. libraries
When compiling libraries such as [libcoral](https://github.com/google-coral/libcoral) and [pycoral](https://github.com/google-coral/pycoral), they are linked to TF and access header files in TF proper. One such case is [schema_conversion_utils.h](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/schema/schema_conversion_utils.h). Unfortunately the visibility of such header is set in [BUILD, line 188](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/schema/BUILD) as:
visibility = [":utils_friends"],
This prevents a successful compilation of the libraries (especially when compiled within a docker), as the libraries are not part of the `utils_friends`. It is notable that a similar issue (i.e. same visibility status) was present in TF2.15 for [schema_util.h](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/schema/schema_util.h), but was recently changed to:
visibility = ["//visibility:public"],
So the visibility status should be changed to `visibility = ["//visibility:public"],` also for the `schema_conversion_utils`.
Reference: https://github.com/tensorflow/tensorflow/issues/63074
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I_kwDOArmXAs6Anr1T
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Change visibility status for lite/schema/schema_conversion_utils to allow compilation of external libraries
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"+1 to gain visibility",
"Hi @feranick, @Namburger \r\n was able to get this change in here: https://github.com/tensorflow/tensorflow/commit/79ecb3f8bb6bd73f0115fa9a97b630a6f745a426, can you test against master to ensure your issue is resolved? Thanks.",
"I believe this issue can be closed, your call @feranick.",
"Closing. Thanks all for your help.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63074\">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/63074\">No</a>\n",
"> Hi @feranick, @Namburger was able to get this change in here: [79ecb3f](https://github.com/tensorflow/tensorflow/commit/79ecb3f8bb6bd73f0115fa9a97b630a6f745a426), can you test against master to ensure your issue is resolved? Thanks.\r\n\r\nYes, I confirm the issue is resolved in master. Compilation works for libcoral. "
] | 2024-02-28T01:10:37 | 2024-03-05T19:55:18 | 2024-03-05T19:25:43 |
CONTRIBUTOR
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
TF2.15
### Custom code
Yes
### OS platform and distribution
Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
6.1
### GCC/compiler version
gcc
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When compiling libraries such as [libcoral](https://github.com/google-coral/libcoral) and [pycoral](https://github.com/google-coral/pycoral), they are linked to TF and access header files in TF proper. One such case is [schema_conversion_utils.h](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/schema/schema_conversion_utils.h). Unfortunately the visibility of such header is set in [BUILD, line 188](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/schema/BUILD) as:
```
visibility = [":utils_friends"],
```
This prevents a successful compilation of the libraries (especially when compiled within a docker), as the libraries are not part of the `utils_friends`. It is notable that a similar issue (i.e. same visibility status) was present in TF2.15 for [schema_util.h](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/schema/schema_util.h), but was recently changed to:
```
visibility = ["//visibility:public"],
```
So the visibility status should be changed to `visibility = ["//visibility:public"],` also for the `schema_conversion_utils`.
### Standalone code to reproduce the issue
```shell
(The libcoral library is outdated, so a fork from the original google version has been created so it can compile in modern systems. It is listed as follow).
git clone https://github.com/feranick/libcoral
cd libcoral
git submodule init
git submodule update
make DOCKER_IMAGE=ubuntu:22.04 DOCKER_CPUS="k8" DOCKER_TARGETS=tests docker-build
```
### Relevant log output
```shell
nicola@carbonio:~/Software/tensorflow-dir/coral/libcoral$ nano ~/.cache/bazel/_bazel_nicola/a151c9dba9406267fd80e5a9bea29d9e/external/org_tensorflow/tensorflow/lite/schema/BUILD
nicola@carbonio:~/Software/tensorflow-dir/coral/libcoral$ make
Loading: 0 packages loaded
Loading: 0 packages loaded
bazel build --compilation_mode=opt --cpu=k8 //coral:bbox_test //coral:error_reporter_test //coral:inference_repeatability_test //coral:inference_stress_test //coral:model_loading_stress_test //coral:multiple_tpus_inference_stress_test //coral:segmentation_models_test //coral:test_utils_test //coral:tflite_utils_test //coral/classification:adapter_test //coral/classification:classification_models_test //coral/classification:cocompiled_classification_models_test //coral/classification:lstm_mnist_models_test //coral/detection:adapter_test //coral/detection:models_test //coral/dmabuf:dmabuf_devboard_test //coral/dmabuf:model_pipelining_dmabuf_devboard_test //coral/learn:imprinting_engine_test //coral/learn:utils_test //coral/learn/backprop:layers_test //coral/learn/backprop:multi_variate_normal_distribution_test //coral/learn/backprop:softmax_regression_model_test //coral/learn/backprop:test_utils_test //coral/pipeline:detection_models_test //coral/pipeline:models_test //coral/pipeline:pipelined_model_runner_test //coral/pipeline/internal:memory_pool_allocator_test //coral/pipeline/internal:segment_runner_test //coral/pose_estimation:bodypix_test //coral/pose_estimation:movenet_test //coral/pose_estimation:posenet_decoder_test //coral/pose_estimation:posenet_test //coral/tools:automl_model_append_rnn_link_test //coral/tools:tflite_graph_util_test //coral/tools/partitioner:parameter_count_based_partitioner_test //coral/tools/partitioner:profiling_based_partitioner_ondevice_test //coral/tools/partitioner:profiling_based_partitioner_test //coral/tools/partitioner:utils_test
WARNING: Download from https://mirror.bazel.build/github.com/bazelbuild/rules_cc/archive/081771d4a0e9d7d3aa0eed2ef389fa4700dfb23e.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found
ERROR: /home/nicola/Software/tensorflow-dir/coral/libcoral/coral/learn/BUILD:21:11: in cc_library rule //coral/learn:utils: target '@org_tensorflow//tensorflow/lite/schema:schema_conversion_utils' is not visible from target '//coral/learn:utils'. Check the visibility declaration of the former target if you think the dependency is legitimate
ERROR: /home/nicola/Software/tensorflow-dir/coral/libcoral/coral/learn/BUILD:21:11: Analysis of target '//coral/learn:utils' failed
ERROR: Analysis of target '//coral/learn/backprop:layers_test' failed; build aborted:
INFO: Elapsed time: 2.310s
INFO: 0 processes.
FAILED: Build did NOT complete successfully (8 packages loaded, 1318 targets configured)
make: *** [Makefile:75: tests] Error 1
```
```
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[oneDNN] Update release notes
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Update release notes to add information about experimental support for float16 auto-mixed precision.
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[
"Im getting the same issue\r\n",
"Hi @delhub ,\r\n\r\nWe came to know the issue persists intermittently and concerned team working on it. Hope it has been resolved already. I am not facing issue now. Could you please try again and let us know.\r\n\r\nThanks!",
"Yes, I got back into it today. Thank you for taking care of it.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63072\">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/63072\">No</a>\n"
] | 2024-02-27T17:21:26 | 2024-03-02T20:15:59 | 2024-03-02T20:15:56 |
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Not really sure where to post this issue since I can't get to discuss.tensorflow.org. Over the past day I've been receiving the subject issue after restarting my browser and system. Was in a conversation over a topic but now can't get back to it.
I'd appreciate if someone would provide some guidance as to whether this is an issue on my side or the server. Tried different systems and browsers. Thanks!
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Fix Checkfail in raw_ops.DecodeAndCropJpeg
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"@sagunb , Done the changes as suggested. Thanks!"
] | 2024-02-27T14:24:50 | 2024-06-05T08:16:31 | null |
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The API `tf.raw_ops.DecodeAndCropJpeg` can lead to assertion failure with -ve values passed to `crop_window` argument. This is true for only debug builds but not with TF official release wheels.
Proposed a fix for this.May please review.
Ref Issue: #63062
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`tf.raw_ops.ArgMax`: Heap buffer overflow
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"@Sehun0819 Could you please have a look at the [gist](https://colab.research.google.com/gist/sushreebarsa/ccbad423d1f928fdf3f24519ba5f9153/63070.ipynb) and confirm the issue?\r\nThank you!",
"@sushreebarsa \r\nEven it does not print out ASAN log(as it wasn't built with ASAN flags), the error message seems to be an evidence.\r\n`Expected dimension in the range [-4, 4), but got 1945239553`\r\n`1945239553` is `0x73F20001` in hexadecimal. I guess the right part, `0x0001` came from 2byte input argument `tf.constant(1,shape=[],dtype=tf.int16)` and the left part `0x73F2` is the 2bytes that was not meant to be read.",
"@Sehun0819 , Thanks for reporting. Proposed a fix which might fix this issue. ",
"@SuryanarayanaY\r\nThank you!\r\nThere is one more code we should handle, [here](https://github.com/tensorflow/tensorflow/blob/50bfa12ba07bcd797be1d3841d059b4418a565de/tensorflow/core/ops/math_ops.cc#L1125-L1130):\r\n```C++\r\n int64_t dimension_val;\r\n if (dim_t->dtype() == DT_INT32) {\r\n dimension_val = dim_t->scalar<int32>()();\r\n } else {\r\n dimension_val = dim_t->scalar<int64_t>()();\r\n }\r\n```\r\nThis leads another heap buffer overflow, because it just assumes int32 and int64.\r\n\r\nReproduction:\r\n```Python\r\nimport tensorflow as tf\r\n\r\ntf.compat.v1.disable_eager_execution()\r\n\r\ntf.raw_ops.ArgMax(\r\n input=tf.random.normal([1,1,1,1]),\r\n dimension=tf.constant(1,shape=[],dtype=tf.int16),\r\n output_type=tf.dtypes.int64,\r\n name=None\r\n)\r\n```\r\nLog:\r\n```bash\r\n=================================================================\r\n==3663542==ERROR: AddressSanitizer: heap-buffer-overflow on address 0x6090000000c0 at pc 0x7f1245ed2a73 bp 0x7ffd5a214450 sp 0x7ffd5a214448\r\nREAD of size 8 at 0x6090000000c0 thread T0\r\n #0 0x7f1245ed2a72 in tensorflow::(anonymous namespace)::ArgOpShape(tensorflow::shape_inference::InferenceContext*) /proc/self/cwd/tensorflow/core/ops/math_ops.cc:1129:21\r\n #1 0x7f124bd6dd80 in absl::lts_20230802::Status std::__invoke_impl<absl::lts_20230802::Status, absl::lts_20230802::Status (*&)(tensorflow::shape_inference::InferenceContext*), tensorflow::shape_inference::InferenceContext*>(std::__invoke_other, absl::lts_20230802::Status (*&)(tensorflow::shape_inference::InferenceContext*), tensorflow::shape_inference::InferenceContext*&&) /usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/invoke.h:61:14\r\n #2 0x7f124bd6dd80 in std::enable_if<is_invocable_r_v<absl::lts_20230802::Status, absl::lts_20230802::Status (*&)(tensorflow::shape_inference::InferenceContext*), tensorflow::shape_inference::InferenceContext*>, absl::lts_20230802::Status>::type std::__invoke_r<absl::lts_20230802::Status, absl::lts_20230802::Status (*&)(tensorflow::shape_inference::InferenceContext*), tensorflow::shape_inference::InferenceContext*>(absl::lts_20230802::Status (*&)(tensorflow::shape_inference::InferenceContext*), tensorflow::shape_inference::InferenceContext*&&) /usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/invoke.h:114:9\r\n #3 0x7f124bd6dd80 in std::_Function_handler<absl::lts_20230802::Status (tensorflow::shape_inference::InferenceContext*), absl::lts_20230802::Status (*)(tensorflow::shape_inference::InferenceContext*)>::_M_invoke(std::_Any_data const&, tensorflow::shape_inference::InferenceContext*&&) /usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/std_function.h:290:9\r\n #4 0x7f124c5b4083 in std::function<absl::lts_20230802::Status (tensorflow::shape_inference::InferenceContext*)>::operator()(tensorflow::shape_inference::InferenceContext*) const /usr/lib/gcc/x86_64-linux-gnu/11/../../../../include/c++/11/bits/std_function.h:590:9\r\n #5 0x7f124c5b4083 in tensorflow::shape_inference::InferenceContext::Run(std::function<absl::lts_20230802::Status (tensorflow::shape_inference::InferenceContext*)> const&) /proc/self/cwd/tensorflow/core/framework/shape_inference.cc:106:14\r\n #6 0x7f124bd65c0b in tensorflow::ShapeRefiner::RunShapeFn(tensorflow::Node const*, tensorflow::OpRegistrationData const*, tensorflow::ExtendedInferenceContext*, tensorflow::shape_inference::InferenceContext*)::$_0::operator()() const /proc/self/cwd/tensorflow/core/common_runtime/shape_refiner.cc:760:7\r\n #7 0x7f124bd5d360 in tensorflow::ShapeRefiner::RunShapeFn(tensorflow::Node const*, tensorflow::OpRegistrationData const*, tensorflow::ExtendedInferenceContext*, tensorflow::shape_inference::InferenceContext*) /proc/self/cwd/tensorflow/core/common_runtime/shape_refiner.cc:827:7\r\n #8 0x7f124bd5a54d in tensorflow::ShapeRefiner::AddNodeInternal(tensorflow::Node const*, tensorflow::shape_inference::InferenceContext*) /proc/self/cwd/tensorflow/core/common_runtime/shape_refiner.cc:270:3\r\n #9 0x7f124bd5c6df in tensorflow::ShapeRefiner::AddNode(tensorflow::Node const*) /proc/self/cwd/tensorflow/core/common_runtime/shape_refiner.cc:212:10\r\n #10 0x7f121547e005 in TF_FinishOperationLocked /proc/self/cwd/tensorflow/c/c_api.cc:1063:45\r\n #11 0x7f121547e5f0 in TF_FinishOperation /proc/self/cwd/tensorflow/c/c_api.cc:1082:10\r\n #12 0x7f11f70af947 in pybind11_init__pywrap_tf_session(pybind11::module_&)::$_52::operator()(TF_OperationDescription*) const /proc/self/cwd/tensorflow/python/client/tf_session_wrapper.cc:1514:32\r\n #13 0x7f11f70af947 in TF_Operation* pybind11::detail::argument_loader<TF_OperationDescription*>::call_impl<TF_Operation*, pybind11_init__pywrap_tf_session(pybind11::module_&)::$_52&, 0ul, pybind11::detail::void_type>(pybind11_init__pywrap_tf_session(pybind11::module_&)::$_52&, std::integer_sequence<unsigned long, 0ul>, pybind11::detail::void_type&&) && /proc/self/cwd/bazel-out/k8-dbg/bin/external/pybind11/_virtual_includes/pybind11/pybind11/cast.h:1443:16\r\n #14 0x7f11f70af947 in std::enable_if<!std::is_void<TF_Operation*>::value, TF_Operation*>::type pybind11::detail::argument_loader<TF_OperationDescription*>::call<TF_Operation*, pybind11::detail::void_type, pybind11_init__pywrap_tf_session(pybind11::module_&)::$_52&>(pybind11_init__pywrap_tf_session(pybind11::module_&)::$_52&) && /proc/self/cwd/bazel-out/k8-dbg/bin/external/pybind11/_virtual_includes/pybind11/pybind11/cast.h:1411:42\r\n #15 0x7f11f70af947 in void pybind11::cpp_function::initialize<pybind11_init__pywrap_tf_session(pybind11::module_&)::$_52, TF_Operation*, TF_OperationDescription*, pybind11::name, pybind11::scope, pybind11::sibling, pybind11::return_value_policy>(pybind11_init__pywrap_tf_session(pybind11::module_&)::$_52&&, TF_Operation* (*)(TF_OperationDescription*), pybind11::name const&, pybind11::scope const&, pybind11::sibling const&, pybind11::return_value_policy const&)::'lambda'(pybind11::detail::function_call&)::operator()(pybind11::detail::function_call&) const /proc/self/cwd/bazel-out/k8-dbg/bin/external/pybind11/_virtual_includes/pybind11/pybind11/pybind11.h:248:69\r\n #16 0x7f11f70af947 in void pybind11::cpp_function::initialize<pybind11_init__pywrap_tf_session(pybind11::module_&)::$_52, TF_Operation*, TF_OperationDescription*, pybind11::name, pybind11::scope, pybind11::sibling, pybind11::return_value_policy>(pybind11_init__pywrap_tf_session(pybind11::module_&)::$_52&&, TF_Operation* (*)(TF_OperationDescription*), pybind11::name const&, pybind11::scope const&, pybind11::sibling const&, pybind11::return_value_policy const&)::'lambda'(pybind11::detail::function_call&)::__invoke(pybind11::detail::function_call&) /proc/self/cwd/bazel-out/k8-dbg/bin/external/pybind11/_virtual_includes/pybind11/pybind11/pybind11.h:223:21\r\n #17 0x7f11f70ea2a4 in pybind11::cpp_function::dispatcher(_object*, _object*, _object*) /proc/self/cwd/bazel-out/k8-dbg/bin/external/pybind11/_virtual_includes/pybind11/pybind11/pybind11.h:939:30\r\n #18 0x528186 in cfunction_call /usr/local/src/conda/python-3.11.7/Objects/methodobject.c:542:18\r\n #19 0x503a0b in _PyObject_MakeTpCall /usr/local/src/conda/python-3.11.7/Objects/call.c:214:18\r\n #20 0x510f32 in _PyEval_EvalFrameDefault /usr/local/src/conda/python-3.11.7/Python/ceval.c:4769:23\r\n #21 0x538732 in _PyEval_EvalFrame /usr/local/src/conda/python-3.11.7/Include/internal/pycore_ceval.h:73:16\r\n #22 0x538732 in _PyEval_Vector /usr/local/src/conda/python-3.11.7/Python/ceval.c:6434:24\r\n #23 0x538732 in _PyFunction_Vectorcall /usr/local/src/conda/python-3.11.7/Objects/call.c:393:16\r\n #24 0x5426bb in _PyVectorcall_Call /usr/local/src/conda/python-3.11.7/Objects/call.c:257:24\r\n #25 0x5426bb in _PyObject_Call /usr/local/src/conda/python-3.11.7/Objects/call.c:328:16\r\n #26 0x5426bb in PyObject_Call /usr/local/src/conda/python-3.11.7/Objects/call.c:355:12\r\n #27 0x514ff0 in do_call_core /usr/local/src/conda/python-3.11.7/Python/ceval.c:7352:12\r\n #28 0x514ff0 in _PyEval_EvalFrameDefault /usr/local/src/conda/python-3.11.7/Python/ceval.c:5376:22\r\n #29 0x538732 in _PyEval_EvalFrame /usr/local/src/conda/python-3.11.7/Include/internal/pycore_ceval.h:73:16\r\n #30 0x538732 in _PyEval_Vector /usr/local/src/conda/python-3.11.7/Python/ceval.c:6434:24\r\n #31 0x538732 in _PyFunction_Vectorcall /usr/local/src/conda/python-3.11.7/Objects/call.c:393:16\r\n #32 0x5426bb in _PyVectorcall_Call /usr/local/src/conda/python-3.11.7/Objects/call.c:257:24\r\n #33 0x5426bb in _PyObject_Call /usr/local/src/conda/python-3.11.7/Objects/call.c:328:16\r\n #34 0x5426bb in PyObject_Call /usr/local/src/conda/python-3.11.7/Objects/call.c:355:12\r\n #35 0x514ff0 in do_call_core /usr/local/src/conda/python-3.11.7/Python/ceval.c:7352:12\r\n #36 0x514ff0 in _PyEval_EvalFrameDefault /usr/local/src/conda/python-3.11.7/Python/ceval.c:5376:22\r\n #37 0x5cb559 in _PyEval_EvalFrame /usr/local/src/conda/python-3.11.7/Include/internal/pycore_ceval.h:73:16\r\n #38 0x5cb559 in _PyEval_Vector /usr/local/src/conda/python-3.11.7/Python/ceval.c:6434:24\r\n #39 0x5cac2e in PyEval_EvalCode /usr/local/src/conda/python-3.11.7/Python/ceval.c:1148:21\r\n #40 0x5ebcf6 in run_eval_code_obj /usr/local/src/conda/python-3.11.7/Python/pythonrun.c:1710:9\r\n #41 0x5e788f in run_mod /usr/local/src/conda/python-3.11.7/Python/pythonrun.c:1731:19\r\n #42 0x5fc831 in pyrun_file /usr/local/src/conda/python-3.11.7/Python/pythonrun.c:1626:15\r\n #43 0x5fbbfe in _PyRun_SimpleFileObject /usr/local/src/conda/python-3.11.7/Python/pythonrun.c:440:13\r\n #44 0x5fb922 in _PyRun_AnyFileObject /usr/local/src/conda/python-3.11.7/Python/pythonrun.c:79:15\r\n #45 0x5f65cd in pymain_run_file_obj /usr/local/src/conda/python-3.11.7/Modules/main.c:360:15\r\n #46 0x5f65cd in pymain_run_file /usr/local/src/conda/python-3.11.7/Modules/main.c:379:15\r\n #47 0x5f65cd in pymain_run_python /usr/local/src/conda/python-3.11.7/Modules/main.c:601:21\r\n #48 0x5f65cd in Py_RunMain /usr/local/src/conda/python-3.11.7/Modules/main.c:680:5\r\n #49 0x5bb3d8 in Py_BytesMain /usr/local/src/conda/python-3.11.7/Modules/main.c:734:12\r\n #50 0x7f1321691d8f in __libc_start_call_main csu/../sysdeps/nptl/libc_start_call_main.h:58:16\r\n #51 0x7f1321691e3f in __libc_start_main csu/../csu/libc-start.c:392:3\r\n #52 0x5bb222 in _start (/home/loft/anaconda3/envs/tf-latest-asan/bin/python3.11+0x5bb222)\r\n\r\n0x6090000000c2 is located 0 bytes after 2-byte region [0x6090000000c0,0x6090000000c2)\r\nallocated by thread T0 here:\r\n #0 0x7f1321a76617 in __interceptor_posix_memalign /home/runner/work/llvm-project/llvm-project/final/llvm-project/compiler-rt/lib/asan/asan_malloc_linux.cpp:145:3\r\n #1 0x7f124e776902 in tsl::port::AlignedMalloc(unsigned long, int) (/home/loft/anaconda3/envs/tf-latest-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x4a18902)\r\n #2 0x7f124c553bcc in tsl::(anonymous namespace)::CPUAllocator::AllocateRaw(unsigned long, unsigned long) cpu_allocator_impl.cc\r\n #3 0x7f124c71e19f in short* tensorflow::TypedAllocator::Allocate<short>(tsl::Allocator*, unsigned long, tsl::AllocationAttributes const&) /proc/self/cwd/./tensorflow/core/framework/typed_allocator.h:47:24\r\n #4 0x7f124c71e19f in tensorflow::(anonymous namespace)::Buffer<short>::Buffer(tsl::Allocator*, long) /proc/self/cwd/tensorflow/core/framework/tensor.cc:568:21\r\n #5 0x7f124c71e19f in tensorflow::TensorBuffer* tensorflow::(anonymous namespace)::FromProtoField<short>(tsl::Allocator*, tensorflow::TensorProto const&, long) /proc/self/cwd/tensorflow/core/framework/tensor.cc:600:24\r\n #6 0x7f124c70ddd1 in tensorflow::Tensor::FromProto(tsl::Allocator*, tensorflow::TensorProto const&) /proc/self/cwd/tensorflow/core/framework/tensor.cc:1140:7\r\n #7 0x7f124bcf4e5b in tensorflow::EvaluateConstantTensor(tensorflow::Node const&, int, tensorflow::ShapeRefiner const&, absl::lts_20230802::FunctionRef<std::optional<tensorflow::Tensor> (tensorflow::Node const&, int)>, std::optional<tensorflow::EvaluateConstantTensorRunner>) /proc/self/cwd/tensorflow/core/common_runtime/eval_const_tensor.cc:375:9\r\n #8 0x7f124bd613ab in tensorflow::ShapeRefiner::EvaluateConstantTensorForEdge(tensorflow::Node const*, int, bool*, tensorflow::Tensor*, tensorflow::shape_inference::InferenceContext*) /proc/self/cwd/tensorflow/core/common_runtime/shape_refiner.cc:454:3\r\n #9 0x7f124bd5cf06 in tensorflow::ShapeRefiner::RunShapeFn(tensorflow::Node const*, tensorflow::OpRegistrationData const*, tensorflow::ExtendedInferenceContext*, tensorflow::shape_inference::InferenceContext*) /proc/self/cwd/tensorflow/core/common_runtime/shape_refiner.cc:799:9\r\n #10 0x7f124bd5a54d in tensorflow::ShapeRefiner::AddNodeInternal(tensorflow::Node const*, tensorflow::shape_inference::InferenceContext*) /proc/self/cwd/tensorflow/core/common_runtime/shape_refiner.cc:270:3\r\n #11 0x7f124bd5c6df in tensorflow::ShapeRefiner::AddNode(tensorflow::Node const*) /proc/self/cwd/tensorflow/core/common_runtime/shape_refiner.cc:212:10\r\n #12 0x7f121547e005 in TF_FinishOperationLocked /proc/self/cwd/tensorflow/c/c_api.cc:1063:45\r\n #13 0x7f121547e5f0 in TF_FinishOperation /proc/self/cwd/tensorflow/c/c_api.cc:1082:10\r\n #14 0x7f11f70af947 in pybind11_init__pywrap_tf_session(pybind11::module_&)::$_52::operator()(TF_OperationDescription*) const /proc/self/cwd/tensorflow/python/client/tf_session_wrapper.cc:1514:32\r\n #15 0x7f11f70af947 in TF_Operation* pybind11::detail::argument_loader<TF_OperationDescription*>::call_impl<TF_Operation*, pybind11_init__pywrap_tf_session(pybind11::module_&)::$_52&, 0ul, pybind11::detail::void_type>(pybind11_init__pywrap_tf_session(pybind11::module_&)::$_52&, std::integer_sequence<unsigned long, 0ul>, pybind11::detail::void_type&&) && /proc/self/cwd/bazel-out/k8-dbg/bin/external/pybind11/_virtual_includes/pybind11/pybind11/cast.h:1443:16\r\n #16 0x7f11f70af947 in std::enable_if<!std::is_void<TF_Operation*>::value, TF_Operation*>::type pybind11::detail::argument_loader<TF_OperationDescription*>::call<TF_Operation*, pybind11::detail::void_type, pybind11_init__pywrap_tf_session(pybind11::module_&)::$_52&>(pybind11_init__pywrap_tf_session(pybind11::module_&)::$_52&) && /proc/self/cwd/bazel-out/k8-dbg/bin/external/pybind11/_virtual_includes/pybind11/pybind11/cast.h:1411:42\r\n #17 0x7f11f70af947 in void pybind11::cpp_function::initialize<pybind11_init__pywrap_tf_session(pybind11::module_&)::$_52, TF_Operation*, TF_OperationDescription*, pybind11::name, pybind11::scope, pybind11::sibling, pybind11::return_value_policy>(pybind11_init__pywrap_tf_session(pybind11::module_&)::$_52&&, TF_Operation* (*)(TF_OperationDescription*), pybind11::name const&, pybind11::scope const&, pybind11::sibling const&, pybind11::return_value_policy const&)::'lambda'(pybind11::detail::function_call&)::operator()(pybind11::detail::function_call&) const /proc/self/cwd/bazel-out/k8-dbg/bin/external/pybind11/_virtual_includes/pybind11/pybind11/pybind11.h:248:69\r\n #18 0x7f11f70af947 in void pybind11::cpp_function::initialize<pybind11_init__pywrap_tf_session(pybind11::module_&)::$_52, TF_Operation*, TF_OperationDescription*, pybind11::name, pybind11::scope, pybind11::sibling, pybind11::return_value_policy>(pybind11_init__pywrap_tf_session(pybind11::module_&)::$_52&&, TF_Operation* (*)(TF_OperationDescription*), pybind11::name const&, pybind11::scope const&, pybind11::sibling const&, pybind11::return_value_policy const&)::'lambda'(pybind11::detail::function_call&)::__invoke(pybind11::detail::function_call&) /proc/self/cwd/bazel-out/k8-dbg/bin/external/pybind11/_virtual_includes/pybind11/pybind11/pybind11.h:223:21\r\n #19 0x7f11f70ea2a4 in pybind11::cpp_function::dispatcher(_object*, _object*, _object*) /proc/self/cwd/bazel-out/k8-dbg/bin/external/pybind11/_virtual_includes/pybind11/pybind11/pybind11.h:939:30\r\n #20 0x528186 in cfunction_call /usr/local/src/conda/python-3.11.7/Objects/methodobject.c:542:18\r\n\r\nSUMMARY: AddressSanitizer: heap-buffer-overflow /proc/self/cwd/tensorflow/core/ops/math_ops.cc:1129:21 in tensorflow::(anonymous namespace)::ArgOpShape(tensorflow::shape_inference::InferenceContext*)\r\nShadow bytes around the buggy address:\r\n 0x608ffffffe00: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00\r\n 0x608ffffffe80: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00\r\n 0x608fffffff00: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00\r\n 0x608fffffff80: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00\r\n 0x609000000000: fa fa fa fa fa fa fa fa 00 00 fa fa fa fa fa fa\r\n=>0x609000000080: fa fa fa fa fa fa fa fa[02]fa fa fa fa fa fa fa\r\n 0x609000000100: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa\r\n 0x609000000180: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa\r\n 0x609000000200: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa\r\n 0x609000000280: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa\r\n 0x609000000300: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa\r\nShadow byte legend (one shadow byte represents 8 application bytes):\r\n Addressable: 00\r\n Partially addressable: 01 02 03 04 05 06 07 \r\n Heap left redzone: fa\r\n Freed heap region: fd\r\n Stack left redzone: f1\r\n Stack mid redzone: f2\r\n Stack right redzone: f3\r\n Stack after return: f5\r\n Stack use after scope: f8\r\n Global redzone: f9\r\n Global init order: f6\r\n Poisoned by user: f7\r\n Container overflow: fc\r\n Array cookie: ac\r\n Intra object redzone: bb\r\n ASan internal: fe\r\n Left alloca redzone: ca\r\n Right alloca redzone: cb\r\n==3663542==ABORTING\r\n```"
] | 2024-02-27T13:06:46 | 2024-03-12T05:28:02 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.17
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04 LTS
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
6.5.0
### GCC/compiler version
clang 16
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`tf.raw_ops.ArgMax` can lead to heap buffer overflow.
[Error location](https://github.com/tensorflow/tensorflow/blob/774b0c3e97b5ef60bfc9c54961347dd0bc3660a8/tensorflow/core/kernels/argmax_op.cc#L59):
```C++
const int32_t dim = internal::SubtleMustCopy(dimension.scalar<int32>()());
```
It copies scalar value of `dimension` without checking exact type. Therefore when `dimension` is an `int16` it reads over the bound.
Note that `int16` is an allowed type for `dimension` according to [opdef](https://github.com/tensorflow/tensorflow/blob/41b93a8b310086f69aab6b6369d2af9d5178881d/tensorflow/core/ops/math_ops.cc#L1153-L1160):
```C++
REGISTER_OP("ArgMax")
.Input("input: T")
.Input("dimension: Tidx")
.Output("output: output_type")
.Attr("T: {realnumbertype, quantizedtype, bool}")
.Attr("Tidx: {int16, int32, int64} = DT_INT32")
.Attr("output_type: {int16, uint16, int32, int64} = DT_INT64")
.SetShapeFn(ArgOpShape);
```
### Standalone code to reproduce the issue
```python
import tensorflow as tf
tf.raw_ops.ArgMax(
input=tf.random.normal([1,1,1,1]),
dimension=tf.constant(1,shape=[],dtype=tf.int16),
output_type=tf.dtypes.int64,
name=None
)
```
### Relevant log output
The below log needs ASAN build.
```shell
=================================================================
==4008222==ERROR: AddressSanitizer: heap-buffer-overflow on address 0x609000000400 at pc 0x7fa6a0dca809 bp 0x7ffe63b29d90 sp 0x7ffe63b29d88
READ of size 4 at 0x609000000400 thread T0
#0 0x7fa6a0dca808 in int const tensorflow::internal::SubtleMustCopy<int>(int const&) /proc/self/cwd/./tensorflow/core/framework/bounds_check.h:49:10
#1 0x7fa6a0dca808 in tensorflow::ArgOp<Eigen::ThreadPoolDevice, float, long, tensorflow::functor::ArgMax<Eigen::ThreadPoolDevice, float, long>>::Compute(tensorflow::OpKernelContext*) /proc/self/cwd/tensorflow/core/kernels/argmax_op.cc:59:25
#2 0x7fa6bbbfab9f in tensorflow::ThreadPoolDevice::Compute(tensorflow::OpKernel*, tensorflow::OpKernelContext*) /proc/self/cwd/tensorflow/core/common_runtime/threadpool_device.cc:185:14
#3 0x7fa6bb91644f in tensorflow::(anonymous namespace)::SingleThreadedExecutorImpl::Run(tensorflow::Executor::Args const&) /proc/self/cwd/tensorflow/core/common_runtime/single_threaded_executor.cc:445:15
#4 0x7fa6bb836ae6 in tensorflow::FunctionLibraryRuntimeImpl::RunSync(tensorflow::FunctionLibraryRuntime::Options, unsigned long, absl::lts_20230802::Span<tensorflow::Tensor const>, std::vector<tensorflow::Tensor, std::allocator<tensorflow::Tensor>>*) /proc/self/cwd/tensorflow/core/common_runtime/function.cc:1339:3
#5 0x7fa6bb866863 in tensorflow::ProcessFunctionLibraryRuntime::RunMultiDeviceSync(tensorflow::FunctionLibraryRuntime::Options const&, unsigned long, std::vector<std::variant<tensorflow::Tensor, tensorflow::TensorShape>, std::allocator<std::variant<tensorflow::Tensor, tensorflow::TensorShape>>>*, std::function<absl::lts_20230802::Status (tensorflow::ProcessFunctionLibraryRuntime::ComponentFunctionData const&, tensorflow::ProcessFunctionLibraryRuntime::InternalArgs*)>) const /proc/self/cwd/tensorflow/core/common_runtime/process_function_library_runtime.cc:931:16
#6 0x7fa6bb877e9b in tensorflow::ProcessFunctionLibraryRuntime::RunSync(tensorflow::FunctionLibraryRuntime::Options const&, unsigned long, absl::lts_20230802::Span<tensorflow::Tensor const>, std::vector<tensorflow::Tensor, std::allocator<tensorflow::Tensor>>*) const /proc/self/cwd/tensorflow/core/common_runtime/process_function_library_runtime.cc:1526:21
#7 0x7fa69db93761 in tensorflow::KernelAndDeviceFunc::Run(tensorflow::ScopedStepContainer*, tensorflow::EagerKernelArgs const&, std::vector<std::variant<tensorflow::Tensor, tensorflow::TensorShape>, std::allocator<std::variant<tensorflow::Tensor, tensorflow::TensorShape>>>*, tsl::CancellationManager*, std::optional<tensorflow::EagerFunctionParams> const&, std::optional<tensorflow::ManagedStackTrace> const&, tsl::CoordinationServiceAgent*) /proc/self/cwd/tensorflow/core/common_runtime/eager/kernel_and_device.cc:464:21
#8 0x7fa69da529f5 in tensorflow::EagerKernelExecute(tensorflow::EagerContext*, absl::lts_20230802::InlinedVector<tensorflow::TensorHandle*, 4ul, std::allocator<tensorflow::TensorHandle*>> const&, std::optional<tensorflow::EagerFunctionParams> const&, tsl::core::RefCountPtr<tensorflow::KernelAndDevice> const&, tensorflow::GraphCollector*, tsl::CancellationManager*, absl::lts_20230802::Span<tensorflow::TensorHandle*>, std::optional<tensorflow::ManagedStackTrace> const&) /proc/self/cwd/tensorflow/core/common_runtime/eager/execute.cc:2167:3
#9 0x7fa69da86630 in tensorflow::ExecuteNode::Run() /proc/self/cwd/./tensorflow/core/common_runtime/eager/execute_node.h:127:12
#10 0x7fa69db7f849 in tensorflow::EagerExecutor::SyncExecute(tensorflow::EagerNode*) /proc/self/cwd/tensorflow/core/common_runtime/eager/eager_executor.cc:128:13
#11 0x7fa69da512d9 in tensorflow::(anonymous namespace)::AddOrExecuteNode(tsl::core::RefCountPtr<tensorflow::KernelAndDevice>, tensorflow::EagerOperation*, tensorflow::TensorHandle**) /proc/self/cwd/tensorflow/core/common_runtime/eager/execute.cc:1685:25
#12 0x7fa69da512d9 in tensorflow::(anonymous namespace)::EagerLocalExecute(tensorflow::EagerOperation*, tensorflow::TensorHandle**, int*) /proc/self/cwd/tensorflow/core/common_runtime/eager/execute.cc:1760:14
#13 0x7fa69da4a107 in tensorflow::DoEagerExecute(tensorflow::EagerOperation*, tensorflow::TensorHandle**, int*) /proc/self/cwd/tensorflow/core/common_runtime/eager/execute.cc:2127:12
#14 0x7fa69da54bd7 in tensorflow::EagerExecute(tensorflow::EagerOperation*, tensorflow::TensorHandle**, int*) /proc/self/cwd/tensorflow/core/common_runtime/eager/execute.cc:2207:10
#15 0x7fa690dd1f99 in tensorflow::EagerOperation::Execute(absl::lts_20230802::Span<tensorflow::AbstractTensorHandle*>, int*) /proc/self/cwd/tensorflow/core/common_runtime/eager/core.cc:187:10
#16 0x7fa69db7959e in tensorflow::CustomDeviceOpHandler::Execute(tensorflow::ImmediateExecutionOperation*, tensorflow::ImmediateExecutionTensorHandle**, int*) /proc/self/cwd/tensorflow/core/common_runtime/eager/custom_device_op_handler.cc
#17 0x7fa6851e18f1 in TFE_Execute /proc/self/cwd/tensorflow/c/eager/c_api.cc:907:62
#18 0x7fa6b989418d in TFE_Py_FastPathExecute_C(_object*) /proc/self/cwd/tensorflow/python/eager/pywrap_tfe_src.cc:3979:3
#19 0x7fa667d2683e in pybind11_init__pywrap_tfe(pybind11::module_&)::$_60::operator()(pybind11::args) const /proc/self/cwd/tensorflow/python/tfe_wrapper.cc:1276:35
#20 0x7fa667d2683e in pybind11::object pybind11::detail::argument_loader<pybind11::args>::call_impl<pybind11::object, pybind11_init__pywrap_tfe(pybind11::module_&)::$_60&, 0ul, pybind11::detail::void_type>(pybind11_init__pywrap_tfe(pybind11::module_&)::$_60&, std::integer_sequence<unsigned long, 0ul>, pybind11::detail::void_type&&) && /proc/self/cwd/bazel-out/k8-dbg/bin/external/pybind11/_virtual_includes/pybind11/pybind11/cast.h:1443:16
#21 0x7fa667d2683e in std::enable_if<!std::is_void<pybind11::object>::value, pybind11::object>::type pybind11::detail::argument_loader<pybind11::args>::call<pybind11::object, pybind11::detail::void_type, pybind11_init__pywrap_tfe(pybind11::module_&)::$_60&>(pybind11_init__pywrap_tfe(pybind11::module_&)::$_60&) && /proc/self/cwd/bazel-out/k8-dbg/bin/external/pybind11/_virtual_includes/pybind11/pybind11/cast.h:1411:42
#22 0x7fa667d2683e in void pybind11::cpp_function::initialize<pybind11_init__pywrap_tfe(pybind11::module_&)::$_60, pybind11::object, pybind11::args, pybind11::name, pybind11::scope, pybind11::sibling>(pybind11_init__pywrap_tfe(pybind11::module_&)::$_60&&, pybind11::object (*)(pybind11::args), pybind11::name const&, pybind11::scope const&, pybind11::sibling const&)::'lambda'(pybind11::detail::function_call&)::operator()(pybind11::detail::function_call&) const /proc/self/cwd/bazel-out/k8-dbg/bin/external/pybind11/_virtual_includes/pybind11/pybind11/pybind11.h:248:69
#23 0x7fa667d2683e in void pybind11::cpp_function::initialize<pybind11_init__pywrap_tfe(pybind11::module_&)::$_60, pybind11::object, pybind11::args, pybind11::name, pybind11::scope, pybind11::sibling>(pybind11_init__pywrap_tfe(pybind11::module_&)::$_60&&, pybind11::object (*)(pybind11::args), pybind11::name const&, pybind11::scope const&, pybind11::sibling const&)::'lambda'(pybind11::detail::function_call&)::__invoke(pybind11::detail::function_call&) /proc/self/cwd/bazel-out/k8-dbg/bin/external/pybind11/_virtual_includes/pybind11/pybind11/pybind11.h:223:21
#24 0x7fa667d67a59 in pybind11::cpp_function::dispatcher(_object*, _object*, _object*) /proc/self/cwd/bazel-out/k8-dbg/bin/external/pybind11/_virtual_includes/pybind11/pybind11/pybind11.h:939:30
#25 0x528186 in cfunction_call /usr/local/src/conda/python-3.11.7/Objects/methodobject.c:542:18
#26 0x503a0b in _PyObject_MakeTpCall /usr/local/src/conda/python-3.11.7/Objects/call.c:214:18
#27 0x510f32 in _PyEval_EvalFrameDefault /usr/local/src/conda/python-3.11.7/Python/ceval.c:4769:23
#28 0x538732 in _PyEval_EvalFrame /usr/local/src/conda/python-3.11.7/Include/internal/pycore_ceval.h:73:16
#29 0x538732 in _PyEval_Vector /usr/local/src/conda/python-3.11.7/Python/ceval.c:6434:24
#30 0x538732 in _PyFunction_Vectorcall /usr/local/src/conda/python-3.11.7/Objects/call.c:393:16
#31 0x5426bb in _PyVectorcall_Call /usr/local/src/conda/python-3.11.7/Objects/call.c:257:24
#32 0x5426bb in _PyObject_Call /usr/local/src/conda/python-3.11.7/Objects/call.c:328:16
#33 0x5426bb in PyObject_Call /usr/local/src/conda/python-3.11.7/Objects/call.c:355:12
#34 0x514ff0 in do_call_core /usr/local/src/conda/python-3.11.7/Python/ceval.c:7352:12
#35 0x514ff0 in _PyEval_EvalFrameDefault /usr/local/src/conda/python-3.11.7/Python/ceval.c:5376:22
#36 0x5cb559 in _PyEval_EvalFrame /usr/local/src/conda/python-3.11.7/Include/internal/pycore_ceval.h:73:16
#37 0x5cb559 in _PyEval_Vector /usr/local/src/conda/python-3.11.7/Python/ceval.c:6434:24
#38 0x5cac2e in PyEval_EvalCode /usr/local/src/conda/python-3.11.7/Python/ceval.c:1148:21
#39 0x5ebcf6 in run_eval_code_obj /usr/local/src/conda/python-3.11.7/Python/pythonrun.c:1710:9
#40 0x5e788f in run_mod /usr/local/src/conda/python-3.11.7/Python/pythonrun.c:1731:19
#41 0x5fc831 in pyrun_file /usr/local/src/conda/python-3.11.7/Python/pythonrun.c:1626:15
#42 0x5fbbfe in _PyRun_SimpleFileObject /usr/local/src/conda/python-3.11.7/Python/pythonrun.c:440:13
#43 0x5fb922 in _PyRun_AnyFileObject /usr/local/src/conda/python-3.11.7/Python/pythonrun.c:79:15
#44 0x5f65cd in pymain_run_file_obj /usr/local/src/conda/python-3.11.7/Modules/main.c:360:15
#45 0x5f65cd in pymain_run_file /usr/local/src/conda/python-3.11.7/Modules/main.c:379:15
#46 0x5f65cd in pymain_run_python /usr/local/src/conda/python-3.11.7/Modules/main.c:601:21
#47 0x5f65cd in Py_RunMain /usr/local/src/conda/python-3.11.7/Modules/main.c:680:5
#48 0x5bb3d8 in Py_BytesMain /usr/local/src/conda/python-3.11.7/Modules/main.c:734:12
#49 0x7fa791408d8f in __libc_start_call_main csu/../sysdeps/nptl/libc_start_call_main.h:58:16
#50 0x7fa791408e3f in __libc_start_main csu/../csu/libc-start.c:392:3
#51 0x5bb222 in _start (/home/loft/anaconda3/envs/tf-latest-asan/bin/python3.11+0x5bb222)
0x609000000402 is located 0 bytes after 2-byte region [0x609000000400,0x609000000402)
allocated by thread T0 here:
#0 0x7fa7917ed617 in __interceptor_posix_memalign /home/runner/work/llvm-project/llvm-project/final/llvm-project/compiler-rt/lib/asan/asan_malloc_linux.cpp:145:3
#1 0x7fa6be4f6902 in tsl::port::AlignedMalloc(unsigned long, int) (/home/loft/anaconda3/envs/tf-latest-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x4a18902)
#2 0x7fa6bc2d3bcc in tsl::(anonymous namespace)::CPUAllocator::AllocateRaw(unsigned long, unsigned long) cpu_allocator_impl.cc
#3 0x7fa6bc4883bc in short* tensorflow::TypedAllocator::Allocate<short>(tsl::Allocator*, unsigned long, tsl::AllocationAttributes const&) /proc/self/cwd/./tensorflow/core/framework/typed_allocator.h:47:24
#4 0x7fa6bc4883bc in tensorflow::(anonymous namespace)::Buffer<short>::Buffer(tsl::Allocator*, long, tsl::AllocationAttributes const&) /proc/self/cwd/tensorflow/core/framework/tensor.cc:574:21
#5 0x7fa6bc4883bc in tensorflow::Tensor::Tensor(tsl::Allocator*, tensorflow::DataType, tensorflow::TensorShape const&, tsl::AllocationAttributes const&) /proc/self/cwd/tensorflow/core/framework/tensor.cc:986:5
#6 0x7fa6bbef871d in tensorflow::OpKernelContext::allocate_tensor(tensorflow::DataType, tensorflow::TensorShape const&, tensorflow::Tensor*, tsl::AllocatorAttributes, tsl::AllocationAttributes const&) /proc/self/cwd/tensorflow/core/framework/op_kernel.cc:764:10
#7 0x7fa6bbef784e in tensorflow::OpKernelContext::allocate_tensor(tensorflow::DataType, tensorflow::TensorShape const&, tensorflow::Tensor*, tsl::AllocatorAttributes) /proc/self/cwd/./tensorflow/core/framework/op_kernel.h:1270:12
#8 0x7fa6bbef784e in tensorflow::OpKernelContext::allocate_output(int, tensorflow::TensorShape const&, tensorflow::Tensor**, tsl::AllocatorAttributes) /proc/self/cwd/tensorflow/core/framework/op_kernel.cc:822:14
#9 0x7fa6bbef4368 in tensorflow::OpKernelContext::allocate_output(int, tensorflow::TensorShape const&, tensorflow::Tensor**) /proc/self/cwd/tensorflow/core/framework/op_kernel.cc:728:10
#10 0x7fa6a9bf0207 in tensorflow::CastOpBase::Compute(tensorflow::OpKernelContext*) (/home/loft/anaconda3/envs/tf-latest-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x3e9fe207)
#11 0x7fa6bbbfab9f in tensorflow::ThreadPoolDevice::Compute(tensorflow::OpKernel*, tensorflow::OpKernelContext*) /proc/self/cwd/tensorflow/core/common_runtime/threadpool_device.cc:185:14
#12 0x7fa6bb91644f in tensorflow::(anonymous namespace)::SingleThreadedExecutorImpl::Run(tensorflow::Executor::Args const&) /proc/self/cwd/tensorflow/core/common_runtime/single_threaded_executor.cc:445:15
#13 0x7fa6bb836ae6 in tensorflow::FunctionLibraryRuntimeImpl::RunSync(tensorflow::FunctionLibraryRuntime::Options, unsigned long, absl::lts_20230802::Span<tensorflow::Tensor const>, std::vector<tensorflow::Tensor, std::allocator<tensorflow::Tensor>>*) /proc/self/cwd/tensorflow/core/common_runtime/function.cc:1339:3
#14 0x7fa6bb866863 in tensorflow::ProcessFunctionLibraryRuntime::RunMultiDeviceSync(tensorflow::FunctionLibraryRuntime::Options const&, unsigned long, std::vector<std::variant<tensorflow::Tensor, tensorflow::TensorShape>, std::allocator<std::variant<tensorflow::Tensor, tensorflow::TensorShape>>>*, std::function<absl::lts_20230802::Status (tensorflow::ProcessFunctionLibraryRuntime::ComponentFunctionData const&, tensorflow::ProcessFunctionLibraryRuntime::InternalArgs*)>) const /proc/self/cwd/tensorflow/core/common_runtime/process_function_library_runtime.cc:931:16
#15 0x7fa6bb877e9b in tensorflow::ProcessFunctionLibraryRuntime::RunSync(tensorflow::FunctionLibraryRuntime::Options const&, unsigned long, absl::lts_20230802::Span<tensorflow::Tensor const>, std::vector<tensorflow::Tensor, std::allocator<tensorflow::Tensor>>*) const /proc/self/cwd/tensorflow/core/common_runtime/process_function_library_runtime.cc:1526:21
#16 0x7fa69db93761 in tensorflow::KernelAndDeviceFunc::Run(tensorflow::ScopedStepContainer*, tensorflow::EagerKernelArgs const&, std::vector<std::variant<tensorflow::Tensor, tensorflow::TensorShape>, std::allocator<std::variant<tensorflow::Tensor, tensorflow::TensorShape>>>*, tsl::CancellationManager*, std::optional<tensorflow::EagerFunctionParams> const&, std::optional<tensorflow::ManagedStackTrace> const&, tsl::CoordinationServiceAgent*) /proc/self/cwd/tensorflow/core/common_runtime/eager/kernel_and_device.cc:464:21
#17 0x7fa69da529f5 in tensorflow::EagerKernelExecute(tensorflow::EagerContext*, absl::lts_20230802::InlinedVector<tensorflow::TensorHandle*, 4ul, std::allocator<tensorflow::TensorHandle*>> const&, std::optional<tensorflow::EagerFunctionParams> const&, tsl::core::RefCountPtr<tensorflow::KernelAndDevice> const&, tensorflow::GraphCollector*, tsl::CancellationManager*, absl::lts_20230802::Span<tensorflow::TensorHandle*>, std::optional<tensorflow::ManagedStackTrace> const&) /proc/self/cwd/tensorflow/core/common_runtime/eager/execute.cc:2167:3
#18 0x7fa69da86630 in tensorflow::ExecuteNode::Run() /proc/self/cwd/./tensorflow/core/common_runtime/eager/execute_node.h:127:12
#19 0x7fa69db7f849 in tensorflow::EagerExecutor::SyncExecute(tensorflow::EagerNode*) /proc/self/cwd/tensorflow/core/common_runtime/eager/eager_executor.cc:128:13
#20 0x7fa69da512d9 in tensorflow::(anonymous namespace)::AddOrExecuteNode(tsl::core::RefCountPtr<tensorflow::KernelAndDevice>, tensorflow::EagerOperation*, tensorflow::TensorHandle**) /proc/self/cwd/tensorflow/core/common_runtime/eager/execute.cc:1685:25
#21 0x7fa69da512d9 in tensorflow::(anonymous namespace)::EagerLocalExecute(tensorflow::EagerOperation*, tensorflow::TensorHandle**, int*) /proc/self/cwd/tensorflow/core/common_runtime/eager/execute.cc:1760:14
#22 0x7fa69da4a107 in tensorflow::DoEagerExecute(tensorflow::EagerOperation*, tensorflow::TensorHandle**, int*) /proc/self/cwd/tensorflow/core/common_runtime/eager/execute.cc:2127:12
#23 0x7fa69da54bd7 in tensorflow::EagerExecute(tensorflow::EagerOperation*, tensorflow::TensorHandle**, int*) /proc/self/cwd/tensorflow/core/common_runtime/eager/execute.cc:2207:10
#24 0x7fa690dd1f99 in tensorflow::EagerOperation::Execute(absl::lts_20230802::Span<tensorflow::AbstractTensorHandle*>, int*) /proc/self/cwd/tensorflow/core/common_runtime/eager/core.cc:187:10
#25 0x7fa69db7959e in tensorflow::CustomDeviceOpHandler::Execute(tensorflow::ImmediateExecutionOperation*, tensorflow::ImmediateExecutionTensorHandle**, int*) /proc/self/cwd/tensorflow/core/common_runtime/eager/custom_device_op_handler.cc
#26 0x7fa6851e18f1 in TFE_Execute /proc/self/cwd/tensorflow/c/eager/c_api.cc:907:62
#27 0x7fa6b985dcbf in tensorflow::EagerCast(TFE_Context*, TFE_TensorHandle*, TF_DataType, TF_DataType, TSL_Status*) /proc/self/cwd/tensorflow/python/eager/pywrap_tensor.cc:259:3
#28 0x7fa6b985e67b in tensorflow::ConvertToEagerTensorUncached(TFE_Context*, _object*, tensorflow::DataType, char const*) /proc/self/cwd/tensorflow/python/eager/pywrap_tensor.cc:317:11
#29 0x7fa6b985f8a1 in tensorflow::ConvertToEagerTensor(TFE_Context*, _object*, tensorflow::DataType, char const*) /proc/self/cwd/tensorflow/python/eager/pywrap_tensor.cc:405:14
#30 0x7fa6b9860088 in EagerTensor_init /proc/self/cwd/tensorflow/python/eager/pywrap_tensor.cc:529:18
#31 0x5039d2 in type_call /usr/local/src/conda/python-3.11.7/Objects/typeobject.c:1103:19
#32 0x5039d2 in _PyObject_MakeTpCall /usr/local/src/conda/python-3.11.7/Objects/call.c:214:18
SUMMARY: AddressSanitizer: heap-buffer-overflow /proc/self/cwd/./tensorflow/core/framework/bounds_check.h:49:10 in int const tensorflow::internal::SubtleMustCopy<int>(int const&)
Shadow bytes around the buggy address:
0x609000000180: fa fa fa fa fa fa fa fa fd fa fa fa fa fa fa fa
0x609000000200: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x609000000280: fd fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x609000000300: 04 fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x609000000380: fd fd fd fd fa fa fa fa fa fa fa fa fa fa fa fa
=>0x609000000400:[02]fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x609000000480: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x609000000500: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x609000000580: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x609000000600: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x609000000680: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
Shadow byte legend (one shadow byte represents 8 application bytes):
Addressable: 00
Partially addressable: 01 02 03 04 05 06 07
Heap left redzone: fa
Freed heap region: fd
Stack left redzone: f1
Stack mid redzone: f2
Stack right redzone: f3
Stack after return: f5
Stack use after scope: f8
Global redzone: f9
Global init order: f6
Poisoned by user: f7
Container overflow: fc
Array cookie: ac
Intra object redzone: bb
ASan internal: fe
Left alloca redzone: ca
Right alloca redzone: cb
==4008222==ABORTING
```
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I_kwDOArmXAs6AiYVJ
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Given Shapes are not Broadcastable, tf to tf-lite conversion error
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[
"I think the bug is more that Resizing is converted to a broadcasting operation. If we remove it and make the shapes equivalent, I am able to convert: [gist](https://colab.sandbox.google.com/gist/pkgoogle/d3c0c4c6459343a0a5f7de1d0305db0f/conversionfails.ipynb)\r\n\r\nHi @arfaian, can you please take a look? Thanks.",
"Thanks for taking a look, @pkgoogle and @arfaian . If I can do anything, let me know. "
] | 2024-02-27T12:44:00 | 2024-03-16T10:12:23 | null |
NONE
| null | null | null |
### 1. System information
Google colab as of 2024-02-27
tf version: 2.15.0
### Problem
I receive the following error when converting a tf module from tf to tfl-lite:
```
RuntimeError: Given shapes, [1,436,1024,3] and [1,218,512,3], are not broadcastable.Node number 1 (SUB) failed to prepare.
```
Below is the relevant class:
```
class Inpainting(Model):
def __init__(self, downsample_factor, **kwargs):
super(Inpainting, self).__init__(**kwargs)
self.downsample_factor = downsample_factor
self.downsize = Resizing(218, 512)
@tf.function(input_signature=[
tf.TensorSpec(shape=(1, 218, 512, 3)),
tf.TensorSpec(shape=(1, 218, 512, 1)),
tf.TensorSpec(shape=(1, 436, 1024, 3)),
tf.TensorSpec(shape=(1, 436, 1024, 1)),
])
def call(self, img_t_lr, depth_t_lr, img_t_w, depth_t_w):
img_t_wlr = self.downsize(img_t_w)
depth_t_wlr = self.downsize(depth_t_w)
assert img_t_wlr.shape[1:] == img_t_lr.shape[1:], "must be same shape"
diff_color = tf.math.subtract(img_t_lr, img_t_wlr)
assert depth_t_lr.shape[1:] == depth_t_wlr.shape[1:], "must be same shape"
diff_depth = tf.math.subtract(depth_t_lr, depth_t_wlr)
diff = tf.concat([diff_color, diff_depth], axis=3)
return diff
```
Please see here for the full [gist](https://colab.research.google.com/drive/18Q2-NvHFre1-1Dk2HYjTjX1MnUveERle?usp=sharing).
I am a bit surprised that this happens. Could this be a bug?
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I_kwDOArmXAs6Ah8vF
| 63,068 |
C++ API `DenseBincount` violates assertion in shape inference step
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[
"I suspect [here](https://github.com/tensorflow/tensorflow/blob/41b93a8b310086f69aab6b6369d2af9d5178881d/tensorflow/core/ops/math_ops.cc#L1895-L1900), a shape function of `DenseBincount`:\r\n```C++\r\n if (c->Rank(c->input(0)) == 1) {\r\n c->set_output(0, c->MakeShape({size_val}));\r\n } else if (c->Rank(c->input(0)) == 2) {\r\n c->set_output(0, c->MakeShape({c->Dim(c->input(0), 0), size_val}));\r\n }\r\n return absl::OkStatus();\r\n```\r\nBecause it assumes 1 or 2 ranked input tensors only, 0-ranked input tensor makes output unset.\r\nHow about to add a guard so that it can make sure only 1 or 2 ranked input tensors reach there?",
"Hi @Sehun0819 ,\r\n\r\nThanks for findings. Proposed a fix in above PR. Thanks!",
"@Sehun0819,\r\nThe PR which was proposed for the API **raw_ops.DenseBincount** lacks validation of input to be a vector has been merged. Also the respective changes are also available in the respective files.\r\n\r\nhttps://github.com/tensorflow/tensorflow/pull/63114/files\r\n\r\n**if (data.dims() <= 1)** in [bincount_op.cc](https://github.com/tensorflow/tensorflow/pull/63114/files#diff-539b05632aa822e749c0d2d78ce544e17fb6f1eb510d1b180098acf60c0c9b3e) and **if (c->Rank(c->input(0)) == 1 || c->Rank(c->input(0)) == 0)** in [math_ops.cc](https://github.com/tensorflow/tensorflow/pull/63114/files#diff-285d834bdca6b7da7f6f950b0fa28d677591e9f5273068148f4f9425282be5b9)\r\n\r\nThank you!"
] | 2024-02-27T11:46:10 | 2024-03-05T01:02:42 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.17
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04 LTS
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
6.5.0
### GCC/compiler version
clang 16
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
C++ API `DenseBincount` violates assertion in shape inference step.
Error location is [here](https://github.com/tensorflow/tensorflow/blob/83f1804f3427ae888e62b26b5bcba8afc9e24ef7/tensorflow/core/framework/shape_inference.cc#L111-L115):
```C++
#ifndef NDEBUG
for (int i = 0; i < num_outputs(); ++i) {
DCHECK(output(i).IsSet()) << i << " for " << attrs_.SummarizeNode();
}
#endif // NDEBUG
```
Seems like output is not set properly in shape function of `DenseBincount`.
Note that in release build, same code terminates normally with another diagnosis.
### Standalone code to reproduce the issue
```C++
#include "tensorflow/cc/framework/scope.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/public/session.h"
#include "tensorflow/cc/ops/array_ops.h"
#include "tensorflow/cc/ops/standard_ops.h"
using namespace tensorflow;
int main() {
SessionOptions options;
std::unique_ptr<tensorflow::Session>
session(tensorflow::NewSession(options));
Scope scope = Scope::NewRootScope();
Input input = 1;
Input size = 1;
Input weights = ops::RandomNormal(scope,{1,1,1,1},DT_FLOAT);
auto target = ops::DenseBincount(scope.WithOpName("target"), input, size, weights);
Status status;
GraphDef graph_def;
status = scope.ToGraphDef(&graph_def);
if (!status.ok()) {
LOG(WARNING) << "Could not build graph: " << status.message();
}
status = session->Create(graph_def);
if (!status.ok()) {
LOG(WARNING) << "Could not create session: " << status.message();
}
std::vector<Tensor> outputs;
status = session->Run({}, {"target"}, {"target"}, &outputs);
if (!status.ok()) {
LOG(WARNING) << "Could not run session: " << status.message();
}
return 0;
}
```
### Relevant log output
Release build:
```shell
2024-02-27 20:40:33.778448: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: INVALID_ARGUMENT: `weights` must be the same shape as `arr` or a length-0 `Tensor`, in which case it acts as all weights equal to 1. Received [1,1,1,1]
[[{{node target}}]]
2024-02-27 20:40:33.781525: W tensorflow/core/kernels/reproduce/DenseBincount.cc:35] Could not run session: `weights` must be the same shape as `arr` or a length-0 `Tensor`, in which case it acts as all weights equal to 1. Received [1,1,1,1]
[[{{node target}}]]
```
Debug build:
```shell
2024-02-27 20:29:28.448037: F tensorflow/core/framework/shape_inference.cc:113] Check failed: output(i).IsSet() 0 for {{node target}} = DenseBincount[T=DT_FLOAT, Tidx=DT_INT32, binary_output=false, _device="/job:localhost/replica:0/task:0/device:CPU:0"](Const_1/Const, Const_1/Const, RandomNormal)
Aborted (core dumped)
```
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I_kwDOArmXAs6Ahs7v
| 63,067 |
`tf.raw_ops.ExtractImagePatches`: Assertion failure in shape inference step
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[
"In release build, we can observe unstable intermediate tensor with invalid dim([gist](https://colab.research.google.com/drive/11NlRWUTovteiN6JNPpGVK2E1zAkHLz4a?usp=sharing)).\r\n```Python\r\nimport tensorflow as tf\r\n\r\ntf.compat.v1.disable_eager_execution()\r\n\r\nx = tf.raw_ops.ExtractImagePatches(\r\n images=tf.random.normal([1,1,1,1]),\r\n ksizes=[1,-1,2,1],\r\n strides=[1,1,1,1],\r\n rates=[1,1,1,1],\r\n padding=\"VALID\",\r\n name=None\r\n)\r\n\r\n# Prints \"True\"\r\nprint(tf.is_tensor(x))\r\n\r\n# Ends up with \"ValueError: Dimension -2 must be >= 0\"\r\nprint(x)\r\n```",
"Hi @Sehun0819 ,\r\n\r\nI think this behaviour exists if negative values passed to strides and rates also. Validation might be costly in that case.Since this behaviour is specific to a debug build and official builds won't have this behaviour, could you please mention the build command used for this build. May be there exists a nice way for fixing this.\r\n\r\nThanks!",
"@SuryanarayanaY\r\nHi!\r\nI built it following [manual](https://www.tensorflow.org/install/source) with `--config=dbg`.\r\n\r\nI just found that it runs without any error message when both `ksizes[1]` and `ksizes[2]` are negative([gist](https://colab.research.google.com/drive/11NlRWUTovteiN6JNPpGVK2E1zAkHLz4a#scrollTo=4Y1QLVSnv2tC&line=1&uniqifier=1)), and it seems even worse.\r\n```Python\r\nimport tensorflow as tf\r\n\r\nx = tf.raw_ops.ExtractImagePatches(\r\n images=tf.random.normal([1,1,1,1]),\r\n ksizes=[1,-1,-2,1],\r\n strides=[1,1,1,1],\r\n rates=[1,1,1,1],\r\n padding=\"VALID\",\r\n name=None\r\n)\r\n\r\n# Tensor(\"ExtractImagePatches_8:0\", shape=(1, 3, 4, 2), dtype=float32)\r\nprint(x)\r\n```",
"Hi @Sehun0819 ,\r\n\r\nThe DCHECK pointed here already addresses in PR #63089. I believe the API still lacks some validations."
] | 2024-02-27T11:12:14 | 2024-03-19T09:51:15 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.17
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04 LTS
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
6.5.0
### GCC/compiler version
clang 16
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`tf.raw_ops.ExtractImagePatches` can lead to assertion failure in shape inference step.
Error location is [here](https://github.com/tensorflow/tensorflow/blob/f5daeae21404e8d672c785cc0c4c469ddc1b1a8a/tensorflow/core/ops/array_ops.cc#L2686-L2687):
```C++
TF_RETURN_IF_ERROR(c->Multiply(
c->Dim(input_shape, 3), ksize_rows * ksize_cols, &output_depth_dim));
```
Because it does not check validity of `ksize_rows * ksize_cols`, the negative value of it is fed to [`Multiply`](https://github.com/tensorflow/tensorflow/blob/83f1804f3427ae888e62b26b5bcba8afc9e24ef7/tensorflow/core/framework/shape_inference.cc#L1096C1-L1098C56):
```C++
Status InferenceContext::Multiply(DimensionHandle first,
DimensionOrConstant second,
DimensionHandle* out)
```
And it ends up with assertion failure at [a constructor of `DimensionOrConstant`](https://github.com/tensorflow/tensorflow/blob/daea84aad67d1fcc8d58c648e1da58ee576445f9/tensorflow/core/framework/shape_inference.h#L891-L896).
### Standalone code to reproduce the issue
```Python
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
tf.raw_ops.ExtractImagePatches(
images=tf.random.normal([1,1,1,1]),
ksizes=[1,-1,2,1],
strides=[1,1,1,1],
rates=[1,1,1,1],
padding="VALID",
name=None
)
```
### Relevant log output
Release build: Outputs nothing
Debug build:
```shell
2024-02-27 20:05:58.701202: F ./tensorflow/core/framework/shape_inference.h:891] Check failed: val >= 0 || val == InferenceContext::kUnknownDim Dimension must be non-negative or equal to InferenceContext::kUnknownDim but got -2
Aborted (core dumped)
```
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I_kwDOArmXAs6AhlgX
| 63,066 |
C++ API `SparseFillEmptyRows` can lead to `std::length_error`
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[
"Hi @Sehun0819 ,\r\n\r\nThanks for reporting. The `dense_shape` can be 1D which means it can have -ve shapes like [-2,3] & [2,-3]. I would like to know the behaviour with the mentioned 2 shapes. It would be appreciated if you test by modifying the Inputs accordingly and report the behaviour.\r\nThanks!"
] | 2024-02-27T10:56:31 | 2024-03-05T09:37:11 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.17
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04 LTS
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
6.5.0
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
C++ API `SparseFillEmptyRows` can lead to `std::length_error`.
Error location is [here](https://github.com/tensorflow/tensorflow/blob/9da417e3f63215efe995b83e7b9f9b34115a424e/tensorflow/core/kernels/fill_empty_rows_functor.h#L114C36-L114C46):
```C++
std::vector<Tindex> csr_offset(dense_rows, 0);
```
Because lack of checking negativity, negative value of `dense_rows` is fed to the constructor of `std::vector`. By integer underflow, the value is converted to extremely large number and it ends up with `std::length_error`.
### Standalone code to reproduce the issue
```C++
#include "tensorflow/cc/framework/scope.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/public/session.h"
#include "tensorflow/cc/ops/array_ops.h"
#include "tensorflow/cc/ops/standard_ops.h"
using namespace tensorflow;
int main() {
SessionOptions options;
std::unique_ptr<tensorflow::Session>
session(tensorflow::NewSession(options));
Scope scope = Scope::NewRootScope();
Input indices = {{1l}};
Input values = {1};
Input dense_shape = {-1l};
Input default_value = 1;
auto target = ops::SparseFillEmptyRows(scope.WithOpName("target"), indices, values, dense_shape, default_value);
Status status;
GraphDef graph_def;
status = scope.ToGraphDef(&graph_def);
if (!status.ok()) {
LOG(WARNING) << "Could not build graph: " << status.message();
}
status = session->Create(graph_def);
if (!status.ok()) {
LOG(WARNING) << "Could not create session: " << status.message();
}
std::vector<Tensor> outputs;
status = session->Run({}, {"target"}, {"target"}, &outputs);
if (!status.ok()) {
LOG(WARNING) << "Could not run session: " << status.message();
}
return 0;
}
```
### Relevant log output
```shell
terminate called after throwing an instance of 'std::length_error'
what(): cannot create std::vector larger than max_size()
Aborted (core dumped)
```
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I_kwDOArmXAs6AhkS_
| 63,065 |
Support for Quantized ELU is missing in TFLite MLIR converter
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[
"Hi @Doomski99,\r\n\r\nI tried `elu` activation on a sample model and converted to tflite with different quantization schemes and it is working fine. Please refer to the [gist](https://colab.sandbox.google.com/gist/LakshmiKalaKadali/cfa3bf0627b74596a8400682ad2b4396/tflite_63065_elu_feature-request.ipynb). If your use case is different from this, please elaborate it w.r.t ` def TFL_EluOp: TFL_Op<\"elu\" ` in [tfl_ops.td](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/mlir/lite/ir/tfl_ops.td) and share a reproducible code for further steps.\r\n\r\nThank You ",
"Hello @LakshmiKalaKadali, \r\n\r\nThanks for taking the time to look at this issue. I was looking at the graphs produced by your gist and they were all the same because I think you were mistakenly saving the original non-quantized model each time:\r\nYou've written: \r\n```\r\nwith open(\"converted_model4.tflite\", \"wb\") as f:\r\n f.write(tflite_model)\r\n```\r\nInstead of:\r\n```\r\nwith open(\"converted_model4.tflite\", \"wb\") as f:\r\n f.write(tflite_model_quant)\r\n```\r\nAfter that modification, you can look at the graph (in this case, converted_model4) and see:\r\n\r\nWhich is odd, we would expect the converter to output an error since you've added to the code this line:\r\n```\r\n# Ensure that if any ops can't be quantized, the converter throws an error\r\nconverter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]\r\n```\r\nand clearly the ELU isn't an INT8 operation in this case. \r\n\r\nConcerning tfl_ops.td, we can confirm that ELU doesn't support quantized inputs from it's definition and by comparing it to LeakyReLU for example:\r\n\r\nELU definition:\r\n```\r\ndef TFL_EluOp: TFL_Op<\"elu\", [\r\n Pure,\r\n SameOperandsAndResultShape,\r\n TFL_SameFirstOperandAndFirstResultElementType]> {\r\n let summary = \"Exponential Linear Unit operator\";\r\n let description = [{\r\n Computes the exponential linear\r\n f(x) -> exp(x) - 1 for x < 0, x for x >= 0.\r\n element-wise.\r\n }];\r\n\r\n let arguments = (ins TFL_TensorOf<[F32, I8]>:$x);\r\n\r\n let results = (outs TFL_TensorOf<[F32, I8]>:$y);\r\n\r\n let hasOptions = 0;\r\n}\r\n```\r\nLeakyReLU definition:\r\n```\r\ndef TFL_LeakyReluOp: TFL_Op<\"leaky_relu\", [\r\n SameOperandsAndResultShape,\r\n QuantizableResult,\r\n Pure,\r\n PredOpTrait<\"input and output must have same element type\",\r\n TFL_TCresVTEtIsSameAsOp<0, 0>>]> {\r\n let summary = \"Leaky Relu operator\";\r\n\r\n let description = [{\r\n Element-wise Leaky ReLU operator\r\n x -> x >= 0 ? x : (alpha * x)\r\n }];\r\n\r\n let arguments = (\r\n ins TFL_TensorOf<[F32, QUI8, QI8, TFL_Quint8, QI16]>:$input,\r\n // Slope of the activation function at x < 0.\r\n F32Attr:$alpha\r\n );\r\n\r\n let results = (outs TFL_TensorOf<[F32, QUI8, QI8, TFL_Quint8, QI16]>:$output);\r\n\r\n let hasOptions = 0b1;\r\n}\r\n```\r\nNote how ELU is missing the \"QuantizableResult\" trait and its arguments are missing the quantized data types \"QUI8\" and \"QI8\". ",
"Hi @Doomski99 ,\r\n\r\nAppreciable your efforts in bringing the feature request to our attention. Yes, as you mentioned `elu` definition doesn't support quantizable trait. The observation is clearly presented in the below screenshot.\r\n\r\n Our team will work on it and will update you.\r\n\r\nThank You\r\n\r\n",
"Hi @Doomski99,\r\n\r\nPlease track the PR [#63126](https://github.com/tensorflow/tensorflow/pull/63126), for feature request.\r\n\r\nThank You"
] | 2024-02-27T10:53:54 | 2024-04-03T09:48:06 | null |
NONE
| null | null | null |
**System information**
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): WSL2 Ubuntu
- TensorFlow installed from (source or binary): pip
- TensorFlow version (or github SHA if from source): Latest
Unlike ReLU for example, currently ELU isn't supported for 8 bit quantization in TFLite:
I think we can confirm this by looking at ELU's definition in [tfl_ops.td](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/mlir/lite/ir/tfl_ops.td) , it doesn't have the quantizable trait.
While researching upon this issue, I stumbled upon an [old stackoverflow issue ](https://stackoverflow.com/questions/67774808/is-elu-int8-quantisation-working-on-tensorflow-lite) implying that quantized ELU was once supported. Maybe it was skipped after the move to MLIR?
Are there plans to support it? If not, will it be straightforward to implement it by my own?
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PR_kwDOArmXAs5oByM6
| 63,064 |
Fix checkfail with -ve perm values in transpose_op.cc
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[
"Hi @SuryanarayanaY Can you please check @mihaimaruseac's comments ? Thank you!",
"Done the changes proposed.",
"Hi @SuryanarayanaY Can you please check @mihaimaruseac's [comments](https://github.com/tensorflow/tensorflow/pull/63064#discussion_r1532352208) ? 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.",
"Not stale. The PR has been amended. ",
"Not changed since last approval. It still fails internally"
] | 2024-02-27T10:48:15 | 2024-05-03T14:46:35 | null |
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Passing negative values to argument perm in tf.raw_ops.Transpose will cause checkfail and coredump error. Hence validation proposed to check and raise exception.
Ref issue #62995
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`tf.raw_ops.ExtractGlimpse`: Assertion failure in shape inference step
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[
"In release build, we can observe unstable intermediate tensor with invalid dim([gist](https://colab.research.google.com/drive/1R454RoZHPVs_ax3WFd-tVEHHp5GHCvpz?usp=sharing)).\r\n```Python\r\nimport tensorflow as tf\r\n\r\ntf.compat.v1.disable_eager_execution()\r\n\r\nx = tf.raw_ops.ExtractGlimpse(\r\n input=tf.random.normal([1,1,1,1]),\r\n size=[-2,-2],\r\n offsets=tf.random.normal([1,2]),\r\n centered=True,\r\n normalized=True,\r\n uniform_noise=True,\r\n noise='uniform',\r\n name=None\r\n)\r\n\r\n# Prints \"True\"\r\nprint(tf.is_tensor(x))\r\n\r\n# Ends up with \"ValueError: Dimension -2 must be >= 0\"\r\nprint(x)\r\n```",
"Note that many other APIs have same problem because `SetOutputToSizedImage`(a function where error location resides) is a common subroutine of APIs in [`image_ops.cc`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/ops/image_ops.cc).\r\nI was able to find below APIs have same behavior.\r\n`tf.raw_ops.QuantizedResizeBilinear`\r\n`tf.raw_ops.ResizeArea`\r\n`tf.raw_ops.ResizeBicubic`\r\n`tf.raw_ops.ResizeBilinear`\r\n`tf.raw_ops.ResizeNearestNeighbor`\r\n`tf.raw_ops.ScaleAndTranslate`\r\n",
"Hi @Sehun0819 ,\r\n\r\nThe DCHEK mentioned has proposed fixed in this PR #63089."
] | 2024-02-27T10:11:25 | 2024-03-05T09:37:22 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.17
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04 LTS
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
6.5.0
### GCC/compiler version
clang 16
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`tf.raw_ops.ExtractGlimpse` can lead to assertion failure in shape inference step.
Error location is [here](https://github.com/tensorflow/tensorflow/blob/daea84aad67d1fcc8d58c648e1da58ee576445f9/tensorflow/core/ops/image_ops.cc#L57):
```C++
height = c->MakeDim(vec(0));
```
It makes a dim from input argument without checking negativity.
In debug build, it ends up with assertion failure in [a constructor of `DimensionOrConstant`](https://github.com/tensorflow/tensorflow/blob/daea84aad67d1fcc8d58c648e1da58ee576445f9/tensorflow/core/framework/shape_inference.h#L891-L896).
### Standalone code to reproduce the issue
```Python
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
tf.raw_ops.ExtractGlimpse(
input=tf.random.normal([1,1,1,1]),
size=[-2,-2],
offsets=tf.random.normal([1,2]),
centered=True,
normalized=True,
uniform_noise=True,
noise='uniform',
name=None
)
```
### Relevant log output
Release build: Outputs nothing
Debug build:
```shell
2024-02-27 19:02:16.866010: F ./tensorflow/core/framework/shape_inference.h:891] Check failed: val >= 0 || val == InferenceContext::kUnknownDim Dimension must be non-negative or equal to InferenceContext::kUnknownDim but got -2
Aborted (core dumped)
```
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I_kwDOArmXAs6AhD0b
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`tf.raw_ops.DecodeAndCropJpeg`: Assertion failure in shape inference step
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"In release build, we can observe an unstable intermediate tensor with invalid dims([gist](https://colab.research.google.com/drive/1xW-D_MJjE-WBbL5pn_SuqP-7mZjgkGLV?usp=sharing)).\r\n```Python\r\nimport tensorflow as tf\r\n\r\ntf.compat.v1.disable_eager_execution()\r\n\r\nx = tf.raw_ops.DecodeAndCropJpeg(\r\n contents=\"abc\",\r\n crop_window=[0,0,0,-2],\r\n channels=0,\r\n ratio=1,\r\n fancy_upscaling=True,\r\n try_recover_truncated=False,\r\n acceptable_fraction=1,\r\n dct_method='',\r\n name=None\r\n)\r\n\r\n# Prints \"True\"\r\nprint(tf.is_tensor(x))\r\n\r\n# Ends up with \"ValueError: Dimension -2 must be >= 0\"\r\nprint(x)\r\n```",
"Hi @Sehun0819 ,\r\nWith official release builds there is exception is raised though message is not much clear. But user can traceback it though.\r\n\r\nI have proposed a fix in linked PR above."
] | 2024-02-27T09:52:15 | 2024-03-05T09:37:37 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.17
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04 LTS
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
6.5.0
### GCC/compiler version
clang 16
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`tf.raw_ops.DecodeAndCropJpeg` can lead to assertion failure in shape inference step.
Error location is [here](https://github.com/tensorflow/tensorflow/blob/daea84aad67d1fcc8d58c648e1da58ee576445f9/tensorflow/core/ops/image_ops.cc#L523):
```C++
w = c->MakeDim(crop_window_vec(3));
```
It makes a dim from an input argument without checking negativity.
In debug build, it ends up with assertion failure in [a constructor of`DimensionOrConstant`](https://github.com/tensorflow/tensorflow/blob/daea84aad67d1fcc8d58c648e1da58ee576445f9/tensorflow/core/framework/shape_inference.h#L891-L896).
### Standalone code to reproduce the issue
```Python
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
tf.raw_ops.DecodeAndCropJpeg(
contents="abc",
crop_window=[0,0,0,-2],
channels=0,
ratio=1,
fancy_upscaling=True,
try_recover_truncated=False,
acceptable_fraction=1,
dct_method='',
name=None
)
```
### Relevant log output
Release build: Outputs nothing
Debug build:
```shell
2024-02-27 18:41:59.133420: F ./tensorflow/core/framework/shape_inference.h:891] Check failed: val >= 0 || val == InferenceContext::kUnknownDim Dimension must be non-negative or equal to InferenceContext::kUnknownDim but got -2
Aborted (core dumped)
```
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I_kwDOArmXAs6AgXYY
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Getting wrong output after tflite conversion
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[
"@sushreebarsa \r\nCould you please suggest, what may be the reason for the wrong results?",
"@sushreebarsa \r\nIs there any update?",
"@priyakansal Sorry for the late response!\r\nThe model was saved and loaded successfully with some warnings about untraced functions. Could you consider disabling debug-level logging if it clutters the output. If you're encountering issues after loading the model, please investigate the missing placeholder values (inputs and Placeholder/_0).\r\n\r\nThank you!",
"Hello @sushreebarsa , \r\n> If you're encountering issues after loading the model, please investigate the missing placeholder values (inputs and Placeholder/_0).\r\n\r\nwhat should I do for the above?",
"@priyakansal Here is an example;\r\n```\r\nimport tensorflow as tf\r\n\r\n# Assuming your loaded model is stored in a graph 'model_graph'\r\n\r\n# Create a session\r\nsess = tf.Session()\r\n\r\n# Prepare your data (replace with your actual data)\r\ninput_data = tf.constant([1.0, 2.0, 3.0]) # Example input\r\n\r\n# Run the model with placeholders\r\ninference_result, input_value, placeholder_0_value = sess.run(\r\n [model_graph(inputs=input_data), \"inputs:0\", \"Placeholder/_0:0\"]\r\n)\r\n\r\n# Check for missing values\r\nif input_value is None:\r\n print(\"Missing value for 'inputs' placeholder\")\r\nif placeholder_0_value is None:\r\n print(\"Missing value for 'Placeholder/_0' placeholder\")\r\n\r\nsess.close()\r\n\r\n```\r\nPlease remember to replace the placeholder names and data preparation with your specific model and code.\r\nBy using either print statements or the session approach, you can identify if the inputs and Placeholder/_0 placeholders are not receiving the necessary data during inference.\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/63061\">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/63061\">No</a>\n"
] | 2024-02-27T08:24:46 | 2024-03-30T01:46:07 | 2024-03-30T01:46:03 |
NONE
| null | null | null |
### 1. System information
- OS Platform and Distribution = **Ubuntu 20.04**
- TensorFlow installation (pip package or built from source): **pip installation**
- TensorFlow library (version, if pip package or github SHA, if built from source):**tf-2.12.1**
### 2. Code
Model is converted using the following code:
```
def representative_data_genRGB():
path = '/content/gdrive/MyDrive/imx_tflite'
dataset_list = tf.data.Dataset.list_files(path + '/*.jpg')
for i in range(len(dataset_list)):
image = next(iter(dataset_list))
image = tf.io.read_file(image)
image = tf.io.decode_jpeg(image, channels=3)
image = tf.image.resize(image, [IMG_HEIGHT, IMG_WIDTH])
image = tf.cast(image,tf.float32)
image = image/255.
image = tf.expand_dims(image, 0)
yield [image]
converter = tf.lite.TFLiteConverter.from_saved_model(MODEL_TF)
# converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter. optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_data_genRGB
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.allow_custom_ops = True
converter.inference_type = tf.uint8
converter.inference_input_type = tf.uint8
converter.inference_output_type = tf.uint8
converter.target_spec.supported_types = [tf.int8]
tflite_model = converter.convert()
with open(MODEL_TFLITE, "wb") as f:
f.write(tflite_model)
```
### 3. Failure after conversion
If the conversion is successful, but the generated model is wrong, then state what is wrong:
Following Code is used for the prediction:
```
try:
import tflite_runtime.interpreter as tfl # prefer tflite_runtime if installed
except ImportError:
import tensorflow.lite as tfl
MODEL_TFLITE = MODEL_TFLITE
interpreter = tfl.Interpreter(model_path=MODEL_TFLITE) # load TFLite model
interpreter.allocate_tensors() # allocate
input_details = interpreter.get_input_details() # inputs
output_details = interpreter.get_output_details()
img = cv2.imread('20221104084515525.jpg')
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
resized = cv2.resize(img,(IMG_WIDTH,IMG_HEIGHT))/255
if(len(img.shape) == 3):
image = np.expand_dims(resized, axis=0)
b,h, w, ch = image.shape # batch, channel, height, width
inp,out = input_details[0], output_details[0]
# inp,out1, out2, out3, out4 = input_details[0], output_details[0], output_details[1], output_details[2], output_details[3]
int8 = inp['dtype'] == np.uint8 # is TFLite quantized uint8 model
if int8:
scale, zero_point = inp['quantization']
image = (image / scale + zero_point).astype(np.uint8) # de-scale
interpreter.set_tensor(inp['index'], image)
interpreter.invoke()
predict = interpreter.get_tensor(out['index'])
if int8:
if out['quantization'] != (0.0, 0):
scale, zero_point = out['quantization']
predict = (predict.astype(np.float32) - zero_point) * scale # re-scale
```
- **Model produces wrong results:**
The attached images shows the results before and after conversion for the same input image:
Before Conversion/Keras Model Results:
After converting into tflite/tflite model results:
### 5. (optional) Any other info / logs
Following are the logs while converting:
```
2024-02-27 17:15:02.854021: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,?]
[[{{node inputs}}]]
2024-02-27 17:15:02.930861: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,?]
[[{{node inputs}}]]
2024-02-27 17:15:03.008753: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,?]
[[{{node inputs}}]]
2024-02-27 17:15:06.708407: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,?]
[[{{node inputs}}]]
2024-02-27 17:15:06.810894: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,?]
[[{{node inputs}}]]
2024-02-27 17:15:06.903451: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,?,?,?]
[[{{node inputs}}]]
WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 31). These functions will not be directly callable after loading.
INFO:tensorflow:Assets written to: /home/couger/2nd_shared_dataset/Custom_modelRGBPruned/202422717152_tf2.12-py3.10_96X144-4-channelRGB/model/assets
INFO:tensorflow:Assets written to: /home/couger/2nd_shared_dataset/Custom_modelRGBPruned/202422717152_tf2.12-py3.10_96X144-4-channelRGB/model/assets
2024-02-27 17:15:11.449645: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:364] Ignored output_format.
2024-02-27 17:15:11.449671: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:367] Ignored drop_control_dependency.
2024-02-27 17:15:11.449833: I tensorflow/cc/saved_model/reader.cc:45] Reading SavedModel from: /home/couger/2nd_shared_dataset/Custom_modelRGBPruned/202422717152_tf2.12-py3.10_96X144-4-channelRGB/model
2024-02-27 17:15:11.461083: I tensorflow/cc/saved_model/reader.cc:89] Reading meta graph with tags { serve }
2024-02-27 17:15:11.461098: I tensorflow/cc/saved_model/reader.cc:130] Reading SavedModel debug info (if present) from: /home/couger/2nd_shared_dataset/Custom_modelRGBPruned/202422717152_tf2.12-py3.10_96X144-4-channelRGB/model
2024-02-27 17:15:11.496457: I tensorflow/cc/saved_model/loader.cc:231] Restoring SavedModel bundle.
2024-02-27 17:15:11.679895: I tensorflow/cc/saved_model/loader.cc:215] Running initialization op on SavedModel bundle at path: /home/couger/2nd_shared_dataset/Custom_modelRGBPruned/202422717152_tf2.12-py3.10_96X144-4-channelRGB/model
2024-02-27 17:15:11.754645: I tensorflow/cc/saved_model/loader.cc:314] SavedModel load for tags { serve }; Status: success: OK. Took 304813 microseconds.
2024-02-27 17:15:12.143006: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [43]
[[{{node Placeholder/_0}}]]
2024-02-27 17:15:12.143348: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [43]
[[{{node Placeholder/_0}}]]
fully_quantize: 0, inference_type: 6, input_inference_type: UINT8, output_inference_type: UINT8
/home/couger/2nd_shared_dataset/Custom_modelRGBPruned/202422717152_tf2.12-py3.10_96X144-4-channelRGB/model.tflite
```
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PR_kwDOArmXAs5n_-m3
| 63,060 |
Fixing the typos, repetitions in interpreter.py
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Fixing the typos, repetitions in interpreter.py
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[oneDNN] Adding a check to verify if gamma and beta are Const
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This PR resolves a segmentation fault for ResNet-DPED performance benchmark. The segmentation fault resulted with the following error: `[libprotobuf FATAL external/com_google_protobuf/src/google/protobuf/map.h:1293] CHECK failed: it != end(): key not found: value.` Adding a check to verify if `gamma` and `beta` are `Const` resolves the issue.
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Update RELEASE.md
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[Tosa] Update Sin/Cos operators legalization
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[
"cc @jpienaar ",
"Hi @rdzhabarov or @jpienaar , could you help take a look at this PR? Thanks ",
"Hi @NatashaKnk, would you be able to do a review on this patch? thanks : )",
"Hi @Jerry-Ge Can you please resolve conflicts? Thank you!",
"This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This PR was closed because it has been inactive for 14 days since being marked as stale. Please reopen if you'd like to work on this further."
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- with the introduction of tosa.sin and tosa.cos ops
- update the legalization to do direct mapping
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autoencoder implementatio
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[
"@menayemeskele Could you please fill the above template with relevant information?\r\nPlease refer to this [doc](https://www.tensorflow.org/guide/keras) for building and training the neural networks.\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."
] | 2024-02-26T18:54:32 | 2024-03-13T01:47:32 | 2024-03-13T01:47:31 |
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I_kwDOArmXAs6AZRYh
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Undefined references to _mlir_ciface* symbols on PPC
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[
"Hi @cdeepali , Could you please confirm the build command used ? Also starting from Tf2.13v onwards Clang is the official built compiler.Please refer [source](https://www.tensorflow.org/install/source#gpu) below.\r\n\r\nVersion | Python version | Compiler | Build tools | cuDNN | CUDA\r\n-- | -- | -- | -- | -- | --\r\ntensorflow-2.16.1 | 3.9-3.12 | Clang 17.0.6 | Bazel 6.5.0 | 8.9 | 12.3\r\ntensorflow-2.15.0 | 3.9-3.11 | Clang 16.0.0 | Bazel 6.1.0 | 8.9 | 12.2\r\ntensorflow-2.14.0 | 3.9-3.11 | Clang 16.0.0 | Bazel 6.1.0 | 8.7 | 11.8\r\ntensorflow-2.13.0 | 3.8-3.11 | Clang 16.0.0 | Bazel 5.3.0 | 8.6 | 11.8\r\n\r\n</div>",
"Thanks @SuryanarayanaY. Yes I have noticed that official compiler is Clang. But TF 2.13 built with GCC and with TF 2.14 we are seeing issues. ",
"@SuryanarayanaY, I do not see this issue on x86. It is happening on PowerPC platform when built with CUDA. CPU build is fine on PowerPC too. \r\n\r\nThe script I am using is - https://github.com/open-ce/tensorflow-feedstock/blob/main/recipe/build-pip-package.sh. \r\n\r\nPlease suggest, if there is something that I am missing in the script, due to which these libs are not getting built for PowerPC.\r\n"
] | 2024-02-26T12:52:16 | 2024-03-11T16:11:13 | null |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.14
### Custom code
No
### OS platform and distribution
RedHat 8.8, ppc64le
### Mobile device
_No response_
### Python version
3.11
### Bazel version
6.1
### GCC/compiler version
11.2
### CUDA/cuDNN version
12.2/8.9.6
### GPU model and memory
_No response_
### Current behavior?
Build fails with numerous undefined reference errors.
### Standalone code to reproduce the issue
```shell
NA
```
### Relevant log output
```shell
Below error seen during build:
<myenv>/bin/../lib/gcc/powerpc64le-conda-linux-gnu/11.2.0/../../../../powerpc64le-conda-linux-gnu/bin/ld.gold: error: bazel-out/ppc-opt/bin/tensorflow/core/kernels/mlir_generated/libless_gpu_less_kernels_gpu_i64_i1_kernel_generator.pic.a(less_gpu_less_kernels_gpu_i64_i1_kernel_generator_kernel.o): incompatible target
<myenv>/bin/../lib/gcc/powerpc64le-conda-linux-gnu/11.2.0/../../../../powerpc64le-conda-linux-gnu/bin/ld.gold: error: bazel-out/ppc-opt/bin/tensorflow/core/kernels/mlir_generated/libless_gpu_less_kernels_gpu_ui8_i1_kernel_generator.pic.a(less_gpu_less_kernels_gpu_ui8_i1_kernel_generator_kernel.o): incompatible target
<myenv>/bin/../lib/gcc/powerpc64le-conda-linux-gnu/11.2.0/../../../../powerpc64le-conda-linux-gnu/bin/ld.gold: error: bazel-out/ppc-opt/bin/tensorflow/core/kernels/mlir_generated/libless_gpu_less_kernels_gpu_ui16_i1_kernel_generator.pic.a(less_gpu_less_kernels_gpu_ui16_i1_kernel_generator_kernel.o): incompatible target
<myenv>/bin/../lib/gcc/powerpc64le-conda-linux-gnu/11.2.0/../../../../powerpc64le-conda-linux-gnu/bin/ld.gold: error: bazel-out/ppc-opt/bin/tensorflow/core/kernels/mlir_generated/libless_gpu_less_kernels_gpu_ui32_i1_kernel_generator.pic.a(less_gpu_less_kernels_gpu_ui32_i1_kernel_generator_kernel.o): incompatible target
<myenv>/bin/../lib/gcc/powerpc64le-conda-linux-gnu/11.2.0/../../../../powerpc64le-conda-linux-gnu/bin/ld.gold: error: bazel-out/ppc-opt/bin/tensorflow/core/kernels/mlir_generated/libless_gpu_less_kernels_gpu_ui64_i1_kernel_generator.pic.a(less_gpu_less_kernels_gpu_ui64_i1_kernel_generator_kernel.o): incompatible target
bazel-out/ppc-opt/bin/tensorflow/core/kernels/mlir_generated/libgpu_nextafter_op.pic.lo(gpu_op_next_after.pic.o):gpu_op_next_after.cc:function tensorflow::(anonymous namespace)::MlirNextAfterGPUDT_FLOATDT_FLOATOp::Invoke(tensorflow::OpKernelContext*, llvm::SmallVectorImpl<tensorflow::UnrankedMemRef>&): error: undefined reference to '_mlir_ciface_NextAfter_GPU_DT_FLOAT_DT_FLOAT'
bazel-out/ppc-opt/bin/tensorflow/core/kernels/mlir_generated/libgpu_nextafter_op.pic.lo(gpu_op_next_after.pic.o):gpu_op_next_after.cc:function tensorflow::(anonymous namespace)::MlirNextAfterGPUDT_DOUBLEDT_DOUBLEOp::Invoke(tensorflow::OpKernelContext*, llvm::SmallVectorImpl<tensorflow::UnrankedMemRef>&): error: undefined reference to '_mlir_ciface_NextAfter_GPU_DT_DOUBLE_DT_DOUBLE'
bazel-out/ppc-opt/bin/tensorflow/core/kernels/mlir_generated/libgpu_nextafter_op.pic.lo(gpu_op_next_after.pic.o):gpu_op_next_after.cc:function tensorflow::MLIROpKernel<(tensorflow::DataType)1, float, (tensorflow::DataType)1>::Compute(tensorflow::OpKernelContext*): error: undefined reference to '_mlir_ciface_NextAfter_GPU_DT_FLOAT_DT_FLOAT'
bazel-out/ppc-opt/bin/tensorflow/core/kernels/mlir_generated/libgpu_nextafter_op.pic.lo(gpu_op_next_after.pic.o):gpu_op_next_after.cc:function tensorflow::MLIROpKernel<(tensorflow::DataType)2, double, (tensorflow::DataType)2>::Compute(tensorflow::OpKernelContext*): error: undefined reference to '_mlir_ciface_NextAfter_GPU_DT_DOUBLE_DT_DOUBLE'
bazel-out/ppc-opt/bin/tensorflow/core/kernels/mlir_generated/libgpu_relu_op.pic.lo(gpu_op_elu.pic.o):gpu_op_elu.cc:function tensorflow::(anonymous namespace)::MlirEluGPUDT_HALFDT_HALFOp::Invoke(tensorflow::OpKernelContext*, llvm::SmallVectorImpl<tensorflow::UnrankedMemRef>&): error: undefined reference to '_mlir_ciface_Elu_GPU_DT_HALF_DT_HALF'
bazel-out/ppc-opt/bin/tensorflow/core/kernels/mlir_generated/libgpu_relu_op.pic.lo(gpu_op_elu.pic.o):gpu_op_elu.cc:function tensorflow::(anonymous namespace)::MlirEluGPUDT_FLOATDT_FLOATOp::Invoke(tensorflow::OpKernelContext*, llvm::SmallVectorImpl<tensorflow::UnrankedMemRef>&): error: undefined reference to '_mlir_ciface_Elu_GPU_DT_FLOAT_DT_FLOAT'
bazel-out/ppc-opt/bin/tensorflow/core/kernels/mlir_generated/libgpu_relu_op.pic.lo(gpu_op_elu.pic.o):gpu_op_elu.cc:function tensorflow::(anonymous namespace)::MlirEluGPUDT_DOUBLEDT_DOUBLEOp::Invoke(tensorflow::OpKernelContext*, llvm::SmallVectorImpl<tensorflow::UnrankedMemRef>&): error: undefined reference to '_mlir_ciface_Elu_GPU_DT_DOUBLE_DT_DOUBLE'
bazel-out/ppc-opt/bin/tensorflow/core/kernels/mlir_generated/libgpu_relu_op.pic.lo(gpu_op_elu.pic.o):gpu_op_elu.cc:function tensorflow::MLIROpKernel<(tensorflow::DataType)19, Eigen::half, (tensorflow::DataType)19>::Compute(tensorflow::OpKernelContext*): error: undefined reference to '_mlir_ciface_Elu_GPU_DT_HALF_DT_HALF'
bazel-out/ppc-opt/bin/tensorflow/core/kernels/mlir_generated/libgpu_relu_op.pic.lo(gpu_op_relu.pic.o):gpu_op_relu.cc:function tensorflow::(anonymous namespace)::MlirReluGPUDT_HALFDT_HALFOp::Invoke(tensorflow::OpKernelContext*, llvm::SmallVectorImpl<tensorflow::UnrankedMemRef>&): error: undefined reference to '_mlir_ciface_Relu_GPU_DT_HALF_DT_HALF'
bazel-out/ppc-opt/bin/tensorflow/core/kernels/mlir_generated/libgpu_relu_op.pic.lo(gpu_op_relu.pic.o):gpu_op_relu.cc:function tensorflow::(anonymous namespace)::MlirReluGPUDT_FLOATDT_FLOATOp::Invoke(tensorflow::OpKernelContext*, llvm::SmallVectorImpl<tensorflow::UnrankedMemRef>&): error: undefined reference to '_mlir_ciface_Relu_GPU_DT_FLOAT_DT_FLOAT'
bazel-out/ppc-opt/bin/tensorflow/core/kernels/mlir_generated/libgpu_relu_op.pic.lo(gpu_op_relu.pic.o):gpu_op_relu.cc:function tensorflow::(anonymous namespace)::MlirReluGPUDT_DOUBLEDT_DOUBLEOp::Invoke(tensorflow::OpKernelContext*, llvm::SmallVectorImpl<tensorflow::UnrankedMemRef>&): error: undefined reference to '_mlir_ciface_Relu_GPU_DT_DOUBLE_DT_DOUBLE'
bazel-out/ppc-opt/bin/tensorflow/core/kernels/mlir_generated/libgpu_relu_op.pic.lo(gpu_op_relu.pic.o):gpu_op_relu.cc:function tensorflow::(anonymous namespace)::MlirReluGPUDT_INT8DT_INT8Op::Invoke(tensorflow::OpKernelContext*, llvm::SmallVectorImpl<tensorflow::UnrankedMemRef>&): error: undefined reference to '_mlir_ciface_Relu_GPU_DT_INT8_DT_INT8'
```
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PR_kwDOArmXAs5n6Wxb
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May fix checkfail in Gatherv2 Op.
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Checkfail can be triggered in GatherV2 Op with inappropriate input from this line below. https://github.com/tensorflow/tensorflow/blob/e193d8ea7776ef5c6f5d769b6fb9c070213e737a/tensorflow/core/ops/array_ops.cc#L1240-L1242.
This will call UnknownShapeOfRank function and checkfail happens at line below as CHECK_GE macro call, which causes core dump error if assertion fails.
https://github.com/tensorflow/tensorflow/blob/8d0c35b8b95086a2a8209fa47580b13ae8241adb/tensorflow/core/framework/shape_inference.cc#L705
Hence proposing validation and raise appropriate message to the user.
May fix #62985
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"@Milehigh-wrld,\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 if you are trying to get the information on the tensorflow lite, I request to have a look at this official document for reference.\r\nhttps://www.tensorflow.org/lite/guide\r\n\r\nTensorFlow Lite is a set of tools that enables on-device machine learning by helping developers run their models on mobile, embedded, and edge devices.\r\n\r\nThank you!",
"Thank you for the clarification 💯👍\r\n\r\nOn Mon, Feb 26, 2024 at 11:12 PM tilakrayal ***@***.***>\r\nwrote:\r\n\r\n> @Milehigh-wrld <https://github.com/Milehigh-wrld>,\r\n> Without the reproducible code, it would be difficult for us to debug the\r\n> issue. In order to expedite the trouble-shooting process, could you please\r\n> provide a minimal code snippet and the TensorFlow version you are using.\r\n>\r\n> Also if you are trying to get the information on the tensorflow lite, I\r\n> request to have a look at this official document for reference.\r\n> https://www.tensorflow.org/lite/guide\r\n>\r\n> TensorFlow Lite is a set of tools that enables on-device machine learning\r\n> by helping developers run their models on mobile, embedded, and edge\r\n> devices.\r\n>\r\n> Thank you!\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/tensorflow/tensorflow/issues/63053#issuecomment-1965918681>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/BFGS7JIG5OSULB3LMDGZSL3YVWBM7AVCNFSM6AAAAABDZ27ZCSVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSNRVHEYTQNRYGE>\r\n> .\r\n> You are receiving this because you were mentioned.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"@Milehigh-wrld,\r\nCould you please confirm if the issue is resolved. if yes, please feel free to move this issue to closed status. 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/63053\">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/63053\">No</a>\n"
] | 2024-02-26T09:40:41 | 2024-02-27T09:22:00 | 2024-02-27T09:21:57 |
NONE
| null | null | null |
**System information**
- Android Device information (use `adb shell getprop ro.build.fingerprint`
if possible):
- TensorFlow Lite in Play Services SDK version (found in `build.gradle`):
- Google Play Services version
(`Settings` > `Apps` > `Google Play Services` > `App details`):
**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 or attach code demonstrating
the problem.
**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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Nan occurs when calculating the determinant of matrix including inf
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"Hi @QuantumCoder4 ,\r\n\r\nI have replicated the reported behaviour and attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/8a29b771b8b907b5a9b2f53fba16bfa5/63052.ipynb) for reference.\r\n\r\nIMO, `inf-inf `should be `Nan` as both inf can be anything and their difference could be anything from -inf to +inf. I have checked this behaviour with numpy and observed `np.inf-np.inf = Nan`. In TF, `tf.linalg.det([[np.inf, np.inf], [1, 1]])=0 `also doesn't seem right IMO.\r\n\r\n`np.inf-1=np.inf` make sense to me.In TF, `tf.linalg.det([np.inf, 1], [1, 1]])=nan` should be `inf` IMO.\r\n\r\nHowever this behaviour with inf may be different in different frameworks. Let's hear from Devteam. Thanks!",
"I agree with @SuryanarayanaY's analysis, the result should be `inf` and `nan` for the two examples.",
"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/63052\">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/63052\">No</a>\n"
] | 2024-02-26T08:37:12 | 2024-02-27T21:58:30 | 2024-02-27T21:58:27 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15.0
### 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 the matrix include np.inf, the tf.linalg.det returns NaN for some cases.
If the matrix is: [[inf, inf], [1,1]], the determinant is 0, which makes sense to me since inf\*1-inf\*1=0.
However, if the matrix is [[inf, 1], [1, 1]], the API will directly outputs NaN instead of inf since: inf\*1 -1\*1 = inf.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
def tensorflow_call(input):
return tf.linalg.det(input)
input = tf.constant([[np.inf, 1], [1, 1]], dtype='float64')
print(tf.linalg.det(input)) # nan
input = tf.constant([[np.inf, np.inf], [1, 1]], dtype='float64')
print(tf.linalg.det(input)) # 0
```
### Relevant log output
_No response_
|
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When receiving infinity complex tensor, tf.math.sigmoid outputs NaN
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"@QuantumCoder4 You're right. The tf.math.sigmoid function outputs NaN when it receives an infinity complex tensor as input. This behavior is expected and aligns with the mathematical properties of the sigmoid function.\r\nThank you!",
"Thank you for your explanation.",
"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/63051\">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/63051\">No</a>\n"
] | 2024-02-26T07:53:51 | 2024-02-27T19:31:53 | 2024-02-27T19:31:50 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15.0
### 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?
Hi,
I was using tf.math.sigmoid and I notice that this function raises NaN when the input is inf+0.j.
Such result looks incorrect to me since I believe sigmoid's output should be within the range [0, 1].
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
x = tf.constant([np.inf+0.j], dtype='complex64')
print(tf.keras.activations.sigmoid(x)) # tf.Tensor([nan+nanj], shape=(1,), dtype=complex64)
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5n35AG
| 63,050 |
Add a note on axis arg of tf.argmax
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[
"I don't think we should clutter our documentation with statements like this. Using a bool won't cause a crash, and shouldn't cause confusion to a legitimate use-case. This is just a case of someone bombarding the API with different inputs to see what happens."
] | 2024-02-26T06:52:34 | 2024-03-12T21:09:08 | 2024-02-26T19:17:41 |
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As per documentation of he API tf.argmax the argument axis accepts integer values.If we pass any data types it is also accepting the same.Hence adding a note on same in the documentation to avoid any confusion as reported by the user in
#63031.
shall fix #63031
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PR_kwDOArmXAs5n2rs9
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Define BUILD_NUM_JOBS in TFLite Cmake script to prevent crash
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Reference: https://github.com/tensorflow/tensorflow/issues/63018
cc: @mihaimaruseac
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I_kwDOArmXAs6AT5x9
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Image upsampling using interpolation function.
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[
"Hi @RocaVincent ,\r\n\r\nYou can use Upsampling layers for it for eg please refer [Upsampling3D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/UpSampling3D) API.\r\n\r\n```\r\ninput_image=x\r\ntf.keras.layers.UpSampling3D(size=2)(x)\r\n```\r\n\r\nIf x is of shape (W,H,D), then the above code will upsample it to (2W,2H,2D).",
"Hi @SuryanarayanaY ,\r\n\r\nIn fact I'm searching for **trilinear interpolation**, that's why I mentioned [this interpolation function](https://www.tensorflow.org/graphics/api_docs/python/tfg/math/interpolation/trilinear/interpolate). [UpSampling3D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/UpSampling3D) simply does nearest-neighbor interpolation.",
"Hi @RocaVincent ,\r\n\r\nWe are not supporting tensorflow_graphics here. Please file an issue at tf-graphics repo following this [link](https://github.com/tensorflow/graphics/issues).\r\n\r\nThanks!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63048\">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/63048\">No</a>\n"
] | 2024-02-25T10:36:01 | 2024-03-23T01:46:44 | 2024-03-23T01:46:38 |
NONE
| null | null | null |
### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
last
### Custom code
No
### 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?
TensorfFlow now provides [a function for trilinear interpolation](https://www.tensorflow.org/graphics/api_docs/python/tfg/math/interpolation/trilinear/interpolate). However, how can we use it to upsample a 3D image ? For example, I have an image `x` of shape `(W,H,D)` and I would like to upsample it to `(W*2,H*2,D*2)`.
Any help appreciated, thanks.
### Standalone code to reproduce the issue
```shell
No code.
```
### Relevant log output
_No response_
|
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I_kwDOArmXAs6ATot-
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Custom Keras RNN with constants changes constants shape when saving
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[
"I've opened the [issue in the keras repo ](https://github.com/keras-team/keras/issues/19224)",
"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/63047\">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/63047\">No</a>\n"
] | 2024-02-25T06:56:30 | 2024-02-25T09:32:45 | 2024-02-25T09:32:19 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15
### Custom code
Yes
### OS platform and distribution
Windows WSL
### 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?
Constants shape changes from rank 2 during inference to rank 3 tensor during model saving
### Standalone code to reproduce the issue
```python
from tensorflow.python.keras.layers.recurrent import (
DropoutRNNCellMixin,
_config_for_enable_caching_device,
_caching_device,
)
class RNNWithConstants(
DropoutRNNCellMixin, tf.keras.__internal__.layers.BaseRandomLayer
):
def __init__(
self,
units,
activation,
recurrent_activation,
dropout,
recurrent_dropout,
**kwargs,
):
super(RNNWithConstants, self).__init__(**kwargs)
self.units = units
self.dropout = dropout
self.recurrent_dropout = recurrent_dropout
self.recurrent_activation = recurrent_activation
self.cell = tf.keras.layers.GRUCell(
units=units,
activation=activation,
recurrent_activation=recurrent_activation,
recurrent_dropout=recurrent_dropout,
dropout=dropout,
)
self.state_size = units
self.output_size = units
@tf.function
def call(self, inputs, states, constants):
print(f"inputs {inputs.shape}")
print(f"states {states[0].shape}")
print(f"constants {constants[0].shape}")
inputs = tf.concat([inputs, constants[0]], axis=-1) # error due to shape change
h, _ = self.cell(inputs, states)
return h, h
class ConstantsModel(tf.keras.models.Model):
def __init__(self, units, **kwargs):
super().__init__(**kwargs)
self.units = units
self.cell = RNNWithConstants(units, "sigmoid", "sigmoid", 0.1, 0.1)
self.rnn = tf.keras.layers.RNN(self.cell)
@tf.function
def call(self, inputs, constants, training):
return self.rnn(inputs, constants=constants)
const = ConstantsModel(10)
print("initializing...")
_ = const(tf.random.normal(shape=(100, 50, 10)), tf.random.normal(shape=(100, 10)))
print("\nsaving....")
const.save("./const_model")
```
### Relevant log output
```shell
initializing...
inputs (100, 10)
states (100, 10)
constants (100, 10)
inputs (100, 10)
states (100, 10)
constants (100, 10)
saving....
inputs (None, 10)
states (None, 10)
constants (None, 10)
inputs (None, 10)
states (None, 10)
constants (None, None, 10)
```
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[Test 2] Internal config change
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[
"Hi @ek-ex ,\r\n\r\nInstead of applying flat_map directly on a WindowDataset could you please try batch seprately.\r\n\r\n```\r\ndataset = tf.data.experimental.make_csv_dataset(\r\n file_pattern=\"/path/stock/*1min*.csv\",\r\n batch_size=1,\r\n num_epochs=1,\r\n shuffle=False,\r\n header=False,\r\n column_names=['timestamp','open','high', 'low', 'close', 'volume'],\r\n column_defaults=[tf.string, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32]\r\n).window(\r\n size=5, # Number of rows per window\r\n shift=1, # Stride for overlapping windows\r\n stride=1\r\n)\r\ndataset=dataset.batch(5)\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.",
"Hi @SuryanarayanaY thank you for your response. \r\nApplying `.batch(5)` to the dataset generates a NestedVariant dataset and I havent found a way to extract the contents of the dataset or iterate over it. \r\n\r\nThe structure that this generates is: \r\n\r\n```\r\ntake = dataset.take(1)\r\nprint (f'{type(dataset)}')\r\nfor data in take:\r\n print (f'\\t{type(data)}')\r\n for key, value in data.items():\r\n print (f'\\t\\t{key} --> {value}')\r\n #for tensor in value:\r\n # print(f'\\t\\t\\t{type(tensor)} --> {tf.size(tensor)} --> {tensor[0]}')\r\n```\r\nprints the following: \r\n```\r\n<class 'tensorflow.python.data.ops.batch_op._BatchDataset'>\r\n\t<class 'collections.OrderedDict'>\r\n\t\ttimestamp --> <tensorflow.python.data.ops.dataset_ops._NestedVariant object at 0x7db68f7c3a00>\r\n\t\topen --> <tensorflow.python.data.ops.dataset_ops._NestedVariant object at 0x7db68f7c22c0>\r\n\t\thigh --> <tensorflow.python.data.ops.dataset_ops._NestedVariant object at 0x7db68f7c0580>\r\n\t\tlow --> <tensorflow.python.data.ops.dataset_ops._NestedVariant object at 0x7db68f7c0310>\r\n\t\tclose --> <tensorflow.python.data.ops.dataset_ops._NestedVariant object at 0x7db68f7c1120>\r\n\t\tvolume --> <tensorflow.python.data.ops.dataset_ops._NestedVariant object at 0x7db68f7c0790>\r\n```",
"Hi @ek-ex ,\r\n\r\nIt would help us to debug if we can get a reproducible code snippet along with the dependent resources. Thanks!",
"Hi @ek-ex ,\r\n\r\nThis still missing the dataset CSV file. Please refer [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/267f370cfa12e131483adc8e640182d7/63044.ipynb) and provide the details. \r\n\r\nPlease also note that this repo is for reporting bugs and performance related issues. If it is pure support you can reachout TF-forum also with all the details.",
"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."
] | 2024-02-24T20:39:20 | 2024-04-13T01:42:04 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.8
### Custom code
No
### 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?
Window dataset coming from the tf.data.experimental.make_csv_dataset is not working as expected
### Standalone code to reproduce the issue
```shell
I'm trying to transform some data read from CSV files using tf.data pipelines and overlapping windows and its not working as expected. All the documentation is not providing clear explanation on how to deal with this case. The columns of the csv files are 'timestamp','open','high', 'low', 'close', 'volume'.
dataset = tf.data.experimental.make_csv_dataset(
file_pattern="/path/stock/*1min*.csv",
batch_size=1,
num_epochs=1,
shuffle=False,
header=False,
column_names=['timestamp','open','high', 'low', 'close', 'volume'],
column_defaults=[tf.string, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32]
).window(
size=5, # Number of rows per window
shift=1, # Stride for overlapping windows
stride=1
)
```
This produces the following structure:
```
-WindowDataset
--OrderedDict
---VariantDataset
----Tensor (single element)
----Tensor...
```
This is not allowing me to transform in a simple way because OrderedDict has not batch method and I cannot flatten following the documentation.
```
dataset = tf.data.experimental.make_csv_dataset(
file_pattern="/path/stock/*1min*.csv",
batch_size=1,
num_epochs=1,
shuffle=False,
header=False,
column_names=['timestamp','open','high', 'low', 'close', 'volume'],
column_defaults=[tf.string, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32]
).window(
size=5, # Number of rows per window
shift=1, # Stride for overlapping windows
stride=1
).flat_map(lambda window: window.batch(5))
```
Gives the following error:
```
AttributeError Traceback (most recent call last)
<ipython-input-47-46d1550f08a0> in <cell line: 1>()
11 shift=1, # Stride for overlapping windows
12 stride=1
---> 13 ).flat_map(lambda window: window.batch(5))
19 frames
/tmp/__autograph_generated_filersrgq3km.py in <lambda>(lscope)
3
4 def inner_factory(ag__):
----> 5 tf__lam = lambda window: ag__.with_function_scope(lambda lscope: ag__.converted_call(window.batch, (5,), None, lscope), 'lscope', ag__.STD)
6 return tf__lam
7 return inner_factory
AttributeError: in user code:
File "<ipython-input-47-46d1550f08a0>", line 13, in None *
lambda window: window.batch(5)
AttributeError: 'collections.OrderedDict' object has no attribute 'batch'
```
If I try to batch the datasets of the OrderedDict, I get the following error
---------------------------------------------------------------------------
```
AttributeError Traceback (most recent call last)
<ipython-input-59-16e14170c082> in <cell line: 25>()
23
24
---> 25 data = dataset.map(extract)
26
27
35 frames
/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/tensor.py in __getattr__(self, name)
259 tf.experimental.numpy.experimental_enable_numpy_behavior()
260 """)
--> 261 self.__getattribute__(name)
262
263 @property
AttributeError: in user code:
File "<ipython-input-59-16e14170c082>", line 3, in extract *
opens = data.get('open').flat_map(lambda x: x.batch(5))
AttributeError: 'SymbolicTensor' object has no attribute 'batch'
```
This is becoming extremely confusing.
What would be a the right way to transform this structure so that I can later apply better transformations to build a timeseries dataset.
```
### Relevant log output
_No response_
|
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Running gemma model in Mac m1 gets xla_compile_on_demand_op error
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[
"@lgemc Please ensure that you're using TensorFlow version 2.16.0 or higher, as it includes better M1 compatibility and might resolve the XLA compilation error.\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/63043\">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/63043\">No</a>\n"
] | 2024-02-24T02:45:55 | 2024-03-13T01:47:36 | 2024-03-13T01:47:33 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.15.0
### Custom code
Yes
### OS platform and distribution
macOS Sonoma 14.1.2
### 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
Macbook pro m1 16G
### Current behavior?
I expect get the model working, if I can help adding support to missing features I probably need some guidance but I can help 😸
Keras version: 3.0.5
I try same TensorFlow and Keras version using cuda on linux and I get the model working 🙈
### Standalone code to reproduce the issue
```shell
import keras
import keras_nlp
import numpy as np
gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset("gemma_2b_en")
out = gemma_lm.generate("Keras is a", max_length=30)
print(out)
```
### Relevant log output
```shell
Low level error:
` W tensorflow/core/framework/op_kernel.cc:1839] OP_REQUIRES failed at xla_compile_on_demand_op.cc:292 : NOT_FOUND: could not find registered platform with id: 0x1320e1750`
Python get this error:
Traceback (most recent call last):
File "/Users/lmanrique/Try/gemma/main.py", line 6, in <module>
gemma_lm.generate("Keras is a", max_length=30)
File "/Users/lmanrique/miniconda3/envs/gemma/lib/python3.9/site-packages/keras_nlp/src/models/generative_task.py", line 269, in generate
outputs = [generate(x) for x in inputs]
File "/Users/lmanrique/miniconda3/envs/gemma/lib/python3.9/site-packages/keras_nlp/src/models/generative_task.py", line 269, in <listcomp>
outputs = [generate(x) for x in inputs]
File "/Users/lmanrique/miniconda3/envs/gemma/lib/python3.9/site-packages/keras_nlp/src/models/generative_task.py", line 253, in generate
return generate_function(x, end_token_id=end_token_id)
File "/Users/lmanrique/miniconda3/envs/gemma/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/Users/lmanrique/miniconda3/envs/gemma/lib/python3.9/site-packages/tensorflow/python/eager/execute.py", line 53, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.NotFoundError: Graph execution error:
```
```
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[
"This is my make file \r\n# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.\r\n#\r\n# This work is made available under the Nvidia Source Code License-NC.\r\n# To view a copy of this license, visit\r\n# https://nvlabs.github.io/stylegan2/license.html\r\n\r\n\"\"\"TensorFlow custom ops builder.\r\n\"\"\"\r\n\r\nimport os\r\nimport re\r\nimport uuid\r\nimport hashlib\r\nimport tempfile\r\nimport shutil\r\nimport tensorflow as tf\r\nfrom tensorflow.python.client import device_lib # pylint: disable=no-name-in-module\r\n\r\n#----------------------------------------------------------------------------\r\n# Global options.\r\n\r\ncuda_cache_path = os.path.join(os.path.dirname(__file__), '_cudacache')\r\ncuda_cache_version_tag = 'v1'\r\ndo_not_hash_included_headers = False # Speed up compilation by assuming that headers included by the CUDA code never change. Unsafe!\r\nverbose = True # Print status messages to stdout.\r\n\r\ncompiler_bindir_search_path = [\r\n 'C:/Program Files (x86)/Microsoft Visual Studio/2017/Community/VC/Tools/MSVC/14.14.26428/bin/Hostx64/x64',\r\n 'C:/Program Files (x86)/Microsoft Visual Studio/2019/Community/VC/Tools/MSVC/14.23.28105/bin/Hostx64/x64',\r\n 'C:/Program Files (x86)/Microsoft Visual Studio 14.0/vc/bin',\r\n]\r\n\r\n#----------------------------------------------------------------------------\r\n# Internal helper funcs.\r\ndef _find_compiler_bindir():\r\n for compiler_path in compiler_bindir_search_path:\r\n if os.path.isdir(compiler_path):\r\n return compiler_path\r\n return None\r\n\r\ndef _get_compute_cap(device):\r\n caps_str = device.physical_device_desc\r\n m = re.search('compute capability: (\\\\d+).(\\\\d+)', caps_str)\r\n major = m.group(1)\r\n minor = m.group(2)\r\n return (major, minor)\r\n\r\ndef _get_cuda_gpu_arch_string():\r\n gpus = [x for x in device_lib.list_local_devices() if x.device_type == 'GPU']\r\n if len(gpus) == 0:\r\n raise RuntimeError('No GPU devices found')\r\n (major, minor) = _get_compute_cap(gpus[0])\r\n return 'sm_%s%s' % (major, minor)\r\ndef _run_cmd(cmd):\r\n with os.popen(cmd) as pipe:\r\n output = pipe.read()\r\n status = pipe.close()\r\n if status is not None:\r\n raise RuntimeError('NVCC returned an error. See below for full command line and output log:\\n\\n%s\\n\\n%s' % (cmd, output))\r\n\r\ndef _prepare_nvcc_cli(opts):\r\n cmd = 'nvcc ' + opts.strip()\r\n cmd += ' --disable-warnings'\r\n cmd += ' --include-path \"%s\"' % tf.sysconfig.get_include()\r\n cmd += ' --include-path \"%s\"' % os.path.join(tf.sysconfig.get_include(), 'external', 'protobuf_archive', 'src')\r\n cmd += ' --include-path \"%s\"' % os.path.join(tf.sysconfig.get_include(), 'external', 'com_google_absl')\r\n cmd += ' --include-path \"%s\"' % os.path.join(tf.sysconfig.get_include(), 'external', 'eigen_archive')\r\n compiler_bindir = _find_compiler_bindir()\r\n if compiler_bindir is None:\r\n # Require that _find_compiler_bindir succeeds on Windows. Allow\r\n # nvcc to use whatever is the default on Linux.\r\n if os.name == 'nt':\r\n raise RuntimeError('Could not find MSVC/GCC/CLANG installation on this computer. Check compiler_bindir_search_path list in \"%s\".' % __file__)\r\n else:\r\n cmd += ' --compiler-bindir \"%s\"' % compiler_bindir\r\n cmd += ' 2>&1'\r\n return cmd\r\n #----------------------------------------------------------------------------\r\n# Main entry point.\r\n\r\n_plugin_cache = dict()\r\n\r\ndef get_plugin(cuda_file):\r\n cuda_file_base = os.path.basename(cuda_file)\r\n cuda_file_name, cuda_file_ext = os.path.splitext(cuda_file_base)\r\n\r\n # Already in cache?\r\n if cuda_file in _plugin_cache:\r\n return _plugin_cache[cuda_file]\r\n\r\n # Setup plugin.\r\n if verbose:\r\n print('Setting up TensorFlow plugin \"%s\": ' % cuda_file_base, end='', flush=True)\r\n try:\r\n # Hash CUDA source.\r\n md5 = hashlib.md5()\r\n with open(cuda_file, 'rb') as f:\r\n md5.update(f.read())\r\n md5.update(b'\\n')\r\n\r\n # Hash headers included by the CUDA code by running it through the preprocessor.\r\n if not do_not_hash_included_headers:\r\n if verbose:\r\n print('Preprocessing... ', end='', flush=True)\r\n with tempfile.TemporaryDirectory() as tmp_dir:\r\n tmp_file = os.path.join(tmp_dir, cuda_file_name + '_tmp' + cuda_file_ext)\r\n _run_cmd(_prepare_nvcc_cli('\"%s\" --preprocess -o \"%s\" --keep --keep-dir \"%s\"' % (cuda_file, tmp_file, tmp_dir)))\r\n with open(tmp_file, 'rb') as f:\r\n bad_file_str = ('\"' + cuda_file.replace('\\\\', '/') + '\"').encode('utf-8') # __FILE__ in error check macros\r\n good_file_str = ('\"' + cuda_file_base + '\"').encode('utf-8')\r\n for ln in f:\r\n if not ln.startswith(b'# ') and not ln.startswith(b'#line '): # ignore line number pragmas\r\n ln = ln.replace(bad_file_str, good_file_str)\r\n md5.update(ln)\r\n md5.update(b'\\n')\r\n\r\n # Select compiler options.\r\n compile_opts = ''\r\n if os.name == 'nt':\r\n compile_opts += '\"%s\"' % os.path.join(tf.sysconfig.get_lib(), 'python', '_pywrap_tensorflow_internal.lib')\r\n elif os.name == 'posix':\r\n compile_opts += '\"%s\"' % os.path.join(tf.sysconfig.get_lib(), 'python', '_pywrap_tensorflow_internal.so')\r\n compile_opts += ' --compiler-options \\'-fPIC -D_GLIBCXX_USE_CXX11_ABI=0\\''\r\n else:\r\n assert False # not Windows or Linux, w00t?\r\n compile_opts += ' --gpu-architecture=%s' % _get_cuda_gpu_arch_string()\r\n compile_opts += ' --use_fast_math'\r\n nvcc_cmd = _prepare_nvcc_cli(compile_opts)\r\n\r\n # Hash build configuration.\r\n md5.update(('nvcc_cmd: ' + nvcc_cmd).encode('utf-8') + b'\\n')\r\n md5.update(('tf.VERSION: ' + tf.VERSION).encode('utf-8') + b'\\n')\r\n md5.update(('cuda_cache_version_tag: ' + cuda_cache_version_tag).encode('utf-8') + b'\\n')\r\n # Compile if not already compiled.\r\n bin_file_ext = '.dll' if os.name == 'nt' else '.so'\r\n bin_file = os.path.join(cuda_cache_path, cuda_file_name + '_' + md5.hexdigest() + bin_file_ext)\r\n if not os.path.isfile(bin_file):\r\n if verbose:\r\n print('Compiling... ', end='', flush=True)\r\n with tempfile.TemporaryDirectory() as tmp_dir:\r\n tmp_file = os.path.join(tmp_dir, cuda_file_name + '_tmp' + bin_file_ext)\r\n _run_cmd(nvcc_cmd + ' \"%s\" --shared -o \"%s\" --keep --keep-dir \"%s\"' % (cuda_file, tmp_file, tmp_dir))\r\n os.makedirs(cuda_cache_path, exist_ok=True)\r\n intermediate_file = os.path.join(cuda_cache_path, cuda_file_name + '_' + uuid.uuid4().hex + '_tmp' + bin_file_ext)\r\n shutil.copyfile(tmp_file, intermediate_file)\r\n os.rename(intermediate_file, bin_file) # atomic\r\n # Load.\r\n if verbose:\r\n print('Loading... ', end='', flush=True)\r\n plugin = tf.load_op_library(bin_file)\r\n\r\n # Add to cache.\r\n _plugin_cache[cuda_file] = plugin\r\n if verbose:\r\n print('Done.', flush=True)\r\n return plugin\r\n\r\n except:\r\n if verbose:\r\n print('Failed!', flush=True)\r\n raise\r\n\r\n#----------------------------------------------------------------------------",
"ldd fused_bias_act_c7a32bd65776e03679106c6556928c8c.so \r\n\tlinux-vdso.so.1 (0x00007ffc6f256000)\r\n\t_pywrap_tensorflow_internal.so => not found\r\n\tlibstdc++.so.6 => /home/chengjun/anaconda3/lib/libstdc++.so.6 (0x00007f190d600000)\r\n\tlibgcc_s.so.1 => /home/chengjun/anaconda3/lib/libgcc_s.so.1 (0x00007f190d9df000)\r\n\tlibc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x00007f190d200000)\r\n\t/lib64/ld-linux-x86-64.so.2 (0x00007f190dad6000)\r\n\tlibm.so.6 => /lib/x86_64-linux-gnu/libm.so.6 (0x00007f190d8f8000)\r\n\r\nI use ldd and find there is nothing linked with _pywrap_tensorflow_internal.so, I don't know whether the problems is about this or not.\r\n\r\nBy the way, I find I have this file \r\nwhich is in /home/chengjun/anaconda3/envs/python3/lib/python3.6/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so"
] | 2024-02-24T00:58:12 | 2024-02-26T22:51:09 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 1.14
### Custom code
Yes
### OS platform and distribution
linux ubuntu 22.04
### Mobile device
_No response_
### Python version
3.6
### Bazel version
_No response_
### GCC/compiler version
11.4.0
### CUDA/cuDNN version
10.0/7.6.5
### GPU model and memory
_No response_
### Current behavior?
When I tried to run stylegan2 on the server, I configured all the environments as required but got an error at runtime.
### Standalone code to reproduce the issue
```shell
https://github.com/NVlabs/stylegan2
CUDA_VISIBLE_DEVICES=0 python run_training.py --num-gpus=1 --data-dir=/home/chengjun/datasets --config=config-f --dataset=my-custom-dataset --total-kimg=100000
this is my running command.
```
### Relevant log output
```shell
tensorflow.python.framework.errors_impl.NotFoundError: /home/chengjun/stylegan2-master/dnnlib/tflib/_cudacache/fused_bias_act_347e82e8919aeb0d5c7dc989e996c091.so: undefined symbol: _ZN10tensorflow12OpDefBuilder4AttrENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE
```
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PR_kwDOArmXAs5nzR-5
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Fix undefined ${BUILD_NUM_JOBS} in TF lite
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[] | 2024-02-23T23:11:38 | 2024-02-24T16:31:16 | 2024-02-24T16:31:16 |
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On branch r2.16
Reference: https://github.com/tensorflow/tensorflow/issues/63018
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PR_kwDOArmXAs5nzRsb
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Fix undefined ${BUILD_NUM_JOBS} in TF lite
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On branch r2.15
Reference: https://github.com/tensorflow/tensorflow/issues/63018
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Fix undefined ${BUILD_NUM_JOBS} in TF lite
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`tf.raw_ops.UnravelIndex` can leads to eigen assertion failure
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[
"@Sehun0819,\r\n\r\nI tried to execute the mentioned code with tensorflow official builds(default) and the code execution was successful with Tf2.15 and tf-nightly \r\nKindly find the [gist](https://colab.research.google.com/gist/tilakrayal/02745a7869988c873a41d08ea7bf2984/untitled1739.ipynb) for the reference.\r\n\r\nAlso this might be an issue with debug builds. We will check and provide more information. 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/63037\">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/63037\">No</a>\n"
] | 2024-02-23T13:57:04 | 2024-03-06T06:43:16 | 2024-03-06T06:43:13 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.17
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04 LTS
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
6.5.0
### GCC/compiler version
clang 16
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Similar to #63036.
`tf.raw_ops.UnravelIndex` can leads to eigen assertion failure [here](https://gitlab.com/libeigen/eigen/-/blob/aa6964bf3a34fd607837dd8123bc42465185c4f8/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h#L156):
```C++
eigen_assert(input_dims[i] > 0);
```
Note that it is executed without error message in release build.
### Standalone code to reproduce the issue
```Python
import tensorflow as tf
tf.raw_ops.UnravelIndex(
indices=tf.constant([],dtype=tf.int32),
dims=[1,1,1,1],
name=None
)
```
### Relevant log output
Release build: outputs nothing
Debug build:
```shell
python: external/eigen_archive/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h:156: Eigen::TensorEvaluator<const Eigen::TensorBroadcastingOp<const Eigen::array<long, 2>, const Eigen::TensorReshapingOp<const Eigen::array<long, 2>, const Eigen::TensorMap<Eigen::Tensor<const int, 1, 1>, 16>>>, Eigen::DefaultDevice>::TensorEvaluator(const XprType &, const Device &) [Derived = const Eigen::TensorBroadcastingOp<const Eigen::array<long, 2>, const Eigen::TensorReshapingOp<const Eigen::array<long, 2>, const Eigen::TensorMap<Eigen::Tensor<const int, 1, 1>, 16>>>, Device = Eigen::DefaultDevice]: Assertion `input_dims[i] > 0' failed.
Aborted (core dumped)
```
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`tf.raw_ops.Substr` can lead to eigen assertion failure
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[
"Hi @Sehun0819 ,\r\n\r\nI have tested the code with Official builds(default/non-debug) and code execution success with Tf2.15 and tf-nightly as well.\r\nAttached [gist](https://colab.research.google.com/gist/SuryanarayanaY/5d3218668209d1ff7439cca6739acdd8/63036.ipynb) for reference.\r\n\r\nThis might be issue with debug builds.Needs to check. 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/63036\">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/63036\">No</a>\n"
] | 2024-02-23T13:48:20 | 2024-03-20T08:57:06 | 2024-03-20T08:57:03 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.17
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04 LTS
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
6.5.0
### GCC/compiler version
clang 16
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
The below code triggers eigen assertion failure [here](https://gitlab.com/libeigen/eigen/-/blob/aa6964bf3a34fd607837dd8123bc42465185c4f8/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h#L156):
```C++
eigen_assert(input_dims[i] > 0);
```
Note that it is executed without error message in release build.
### Standalone code to reproduce the issue
```Python
import tensorflow as tf
tf.raw_ops.Substr(
input="abc",
pos=tf.constant([],dtype=tf.int32),
len=tf.constant([],dtype=tf.int32),
unit='BYTE',
name=None
)
```
### Relevant log output
Release build: Outputs nothing
Debug build:
```shell
python: external/eigen_archive/unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h:156: Eigen::TensorEvaluator<const Eigen::TensorBroadcastingOp<const Eigen::array<long, 1>, const Eigen::TensorMap<Eigen::Tensor<const int, 1, 1>, 16>>, Eigen::DefaultDevice>::TensorEvaluator(const XprType &, const Device &) [Derived = const Eigen::TensorBroadcastingOp<const Eigen::array<long, 1>, const Eigen::TensorMap<Eigen::Tensor<const int, 1, 1>, 16>>, Device = Eigen::DefaultDevice]: Assertion `input_dims[i] > 0' failed.
Aborted (core dumped)
```
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I_kwDOArmXAs6AN0DE
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SVDF layer implementation compatible with TFLite’s SVDF operator
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[
"@VictorDominguite TensorFlow doesn't have a dedicated SVDF layer in Keras, you can achieve a similar functionality by manually defining the sequence of operations and convert them to TFLite, where the SVDF fusion might occur.\r\nThank you!",
"Hi @sushreebarsa, thank you for the response! \r\nActually, my main doubt here is precisely defining what is the sequence of operations that would lead to the SVDF fusion when converting to TFLite. I already tried using [Google Research's implementation](https://github.com/google-research/google-research/blob/master/kws_streaming/layers/svdf.py) and the implementation of a \"low latency svdf model\" I found in [this tutorial](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/speech_commands/models.py#L458) from Tensorflow's repository (for this one I had to adapt the code to use TF2 operations equivalent to the ones used in the TF1 implementation), but none of them got fused as an SVDF operator when converting to TFLite. In fact, when listing the set of operations from the TFLite models generated from Keras models with these SVDF layers, I get all separate ops, such as `'DEPTHWISE_CONV_2D', 'FULLY_CONNECTED', 'PAD', ...`.\r\nSo I was wondering what would be the correct sequence of operations that would be recognized as an SVDF op after converting to TFLite.",
"Hi @VictorDominguite, feels like a bug or the converter doesn't properly recognize this situation, you mentioned you had to adapt the tutorial code to TF2 operations -- would you happen to have an example model creation + conversion for this situation handy that you can share on Colab? Figured you can help us save some time since you have already done the work there.",
"Hi @pkgoogle! So, the first few lines from the `create_low_latency_svdf_model` function found in [this file](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/speech_commands/models.py#L458-L618) correspond to the SVDF operation. What I tried to do was write a layer in TF2 that performed operations equivalent to the ones from the TF1 example. One difference worth mentioning is concerning the `is_training` flag present in the TF1 implementation. It slightly alters the behavior of the model. For simplicity, I only implemented the case where `is_training = True`.\r\nThe implementation for the TF2 layer and its conversion to TFLite can be seen in [this Colab notebook](https://colab.research.google.com/drive/1hJynxjcfrplbyif20bZZyznk-Ewl0NNG?usp=sharing).",
"I was able to replicate with your notebook, thanks! I tried with tf-nightly and it is running into a different bug. @haozha111 can you please take a look? Thanks.",
"one more info, I tried on docker based tensorflow 2.12.0, also can't fused to TFLite's SVDF operator"
] | 2024-02-23T13:33:04 | 2024-05-12T05:05:53 | null |
NONE
| null | null | null |
Although TFLite has a built-in SVDF operator (as listed [here](https://www.tensorflow.org/mlir/tfl_ops#tflsvdf_tflsvdfop), and whose implementation can be seen [here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/kernels/internal/reference/svdf.h)), currently, Tensorflow doesn't have a keras layer implementation for the SVDF operation.
I was wondering what would be the sequence of operations (using Tensorflow's Python library) needed so that they could be fused and recognized as an SVDF operator after converting to TFLite.
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I_kwDOArmXAs6ANyL_
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`tf.raw_ops.AvgPool`: negative kernel size is not checked at shape inference step
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"Hi **@Sehun0819** ,\r\n\r\nSorry for the delay, I tried to run your code on Colab using TF v2.15 and nightly. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/9f60934e6a7e51d8012e1f9214670383/63034_2-15-nightly.ipynb) here for reference.\r\nThank you!"
] | 2024-02-23T13:29:12 | 2024-03-05T09:35:39 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.17
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04 LTS
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
6.5.0
### GCC/compiler version
clang 16
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Currently shape inference step of `tf.raw_ops.AvgPool` allows negative kernel size.
Note that debug build rejects it [here](https://github.com/tensorflow/tensorflow/blob/7ba14e559de0112cbf59dc6db6f8c6a18283642a/tensorflow/core/framework/common_shape_fns.cc#L1393-L1394):
```C++
TF_RETURN_IF_ERROR(GetWindowedOutputSizeFromDims(
c, in_rows_dim, kernel_rows, stride_rows, padding, &output_rows));
```
, where `kernel_rows` is converted to `DimensionOrConstant` and ends up with assertion failure [here](https://github.com/tensorflow/tensorflow/blob/7ba14e559de0112cbf59dc6db6f8c6a18283642a/tensorflow/core/framework/shape_inference.h#L890-L895):
```C++
inline DimensionOrConstant::DimensionOrConstant(int64_t val) : val(val) {
DCHECK(val >= 0 || val == InferenceContext::kUnknownDim)
<< "Dimension must be non-negative or equal to "
"InferenceContext::kUnknownDim but got "
<< val;
}
```
### Standalone code to reproduce the issue
```Python
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
x = tf.raw_ops.AvgPool(
value=tf.random.normal([1,1,1,1]),
ksize=[1,-2,1,1],
strides=[1,1,1,1],
padding="SAME",
data_format='NHWC',
name=None
)
print(x)
```
### Relevant log output
Release Build:
```shell
Tensor("AvgPool:0", shape=(1, 1, 1, 1), dtype=float32)
```
Debug Build:
```shell
2024-02-23 22:18:43.783609: F ./tensorflow/core/framework/shape_inference.h:891] Check failed: val >= 0 || val == InferenceContext::kUnknownDim Dimension must be non-negative or equal to InferenceContext::kUnknownDim but got -2
Aborted (core dumped)
```
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[
"@Sehun0819,\r\n\r\nI tried to execute the mentioned code with tensorflow official builds(default) and the code execution was successful with Tf2.15 and tf-nightly. Also the output is also displayed.\r\nKindly find the [gist](https://colab.research.google.com/gist/tilakrayal/2259609ffe3e68eee0a666ad1faea3f3/untitled1740.ipynb) for the reference.\r\n\r\nAlso this might be an issue with debug builds. We will check and provide more information. Thank you!",
"This might be probably fixed in the PR #63064"
] | 2024-02-23T13:09:32 | 2024-03-08T15:11:32 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.17
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04 LTS
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
6.5.0
### GCC/compiler version
clang 16
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
In `tf.raw_ops.ConjugateTranspose`, negative value of `perm` can lead to out-of-bounds read.
[Here](https://github.com/tensorflow/tensorflow/blob/617b5e97d6a9a71e1972dfe6fead5bf460094658/tensorflow/core/kernels/transpose_functor_cpu.cc#L52),
```C++
i_idx += ratio * in_strides[perm[i]];
```
as there is no guard which checks validity of `perm`, when its value is -1 `in_strides[perm[i]]` can be an out-of-bounds reading(I guess -1 would be interpreted as an `SIZE_T_MAX` or something).
Note that the below code ends up with absl assertion failure in debug build.
### Standalone code to reproduce the issue
```Python
import tensorflow as tf
tf.raw_ops.ConjugateTranspose(
x=tf.random.normal([2]),
perm=[-1])
```
### Relevant log output
Release Build:
```shell
Outputs nothing
```
Debug Build:
```shell
python: external/com_google_absl/absl/container/inlined_vector.h:363: auto absl::InlinedVector<long, 8>::operator[](size_type)::(anonymous class)::operator()() const [T = long, N = 8, A = std::allocator<long>]: Assertion `false && "i < size()"' failed.
Aborted (core dumped)
```
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| 2,150,536,441 |
PR_kwDOArmXAs5nueBB
| 63,032 |
add go protobuf-files
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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/63032/checks?check_run_id=21897307220) 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 @rohan100jain Can you please review this PR ? Thank you!"
] | 2024-02-23T07:35:43 | 2024-03-21T14:11:13 | 2024-03-21T14:11:10 |
NONE
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I_kwDOArmXAs6AJpdV
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Confusing result of tf.argsort/argmax/argmin/ given a boolean axis
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[
"Hi @drewshark ,\r\n\r\nThe mentioned APIs also works for `bool` data types for `axis` argument even though it's not explicitly mentioned in the documentation.This may be true for some of other APIs also.May be we can add a note in the documentation regarding bool as accepted datatype.Attached below is the line of code where `argmax_op` is registered for bool data type.\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/216a1a0d4633a068fa671252541305438b353954/tensorflow/core/kernels/argmax_op.cc#L173\r\n\r\nWhen it comes to string ,the API tf.argsort will try to convert this into a numeric in the line of code. This may be due to the reason that mentioned in the comment in the same line.If the string contains chars other than numbers then it will raise intended error.Only numerics in the form of strings are accepted here and these values also validated after conversion into integers.Attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/1390af7af4ac75698a542269d84c0d77/63031.ipynb) for reference.\r\n\r\n`\r\naxis_static = int(axis_static) # Avoids NumPy casting error\r\n `",
"@drewshark, Please note that the API tf.argmin accepts axis as Tensor unlike argmax which accepts integer. If you pass a boolean Tensor for `tf.argmin` then it will raise error below.\r\n`InvalidArgumentError: Value for attr 'Tidx' of bool is not in the list of allowed values: int32, int64`\r\n\r\n However if we pass python boolean then its accepting same.This behaviour also different and needs a fix IMO.",
"The documentation of tf.argsort not mentioning any dtype of the argument axis explicitly.IMO, we can leave this as it is.\r\n\r\n\r\naxis | The axis along which to sort. The default is -1, which sorts the last axis. \r\n-- | --\r\n\r\n\r\n",
"There is no legitimate use-case for passing in a bool. As with numpy, bools are a form of integer type, so it is being interpreted as such. There shouldn't be any confusion for legitimate use-cases.",
"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/63031\">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/63031\">No</a>\n"
] | 2024-02-22T22:00:23 | 2024-02-26T19:17:45 | 2024-02-26T19:17:37 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15.0
### 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?
These three APIs tf.argsort/argmax/argmin will accept boolean axis such as True and False, which confuses me. Indeed, the documentation claims that the type of axis for tf.argmax and tf.argmin should be integer (https://www.tensorflow.org/api_docs/python/tf/math/argmax, https://www.tensorflow.org/api_docs/python/tf/math/argmin).
Moreover, tf.argsort can also accept string variable.
Taking a closer look, I find this code: https://github.com/tensorflow/tensorflow/blob/74cafb3ee7ce22dc2593127a2a6d8e78425e2640/tensorflow/python/ops/sort_ops.py#L179
It seems that argsort will convert any value of axis to integer using the `int()` call.
However, such silent handling might make user confuse because the actual behavior deviates from the documentation.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
values = tf.constant([[3,1,4,1,5,9,2,6], [3,4,5,1,2,3,4,0]])
axis = True
print(tf.argsort(values,axis)) # tf.Tensor([[1 3 0 2] [3 0 1 2]], shape=(2, 4), dtype=int32)
print(tf.argmax(values,axis)) # tf.Tensor([2 2], shape=(2,), dtype=int64)
print(tf.argmin(values,axis)) # tf.Tensor([1 3], shape=(2,), dtype=int64)
axis = '-1'
print(tf.argsort(values, axis)) # tf.Tensor([[1 3 0 2] [3 0 1 2]], shape=(2, 4),dtype=int32)
```
### Relevant log output
_No response_
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PR_kwDOArmXAs5nsQgW
| 63,030 |
[oneDNN] Add oneDNN version of SparseMatrixMatMul (v2)
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"@milpuz01 @jondea, JFYI, this PR also enables an opt-in CSR matmul for ARM. Currently, a reference implementation will be used when running on ARM.",
"@cantonios This is version 2 of the PR; should have fixed the ARM64 build issue that arose in overnight testing from the last version.",
"Great -- hopefully this time, we've fixed the nightly tests!",
"Thanks for the heads up and for fixing the failure! I should say that if this flag goes from opt-in to opt-out before there’s an optimized implementation of SPMM in oneDNN for AArch64, then it will need a platform specific guard to stick with Eigen.",
"@jondea No problem! Yes, definitely: that's exactly one of the reasons that it's opt-in.",
"So all of the internal overnight checks are successful, now?"
] | 2024-02-22T20:34:18 | 2024-03-20T11:23:24 | 2024-02-27T18:47:35 |
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This is version 2 of #62883, which should fix the build on ARM64.
Adds `_MklNativeSparseMatrixMatMul` and its accompanying kernel, which uses oneDNN to multiply a CSR sparse matrix by a dense tensor. The op is enabled with an environment variable (`TF_ENABLE_ONEDNN_SPMM`), so is entirely opt-in. It also includes tests and a benchmark, which we've used below to measure its performance against the existing kernel.
The performance looks promising particularly for larger shapes, and is optimized to use the AVX2 and AVX512 ISAs. These results were collected using the new benchmark in `tensorflow/core/kernels/mkl/mkl_sparse_matrix_matmul_op_benchmark.cc` on an Intel Xeon Platinum 8480 with hyperthreading enabled. To minimize NUMA effects, we bound it to the first socket.
Configuration (NNZ_M_K_N) | Eigen Time (ns) | oneDNN Time (ns) | Ratio
-- | -- | -- | --
128_8_512_1 | 15300 | 18598 | 0.82
128_16_512_1 | 14778 | 18551 | 0.80
128_128_512_1 | 18613 | 19165 | 0.97
128_4096_4096_1 | 151525 | 35904 | 4.22
1024_4096_4096_1 | 161593 | 37041 | 4.36
16384_4096_4096_1 | 163055 | 49355 | 3.30
128_8_1024_16 | 17307 | 18420 | 0.94
128_16_1024_16 | 17765 | 18678 | 0.95
128_128_1024_16 | 19247 | 19200 | 1.00
128_4096_4096_128 | 160341 | 140894 | 1.14
128_4096_4096_1024 | 181590 | 156980 | 1.16
1024_8_1024_16 | 24265 | 19502 | 1.24
1024_16_1024_16 | 24223 | 20448 | 1.18
1024_128_1024_16 | 26013 | 20396 | 1.28
1024_4096_4096_128 | 157612 | 139688 | 1.13
1024_4096_4096_1024 | 177549 | 160973 | 1.10
16384_8_1024_16 | 153005 | 36643 | 4.18
16384_16_1024_16 | 152853 | 36597 | 4.18
16384_128_1024_16 | 153600 | 31928 | 4.81
16384_4096_4096_128 | 166061 | 142494 | 1.17
16384_4096_4096_1024 | 244243 | 194536 | 1.26
16384_4096_4096_4096 | 654950 | 536546 | 1.22
100_1_1000000_100 | 18230 | 19991 | 0.91
200_1_2000000_100 | 20509 | 20818 | 0.99
400_1_4000000_100 | 23023 | 21638 | 1.06
400_4_1000000_100 | 22164 | 21349 | 1.04
800_4_2000000_100 | 26180 | 23374 | 1.12
1600_4_4000000_100 | 40538 | 28622 | 1.42
800_8_1000000_100 | 27015 | 23231 | 1.16
1600_8_2000000_100 | 39953 | 27894 | 1.43
3200_8_4000000_100 | 77708 | 42709 | 1.82
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I_kwDOArmXAs6AInzD
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How the data member `data_` of TensorBuffer is destroyed?
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"The `data_ `member is indeed a `void`* pointer, but it doesn't directly own the memory it points to. The actual memory management depends on the T`ensorBuffer'`s underlying data type and allocation strategy. The RefCounted::Unref function you mentioned does indeed call delete this when the reference count reaches zero. However, this delete only deallocates the TensorBuffer object itself, not the memory pointed to by data_.\r\nThe responsibility of de-allocating the actual data belongs to the specific allocation mechanism used for that data type. TensorFlow internally handles this through its memory management system.\r\n\r\nPlease let us know if it clarifies your query?\r\nThank you!",
"@sushreebarsa Thanks for your reply. I got some idea. Here is my description:\r\n\r\nTake this [constructor](https://github.com/tensorflow/tensorflow/blob/23bc3342fcc7d4bf1da6f2f11da61da55d290d34/tensorflow/core/framework/tensor.cc#L967) as an example,\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/23bc3342fcc7d4bf1da6f2f11da61da55d290d34/tensorflow/core/framework/tensor.cc#L967-L978\r\n\r\n`buf_ = new Buffer<T>(a, shape.num_elements())` sets the `buf_` member of `Tensor`. Here `Buffer` is a subclass of `TensorBuffer`, its destructor:\r\n\r\nhttps://github.com/tensorflow/tensorflow/blob/23bc3342fcc7d4bf1da6f2f11da61da55d290d34/tensorflow/core/framework/tensor.cc#L577-L585\r\n\r\nWhen `Tensor` object is destroyed, it will call `buf_ `'s destructor.\r\n\r\nThe `TypedAllocator::Deallocate` then deallocate the memory which ultimately calls `DeallocateRaw` method of the allocator that is used in constructor.\r\n\r\nIs it looks this what I describe above?\r\n"
] | 2024-02-22T18:57:36 | 2024-02-27T09:08:16 | null |
NONE
| null | null | null |
From the `TensorBuffer ` definition, I notice there is a member `data_`
https://github.com/tensorflow/tensorflow/blob/b7ee894eba3b725eafdd70b088029e907f7943c7/tensorflow/core/framework/tensor.h#L108
I am curious about how this void pointer will be destroyed?
I find `RefCounted::Unref` has the logic of `delete`, especially the following line:
https://github.com/tensorflow/tensorflow/blob/b7ee894eba3b725eafdd70b088029e907f7943c7/third_party/xla/third_party/tsl/tsl/platform/refcount.h#L336
but it seems the above line did not delete what the void pointer `data_` is pointing to.
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[
"Hi @kanglant This PR is in draft, any update on this? Please. Thank you!",
"Hi @kanglant This PR is in draft, any update on this? Please. Thank you!",
"Hi @kanglant This PR is in draft, any update on this? Please. Thank you!"
] | 2024-02-22T18:53:04 | 2024-06-07T17:15:19 | 2024-06-07T17:15:19 |
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Tensorflow cannot find libdevice.so consistently
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[
"Hi **@ruslankotl** ,\r\n\r\nSorry for the delay, The problem might be arising due to a combination of factors including the TensorFlow version, the CUDA and cuDNN setup, and perhaps how the environment variables are set in your conda environment.\r\nCould you to check the compatibility of these versions with TensorFlow installation. Here i am providing [document](https://www.tensorflow.org/install/source#ubuntu) for your reference.\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/63027\">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/63027\">No</a>\n"
] | 2024-02-22T16:04:42 | 2024-03-15T01:47:21 | 2024-03-15T01:47:16 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.14.0
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04.1
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
CUDA 11.8, cuDNN 8.7.0.84
### GPU model and memory
_No response_
### Current behavior?
I am running inference using Tensorflow 2.14 installed via `pip` on a model written for Tensorflow 2.4. The model works but sometimes crashes unexpectedly. Tensorflow log suggests it cannot find a file at the path throwing an error:
`${CONDA_PREFIX}/envs/dp5_cascade/lib/python3.10/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2/../../nvidia/cuda_nvcc/nvvm/libdevice/libdevice.10.bc:-1:-1: Could not open input file: Not a directory`
or inserts a mystery character and then also throws an error:
`2024-02-22 14:00:25.171585: F tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/utils.cc:32] /home/rk582/miniconda3/envs/dp5_cascade/lib/python3.10/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2�/../../nvidia/cuda_nvcc/nvvm/libdevice/libdevice.10.bc:-1:-1: Could not open input file: No such file or directory`
However, there is definitely a file at `${CONDA_PREFIX}/envs/dp5_cascade/lib/python3.10/site-packages/nvidia/cuda_nvcc/nvvm/libdevice/libdevice.10.bc` so I do not know why would the code look for a file in such a roundabout way.
Are there any environment variables one can set to ensure consistent function?
nb I am dealing with a legacy code base with the author long gone and it is too hard to untangle it from a larger package due to their design decisions
### Standalone code to reproduce the issue
```shell
from tensorflow.keras.models import load_model
model = load_model(path_to_model, custom_objects = (some custom objects), compile = False)
model.compile()
yhat = model(input)
```
### Relevant log output
```shell
2024-02-21 16:44:51.345095: I tensorflow/core/util/port.cc:111] 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`.
2024-02-21 16:44:51.366345: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-02-21 16:44:51.366366: 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
2024-02-21 16:44:51.366380: 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
2024-02-21 16:44:51.370317: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-02-21 16:44:52.290366: 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
2024-02-21 16:44:52.293186: 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
2024-02-21 16:44:52.293275: 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
2024-02-21 16:44:52.294092: 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
2024-02-21 16:44:52.294204: 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
2024-02-21 16:44:52.294275: 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
2024-02-21 16:44:52.330931: 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
2024-02-21 16:44:52.331051: 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
2024-02-21 16:44:52.331135: 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
2024-02-21 16:44:52.331200: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5503 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3080, pci bus id: 0000:01:00.0, compute capability: 8.6
==========================
2024-02-21 16:44:52.658956: F tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/utils.cc:32] /home/rk582/miniconda3/envs/dp5_cascade/lib/python3.10/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2�/../../nvidia/cuda_nvcc/nvvm/libdevice/libdevice.10.bc:-1:-1: Could not open input file: No such file or directory
```
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PR_kwDOArmXAs5nnVQP
| 63,026 |
Support legalization of tf.SplitV op for dynamic shapes
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"For this example\r\n```\r\nfunc.func @splitv_dynamic(%input: tensor<?x6xf32>) -> (tensor<?x1xf32>, tensor<?x2xf32>, tensor<?x3xf32>) {\r\n %split_sizes = \"tf.Const\"() {value = dense<[1, 2, 3]> : tensor<3xi32>} : () -> tensor<3xi32>\r\n %split_dim = \"tf.Const\"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>\r\n %0:3 = \"tf.SplitV\"(%input, %split_sizes, %split_dim) : (tensor<?x6xf32>, tensor<3xi32>, tensor<i32>) -> (tensor<?x1xf32>, tensor<?x2xf32>, tensor<?x3xf32>)\r\n func.return %0#0, %0#1, %0#2 : tensor<?x1xf32>, tensor<?x2xf32>, tensor<?x3xf32>\r\n}\r\n```\r\n\r\n\r\nit will be converted to\r\n```\r\nmodule {\r\n func.func @splitv_dynamic(%arg0: tensor<?x6xf32>) -> (tensor<?x1xf32>, tensor<?x2xf32>, tensor<?x3xf32>) {\r\n %0 = mhlo.constant dense<[1, 2, 3]> : tensor<3xi32>\r\n %1 = mhlo.constant dense<1> : tensor<i32>\r\n %cst = arith.constant dense<1> : tensor<2xindex>\r\n %cst_0 = arith.constant dense<0> : tensor<2xindex>\r\n %cst_1 = arith.constant dense<[0, 1]> : tensor<2xindex>\r\n %cst_2 = arith.constant dense<[0, 3]> : tensor<2xindex>\r\n %c0 = arith.constant 0 : index\r\n %dim = tensor.dim %arg0, %c0 : tensor<?x6xf32>\r\n %c1 = arith.constant 1 : index\r\n %from_elements = tensor.from_elements %dim, %c1 : tensor<2xindex>\r\n %2 = mhlo.real_dynamic_slice %arg0, %cst_0, %from_elements, %cst : (tensor<?x6xf32>, tensor<2xindex>, tensor<2xindex>, tensor<2xindex>) -> tensor<?x1xf32>\r\n %c3 = arith.constant 3 : index\r\n %from_elements_3 = tensor.from_elements %dim, %c3 : tensor<2xindex>\r\n %3 = mhlo.real_dynamic_slice %arg0, %cst_1, %from_elements_3, %cst : (tensor<?x6xf32>, tensor<2xindex>, tensor<2xindex>, tensor<2xindex>) -> tensor<?x2xf32>\r\n %c6 = arith.constant 6 : index\r\n %from_elements_4 = tensor.from_elements %dim, %c6 : tensor<2xindex>\r\n %4 = mhlo.real_dynamic_slice %arg0, %cst_2, %from_elements_4, %cst : (tensor<?x6xf32>, tensor<2xindex>, tensor<2xindex>, tensor<2xindex>) -> tensor<?x3xf32>\r\n return %2, %3, %4 : tensor<?x1xf32>, tensor<?x2xf32>, tensor<?x3xf32>\r\n }\r\n}\r\n```",
"AMD ROCm -- Community CI Build — rocm CI build failure was caused by `unrecognized gcc command line option`, which shouldn't be caused by my patch.\r\n<img width=\"1147\" alt=\"image\" src=\"https://github.com/tensorflow/tensorflow/assets/19923746/b9223db6-7a6b-4053-b9ba-3ad488b21260\">\r\n",
"Hi @rdzhabarov , could you please help to review this change?\r\nThanks.",
"Hi @rdzhabarov, Can you please review this PR ? Thank you!",
"Hi @rdzhabarov , any comments on this change?\r\nThanks.",
"Hi @rdzhabarov, Can you please review this PR ? Thank you!",
"Hi @rdzhabarov , could you please taking a look at this PR?\r\nThanks."
] | 2024-02-22T08:03:19 | 2024-06-11T01:06:01 | null |
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Hi all,
This patch implements the legalization of `tf.SplitV` op for dynamic shapes, which is helpful for dynamic shape compilation
May I get reviews for it?
- Testing: the following tests all passed
```
tensorflow/compiler/mlir/tf2xla/tests:all_tests \
tensorflow/compiler/mlir/tensorflow/tests:all_tests
```
Thanks.
Best regards,
Jie
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Failure in convert Gemma 2B models to TfLite
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[
"Hi @RageshAntonyHM,\r\n\r\nI am trying to reproduce the issue while I had another error `ModuleNotFoundError: No module named 'keras_nlp.backend`, could you please confirm the version of it.\r\n\r\nThank You",
"@LakshmiKalaKadali \r\n\r\nit is keras 3.0.5 and installed keras-nlp via pip install git+https://github.com/keras-team/keras-nlp (0.8.1)",
"@LakshmiKalaKadali \r\n\r\nfirst install ` pip install git+https://github.com/keras-team/keras-nlp` and then update Keras (`pip install -U keras`)",
"Then install tensorflow-datasets also @LakshmiKalaKadali ",
"Also crashing in Colab with or without quantization.",
"@farmaker47 \r\n\r\nThis conversion pipeline needs lot of Vram. At Least 24 GB. \r\n\r\n@LakshmiKalaKadali any updates on this please?",
"@RageshAntonyHM \r\nI got same crash in colab A100(40GB GPU RAM).\r\n",
"@urim85 \r\n\r\nYeah. Actually, till it is crashing for me in 48 GB RTX 6000. \r\n\r\n(What I told to @farmaker47 was, it will crash prematurally if VRAM is low. But also crashes in final step even if you have enough VRAM) ",
"I saw that training is working OK having installed first TensorFlow nightly version (2.17.0-dev20240223). @RageshAntonyHM can you try with nightly version and check again the conversion?",
"@farmaker47 \r\n\r\nHow to install TensorFlow nightly version? I tried pip install tf-nightly, but I am getting error \r\n\r\n File \"/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/core.py\", line 5, in <module>\r\n from tensorflow.compiler.tf2xla.python.xla import dynamic_update_slice\r\nModuleNotFoundError: No module named 'tensorflow.compiler.tf2xla'\r\n\r\nName: tf-nightly\r\nVersion: 2.17.0.dev20240223",
"I work with Colab.\r\nSo it is\r\n\r\n!pip install tf-nightly\r\n!pip install -q --upgrade keras-nlp\r\n!pip install -q -U keras>=3",
"@farmaker47 \r\n\r\nNow, again I am getting that first mentioned error \r\n\r\ncould you please share your notebook link ?\r\n",
"The colab is from this example\r\n\r\nhttps://ai.google.dev/gemma/docs/lora_tuning\r\n\r\nI have changed nothing. So the idea is if you install tf-nightly the error for conversion disappears? I don't understand from your previous answer if the error is during tf-nightly installation or during conversion.",
"@farmaker47 \r\n\r\nI hope some package conflicts ,like some packages reinstall 'stable' version of tensorflow. Let me check\r\n",
"@farmaker47 \r\n\r\nI able to ran inference already. my problem is, i need to create a TFlite model for Gemma 2B. I think there is some problem still in conversion \r\n\r\ni am very new to AI and even python. ",
"> @farmaker47\r\n> \r\n> I able to ran inference already. my problem is, i need to create a TFlite model for Gemma 2B. I think there is some problem still in conversion\r\n> \r\n> i am very new to AI and even python.\r\n\r\nThen we have to wait a little bit so the TF team solves this and provide us the tf-nightly version we can use to convert it.",
"@LakshmiKalaKadali \r\n\r\n`import keras_nlp.backend import ops \r\n`\r\nis not needed. Sorry \r\n\r\nBut when using all nightly versions, I got some \"GraphDef\" issue ",
"a minimal script to reproduce the issue\r\n```python\r\nimport keras\r\nimport keras_nlp\r\nimport tensorflow as tf\r\n\r\nos.environ[\"KAGGLE_USERNAME\"] = '....'\r\nos.environ[\"KAGGLE_KEY\"] = '...'\r\nos.environ[\"KERAS_BACKEND\"] = \"tensorflow\" \r\n\r\ngemma_lm = keras_nlp.models.GemmaCausalLM.from_preset(\"gemma_2b_en\")\r\ntf.saved_model.save(gemma_lm.backbone, '/tmp/gemma_saved_model/')\r\n\r\nf = tf.lite.TFLiteConverter.from_saved_model('/tmp/gemma_saved_model/').convert()\r\n\r\n```\r\n\r\nI tested with tf-2.15, 2.16, and 2.17 nightly and their corresponding packages. None of them works.",
"Hi @pkgoogle,\r\n\r\nI have reproduced the issue in Colab with TF 2.15, the session crashed at the step `generator = keras_nlp.models.GemmaCausalLM.from_preset(\"gemma_2b_en\")` . Please take a look.\r\n\r\nThank You",
"Adding @advaitjain and @paulinesho for visibility.",
"I believe colab is running out of memory for @LakshmiKalaKadali 's case,\r\n\r\nIn attempting to replicate the below, I am running into tensorflow-text installation issues (apparently the Gemma tokenizer uses it for the tokenizer), this may be because of the new 2.16 release.\r\n```py\r\nimport keras\r\nimport keras_nlp\r\nimport tensorflow as tf\r\n\r\nos.environ[\"KAGGLE_USERNAME\"] = '....'\r\nos.environ[\"KAGGLE_KEY\"] = '...'\r\nos.environ[\"KERAS_BACKEND\"] = \"tensorflow\" \r\n\r\ngemma_lm = keras_nlp.models.GemmaCausalLM.from_preset(\"gemma_2b_en\")\r\ntf.saved_model.save(gemma_lm.backbone, '/tmp/gemma_saved_model/')\r\n\r\nf = tf.lite.TFLiteConverter.from_saved_model('/tmp/gemma_saved_model/').convert()\r\n```\r\n\r\nmy error:\r\n```py\r\nTypeError: <class 'keras_nlp.src.models.gemma.gemma_tokenizer.GemmaTokenizer'> could not be deserialized properly. Please ensure that components that are Python object instances (layers, models, etc.) returned by `get_config()` are explicitly deserialized in the model's `from_config()` method.\r\n\r\nconfig={'module': 'keras_nlp.src.models.gemma.gemma_tokenizer', 'class_name': 'GemmaTokenizer', 'config': {'name': 'gemma_tokenizer', 'trainable': True, 'dtype': 'int32', 'proto': None, 'sequence_length': None}, 'registered_name': 'keras_nlp>GemmaTokenizer', 'assets': ['assets/tokenizer/vocabulary.spm'], 'weights': None}.\r\n\r\nException encountered: Error when deserializing class 'GemmaTokenizer' using config={'name': 'gemma_tokenizer', 'trainable': True, 'dtype': 'int32', 'proto': None, 'sequence_length': None}.\r\n```",
"I think there is an answer here that it is working:\r\nhttps://github.com/keras-team/keras/issues/19108\r\nIt is based on this comment:\r\nhttps://github.com/keras-team/keras/issues/19108#issuecomment-1913421572\r\n\r\nSo my code now is:\r\n```\r\nmodel.export(\"test\", \"tf_saved_model\")\r\nconverter = tf.lite.TFLiteConverter.from_saved_model(\"test\")\r\ntflite_model = converter.convert()\r\nwith open(\"model.tflite\", \"wb\") as f:\r\n f.write(tflite_model)\r\n```\r\n\r\nWith the above the conversion finishes and the .tflite model is running into android. I have not used quantization since it is failing into android.",
"@farmaker47 \r\n\r\nI ran like this:\r\n\r\n```\r\nimport os\r\nimport keras\r\nimport os\r\nimport numpy as np\r\nimport keras_nlp\r\nimport tensorflow as tf\r\nimport tensorflow_text as tf_text\r\nfrom tensorflow import keras\r\nfrom tensorflow.lite.python import interpreter\r\nimport time\r\n\r\nos.environ[\"KAGGLE_USERNAME\"] = \"rag\"\r\nos.environ[\"KAGGLE_KEY\"] = 'e7c'\r\nos.environ[\"KERAS_BACKEND\"] = \"tensorflow\" # Or \"tensorflow\" or \"torch\".\r\n\r\npreprocessor = keras_nlp.models.GemmaCausalLMPreprocessor.from_preset('gemma_2b_en', sequence_length=4096, add_end_token=True\r\n)\r\nmodel = keras_nlp.models.GemmaCausalLM.from_preset(\"gemma_2b_en\")\r\nprint(\"exporting\")\r\nmodel.export(\"test\", \"tf_saved_model\")\r\nprint(\"converting\")\r\nconverter = tf.lite.TFLiteConverter.from_saved_model(\"test\")\r\ntflite_model = converter.convert()\r\nprint(\"writiing\")\r\n\r\nwith open(\"model.tflite\", \"wb\") as f:\r\n f.write(tflite_model)\r\n```\r\n\r\nIit fails at \"converting\" with this error \"GPU:0 in order to run Identity: Dst tensor is not initialized. [Op:Identity] name: \" :\r\n\r\n\r\n```\r\n2024-02-28 17:40:21.522813: I external/local_tsl/tsl/framework/bfc_allocator.cc:1114] Stats: \r\nLimit: 23553966080\r\nInUse: 23553959680\r\nMaxInUse: 23553959680\r\nNumAllocs: 1629\r\nMaxAllocSize: 2097152000\r\nReserved: 0\r\nPeakReserved: 0\r\nLargestFreeBlock: 0\r\n\r\n2024-02-28 17:40:21.522846: W external/local_tsl/tsl/framework/bfc_allocator.cc:497] ****************************************************************************************************\r\nTraceback (most recent call last):\r\n File \"/workspace/gem.py\", line 22, in <module>\r\n converter = tf.lite.TFLiteConverter.from_saved_model(\"test\")\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/lite.py\", line 2087, in from_saved_model\r\n saved_model = _load(saved_model_dir, tags)\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/saved_model/load.py\", line 912, in load\r\n result = load_partial(export_dir, None, tags, options)[\"root\"]\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/saved_model/load.py\", line 1043, in load_partial\r\n loader = Loader(object_graph_proto, saved_model_proto, export_dir,\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/saved_model/load.py\", line 226, in __init__\r\n self._restore_checkpoint()\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/saved_model/load.py\", line 561, in _restore_checkpoint\r\n load_status = saver.restore(variables_path, self._checkpoint_options)\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/checkpoint/checkpoint.py\", line 1479, in restore\r\n checkpoint=checkpoint, proto_id=0).restore(self._graph_view.root,\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/checkpoint/restore.py\", line 62, in restore\r\n restore_ops = self._restore_descendants(reader)\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/checkpoint/restore.py\", line 463, in _restore_descendants\r\n current_position.checkpoint.restore_saveables(\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/checkpoint/checkpoint.py\", line 379, in restore_saveables\r\n registered_savers).restore(self.save_path_tensor, self.options)\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/checkpoint/functional_saver.py\", line 499, in restore\r\n restore_ops = restore_fn()\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/checkpoint/functional_saver.py\", line 467, in restore_fn\r\n ret = restore_fn(restored_tensors)\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/training/saving/saveable_object_util.py\", line 747, in _restore_from_tensors\r\n return saveable_object_to_restore_fn(self.saveables)(restored_tensors)\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/training/saving/saveable_object_util.py\", line 784, in _restore_from_tensors\r\n restore_ops[saveable.name] = saveable.restore(\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/training/saving/saveable_object_util.py\", line 602, in restore\r\n ret = restore_fn(restored_tensor_dict)\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/ops/resource_variable_ops.py\", line 779, in _restore_from_tensors\r\n restored_tensor = array_ops.identity(\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/ops/weak_tensor_ops.py\", line 88, in wrapper\r\n return op(*args, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/util/traceback_utils.py\", line 153, in error_handler\r\n raise e.with_traceback(filtered_tb) from None\r\n File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\", line 5883, in raise_from_not_ok_status\r\n raise core._status_to_exception(e) from None # pylint: disable=protected-access\r\ntensorflow.python.framework.errors_impl.InternalError: Failed copying input tensor from \r\n\r\n/job:localhost/replica:0/task:0/device:CPU:0 to /job:localhost/replica:0/task:0/device:GPU:0 in order to run Identity: Dst tensor is not initialized. [Op:Identity] name: \r\n```\r\n\r\nAm I doing something wrong ?",
"You can skip the Kaggle_key...😀\r\n\r\nI think it's a memory error",
"@farmaker47 \r\n\r\nI rented 48 GB GPU, now got another error : \r\n\r\n```\r\nconverting\r\n2024-02-28 17:51:39.439053: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:378] Ignored output_format.\r\n2024-02-28 17:51:39.439136: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:381] Ignored drop_control_dependency.\r\n2024-02-28 17:51:39.440216: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: test\r\n2024-02-28 17:51:39.459869: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve }\r\n2024-02-28 17:51:39.459902: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: test\r\n2024-02-28 17:51:39.760067: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled\r\n2024-02-28 17:51:39.808678: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle.\r\n2024-02-28 17:51:44.034223: I tensorflow/cc/saved_model/loader.cc:217] Running initialization op on SavedModel bundle at path: test\r\n2024-02-28 17:51:44.392812: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 4952596 microseconds.\r\n2024-02-28 17:51:44.754857: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\r\nSummary on the non-converted ops:\r\n---------------------------------\r\n * Accepted dialects: tfl, builtin, func\r\n * Non-Converted Ops: 220, Total Ops 2416, % non-converted = 9.11 %\r\n * 184 ARITH ops, 36 TF ops\r\n\r\n- arith.constant: 184 occurrences (f32: 153, i32: 31)\r\n\r\n\r\n\r\n- tf.StridedSlice: 36 occurrences (i1: 18, i32: 18)\r\n (f32: 127)\r\n (f32: 36)\r\n (i1: 18)\r\n (f32: 36, i32: 20)\r\n (i32: 72)\r\n (f32: 18)\r\n (f32: 37)\r\n (i1: 18)\r\n (f32: 19, i1: 18)\r\n (f32: 127)\r\n (f32: 1, i32: 90)\r\n (f32: 18)\r\n (i1: 18)\r\n (i32: 1)\r\n (f32: 37)\r\n (i32: 19)\r\n (f32: 272)\r\n\r\n (f32: 36, i32: 90)\r\n (f32: 18)\r\n (i1: 18)\r\n (f32: 252)\r\n (f32: 18)\r\n (i32: 180)\r\n (f32: 18)\r\n (f32: 18)\r\n (f32: 36)\r\n (f32: 37)\r\n (f32: 37)\r\n (f32: 36, i32: 180)\r\n (f32: 54)\r\n (f32: 90)\r\n (i32: 54)\r\n (f32: 18)\r\nKilled\r\n```\r\n\r\nThe process terminates with \"killed\" message. Didn't enter \"writing\" ! ",
"@RageshAntonyHM this looks like the OS terminated the process, maybe due to memory consumption/cpu time limitation?",
"@RageshAntonyHM looks like it is still memory and compute issue",
"> I think there is an answer here that it is working: [keras-team/keras#19108](https://github.com/keras-team/keras/issues/19108) It is based on this comment: [keras-team/keras#19108 (comment)](https://github.com/keras-team/keras/issues/19108#issuecomment-1913421572)\r\n> \r\n> So my code now is:\r\n> \r\n> ```\r\n> model.export(\"test\", \"tf_saved_model\")\r\n> converter = tf.lite.TFLiteConverter.from_saved_model(\"test\")\r\n> tflite_model = converter.convert()\r\n> with open(\"model.tflite\", \"wb\") as f:\r\n> f.write(tflite_model)\r\n> ```\r\n> \r\n> With the above the conversion finishes and the .tflite model is running into android. I have not used quantization since it is failing into android.\r\n\r\nI can cofirm that I could get tflite by using:\r\n```python\r\ngemma_lm.backbone.export('/tmp/gemma_saved_model')\r\n```\r\ninstead of \r\n```python\r\ntf.saved_model.save(gemma_lm.backbone, '/tmp/gemma_saved_model/')\r\n```\r\n\r\nNote that the converter seems not memory efficient; I observed more than 90 GiB virtual memory was needed on my desktop machine.",
"@freedomtan can you share your working colab here ",
"> @freedomtan can you share your working colab here\r\n\r\nnope, because I tested it with a simple script on my local machine; didn't try to deal with memory issues in Colab :-)\r\n```python\r\nimport keras\r\nimport keras_nlp\r\nimport tensorflow as tf\r\n\r\nos.environ[\"KAGGLE_USERNAME\"] = '....'\r\nos.environ[\"KAGGLE_KEY\"] = '...'\r\nos.environ[\"KERAS_BACKEND\"] = \"tensorflow\" \r\n\r\ngemma_lm = keras_nlp.models.GemmaCausalLM.from_preset(\"gemma_2b_en\")\r\ngemma_lm.backbone.export('/tmp/gemma_saved_model')\r\n\r\ntflite_model = tf.lite.TFLiteConverter.from_saved_model('/tmp/gemma_saved_model/').convert()\r\nwith open(\"model.tflite\", \"wb\") as f:\r\n f.write(tflite_model)\r\n```\r\n\r\nFor test, I modified keras_nlp to have fixed tensor dimensions. That's it.\r\n\r\n"
] | 2024-02-22T07:33:01 | 2024-06-11T20:32:17 | null |
NONE
| null | null | null |
I tried converting Google Gemma 2B models to TfLite. Found it ending in failure
### 1. System information
- Ubuntu 22.04
- TensorFlow installation (installed with keras-nlp) :
- TensorFlow library (installed with keras-nlp):
### 2. Code
```
import os
import keras
import os
import numpy as np
import keras_nlp
import tensorflow as tf
import tensorflow_text as tf_text
from tensorflow import keras
from tensorflow.lite.python import interpreter
import time
os.environ["KAGGLE_USERNAME"] = "rag"
os.environ["KAGGLE_KEY"] = 'e7c'
os.environ["KERAS_BACKEND"] = "tensorflow" # Or "tensorflow" or "torch".
preprocessor = keras_nlp.models.GemmaCausalLMPreprocessor.from_preset('gemma_2b_en', sequence_length=4096, add_end_token=True
)
generator = keras_nlp.models.GemmaCausalLM.from_preset("gemma_2b_en")
def run_inference(input, generate_tflite):
interp = interpreter.InterpreterWithCustomOps(
model_content=generate_tflite,
custom_op_registerers=tf_text.tflite_registrar.SELECT_TFTEXT_OPS)
interp.get_signature_list()
preprocessor_output = preprocessor.generate_preprocess(
input, sequence_length=preprocessor.sequence_length
)
generator = interp.get_signature_runner('serving_default')
output = generator(preprocessor_output)
output = preprocessor.generate_postprocess(output["output_0"])
print("\nGenerated with TFLite:\n", output)
generate_function = generator.make_generate_function()
concrete_func = generate_function.get_concrete_function({
"token_ids": tf.TensorSpec([None, 4096]),
"padding_mask": tf.TensorSpec([None, 4096])
})
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func],
generator)
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.
tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.
]
converter.allow_custom_ops = True
converter.target_spec.experimental_select_user_tf_ops = ["UnsortedSegmentJoin", "UpperBound"]
converter._experimental_guarantee_all_funcs_one_use = True
generate_tflite = converter.convert()
run_inference("I'm enjoying a", generate_tflite)
with open('unquantized_mistral.tflite', 'wb') as f:
f.write(generate_tflite)
```
### 3. Failure after conversion
I am getting this error:
tensorflow/core.py":65:1))))))))))))))))))))))))))]): error: missing attribute 'value'
LLVM ERROR: Failed to infer result type(s).
Aborted (core dumped)
### 5. (optional) Any other info / logs
```
2024-02-22 06:34:41.094712: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:378] Ignored output_format.
2024-02-22 06:34:41.094742: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:381] Ignored drop_control_dependency.
2024-02-22 06:34:41.095691: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: /tmp/tmp58p378bn
2024-02-22 06:34:41.140303: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve }
2024-02-22 06:34:41.140329: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: /tmp/tmp58p378bn
2024-02-22 06:34:41.233389: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled
2024-02-22 06:34:41.264724: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle.
2024-02-22 06:34:43.697440: I tensorflow/cc/saved_model/loader.cc:217] Running initialization op on SavedModel bundle at path: /tmp/tmp58p378bn
2024-02-22 06:34:44.189111: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 3093423 microseconds.
2024-02-22 06:34:45.009212: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
loc(fused["ReadVariableOp:", callsite("decoder_block_0_1/attention_1/attention_output_1/Cast/ReadVariableOp@__inference_generate_step_12229"("/workspace/gem.py":38:1) at callsite("/usr/local/lib/python3.10/dist-packages/keras_nlp/models/gemma/gemma_causal_lm.py":258:1 at callsite("/usr/local/lib/python3.10/dist-packages/keras_nlp/models/gemma/gemma_causal_lm.py":235:1 at callsite("/usr/local/lib/python3.10/dist-packages/keras_nlp/models/gemma/gemma_causal_lm.py":212:1 at callsite("/usr/local/lib/python3.10/dist-packages/keras_nlp/models/gemma/gemma_causal_lm.py":214:1 at callsite("/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py":118:1 at callsite("/usr/local/lib/python3.10/dist-packages/keras/src/layers/layer.py":816:1 at callsite("/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py":118:1 at callsite("/usr/local/lib/python3.10/dist-packages/keras/src/ops/operation.py":42:1 at callsite("/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py":157:1 at callsite("/usr/local/lib/python3.10/dist-packages/keras_nlp/models/gemma/gemma_decoder_block.py":147:1 at callsite("/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py":118:1 at callsite("/usr/local/lib/python3.10/dist-packages/keras/src/layers/layer.py":816:1 at callsite("/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py":118:1 at callsite("/usr/local/lib/python3.10/dist-packages/keras/src/ops/operation.py":42:1 at callsite("/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py":157:1 at callsite("/usr/local/lib/python3.10/dist-packages/keras_nlp/models/gemma/gemma_attention.py":193:1 at callsite("/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py":118:1 at callsite("/usr/local/lib/python3.10/dist-packages/keras/src/layers/layer.py":816:1 at callsite("/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py":118:1 at callsite("/usr/local/lib/python3.10/dist-packages/keras/src/ops/operation.py":42:1 at callsite("/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py":157:1 at callsite("/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/einsum_dense.py":218:1 at callsite("/usr/local/lib/python3.10/dist-packages/keras/src/ops/numpy.py":2414:1 at callsite("/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/numpy.py":90:1 at callsite("/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/numpy.py":91:1 at "/usr/local/lib/python3.10/dist-packages/keras/src/backend/tensorflow/core.py":65:1))))))))))))))))))))))))))]): error: missing attribute 'value'
LLVM ERROR: Failed to infer result type(s).
Aborted (core dumped)
```
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I_kwDOArmXAs6ADPUG
| 63,024 |
How to use Hand pose detection module in iOS?
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[
"Hi @vnanaware111 ,\r\n\r\nThere are three CocoaPods for TensorFlow Lite:\r\n\r\n`TensorFlowLiteSwift`: Provides the Swift APIs for TensorFlow Lite.\r\n`TensorFlowLiteObjC`: Provides the Objective-C APIs for TensorFlow Lite.\r\n`TensorFlowLiteC`: Common base pod, which embeds the TensorFlow Lite core runtime and exposes the base C APIs used by the above two pods. \r\nYou should choose either `TensorFlowLiteSwift` or `TensorFlowLiteC` pod based on the language in which your app is written, but not both. The exact steps for using local builds of TensorFlow Lite differ, depending on which exact part you would like to build.\r\nPlease go through the reference [document](https://www.tensorflow.org/lite/guide/build_ios) and reference example [code](https://github.com/tensorflow/examples/tree/master/lite/examples/pose_estimation/ios) .\r\n\r\nThank You",
"<img width=\"587\" alt=\"Screenshot 2024-02-23 at 3 56 16 PM\" src=\"https://github.com/tensorflow/tensorflow/assets/156997549/c0f3bfc6-0777-41bf-8601-0681555d4121\">\r\n\r\nI want like this in iOS swift using TensorFlow. is it possible?",
"Hi @vnanaware111,\r\n\r\nAs mentioned in the earlier post, in this [link](https://github.com/tensorflow/examples/tree/master/lite/examples/pose_estimation/ios), pose estimation tflite example is given. There is no pretrained model for hand pose detection available. For your usecase you can customize the pose detection example. For other sources of hand pose detection please refer [link1](https://developers.google.com/mediapipe/solutions/vision/hand_landmarker/ios\r\n), [link2](https://mediapipe-studio.webapps.google.com/studio/demo/hand_landmarker).\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."
] | 2024-02-22T06:40:45 | 2024-03-13T01:47:37 | 2024-03-13T01:47:36 |
NONE
| null | null | null |
I want to use Hand Pose Detection module in iOS (Swift), So which pod I use or any reference source code available.
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I_kwDOArmXAs6ACn6B
| 63,023 |
Pallas like kernel language for TensorFlow
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[] | 2024-02-22T04:33:47 | 2024-02-26T03:50:21 | null |
CONTRIBUTOR
| null | null | null |
### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.15
### 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?
JAX provided [Pallas](https://jax.readthedocs.io/en/latest/pallas/tpu.html) to write TPU and GPU kernel. It would be great if TensorFlow had a similar feature.
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| 2,147,940,783 |
PR_kwDOArmXAs5nlk7n
| 63,022 |
tfl: mul_test: optimize the MultiDimBroadcastSubshard tests
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[
"You describe a 0.6% improvement on the performance of a test. I am not approving this.",
"> You describe a 0.6% improvement on the performance of a test. I am not approving this.\r\n\r\nI didn't run too much iterations, just tried more and the improvement can be 3% to 4% on my end now.\r\n\r\nHi @qukhan in case of it is a correct change, why don't you want to take it? Improvemence accumulates step by step, no?"
] | 2024-02-22T00:26:45 | 2024-02-27T20:17:48 | 2024-02-27T14:18:12 |
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Optimize to avoid doing duplicate calculation to speed up the cases *MultiDimBroadcastSubshard*.
This improves the execution time for example from 1m11.090s to 1m10.645s.
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load_model() cannot load model from previous version
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[
"Hi **@eneskelestemur** ,\r\nSorry for late reply, I tried to run your code on Colab using TF v2.14 and 2.15 faced the same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/71d4dbeea1ad6d3e712ce4554d783763/63021_2-14-v-2-15.ipynb) here for reference. \r\nThanks you!",
"Hi @eneskelestemur ,\r\n\r\nI tried to run your code on colab using TF v2.16.1 with nightly now it is working fine. Could you please check with recent version. Here i providing [gist](https://colab.sandbox.google.com/gist/Venkat6871/37e51a98566ab8faefa4ddd3d9397d02/63021_2-16-1-nightly.ipynb) for your reference.\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/63021\">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/63021\">No</a>\n"
] | 2024-02-21T23:21:33 | 2024-05-10T01:49:24 | 2024-05-10T01:49:21 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.14 and tf 2.15
### 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?
Hello,
I was trying to load a model I previously saved with tf 2.14, and got the displayed error. I realized that the model was saved with tf 2.14, and the environment I was trying to load the model in was tf 2.15. I'm not sure if this is an expected behavior, but it seemed odd since the version difference wasn't that big.
### Standalone code to reproduce the issue
```shell
# previously saved model with tf 2.14
# import tensorflow as tf
# from tensorflow import keras
# import numpy as np
# # print version
# print(tf.__version__)
# # random data
# data = np.random.random((100, 10))
# labels = np.random.random((100, ))
# # simple model
# normalizer = keras.layers.Normalization()
# normalizer.adapt(data)
# model = keras.Sequential()
# model.add(normalizer)
# model.add(keras.layers.Dense(10))
# model.add(keras.layers.Dense(1))
# model.compile(loss='mean_squared_error', optimizer='adam')
# model.fit(data, labels, epochs=10, verbose=2)
# model.save('test.keras')
### loading with tf 2.15 ###
import tensorflow as tf
from tensorflow import keras
# print version
print(tf.__version__)
# load model
model = keras.models.load_model('test.keras')
```
### Relevant log output
```shell
2.15.0
Traceback (most recent call last):
File "/work/users/e/n/enesk/phakinpro/test_load.py", line 8, in <module>
model = keras.models.load_model('test.keras')
File "/nas/longleaf/home/enesk/miniforge3/envs/tf/lib/python3.9/site-packages/keras/src/saving/saving_api.py", line 254, in load_model
return saving_lib.load_model(
File "/nas/longleaf/home/enesk/miniforge3/envs/tf/lib/python3.9/site-packages/keras/src/saving/saving_lib.py", line 281, in load_model
raise e
File "/nas/longleaf/home/enesk/miniforge3/envs/tf/lib/python3.9/site-packages/keras/src/saving/saving_lib.py", line 246, in load_model
model = deserialize_keras_object(
File "/nas/longleaf/home/enesk/miniforge3/envs/tf/lib/python3.9/site-packages/keras/src/saving/serialization_lib.py", line 728, in deserialize_keras_object
instance = cls.from_config(inner_config)
File "/nas/longleaf/home/enesk/miniforge3/envs/tf/lib/python3.9/site-packages/keras/src/engine/sequential.py", line 471, in from_config
model.add(layer)
File "/nas/longleaf/home/enesk/miniforge3/envs/tf/lib/python3.9/site-packages/tensorflow/python/trackable/base.py", line 204, in _method_wrapper
result = method(self, *args, **kwargs)
File "/nas/longleaf/home/enesk/miniforge3/envs/tf/lib/python3.9/site-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/nas/longleaf/home/enesk/miniforge3/envs/tf/lib/python3.9/site-packages/keras/src/layers/preprocessing/normalization.py", line 188, in build
raise ValueError(
ValueError: All `axis` values to be kept must have known shape. Got axis: (-1,), input shape: [None, None], with unknown axis at index: 1
```
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PR_kwDOArmXAs5nlSxA
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Cherrypick r2.15: Update ml_dtypes runtime dependency to 0.3.1 to fix package conflict issues
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Fixes #62746
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Dataset is never fully read when caching to disk
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[
"@vanskarner,\r\nI tried to execute the mentioned code with **train_data.take(1) or train_data.take(1).cache()** on both tensorflow [v2.15](https://colab.research.google.com/gist/tilakrayal/53f5ab525c088b7fd7f959f8a32c5cf6/untitled1734.ipynb) and v2.13, and observed that the output is intended and also the image is also visible. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/a10ec69e60ef2fe3c67c02f3844e7669/untitled1735.ipynb) and let me know. Thank you!",
"> @vanskarner, I tried to execute the mentioned code with **train_data.take(1) or train_data.take(1).cache()** on both tensorflow [v2.15](https://colab.research.google.com/gist/tilakrayal/53f5ab525c088b7fd7f959f8a32c5cf6/untitled1734.ipynb) and v2.13, and observed that the output is intended and also the image is also visible. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/a10ec69e60ef2fe3c67c02f3844e7669/untitled1735.ipynb) and let me know. Thank you!\r\n\r\nHi, when the code is executed in the google colab there are no resulting messages, however based on the template I filled in(OS platform and distribution) for this issue I still get the same message even if I upgrade to tensorflow v2.15, either using train_data.take(1) or train_data.take(1).cache() generate the same message described in Relevant log output.",
"@vanskarner,\r\nI suspect this is happening only with the Windows OS. When I tried to execute the same code on the other environments like Linux, MacOS and Colab, I don't face any issue/warning with the tensorflow 2.15.\r\n\r\nAlso the message is the Warning(W) which might not affect the execution of the code. 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/63019\">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/63019\">No</a>\n"
] | 2024-02-21T19:14:40 | 2024-03-27T01:47:23 | 2024-03-27T01:47:19 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.13.1
### Custom code
Yes
### OS platform and distribution
Windows 11
### Mobile device
_No response_
### Python version
Python 3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When displaying the dataset elements for viewing with either `train_data.take(1)` or `train_data.take(1).cache()` it always shows the message "The calling iterator did not fully read the dataset being cached...".
The following has already been tried, but the same message always appears:
```python
# train_data = train_data.cache()
# train_data = train_data.take(1)
# train_data = train_data.take(1).cache()
# train_data = train_data.take(1).prefetch(tf.data.experimental.AUTOTUNE)
```
There is a similar problem but the bot closed it due to inactivity: #60174
### Standalone code to reproduce the issue
```shell
import tensorflow_datasets as tfds
import matplotlib.pyplot as plt
data, metadata = tfds.load(
name='fashion_mnist',
as_supervised=True,
with_info=True)
train_data = data['train']
categories = metadata.features['label'].names
plt.suptitle('First Item')
for image, label_index in train_data.take(1):
plt.imshow(image, cmap='binary')
plt.xlabel(categories[label_index])
plt.colorbar()
plt.grid(visible=False)
plt.show()
```
### Relevant log output
```shell
2024-02-21 14:07:32.650452: W tensorflow/core/kernels/data/cache_dataset_ops.cc:854] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
```
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I_kwDOArmXAs5__ySz
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${BUILD_NUM_JOBS} not defined in TF lite building script leads to crash
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[
"Possible fix: In line 126 of file: `tensorflow/lite/tools/pip_package/build_pip_package_with_cmake.sh`, replace:\r\n```\r\ncmake --build . --verbose -j ${BUILD_NUM_JOBS} -t _pywrap_tensorflow_interpreter_wrapper\r\n```\r\nwith:\r\n```\r\ncmake --build . --verbose -j $(nproc) -t _pywrap_tensorflow_interpreter_wrapper\r\n```",
"Pull request:\r\nhttps://github.com/tensorflow/tensorflow/pull/63038",
"Posted 3 PR for head, r2.15 and r2.16",
"You can also define the envvar before building:\r\n\r\n```bash\r\nBUILD_NUM_JOBS=4 PYTHON=python3 tensorflow/lite/tools/pip_package/build_pip_package_with_cmake.sh native\r\n```\r\n\r\nIn fact, this is the way the scripts have been designed to be run a long time ago. One setup script defines the variables and then calls the exported scripts.",
"This is all fine and well. Yet, it's completely undocumented. People will find the following in the README.md page for tensorflow/lite:\r\n```\r\nPYTHON=python3 tensorflow/lite/tools/pip_package/build_pip_package_with_cmake.sh native\r\n```\r\nand in more cases than not, their system will crash as the compiler sees a `-j` option with no actual value. At the very least, the documentation should indicate a value is needed. Same for the following pages:\r\nhttps://www.tensorflow.org/lite/guide/build_cmake_pip\r\nhttps://www.tensorflow.org/lite/guide/build_cmake",
"Yes, that's why I recommended in #63039 to define a default smallish (~2 / 4?) value in case users are not passing one, instead of pinning to `nproc`.",
"I'd be happy to push a PR with the added definition. On Feb 25, 2024 10:11 AM, Mihai Maruseac ***@***.***> wrote:\r\nYes, that's why I recommended in #63039 to define a default smallish (~2 / 4?) value in case users are not passing one, instead of pinning to nproc.\r\n\r\n—Reply to this email directly, view it on GitHub, or unsubscribe.You are receiving this because you authored the thread.Message ID: ***@***.***>",
"Please CC me on the PR and I'll try to speed it up through the internal systems.\r\n\r\nThank you",
"Done. Thank you, @mihaimaruseac",
"This [PR](https://github.com/tensorflow/tensorflow/pull/63049) addressing the issue is now merged. Thanks very much, @mihaimaruseac. Closing.",
"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/63018\">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/63018\">No</a>\n",
"Thank you for the PRs"
] | 2024-02-21T18:27:02 | 2024-02-26T19:17:00 | 2024-02-26T19:13:40 |
CONTRIBUTOR
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15.0
### 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?
in line 126 of file: `tensorflow/lite/tools/pip_package/build_pip_package_with_cmake.sh`
```
cmake --build . --verbose -j ${BUILD_NUM_JOBS} -t _pywrap_tensorflow_interpreter_wrapper
```
the variable `${BUILD_NUM_JOBS}` is never defined. When compilation fo TF lite is carried out with cmake (docker, native) there is no limit in the number of processes used for compilation. This leads to a massive RAM/swap usage and in some cases when these resources run out, a crash (with logged error: `Killed signal terminated program cc1plus`).
The `${BUILD_NUM_JOBS}` variable should be set earlier in the script or replaced with a `-j 4`.
### Standalone code to reproduce the issue
```shell
PYTHON=python3 tensorflow/lite/tools/pip_package/build_pip_package_with_cmake.sh native
```
### Relevant log output
_No response_
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| 63,017 |
Build/release Python 3.11 tflite-runtime MacOS wheels to PyPI
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[
"This repo below is certainly not a replacement of a proper official support for `tflite_runtime` from Google. Yet, it provides help on making up-to-date `whl` packages yourself (and some unofficial builds, including for python 3.11).\r\n\r\nhttps://github.com/feranick/TFlite-builds\r\nhttps://github.com/feranick/TFlite-builds/releases/tag/v2.15.0",
"@chatnord,\r\nI request you to take a look at this [issue](https://github.com/tensorflow/tensorflow/issues/61860) where a similar feature has been proposed and it is still open at the developer end. Also I request to follow the similar feature which has been proposed to have the updates on the similar issue. Thank you!\r\n\r\n",
"@tilakrayal the issue you mentioned is not the same. It refers to adding support for Apple M2 (or at least verify it is supported). This issue is about providing python 3.11 runtimes even for platforms that are currently supported, such as x86_64 and Apple M1. This infact goes beyond MacOS. Runtimes binaries for Linux using 3.11 are missing as well. In other words, Google needs to simply compile and provide the binaries, see comment above.",
"To be clear, there are a number of issues reported and pointed to the same state of abandonment of the repo for modern platforms. ",
"Yes, the issue is the lack of executables for Python 3.11 on PiPy, as you can see here: \r\nhttps://pypi.org/project/tflite-runtime/2.14.0/#files\r\n(2.14.0 is the only release that supports Python 3.11).\r\nIt's not related to the above linked issue - I will have a look at the alternative repo.\r\n",
"Hi @terryheo, can you please take a look? Thanks.",
"Hi there, are there any updates on this? Still stuck :) Thank you!"
] | 2024-02-21T17:38:48 | 2024-04-22T17:20:23 | null |
NONE
| null | null | null |
### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.14.0
### Custom code
Yes
### OS platform and distribution
MacOs
### Mobile device
_No response_
### Python version
4.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
We are currently needing to run automated jobs on MacOS using tflite-runtime and Python 3.11, but the wheels on Pypi only cover Linux.
### Standalone code to reproduce the issue
```shell
pip install tflite-runtime
```
### Relevant log output
```shell
ERROR: Could not find a version that satisfies the requirement tflite-runtime (from versions: none)
ERROR: No matching distribution found for tflite-runtime
```
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PR_kwDOArmXAs5niVxQ
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tf-lite CMake: add headers into sources to correctly install them
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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/63016/checks?check_run_id=21822089891) 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 @lbertho-gpsw Can you please sign CLA? Thank you!",
"> Hi @lbertho-gpsw Can you please sign CLA? Thank you!\r\n\r\nI'm still waiting for the approval of the legal team of my company (it should not be a problem, but it can take some time...) but as soon as I have it I will sign it. ",
"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 @lbertho-gpsw I'm going to go ahead and close this PR, because it seems to have stalled. If you're still interested in pursing this (and responding to my comments), please feel free to reopen! Thank you for your contribution. ",
"The CLA is signed now, but apparently I can't reopen the PR, should I create a new one ?",
"> The CLA is signed now, but apparently I can't reopen the PR, should I create a new one ?\r\n\r\nHi @lbertho-gpsw Sure, please create a new one. Thank you!"
] | 2024-02-21T15:13:00 | 2024-05-27T05:51:07 | 2024-04-02T06:17:42 |
NONE
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The headers `delegate.h` and `delegate_options.h` were missing when tensorflow lite was installed with the GPU delegates using CMake.
This PR just add these 2 files into `TFLITE_DELEGATES_GPU_SRCS` variable and it fixes the issue. It works because `TFLITE_DELEGATES_GPU_SRCS` is used to create `_ALL_TFLITE_SRCS` which is used to have `_ALL_TFLITE_HDRS` and then all headers from this variables are installed.
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PR_kwDOArmXAs5niI0a
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[TFLite] Support for fully quantized fused custom operators
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"Hi @Tessil Can you please resolve conflicts? Thank you!",
"Hi @Tessil Can you please resolve conflicts? Thank you!",
"@gbaned Done, thanks",
"Hi @yijie-yang Can you please review this PR ? Thank you!"
] | 2024-02-21T14:48:57 | 2024-06-07T16:50:30 | null |
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Hi,
This PR adds support for fully quantized fused custom ops with two commits:
* The first one adds a `_tfl_no_side_effect` trait to mark a function as having no side-effect so that the dangling outputs are correctly pruned. This can also be done through the `tfl-post-quantize` pass with `enable-no-side-effect=custom_op_name=true`, the commit provides a way to do it directly from the Python API.
* The second one propagate the `_tfl_quant_trait` and `_tfl_no_side_effect` traits on a custom op as operator attribute instead of as a custom option of the custom operator.
We can then annotate a function as follow to have a fully quantizated fused op:
```python
def get_implements_signature():
implements_signature = [
'name: "tan"',
'attr {key: "tfl_fusable_op" value { b: true } }',
'attr {key: "_tfl_quant_trait" value { s: "fully_quantizable" } }',
'attr {key: "_tfl_no_side_effect" value { b: true } }',
]
return " ".join(implements_signature)
@tf.function(experimental_implements=get_implements_signature())
def tan_f(x):
return tf.math.sin(x) / tf.math.cos(x)
```
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Compilation error on macos with GPU delegate
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"I followed these directions on MacOS: https://www.tensorflow.org/lite/guide/build_cmake#opencl_gpu_delegate on r2.15 and nightly. I'm running into a different issue actually:\r\n\r\nThis part works fine:\r\n```\r\ncmake ../tensorflow_src/tensorflow/lite -DTFLITE_ENABLE_GPU=ON\r\n```\r\nbut\r\n```\r\ncmake --build . -j\r\n```\r\nfails here:\r\n```\r\ngmake[1]: *** [CMakeFiles/Makefile2:6653: _deps/xnnpack-build/CMakeFiles/microkernels-all.dir/all] Error 2\r\ngmake[1]: *** Waiting for unfinished jobs....\r\n[ 56%] Linking CXX static library libabsl_stacktrace.a\r\n[ 56%] Built target ruy_tune\r\n[ 56%] Building CXX object _deps/abseil-cpp-build/absl/base/CMakeFiles/absl_malloc_internal.dir/internal/low_level_alloc.cc.o\r\n[ 56%] Built target ruy_thread_pool\r\n[ 56%] Building CXX object _deps/abseil-cpp-build/absl/strings/CMakeFiles/absl_strings_internal.dir/internal/ostringstream.cc.o\r\n[ 56%] Building CXX object _deps/abseil-cpp-build/absl/strings/CMakeFiles/absl_strings_internal.dir/internal/escaping.cc.o\r\n[ 56%] Building CXX object _deps/abseil-cpp-build/absl/strings/CMakeFiles/absl_strings_internal.dir/internal/utf8.cc.o\r\n[ 56%] Building CXX object _deps/abseil-cpp-build/absl/hash/CMakeFiles/absl_city.dir/internal/city.cc.o\r\n[ 56%] Building CXX object _deps/abseil-cpp-build/absl/crc/CMakeFiles/absl_crc_cpu_detect.dir/internal/cpu_detect.cc.o\r\n[ 56%] Building CXX object _deps/abseil-cpp-build/absl/log/CMakeFiles/absl_log_internal_conditions.dir/internal/conditions.cc.o\r\n[ 56%] Building CXX object _deps/abseil-cpp-build/absl/debugging/CMakeFiles/absl_demangle_internal.dir/internal/demangle.cc.o\r\n[ 56%] Building CXX object _deps/abseil-cpp-build/absl/hash/CMakeFiles/absl_low_level_hash.dir/internal/low_level_hash.cc.o\r\n[ 56%] Built target absl_random_internal_randen_hwaes\r\n[ 56%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/abs.c.o\r\n[ 56%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph.c.o\r\n[ 56%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/clamp.c.o\r\n[ 56%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/average-pooling-2d.c.o\r\n[ 56%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/memory-planner.c.o\r\n[ 56%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/tensor.c.o\r\n[ 56%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/static-transpose.c.o\r\n[ 56%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/ceiling.c.o\r\n[ 56%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/depthwise-convolution-2d.c.o\r\n[ 56%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/copy.c.o\r\n[ 56%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/global-sum-pooling.c.o\r\n[ 56%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/static-reshape.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/add2.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/global-average-pooling.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/softmax.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/elu.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/hardswish.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/convert.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/unpooling-2d.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/concatenate.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/leaky-relu.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/fully-connected.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/maximum2.c.o\r\n[ 57%] Linking CXX static library libabsl_log_internal_conditions.a\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/depth-to-space.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/sigmoid.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/bankers-rounding.c.o\r\n[ 57%] Linking CXX static library libabsl_low_level_hash.a\r\n[ 57%] Linking CXX static library libabsl_malloc_internal.a\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/deconvolution-2d.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/squared-difference.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/max-pooling-2d.c.o\r\n[ 57%] Linking CXX static library libabsl_strings_internal.a\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/square-root.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/subtract.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/prelu.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/tanh.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/static-constant-pad.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/static-resize-bilinear-2d.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/negate.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/validation.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/runtime.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/static-slice.c.o\r\n[ 57%] Linking CXX static library libabsl_demangle_internal.a\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/minimum2.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/divide.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/square.c.o\r\n[ 57%] Linking CXX static library libabsl_city.a\r\n[ 57%] Built target absl_malloc_internal\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/space-to-depth-2d.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/argmax-pooling-2d.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/convolution-2d.c.o\r\n[ 57%] Built target absl_low_level_hash\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/even-split.c.o\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/multiply2.c.o\r\n[ 57%] Linking CXX static library libabsl_crc_cpu_detect.a\r\n[ 57%] Built target absl_strings_internal\r\n[ 57%] Built target absl_city\r\n[ 57%] Built target absl_stacktrace\r\n[ 57%] Building C object _deps/xnnpack-build/CMakeFiles/subgraph.dir/src/subgraph/floor.c.o\r\n[ 57%] Built target absl_demangle_internal\r\n[ 57%] Built target absl_log_internal_conditions\r\n[ 57%] Built target absl_crc_cpu_detect\r\n[ 57%] Built target subgraph\r\ngmake: *** [Makefile:136: all] Error 2\r\n```\r\n\r\n@terryheo, can you please take a look? Thanks."
] | 2024-02-21T14:45:55 | 2024-02-23T21:28:02 | null |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15.0
### Custom code
No
### OS platform and distribution
Macos 14.3.1
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
Apple clang 15.0.0
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I'm trying to build tensorflow-lite with the GPU delegates with CMake. It works well with Linux and Android (gcc), but for Macos (apple clang compiler) , I have a compilation error:
`tensorflow-lite/tensorflow/lite/delegates/gpu/api.cc:72:10: error: no matching function for call to 'visit'
return std::visit(ObjectTypeGetter{}, object);`
It complains about using std::visit on a absl::variant.
My guess is that std::visit with gcc is more permissive than the standard about std::visit and allow to use it with not only std::variant but also with other variant-like. On the other side, Clang is not that permissive.
I tried with the source from the 2.15.0 and also with the sources of the latest commit.
### Standalone code to reproduce the issue
```shell
cd tensorflow/lite && cmake -B build -DTFLITE_ENABLE_GPU=ON && cmake --build build
```
### Relevant log output
```shell
/Users/lbertho/devel/.sx/build/tensorflow-lite/tensorflow/lite/delegates/gpu/api.cc:72:10: error: no matching function for call to 'visit'
return std::visit(ObjectTypeGetter{}, object);
^~~~~~~~~~
/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX14.2.sdk/usr/include/c++/v1/variant:1753:26: note: candidate template ignored: substitution failure [with _Visitor = tflite::gpu::(anonymous namespace)::ObjectTypeGetter, _Vs = <const absl::variant<std::monostate, tflite::gpu::OpenGlBuffer, tflite::gpu::OpenGlTexture, tflite::gpu::CpuMemory, tflite::gpu::OpenClBuffer, tflite::gpu::OpenClTexture, tflite::gpu::VulkanBuffer, tflite::gpu::VulkanTexture> &>]: no matching function for call to '__as_variant'
constexpr decltype(auto) visit(_Visitor&& __visitor, _Vs&&... __vs) {
^
/Users/lbertho/devel/.sx/build/tensorflow-lite/tensorflow/lite/delegates/gpu/api.cc:79:10: error: no matching function for call to 'visit'
std::visit(ObjectValidityChecker{def.object_def.data_type}, object);
^~~~~~~~~~
/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX14.2.sdk/usr/include/c++/v1/variant:1753:26: note: candidate template ignored: substitution failure [with _Visitor = tflite::gpu::(anonymous namespace)::ObjectValidityChecker, _Vs = <const absl::variant<std::monostate, tflite::gpu::OpenGlBuffer, tflite::gpu::OpenGlTexture, tflite::gpu::CpuMemory, tflite::gpu::OpenClBuffer, tflite::gpu::OpenClTexture, tflite::gpu::VulkanBuffer, tflite::gpu::VulkanTexture> &>]: no matching function for call to '__as_variant'
constexpr decltype(auto) visit(_Visitor&& __visitor, _Vs&&... __vs) {
^
/Users/lbertho/devel/.sx/build/tensorflow-lite/tensorflow/lite/delegates/gpu/api.cc:85:14: error: no matching function for call to 'holds_alternative'
return std::holds_alternative<CpuMemory>(obj);
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX14.2.sdk/usr/include/c++/v1/variant:1502:16: note: candidate template ignored: could not match 'std::variant' against 'absl::variant'
constexpr bool holds_alternative(const variant<_Types...>& __v) noexcept {
^
/Users/lbertho/devel/.sx/build/tensorflow-lite/tensorflow/lite/delegates/gpu/api.cc:87:14: error: no matching function for call to 'holds_alternative'
return std::holds_alternative<OpenGlBuffer>(obj);
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX14.2.sdk/usr/include/c++/v1/variant:1502:16: note: candidate template ignored: could not match 'std::variant' against 'absl::variant'
constexpr bool holds_alternative(const variant<_Types...>& __v) noexcept {
^
/Users/lbertho/devel/.sx/build/tensorflow-lite/tensorflow/lite/delegates/gpu/api.cc:89:14: error: no matching function for call to 'holds_alternative'
return std::holds_alternative<OpenGlTexture>(obj);
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX14.2.sdk/usr/include/c++/v1/variant:1502:16: note: candidate template ignored: could not match 'std::variant' against 'absl::variant'
constexpr bool holds_alternative(const variant<_Types...>& __v) noexcept {
^
/Users/lbertho/devel/.sx/build/tensorflow-lite/tensorflow/lite/delegates/gpu/api.cc:91:14: error: no matching function for call to 'holds_alternative'
return std::holds_alternative<OpenClBuffer>(obj);
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX14.2.sdk/usr/include/c++/v1/variant:1502:16: note: candidate template ignored: could not match 'std::variant' against 'absl::variant'
constexpr bool holds_alternative(const variant<_Types...>& __v) noexcept {
^
/Users/lbertho/devel/.sx/build/tensorflow-lite/tensorflow/lite/delegates/gpu/api.cc:93:14: error: no matching function for call to 'holds_alternative'
return std::holds_alternative<OpenClTexture>(obj);
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX14.2.sdk/usr/include/c++/v1/variant:1502:16: note: candidate template ignored: could not match 'std::variant' against 'absl::variant'
constexpr bool holds_alternative(const variant<_Types...>& __v) noexcept {
^
/Users/lbertho/devel/.sx/build/tensorflow-lite/tensorflow/lite/delegates/gpu/api.cc:95:14: error: no matching function for call to 'holds_alternative'
return std::holds_alternative<VulkanBuffer>(obj);
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX14.2.sdk/usr/include/c++/v1/variant:1502:16: note: candidate template ignored: could not match 'std::variant' against 'absl::variant'
constexpr bool holds_alternative(const variant<_Types...>& __v) noexcept {
^
/Users/lbertho/devel/.sx/build/tensorflow-lite/tensorflow/lite/delegates/gpu/api.cc:97:14: error: no matching function for call to 'holds_alternative'
return std::holds_alternative<VulkanTexture>(obj);
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX14.2.sdk/usr/include/c++/v1/variant:1502:16: note: candidate template ignored: could not match 'std::variant' against 'absl::variant'
constexpr bool holds_alternative(const variant<_Types...>& __v) noexcept {
```
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| 63,013 |
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[
"@cbreak-black,\r\nI tried to execute the mentioned code on tensorflow v2.15 on both colab and the Ubuntu22.04 & observed that the output is as intended. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/12e749b2b659a99b69ad6e3ed15a3aa4/untitled.ipynb) and screen shot for the reference.\r\n\r\n\r\n\r\nThank you!\r\n",
"Interesting! I can reproduce the problem only when running on my GPU (RTX 3090Ti, CUDA 12.0, Driver 525.60.13, it works fine on CPU. Since it works on the GPU used by colab, it seems like it's something hardware / compute capability specific, or maybe cuda version specific. I'll try to isolate it further.\r\n\r\nHere the output of `tf.sysconfig.get_build_info()` from my local system, where it fails:\r\n```\r\nOrderedDict([('cpu_compiler', '/usr/bin/x86_64-linux-gnu-gcc-11'),\r\n ('cuda_compute_capabilities', ['sm_61', 'sm_75', 'compute_86']),\r\n ('cuda_version', '12.0'),\r\n ('cudnn_version', '8'),\r\n ('is_cuda_build', True),\r\n ('is_rocm_build', False),\r\n ('is_tensorrt_build', False)])\r\n```\r\n\r\nAnd from colab, where it seems to work:\r\n```\r\nOrderedDict([('cpu_compiler', '/usr/lib/llvm-17/bin/clang'),\r\n ('cuda_compute_capabilities',\r\n ['sm_50', 'sm_60', 'sm_70', 'sm_75', 'compute_80']),\r\n ('cuda_version', '12.2'),\r\n ('cudnn_version', '8'),\r\n ('is_cuda_build', True),\r\n ('is_rocm_build', False),\r\n ('is_tensorrt_build', True)])\r\n```",
"Alright, had the time to update my system to CUDA 12.3, and recompile tensorflow 2.14 (with CUDA 12.2), and the result is correct:\r\n```\r\n>>> tf.sysconfig.get_build_info()\r\nOrderedDict([('cpu_compiler', '/usr/bin/x86_64-linux-gnu-gcc-11'), ('cuda_compute_capabilities', ['sm_61', 'sm_75', 'compute_86']), ('cuda_version', '12.2'), ('cudnn_version', '8'), ('is_cuda_build', True), ('is_rocm_build', False), ('is_tensorrt_build', False)])\r\n>>> tf.reduce_sum(tf.math.bincount(tf.range(50000)))\r\n<tf.Tensor: shape=(), dtype=int32, numpy=50000>\r\n```\r\n\r\nIt seems the problem is cuda related, and possibly cuda-caused. Don't know if there's any reason to keep this issue open.",
"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/63013\">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/63013\">No</a>\n",
"@cbreak-black,\r\nYes as you mentioned, this issue is not related to tensorflow and more likely related to CUDA version which is not handled by the tensorflow team. Also I request to follow the tensorflow installation with the compatible test build configurations to avoid such type of errors/warnings.\r\n Thank you!"
] | 2024-02-21T13:04:28 | 2024-02-28T04:12:14 | 2024-02-26T22:59:00 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
v2.15.0
### 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
12.0
### GPU model and memory
RTX 3090Ti
### Current behavior?
The `tf.math.bincount` function returns incorrect results with medium size values, even though those don't exceed int32. Using int64 seems to work around the bug, at least at the number ranges that I tested.
This problem seems to have been introduced between 2.13 and 2.14, but still exists with 2.15.
### Standalone code to reproduce the issue
```shell
The expected result of the following function is `100000`.
tf.reduce_sum(tf.math.bincount(tf.range(100000)))
```
```
### Relevant log output
```shell
>>> tf.reduce_sum(tf.math.bincount(tf.range(50000)))
<tf.Tensor: shape=(), dtype=int32, numpy=42950>
>>> tf.reduce_sum(tf.math.bincount(tf.range(45000)))
<tf.Tensor: shape=(), dtype=int32, numpy=45000>
>>> tf.reduce_sum(tf.math.bincount(tf.range(47000)))
<tf.Tensor: shape=(), dtype=int32, numpy=45692>
>>> tf.reduce_sum(tf.math.bincount(tf.range(47000), dtype=tf.int64))
<tf.Tensor: shape=(), dtype=int64, numpy=47000>
```
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Unit test failures with Python 3.12 and gcc
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[
"Building with debug enabled makes the tests pass. This is an indication that there is a programming error causing undefined behaviour.",
"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/63012\">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/63012\">No</a>\n"
] | 2024-02-21T13:02:23 | 2024-02-29T09:00:15 | 2024-02-29T09:00:11 |
CONTRIBUTOR
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
git HEAD
### Custom code
No
### OS platform and distribution
Ubuntu 20.04
### Mobile device
n/a
### Python version
3.12.1
### Bazel version
6.5.0
### GCC/compiler version
10.2.1
### CUDA/cuDNN version
n/a
### GPU model and memory
n/a
### Current behavior?
The following tests fail
//tensorflow/python/eager:backprop_test_cpu
//tensorflow/python/eager/polymorphic_function:tracing_compilation_test
//tensorflow/python/eager:forwardprop_test_cpu
//tensorflow/python/saved_model:load_test_cpu
//tensorflow/python/eager/polymorphic_function:polymorphic_function_test_cpu
### Standalone code to reproduce the issue
```shell
bazel test --test_timeout=500,900,3000,-1 --copt=-flax-vector-conversions --test_env=TF2_BEHAVIOR=1 --define=tf_api_version=2 --test_size_filters=small,medium --test_lang_filters=py,cc --test_output=errors --verbose_failures=true --notest_verbose_timeout_warnings --action_env=PYTHON_BIN_PATH=/usr/local/bin/python3 --test_env=PORTSERVER_ADDRESS=@unittest-portserver --build_tag_filters=-no_oss,-oss_excluded,-oss_serial,-v1only,-benchmark-test,-no_aarch64,-gpu,-tpu,-no_oss_py39,-no_oss_py310 --test_tag_filters=-no_oss,-oss_excluded,-oss_serial,-v1only,-benchmark-test,-no_aarch64,-gpu,-tpu,-no_oss_py39,-no_oss_py310 --build_tests_only -- //tensorflow/... -//tensorflow/compiler/tf2tensorrt/... -//tensorflow/core/tpu/... -//tensorflow/go/... -//tensorflow/java/... -//tensorflow/python/integration_testing/... -//tensorflow/tools/toolchains/... -//tensorflow/lite/...
```
### Relevant log output
```shell
FAIL: test_functions_cleaned_LoadWithPython (__main__.SingleCycleTests)
SingleCycleTests.test_functions_cleaned_LoadWithPython
test_functions_cleaned_LoadWithPython(use_cpp_bindings=False)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/andrew/src/tf_test/tensorflow-git/bazel-ci_build-cache/.cache/bazel/_bazel_andrew/eab0d61a99b6696edb3d2aff87b585e8/execroot/org_tensorflow/bazel-out/aarch64-opt/bin/tensorflow/python/saved_model/load_test_cpu.runfiles/absl_py/absl/testing/parameterized.py", line 314, in bound_param_test
return test_method(self, **testcase_params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/andrew/src/tf_test/tensorflow-git/bazel-ci_build-cache/.cache/bazel/_bazel_andrew/eab0d61a99b6696edb3d2aff87b585e8/execroot/org_tensorflow/bazel-out/aarch64-opt/bin/tensorflow/python/saved_model/load_test_cpu.runfiles/org_tensorflow/tensorflow/python/framework/test_util.py", line 823, in decorator
assert not obj_count_by_type, (
AssertionError: The following objects were newly created: Counter({'list': 330, 'Operation': 270, 'TensorShape': 147})
----------------------------------------------------------------------
Ran 2 tests in 1.571s
FAILED (failures=1, skipped=1)
```
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PR_kwDOArmXAs5ngLo9
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[Linaro:ARM_CI] Tests now pass
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Remove these tests from the skip list as they now pass
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| 2,146,294,888 |
I_kwDOArmXAs5_7dxo
| 63,010 |
Load and do inference of model.hdf5 in c++
|
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[
"@devahh,\r\nCould you please let us know if there is any specific use-case to use C++ for CNN's. Most of the community members prefer python over C++.\r\n\r\nAFAIK there are few external sources which provide the knowledge on how to write the code on C++ and also the compilation process.\r\nhttps://discuss.tensorflow.org/t/neural-network-training-back-propagation-in-c/2283/2\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/63010\">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/63010\">No</a>\n"
] | 2024-02-21T09:51:49 | 2024-03-09T01:45:22 | 2024-03-09T01:45:19 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.14.0
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
3.11.5
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Hi,
Please provide instructions on how to load a CNN model in C++ script to run and do inference.
Thanks
### Standalone code to reproduce the issue
```shell
-
```
### Relevant log output
_No response_
|
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| 2,146,121,500 |
I_kwDOArmXAs5_6zcc
| 63,009 |
CONV1D , MAX POOLING 1D
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[
"Hi @hammnii-study,\r\n\r\nConv1D, Conv2D and Maxpooling1D, Maxpooling2D are the in-built ops. If you want to build any custom op for your usecase, please refer to the [document](https://www.tensorflow.org/lite/guide/ops_custom). To better understand the issue please share your reproducible code and any other related information.\r\n\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."
] | 2024-02-21T08:36:39 | 2024-03-09T01:45:20 | 2024-03-09T01:45:20 |
NONE
| null | null | null |
I want to embed tflite model in my mcu, but conv1D, MAX Pooling 1D was used in the model. But that function doesn't exist in the mutable ops list.
Going Googling, I saw the answer that Conv1D wraps to Conv2D automatically when converting from Stackoverflow to tflite(https://stackoverflow.com/questions/67481996/custom-op-is-replaced-by-another-ops).
So should I use 'conv2d' and 'maxpooling2D' for mutable ops when embedding my model (conv1D, with maxpooling1D)?
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I_kwDOArmXAs5_6fG0
| 63,008 |
`tf.keras.utils.image_dataset_from_directory` doesn't support 8-bit BMP when `color_mode` is grayscale
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[
"In my opinion, this problem is cause by the **num_channels** has different definition in `image_dataset_from_directory` and `tf.image.decode_image`.\r\n\r\nIf **color_mode** set as grayscale, **num_channels** will be 1, which cause a problem when `tf.image.decode_image` try to load bmp images in `tf.keras.utils.image_dataset_from_directory`.\r\n\r\nMy way to solve this problem is to add a condition in `image_dataset_from_directory` to set **num_channels** as 0 when image format is bmp and **color_mode** is grayscale. \r\nBut it raise another problem since the output shape should be (None, image_size, image_size, 1), so I also add a condition in `load_image` to set the shape to (None, image_size, image_size, 1) if output shape is (None, image_size, image_size, 0).",
"@yyingci,\r\nThank you for the issue. We are working on this. Could you please allow some time to deep dive into this issue and come back with the resolution. Thank you!",
"I solve this problem by editing the **datasetLoader.py** script directly.\r\nTo load grayscale bmp image correctly, I add a condition in `image_dataset_from_directory` function\r\n\r\n\r\nTo keep the output shape, I add a condition in `load_image` function\r\n\r\n\r\nHere is the entire script, hope it helps.\r\n```\r\ndef image_dataset_from_directory(\r\n directory,\r\n labels=\"inferred\",\r\n label_mode=\"int\",\r\n class_names=None,\r\n color_mode=\"rgb\",\r\n batch_size=32,\r\n image_size=(256, 256),\r\n shuffle=True,\r\n seed=None,\r\n validation_split=None,\r\n subset=None,\r\n interpolation=\"bilinear\",\r\n follow_links=False,\r\n crop_to_aspect_ratio=False,\r\n **kwargs,\r\n):\r\n \r\n if \"smart_resize\" in kwargs:\r\n crop_to_aspect_ratio = kwargs.pop(\"smart_resize\")\r\n if kwargs:\r\n raise TypeError(f\"Unknown keywords argument(s): {tuple(kwargs.keys())}\")\r\n if labels not in (\"inferred\", None):\r\n if not isinstance(labels, (list, tuple)):\r\n raise ValueError(\r\n \"`labels` argument should be a list/tuple of integer labels, \"\r\n \"of the same size as the number of image files in the target \"\r\n \"directory. If you wish to infer the labels from the \"\r\n \"subdirectory \"\r\n 'names in the target directory, pass `labels=\"inferred\"`. '\r\n \"If you wish to get a dataset that only contains images \"\r\n f\"(no labels), pass `labels=None`. Received: labels={labels}\"\r\n )\r\n if class_names:\r\n raise ValueError(\r\n \"You can only pass `class_names` if \"\r\n f'`labels=\"inferred\"`. Received: labels={labels}, and '\r\n f\"class_names={class_names}\"\r\n )\r\n if label_mode not in {\"int\", \"categorical\", \"binary\", None}:\r\n raise ValueError(\r\n '`label_mode` argument must be one of \"int\", '\r\n '\"categorical\", \"binary\", '\r\n f\"or None. Received: label_mode={label_mode}\"\r\n )\r\n if labels is None or label_mode is None:\r\n labels = None\r\n label_mode = None\r\n\r\n if color_mode == \"rgb\":\r\n num_channels = 3\r\n elif color_mode == \"rgba\":\r\n num_channels = 4\r\n elif color_mode == \"grayscale\":\r\n num_channels = 1\r\n elif color_mode == \"grayscale_bmp\":\r\n num_channels = 0\r\n else:\r\n raise ValueError(\r\n '`color_mode` must be one of {\"rgb\", \"rgba\", \"grayscale\"}. '\r\n f\"Received: color_mode={color_mode}\"\r\n )\r\n interpolation = image_utils.get_interpolation(interpolation)\r\n dataset_utils.check_validation_split_arg(\r\n validation_split, subset, shuffle, seed\r\n )\r\n\r\n if seed is None:\r\n seed = np.random.randint(1e6)\r\n image_paths, labels, class_names = dataset_utils.index_directory(\r\n directory,\r\n labels,\r\n formats=ALLOWLIST_FORMATS,\r\n class_names=class_names,\r\n shuffle=shuffle,\r\n seed=seed,\r\n follow_links=follow_links,\r\n )\r\n\r\n if label_mode == \"binary\" and len(class_names) != 2:\r\n raise ValueError(\r\n 'When passing `label_mode=\"binary\"`, there must be exactly 2 '\r\n f\"class_names. Received: class_names={class_names}\"\r\n )\r\n\r\n if subset == \"both\":\r\n (\r\n image_paths_train,\r\n labels_train,\r\n ) = dataset_utils.get_training_or_validation_split(\r\n image_paths, labels, validation_split, \"training\"\r\n )\r\n (\r\n image_paths_val,\r\n labels_val,\r\n ) = dataset_utils.get_training_or_validation_split(\r\n image_paths, labels, validation_split, \"validation\"\r\n )\r\n if not image_paths_train:\r\n raise ValueError(\r\n f\"No training images found in directory {directory}. \"\r\n f\"Allowed formats: {ALLOWLIST_FORMATS}\"\r\n )\r\n if not image_paths_val:\r\n raise ValueError(\r\n f\"No validation images found in directory {directory}. \"\r\n f\"Allowed formats: {ALLOWLIST_FORMATS}\"\r\n )\r\n train_dataset = paths_and_labels_to_dataset(\r\n image_paths=image_paths_train,\r\n image_size=image_size,\r\n num_channels=num_channels,\r\n labels=labels_train,\r\n label_mode=label_mode,\r\n num_classes=len(class_names),\r\n interpolation=interpolation,\r\n crop_to_aspect_ratio=crop_to_aspect_ratio,\r\n )\r\n val_dataset = paths_and_labels_to_dataset(\r\n image_paths=image_paths_val,\r\n image_size=image_size,\r\n num_channels=num_channels,\r\n labels=labels_val,\r\n label_mode=label_mode,\r\n num_classes=len(class_names),\r\n interpolation=interpolation,\r\n crop_to_aspect_ratio=crop_to_aspect_ratio,\r\n )\r\n train_dataset = train_dataset.prefetch(tf.data.AUTOTUNE)\r\n val_dataset = val_dataset.prefetch(tf.data.AUTOTUNE)\r\n\r\n if batch_size is not None:\r\n if shuffle:\r\n # Shuffle locally at each iteration\r\n train_dataset = train_dataset.shuffle(\r\n buffer_size=batch_size * 8, seed=seed\r\n )\r\n train_dataset = train_dataset.batch(batch_size)\r\n val_dataset = val_dataset.batch(batch_size)\r\n else:\r\n if shuffle:\r\n train_dataset = train_dataset.shuffle(\r\n buffer_size=1024, seed=seed\r\n )\r\n\r\n # Users may need to reference `class_names`.\r\n train_dataset.class_names = class_names\r\n val_dataset.class_names = class_names\r\n\r\n # Include file paths for images as attribute.\r\n train_dataset.file_paths = image_paths_train\r\n val_dataset.file_paths = image_paths_val\r\n dataset = [train_dataset, val_dataset]\r\n else:\r\n image_paths, labels = dataset_utils.get_training_or_validation_split(\r\n image_paths, labels, validation_split, subset\r\n )\r\n if not image_paths:\r\n raise ValueError(\r\n f\"No images found in directory {directory}. \"\r\n f\"Allowed formats: {ALLOWLIST_FORMATS}\"\r\n )\r\n\r\n dataset = paths_and_labels_to_dataset(\r\n image_paths=image_paths,\r\n image_size=image_size,\r\n num_channels=num_channels,\r\n labels=labels,\r\n label_mode=label_mode,\r\n num_classes=len(class_names),\r\n interpolation=interpolation,\r\n crop_to_aspect_ratio=crop_to_aspect_ratio,\r\n )\r\n dataset = dataset.prefetch(tf.data.AUTOTUNE)\r\n if batch_size is not None:\r\n if shuffle:\r\n # Shuffle locally at each iteration\r\n dataset = dataset.shuffle(buffer_size=batch_size * 8, seed=seed)\r\n dataset = dataset.batch(batch_size)\r\n else:\r\n if shuffle:\r\n dataset = dataset.shuffle(buffer_size=1024, seed=seed)\r\n\r\n # Users may need to reference `class_names`.\r\n dataset.class_names = class_names\r\n\r\n # Include file paths for images as attribute.\r\n dataset.file_paths = image_paths\r\n return dataset\r\n\r\ndef load_image(\r\n path, image_size, num_channels, interpolation, crop_to_aspect_ratio=False\r\n):\r\n \"\"\"Load an image from a path and resize it.\"\"\"\r\n img = tf.io.read_file(path)\r\n img = tf.image.decode_image(img, channels=num_channels, expand_animations=False)\r\n if crop_to_aspect_ratio:\r\n img = image_utils.smart_resize(\r\n img, image_size, interpolation=interpolation\r\n )\r\n else:\r\n img = tf.image.resize(img, image_size, method=interpolation)\r\n if num_channels == 0:\r\n img.set_shape((image_size[0], image_size[1], 1))\r\n else:\r\n img.set_shape((image_size[0], image_size[1], num_channels))\r\n return img\r\n```",
"@yyingci,\r\nAs mentioned it is an error provided by **tensorflow.image.decode_image** where there is a limitation to not support for BMP formats.\r\nSimilar issue on the same feature. https://github.com/tensorflow/tensorflow/issues/61893\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/63008\">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/63008\">No</a>\n"
] | 2024-02-21T07:45:36 | 2024-03-14T01:46:50 | 2024-03-14T01:46:46 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.8
### Custom code
Yes
### OS platform and distribution
Window 10
### Mobile device
Window 10
### Python version
3.8
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.3/8.2
### GPU model and memory
_No response_
### Current behavior?
`tf.keras.utils.image_dataset_from_directory` doesn't support 8-bit BMP when `color_mode` is grayscale
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
dataset_dir = 'your_dataset_path'
def get_dataset(dataset_dir):
return tf.keras.utils.image_dataset_from_directory(
directory=dataset_dir ,
label_mode='int',
class_names=['ok', 'ng'],
color_mode='grayscale',
batch_size=4,
image_size=(64, 64),
shuffle=True,
seed=369,
validation_split=0.2,
subset='training',
interpolation='bicubic'
)
dataset = get_dataset(dataset_dir)
for images, labels in dataset.take(1):
print('Image shape', images.shape)
```
### Relevant log output
```shell
tensorflow.python.framework.errors_impl.InvalidArgumentError: `channels` must be 0, 3 or 4 for BMP, but got 1
[[{{node decode_image/DecodeImage}}]] [Op:IteratorGetNext]
```
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PR_kwDOArmXAs5ne7SW
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r2.16 cherry-pick: a0d7ace8e2f "Update libtpu index and release version"
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Refer to the original commit: https://github.com/tensorflow/tensorflow/commit/a0d7ace8e2fd948920a0fee3618cb4fcc98eb513
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PR_kwDOArmXAs5ndrZQ
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Convert nested if block to nested match block
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[
"Hi @rohan100jain Can you please review this PR ? Thank you!"
] | 2024-02-21T02:27:05 | 2024-06-07T16:49:33 | null |
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A nested if block is rewritten as a more readable nested match block. `match` blocks are also generally more performant than trails of `elif`'s.
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Disables flaky test flag for TF 2.16 release.
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Export this one as a draft PR
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"Hi @kanglant This PR is duplicate of PR[#63003](https://github.com/tensorflow/tensorflow/pull/63003). Hence closing this PR. Thank you for your contribution!"
] | 2024-02-20T21:05:13 | 2024-02-27T03:32:49 | 2024-02-22T08:04:30 |
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Tensorflow v2.15.0 build fail
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[
"Hi @zahed327 ,\r\n\r\nFor TF2.15V builds are done and tested with Bazel 6.1.0 . Please try build with tested version only as mentioned [here](https://www.tensorflow.org/install/source#cpu_2) as forward versions may raise compatibility issues.\r\n\r\n\r\nVersion | Python version | Compiler | Build tools\r\n-- | -- | -- | --\r\ntensorflow-2.15.0 | 3.9-3.11 | Clang from xcode 10.15 | Bazel 6.1.0\r\n\r\n",
"I recommend to use [Bazelisk](https://github.com/bazelbuild/bazelisk) which can automatically select the compatible bazel version for the Tensorflow version that is being built.\r\n\r\nThanks!",
"Thank you, I'll probably try using Docker to avoid unforseen issues in the future",
"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/63002\">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/63002\">No</a>\n"
] | 2024-02-20T19:14:19 | 2024-02-21T20:03:22 | 2024-02-21T20:03:18 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15.0
### Custom code
No
### OS platform and distribution
macOS 14.0
### Mobile device
_No response_
### Python version
python 3.10
### Bazel version
6.5.0
### GCC/compiler version
_No response_
### CUDA/cuDNN version
none
### GPU model and memory
none
### Current behavior?
Unable to build tf v2.15.0
### Standalone code to reproduce the issue
```shell
Here's the terminal commands :
https://colab.research.google.com/drive/1CwOxD4shP4VzeLdkhZm6XXQl6BH_XdGi#scrollTo=7i3ykE297nzV
```
### Relevant log output
_No response_
|
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[
"@igormintz when you assign a name to a dataset using tf.data.Dataset.from_tensor_slices, the actual name gets stored in an internal attribute ._name instead of the public name attribute. This can be confusing, as printing the dataset won't show the assigned name.\r\n\r\nCould you use the `._name `attribute:\r\n```\r\nds = tf.data.Dataset.from_tensor_slices(..., name=\"my_dataset\")\r\nactual_name = ds._name\r\nprint(f\"Actual name: {actual_name}\")\r\n\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.",
"why not change the method form ._name to .name?\r\nhow to make the name stick to the tensor so it will show up when printed?",
"@igormintz TensorFlow maintains a consistent API for interacting with tensor attributes. Using a public property like .name ensures uniformity across different operations and functionalities. You can make the name stick to the tensor so it shows up when printed by assigning a name during its creation. You can assign names to any type of tensor created using TensorFlow functions like tf.constant, tf.zeros, tf.random.normal, etc.\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/63001\">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/63001\">No</a>\n"
] | 2024-02-20T18:48:52 | 2024-03-29T01:47:09 | 2024-03-29T01:47:01 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.17.0-dev20240220
### Custom code
Yes
### OS platform and distribution
colab, mac
### Mobile device
_No response_
### Python version
3.10.5
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
when trying to name a datset using `tf.data.Dataset.from_tensor_slices`, the name is saved under ._name and not name. When printing the ds, the name is None
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
train_ds = tf.data.Dataset.from_tensor_slices((['a','b']), name="train_ds")
print(train_ds)
print(train_ds._name) #prints train_ds
# print(train_ds.name) # AttributeError: '_TensorSliceDataset' object has no attribute 'name'
```
### Relevant log output
```shell
<_TensorSliceDataset element_spec=TensorSpec(shape=(), dtype=tf.string, name=None)>
```
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| 63,000 |
tf.keras.layers.Add with constant fails in tf.keras.models.load_model
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[
"Hi @elad-c ,\r\nI tried to run your code on Colab using TF v2.15 and nightly versions, In nightly version it is working fine. Could you please check with nightly version. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/503fcbc95d8a64d692d252ac4ca83493/63000_2-15-nightly.ipynb) here for reference.\r\n\r\nThank you!",
"Thanks!\r\nSo I should expect it to be solved in TF 2.17?",
"Hi **@elad-c** ,\r\n\r\nCould you please confirm if this issue is resolved for you ? Please feel free to close the issue if it is resolved ? \r\n\r\nThank you!",
"It's not resolved since I'm using a formal release, but I guess it will be resolved in the coming releases, so I'm closing the issue.\r\nThanks for your response.",
"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/63000\">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/63000\">No</a>\n"
] | 2024-02-20T15:34:37 | 2024-02-23T07:24:09 | 2024-02-23T07:24:05 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15.0
### 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?
Currently, it fails in building the Add layer:
> [/usr/local/lib/python3.10/dist-packages/keras/src/layers/merging/base_merge.py](https://localhost:8080/#) in <setcomp>(.0)
> 94 f"Full input_shape received: {input_shape}"
> 95 )
> ---> 96 batch_sizes = {s[0] for s in input_shape if s} - {None}
> 97 if len(batch_sizes) > 1:
> 98 raise ValueError(
>
> TypeError: unhashable type: 'list'
Expect not to fails...
### Standalone code to reproduce the issue
```shell
import os
import numpy as np
import tempfile
import tensorflow as tf
print(tf.__version__)
x = np.random.randn(1, 2, 2, 3)
_input = tf.keras.layers.Input(x.shape[1:])
const = tf.convert_to_tensor(np.ones((1, 1, 1, 3)).astype(np.float32))
_out = tf.keras.layers.Add()([_input, const])
model = tf.keras.Model(inputs=_input, outputs=_out)
_, tmp_h5_file = tempfile.mkstemp('.keras')
tf.keras.models.save_model(model, tmp_h5_file)
loaded_model = tf.keras.models.load_model(tmp_h5_file) # <== this line fails
os.remove(tmp_h5_file)
```
### Relevant log output
```shell
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-b3b2b0b93053> in <cell line: 9>()
7 _, tmp_h5_file = tempfile.mkstemp('.keras')
8 tf.keras.models.save_model(model, tmp_h5_file)
----> 9 loaded_model = tf.keras.models.load_model(tmp_h5_file)
10 os.remove(tmp_h5_file)
8 frames
/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_api.py in load_model(filepath, custom_objects, compile, safe_mode, **kwargs)
252 f"with the native Keras format: {list(kwargs.keys())}"
253 )
--> 254 return saving_lib.load_model(
255 filepath,
256 custom_objects=custom_objects,
/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py in load_model(filepath, custom_objects, compile, safe_mode)
279
280 except Exception as e:
--> 281 raise e
282 else:
283 return model
/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py in load_model(filepath, custom_objects, compile, safe_mode)
244 # Construct the model from the configuration file in the archive.
245 with ObjectSharingScope():
--> 246 model = deserialize_keras_object(
247 config_dict, custom_objects, safe_mode=safe_mode
248 )
/usr/local/lib/python3.10/dist-packages/keras/src/saving/serialization_lib.py in deserialize_keras_object(config, custom_objects, safe_mode, **kwargs)
726 safe_mode_scope = SafeModeScope(safe_mode)
727 with custom_obj_scope, safe_mode_scope:
--> 728 instance = cls.from_config(inner_config)
729 build_config = config.get("build_config", None)
730 if build_config:
/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py in from_config(cls, config, custom_objects)
3328 # Revive Functional model
3329 # (but not Functional subclasses with a custom __init__)
-> 3330 inputs, outputs, layers = functional.reconstruct_from_config(
3331 config, custom_objects
3332 )
/usr/local/lib/python3.10/dist-packages/keras/src/engine/functional.py in reconstruct_from_config(config, custom_objects, created_layers)
1503 while layer_nodes:
1504 node_data = layer_nodes[0]
-> 1505 if process_node(layer, node_data):
1506 layer_nodes.pop(0)
1507 else:
/usr/local/lib/python3.10/dist-packages/keras/src/engine/functional.py in process_node(layer, node_data)
1443 input_tensors
1444 )
-> 1445 output_tensors = layer(input_tensors, **kwargs)
1446
1447 # Update node index map.
/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py in error_handler(*args, **kwargs)
68 # To get the full stack trace, call:
69 # `tf.debugging.disable_traceback_filtering()`
---> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb
/usr/local/lib/python3.10/dist-packages/keras/src/layers/merging/base_merge.py in <setcomp>(.0)
94 f"Full input_shape received: {input_shape}"
95 )
---> 96 batch_sizes = {s[0] for s in input_shape if s} - {None}
97 if len(batch_sizes) > 1:
98 raise ValueError(
TypeError: unhashable type: 'list'
```
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I_kwDOArmXAs5_1Fr1
| 62,999 |
Micro_Speech error on Sparkfun Edge when using new model
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[
"Hi @ldbrouwer,\r\n\r\nThere is a mismatch between the expected and actual feature data sizes while inferencing on Sparkfun Edge. This might be the reason for `Didn't find op for builtin opcode 'RESHAPE' version '1'` so please make sure input and output shapes while inferencing should match that of your model. No. of features(1960) should match the dimensions as well. \r\n\r\nUse TFLite Micro benchmarking tool to analyze your model and identify potential compatibility issues. Also, there is a dedicated [repo](https://github.com/tensorflow/tflite-micro/issues) for TFLite micro issues. Please try to post there for quick response.\r\n\r\nThank You",
"LakshmiKalaKadali, Thank you for your response. I am working through some additions to check on input and output shapes. As I am new to working with Tensorflow, I do have a couple of follow-on questions as I work this issue. Additionally, these questions may provide clarity to you on what is happening: 1) I am using the Sparkfun Edge micro-speech code in both instances - a) the demo cdde that uses the \"yes/no\" model and b) for my \"On/Off\" model. The only data I changed (using the model I trained in Google Colab) was in the \"tiny_conv_micro_features_model_data.cc\" file (substituting the hex data and the size of the hex data) and the corresponding info in the \"micro_model_settings.cc\" (substituting ON for Yes and Off for No) and the same in the \"command_responder.cc\" files. My understanding is that these changes will not change the shape of the input or output. Could you please help me understand?\r\n2) I was getting very similar errors using the Arduino Nano BLE Sense and Arduino IDE. That error was solved by adding the opcode for CONV-2D to the micro_op_resolver as recommended in this forum. Again the only data I changed was using the same ON/OFF model trained on the Colab page. Since the model works on the Arduino, I am puzzled how the ON/OFF model shape would be the cause of the error on the Sparkfun Edge.\r\nThanks in advance,\r\nLonnie",
"LakshmiKalaKadali, \r\nI have discovered a great deal about my issue, but I have not solved it. Please provide guidance:\r\n**For clarity - I am running the example code for the Sparkfun Edge provided in the tensorflow repository\r\n\"tensorflow/tensorflow/lite/experimental/micro/examples/micro_speech\".** \r\n\r\n1) Upon compiling the test example code with the newly trained model, the first error encountered was \"Segmentation fault (core dumped)\"\r\nThis suggests that the first problem is incorrectly allocated memory. You can see the error messages in paragraph A below.\r\nAlthough I still get the opcode RESHAPE error and the dims errors. I believe these to be related to the memory allocation error.\r\n\r\n2) I then used the tflite quantized model (not the micro model, but the tflite model) and ran it through the TFLite model analyzer.\r\nI believe it shows that the model has the correct shape for all tensors - this is shown in paragraph B below\r\n\r\n3) I was not able to use the TFLite Micro benchmarking tool you recommended. I found several references on Google, but was not able to apply them.\r\nCould you direct me to a location and an example?\r\n\r\n4) I also played with the arena size. When I changed it to 50, I got a segmentation error and similar \"dims\" results. \r\nHowever, when I changed the arena size to 20, I still got the segmentation error, but the \"dims\" errors changed to more \"normal\" numbers\r\nas shown in paragraph C below.\r\n\r\nAgain, I would appreciate guidance,\r\n\r\nLonnie\r\n\r\nA) When compiling with newly trained model from the Colab page, the compile fails with the following info at the end of the compiler messages: \r\n ************************* \r\ntensorflow/lite/experimental/micro/testing/test_linux_binary.sh tensorflow/lite/experimental/micro/tools/make/gen/linux_x86_64/bin/micro_speech_test '~~~ALL TESTS PASSED~~~'\r\nSegmentation fault (core dumped)\r\ntensorflow/lite/experimental/micro/tools/make/gen/linux_x86_64/bin/micro_speech_test: FAIL - '~~~ALL TESTS PASSED~~~' not found in logs.\r\nTesting TestInvoke\r\nDidn't find op for builtin opcode 'RESHAPE' version '1'\r\n\r\nFailed to get registration from op code d\r\n \r\n4 == input->dims->size failed at tensorflow/lite/experimental/micro/examples/micro_speech/micro_speech_test.cc:77 (4 vs 1584022240)\r\n1 == input->dims->data[0] failed at tensorflow/lite/experimental/micro/examples/micro_speech/micro_speech_test.cc:78 (1 vs 32677)\r\n49 == input->dims->data[1] failed at tensorflow/lite/experimental/micro/examples/micro_speech/micro_speech_test.cc:79 (49 vs 1583772544)\r\n40 == input->dims->data[2] failed at tensorflow/lite/experimental/micro/examples/micro_speech/micro_speech_test.cc:80 (40 vs 32677)\r\n1 == input->dims->data[3] failed at tensorflow/lite/experimental/micro/examples/micro_speech/micro_speech_test.cc:81 (1 vs 1583773840)\r\nkTfLiteUInt8 == input->type failed at tensorflow/lite/experimental/micro/examples/micro_speech/micro_speech_test.cc:82 (3 vs 1540943972)\r\nSegmentation fault\r\nmake: *** [tensorflow/lite/experimental/micro/examples/micro_speech/Makefile.inc:246: test_micro_speech_test] Error 1\r\n*********************\r\n\r\nB) I also ran the TF Lite model analyzer on the tflite model (not the micro model) and it showed the following:\r\n=== TFLite ModelAnalyzer ===\r\n\r\nYour TFLite model has '1' subgraph(s). In the subgraph description below,\r\nT# represents the Tensor numbers. For example, in Subgraph#0, the RESHAPE op takes\r\ntensor #0 and tensor #1 as input and produces tensor #7 as output.\r\n\r\nSubgraph#0 main(T#0) -> [T#11]\r\n Op#0 RESHAPE(T#0, T#1[-1, 49, 40, 1]) -> [T#7]\r\n Op#1 CONV_2D(T#7, T#6, T#5[-19, 14, -425, 39, 0, ...]) -> [T#8]\r\n Op#2 RESHAPE(T#8, T#2[-1, 4000]) -> [T#9]\r\n Op#3 FULLY_CONNECTED(T#9, T#4, T#3[469, -685, 534, -317]) -> [T#10]\r\n Op#4 SOFTMAX(T#10) -> [T#11]\r\n\r\nTensors of Subgraph#0\r\n T#0(Reshape_1) shape:[1, 1960], type:INT8\r\n T#1(Reshape_2/shape) shape:[4], type:INT32 RO 16 bytes, buffer: 2, data:[-1, 49, 40, 1]\r\n T#2(Reshape_3/shape) shape:[2], type:INT32 RO 8 bytes, buffer: 3, data:[-1, 4000]\r\n T#3(final_fc_bias) shape:[4], type:INT32 RO 16 bytes, buffer: 4, data:[469, -685, 534, -317]\r\n T#4(MatMul) shape:[4, 4000], type:INT8 RO 16000 bytes, buffer: 5, data:[., ., ., ., ., ...]\r\n T#5(first_bias) shape:[8], type:INT32 RO 32 bytes, buffer: 6, data:[-19, 14, -425, 39, 0, ...]\r\n T#6(Conv2D) shape:[8, 10, 8, 1], type:INT8 RO 640 bytes, buffer: 7, data:[., ., ., ., ., ...]\r\n T#7(Reshape_2) shape:[1, 49, 40, 1], type:INT8\r\n T#8(Relu;add;Conv2D;first_bias) shape:[1, 25, 20, 8], type:INT8\r\n T#9(Reshape_3) shape:[1, 4000], type:INT8\r\n T#10(MatMul;add_1) shape:[1, 4], type:INT8\r\n T#11(labels_softmax) shape:[1, 4], type:INT8\r\n\r\n---------------------------------------------------------------\r\n Model size: 18960 bytes\r\n Non-data buffer size: 2160 bytes (11.39 %)\r\n Total data buffer size: 16800 bytes (88.61 %)\r\n (Zero value buffers): 0 bytes (00.00 %)\r\n\r\n* Buffers of TFLite model are mostly used for constant tensors.\r\n And zero value buffers are buffers filled with zeros.\r\n Non-data buffers area are used to store operators, subgraphs and etc.\r\n You can find more details from https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/schema/schema.fbs\r\n===================================================\r\n\r\nC)\r\ntensorflow/lite/experimental/micro/testing/test_linux_binary.sh tensorflow/lite/experimental/micro/tools/make/gen/linux_x86_64/bin/micro_speech_test '~~~ALL TESTS PASSED~~~'\r\nSegmentation fault (core dumped)\r\ntensorflow/lite/experimental/micro/tools/make/gen/linux_x86_64/bin/micro_speech_test: FAIL - '~~~ALL TESTS PASSED~~~' not found in logs.\r\nTesting TestInvoke\r\nDidn't find op for builtin opcode 'RESHAPE' version '1'\r\n\r\nFailed to get registration from op code d\r\n \r\n4 == input->dims->size failed at tensorflow/lite/experimental/micro/examples/micro_speech/micro_speech_test.cc:77 (4 vs 0)\r\n1 == input->dims->data[0] failed at tensorflow/lite/experimental/micro/examples/micro_speech/micro_speech_test.cc:78 (1 vs 0)\r\n49 == input->dims->data[1] failed at tensorflow/lite/experimental/micro/examples/micro_speech/micro_speech_test.cc:79 (49 vs 0)\r\n40 == input->dims->data[2] failed at tensorflow/lite/experimental/micro/examples/micro_speech/micro_speech_test.cc:80 (40 vs 4)\r\n1 == input->dims->data[3] failed at tensorflow/lite/experimental/micro/examples/micro_speech/micro_speech_test.cc:81 (1 vs 0)\r\nkTfLiteUInt8 == input->type failed at tensorflow/lite/experimental/micro/examples/micro_speech/micro_speech_test.cc:82 (3 vs -905926320)\r\nSegmentation fault\r\nmake: *** [tensorflow/lite/experimental/micro/examples/micro_speech/Makefile.inc:246: test_micro_speech_test] Error 1\r\n",
"Hi @Idbrouwer, I think that part of the repo is outdated/deprecated, do you have a link to that part of the repo? Reason being here are the current examples: \r\n\r\nhttps://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/experimental\r\nhttps://github.com/tensorflow/tflite-micro/tree/main/tensorflow/lite/experimental\r\n\r\nThanks for any additional information you can provide.",
"Hello, after ALOT of Google searches and going through GitHub repos and through Tensorflow and Sparkfun forums, I have determined that all of the info on the Sparkfun Edge board is outdated. On your current Github site, you have instructions on how to compile and run Micro_Speech_Test on a development machine, and how to create new models on Colab and other platforms, but there is no instruction/guidance or means to compile the Micro_Speech code for the Sparkfun Edge - or any microcontroller. I can find no \"make\" file for a TARGET=SparkfunEdge. I guess neither tensorflow not sparkfun support that board any longer and there is no way to compile and use the existing code in any expansion. My understanding has changed recently, such that I now believe that Tensorflow writes the TFLM C++ code that a microcontroller producer must then use to build a library/ compiler/loader for their specific microcontroller. Therefore, if Sparkfun no longer supports/updates a library for the Edge, any buyer is out of luck.\r\nOh, here is a link to the repo I was using - it is old: tensorflow/lite/experimental/micro/examples/micro_speech/micro_features/micro_model_settings.h",
"Hi @ldbrouwer, would you happen to know the branch/commit your code is referring to? I just want to ensure I have the right context on what's happening in your situation.",
"I think I'm in the main branch - but I'm far from a github guru",
"Hi @ldbrouwer, can you go to your root directory/folder of where you cloned the repo and try this:\r\n\r\n```\r\ngit branch\r\n```\r\nYour current branch should have an asterisk next to it\r\n\r\nthen try:\r\n```\r\ngit log\r\n```\r\nTell me the latest commit hash that is not yours, (If you haven't made a commit to your current branch, then they are all not yours), maybe give me the top 3 so that I may gather the correct context for your situation.\r\n\r\nThanks.",
"I did the pull from the webpage. What I see on the page is the commit is be4f687\r\nLet me know if that helps.\r\nAlso, the zip folder that I cloned is named \"tensorflow-be4f6874533d78f662d9777b66abe3cdde98f901\"\r\n\r\nLonnie",
"Hi @ldbrouwer, you are using a very old version... of well pretty much everything as that commit is very old (Dec 2019). We have since migrated tflite-micro to its own repo: https://github.com/tensorflow/tflite-micro/tree/main/tensorflow/lite/micro/examples/micro_speech Is the current equivalent of your example. Can you clone that repo and ensure you are on the main branch and then try to continue your project/work? Thanks for your help.",
"Hello. In fact I did clone the repo you referenced above before I wrote my comment on Feb 28. As I said there, \" you have instructions on how to compile and run Micro_Speech_Test on a development machine (in python), and how to create new models on Colab and other platforms, but there is no instruction/guidance or means to compile the Micro_Speech code (in C++) for the Sparkfun Edge - or any microcontroller. I can find no \"make\" file for a TARGET=SparkfunEdge. \r\nAm I missing something - I'd appreciate any guidance you can provide.",
"Hi @ldbrouwer, If I'm understanding your problem correctly, You are have made and trained a model with Colab, then when attempting to infer on a SparkfunEdge you are running into issues during inference? If that is correct, can you please share your exact steps (including commands) to do everything? Starting from compiling the code and training the model on colab and the commands/actions used to run it on your SparkfunEdge. Generally the more information you share the better context we have to help you. Thanks for your help.",
"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.",
"Sorry for the delayed response; thank you for your advice and guidance. I have taken a detour on my journey with TFLu that will hopefully yield alternative solutions - but I plan to return to this project later. Regardless, please close this thread for now - I'll get back to you with my progress as you directed above at a later date.",
"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/62999\">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/62999\">No</a>\n"
] | 2024-02-20T15:18:05 | 2024-03-15T11:51:59 | 2024-03-15T11:51:55 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
Tensorflow for microcomputers - from micro_speech repository
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.10.12
### Bazel version
_No response_
### GCC/compiler version
11.4.0
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Initial "make" and upload of micro_speech code works fine. Upon training a new model on the Google Colab (with guidance from Tiny ML book), code compiles fine, but does not run. Error reporting shows: Didn't find op for builtin opcode 'RESHAPE' version '1', Failed to get registration from op code d, AllocateTensors() failed.
And then a stream of : Requested feature_data_size -1560243888 doesn't match 1960, Feature generation failed - messages.
I had similar issue with Arduino Nano BLE on Arduino IDE but was able to solve the issue by adding the opcode for CONV-2D to the micro_op_resolver as recommended in this forum. However when using the Sparkfun Edge on Linux, I cannot solve the problem. I have tried to add the CONV2D op to the micro_op_resolver, but this didn't work. I also tried in increase the Arena Size from 10 to 50, but this didn't work either.
### Standalone code to reproduce the issue
```shell
Code is directly from the tensorflow/lite/experimental/micro/examples/micro_speech repo
```
### Relevant log output
```shell
log data from serial port is included above
```
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PR_kwDOArmXAs5nZnF8
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Fix TensorFlowLiteSwift `Interpreter` use-after-free bug
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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/62998/checks?check_run_id=21771614612) 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 @yishuangP Can you please review this PR ? Thank you!",
"Are there any internal updates for this issue?",
"Hi @yishuangP Can you please review this PR ? Thank you!",
"Hi @yishuangP Can you please review this PR ? Thank you!"
] | 2024-02-20T14:11:02 | 2024-06-07T16:49:05 | null |
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TensorFlowLiteSwift `Interpreter` has use-after-free bug, releasing underlying `Model` after `init(modelData: Data)`, causing `EXC_BAD_ACCESS`.
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Fix typos in multiple files
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[
"Hi @SuryanarayanaY Please fix the below PyLint errors. Thank you!\r\n\r\n\r\n",
"Hi @gbaned ,\r\nThe lint errors not introduced by this commit.I haven't done any changes to those lines causing lint errors.They seems exists since before.Please check."
] | 2024-02-20T13:04:41 | 2024-02-29T16:09:30 | 2024-02-29T16:09:30 |
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Full Integer Quantization Issue with Multiple Signatures in TensorFlow Lite
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[
"Hi @pkgoogle,\r\n\r\nI have reproduced the issue without signatures and with signatures. Without signatures the model is behaving as intended but the full integer model is not quantized properly with signatures. Here is the [gist](https://colab.research.google.com/gist/LakshmiKalaKadali/5fa6c17f8635776539f72f3b2853b125/tflite_quant_keras2.ipynb#scrollTo=nM9CkvDGrpsd) for reference. Tried with the 'tf nightly' version but the session is getting crashed. Here is the [gist](https://colab.research.google.com/gist/LakshmiKalaKadali/35856a3c934e9ccb647dcf87e5c7d033/copy-of-tflite_62996_full-integer_multiple-signatures_keras2.ipynb) with nightly version.\r\n\r\nThank You",
"I was able to replicate with the first gist, @abattery can you please take a look? Thanks.",
"@LakshmiKalaKadali @pkgoogle \r\n\r\nRegarding the issue encountered with the 'tf nightly' version, it's important to note that this version utilizes Keras 3, which has known compatibility issues when using the `tf.lite.TFLiteConverter.from_saved_model` or `tf.lite.TFLiteConverter.from_keras_model` method, as reported in the Keras GitHub issue [#19108](https://github.com/keras-team/keras/issues/19108). As a workaround for this compatibility issue, the `model.export` or `keras.export.ExportArchive` methods can be used for exporting models for TFLite conversion.\r\n\r\nHere is a gist using Keras 3 to reproduce the issue with that method: [gist](https://colab.research.google.com/drive/1vrjBGoFUUXJQZacnAUFxMKmLpRiAr-km?usp=sharing).\r\n"
] | 2024-02-20T12:58:00 | 2024-02-22T06:37:17 | null |
NONE
| null | null | null |
### 1. System information
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):Ubuntu 22.04.3 LTS
- TensorFlow installation (pip package or built from source):pip
- TensorFlow library (version, if pip package or github SHA, if built from source):2.15.0
### 2. Code
To help reproduce this issue, I am providing a link to a custom Colab notebook:
[Full Integer Quantization Issue with Multiple Signatures in TensorFlow Lite](https://colab.research.google.com/drive/1zsuqY90d7xFaWb3HPPhiZJcyuFt_a9Ja?usp=sharing)
### 3. Failure after conversion
In the dynamic range quantization process of TensorFlow Lite, it appears that for models with multiple signatures (including aliased ones), the quantization treats references to the same computational graph as a single entity. This is evidenced by the TFLite ModelAnalyzer report showing two subgraphs with identical sizes, yet the overall model size corresponds roughly to the size of a single subgraph. Specifically:
TFLite ModelAnalyzer Output of Dynamic Range Quantization:
```
Model size: 56528 bytes
Non-data buffer size: 3384 bytes (05.99 %)
Total data buffer size: 53144 bytes (94.01 %)
- Subgraph#0 : 53040 bytes (93.83 %)
- Subgraph#1 : 53040 bytes (93.83 %)
(Zero value buffers): 0 bytes (00.00 %)
```
The total model size is not the sum of the two subgraphs, suggesting that the same subgraph is counted twice but only stored once.
However, the situation is markedly different in the full integer quantization process. Here, the quantization leads to two subgraphs with significantly different sizes, which indicates a distinct treatment of the computational graph segments during quantization. This behavior contrasts with the dynamic range quantization and suggests that the full integer quantization process might interpret or handle the aliased signatures differently, resulting in varied optimization or quantization strategies for the subgraphs. The detailed output is as follows:
TFLite ModelAnalyzer Output of Full Integer Quantization:
```
Model size: 259144 bytes
Non-data buffer size: 4360 bytes (01.68 %)
Total data buffer size: 254784 bytes (98.32 %)
- Subgraph#0 : 51120 bytes (19.73 %)
- Subgraph#1 : 203568 bytes (78.55 %)
(Zero value buffers): 0 bytes (00.00 %)
```
Here, the total model size reflects the sum of two distinctly sized subgraphs, highlighting a genuine differentiation in how each subgraph is quantized and stored.
This discrepancy between dynamic range and full integer quantization processes raises questions about the underlying mechanisms TensorFlow Lite employs for handling multiple signatures, especially when they reference the same computational graph segment. The difference in subgraph sizes under full integer quantization suggests that the process may inadvertently treat aliased signatures or multiple references as distinct computational entities, potentially leading to inefficiencies in model size and performance.
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I_kwDOArmXAs5_y-ea
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`tf.raw_ops.Transpose` aborts with negative `perm` value
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[
"@Sehun0819 If removing negative values from the perm argument isn't possible, could you define a mapping function that converts negative values to their positive counterparts based on the dimensions of your tensor. If the negative values are intentional and represent specific transposition directions, please consider using alternative operations that support them. For example, tf.reverse might be suitable depending on your use case?\r\nThank you!",
"@sushreebarsa Thank you for your response. But I just wanted to inform you it crashes. Please check [it](https://colab.research.google.com/drive/1XtuhjL-l3x2r4KVWj2xeO6xlMD7b0Evh?usp=sharing).",
"@sachinprasadhs I was able to replicate this issue [here](https://colab.research.google.com/gist/sushreebarsa/b6e329989605b868659650bab7a09bb1/untitled9.ipynb), please have a look.\r\nThank you! "
] | 2024-02-20T10:39:53 | 2024-03-05T09:38:38 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.17
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04 LTS
### Mobile device
_No response_
### Python version
3.11.7
### Bazel version
6.5.0
### GCC/compiler version
clang 16
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`tf.raw_ops.Transpose` aborts with negative `perm` value. [gist](https://colab.research.google.com/drive/1r0oUxDcu-uWHgjQGOU8qvcSzD1z2fv-a?usp=sharing)
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
tf.raw_ops.Transpose(
x=tf.random.normal([2,1]),
perm=[-1,0],
name=None
)
```
### Relevant log output
```shell
2024-02-20 19:35:25.981306: F tensorflow/core/framework/tensor_shape.cc:356] Check failed: d >= 0 (0 vs. -1)
Aborted (core dumped)
```
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"Skipping registering GPU devices"...message in JupyterLab v 3.6.3
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"@MessDeveloper,\r\nCould you please confirm whether you are trying to execute the mentioned code on the base environment or the CUDA, tensorflow installed environment. Also make sure to execute the code in the environment where you installed the tensorflow and CUDA.\r\n\r\nI tried to execute in our local environment and it was executed without any issue/error. Thank you! \r\n\r\n",
"Have the same issue :\r\n\r\n2024-02-24 17:46:11.024256: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\r\n2024-02-24 17:46:11.044842: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\r\n2024-02-24 17:46:11.045061: 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\n2024-02-24 17:46:11.491969: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n>>> gpus = tf.config.experimental.list_physical_devices('GPU')\r\n2024-02-24 17:46:11.976038: 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\n2024-02-24 17:46:11.992328: 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\n\r\nwhereas NVIDIA-SMI 545.23.08 Driver Version: 545.23.08 CUDA Version: 12.3\r\ninstalled and \r\nnvidia-tensorrt 99.0.0 pypi_0 pypi\r\ntensorboard 2.13.0 pypi_0 pypi\r\ntensorboard-data-server 0.7.2 pypi_0 pypi\r\ntensorboard-plugin-wit 1.8.1 pypi_0 pypi\r\ntensorboardx 2.6.2.2 pypi_0 pypi\r\ntensorflow-gpu 2.8.0 pypi_0 pypi\r\ntensorrt 8.6.1.post1 pypi_0 pypi\r\ntensorrt-bindings 8.6.1 pypi_0 pypi\r\ntensorrt-libs 8.6.1 pypi_0 pypi\r\n",
"> @MessDeveloper, Could you please confirm whether you are trying to execute the mentioned code on the base environment or the CUDA, tensorflow installed environment. Also make sure to execute the code in the environment where you installed the tensorflow and CUDA.\r\n> \r\n> I tried to execute in our local environment and it was executed without any issue/error. Thank you!\r\n\r\nHello, despite appearing as a \"base\" environment in the screenshot, I'm running it in a specially created TensorFlow environment under Anaconda Navigator and running JupyterLab, I believe that dependences were downloaded, such as CUDA, how can I check it?\r\n\r\nThank you for your help.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62994\">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/62994\">No</a>\n",
"I closed it by mistake, sorry. Could you please check again?\r\n\r\nThanks!",
"@MessDeveloper,\r\nFrom the information provided above, I can see you are trying with the **tf-nightly**. I faced the same error with tf-nightly in the jupyter notebook. So I will check with the developer team on the same and provide more information.\r\n\r\n\r\n\r\n\r\nAs a temporary workaround, you can try using the latest stable version **2.15** where it was detecting and executing the code on the GPU.\r\n\r\n\r\n\r\nThank you!\r\n\r\n",
"Hi @tilakrayal , I'm having the same problem. But in my case, CUDA libraries are loaded properly, it's just tensorflow does not detect any gpus (but it works normally with torch and nvidia-smi). Do you have any idea ?\r\nTerminal outputs look like this:\r\n```\r\n(my_env) $ python3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU')); print(tf.__version__)\" \r\n2024-02-29 15:36:16.385941: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used. 2024-02-29 15:36:16.435959: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used. \r\n2024-02-29 15:36:16.436407: 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\n2024-02-29 15:36:19.414065: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT \r\n2024-02-29 15:36:24.877011: 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\n2.13.1 \r\n```",
"@thangld201,\r\nFor tensorflow v2.13 you can try installing cuda toolkit and cudnn, & update the LD_LIBRARY_PATH and it works.\r\n``` python\r\n> conda activate tf\r\n> conda install cudatoolkit=11.8\r\n> export LD_LIBRARY_PATH=\"$HOME/miniconda3/envs/tf-test/lib\"\r\n> conda install cudnn=8.6\r\n```\r\nAnd also please follow the tested build configurations which are compatible for tensorflow v2.13\r\nhttps://www.tensorflow.org/install/source#gpu\r\nThank you!\r\n\r\n\r\n",
"@tilakrayal, I forgot to add cudnn to LD_LIBRARY_PATH, when I updated the following:\r\n```\r\nLD_LIBRARY_PATH=$CONDA_PREFIX/lib/python3.8/site-packages/nvidia/cudnn/lib:$LD_LIBRARY_PATH\r\n```\r\nGpus were detected!\r\n\r\n",
"@thangld201,\r\nThank you for the response!",
"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/62994\">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/62994\">No</a>\n"
] | 2024-02-19T20:24:49 | 2024-03-15T01:47:23 | 2024-03-15T01:47:18 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
v1.12.1-106195-g9060e62bf4e 2.17.0-dev20240219
### Custom code
No
### OS platform and distribution
Linux Ubuntu 22.04.4 LTS
### Mobile device
_No response_
### Python version
Python 3.11.5
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.2 / I don't know cuDNN version
### GPU model and memory
NVIDIA GeForce RTX 4070 Ti 12282MiB
### Current behavior?
While working in Jupyter Notebook throught Anaconda, when importing tensorflow I get this error. I spent a lot of money to be able to use GPU in Data Science works and I'm unable to use it at all...not sure if enviroment is OK...
Driver is:
GPU model:
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
print("Name:", gpu.name, " Type:", gpu.device_type)
```
### Relevant log output
```shell
2024-02-19 20:35:30.820121: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
2024-02-19 20:35:30.851745: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-02-19 20:35:30.851772: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-02-19 20:35:30.852727: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-02-19 20:35:30.857979: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
2024-02-19 20:35:30.858297: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-02-19 20:35:31.682954: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-02-19 20:35:32.799765: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] 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
2024-02-19 20:35:32.800463: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2256] 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.
Skipping registering GPU devices...
```
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Avoid deprecated `std::iterator`
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https://en.cppreference.com/w/cpp/iterator/iterator is deprecated in c++17 and beyond.
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AssertionError: Found 312 Python objects that were not bound to checkpointed values, likely due to changes in the Python program. Showing 10 of 312 unmatched objects
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[
"Hi @Dibyajyoti227, I am unsure how to use/ingest your config file, do you have a program/tool/documentation on how to use your config file? If so please share so that we may help you. Thanks for your help.",
"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."
] | 2024-02-19T16:45:41 | 2024-03-10T01:47:54 | 2024-03-10T01:47:53 |
NONE
| null | null | null |
Tensorflow = 2.13.1
OS: Ubuntu 20.04
Python: 3.8
Code used to convert to tflite framework before converting to tflite file:
`python3 object_detection/export_tflite_graph_tf2.py --pipeline_config_path /home/export/best_model_d0_198_0.807/ssd_efficientdet_d0_512x512_coco17_mod.config --trained_checkpoint_dir /home/checkpoints/ --output_directory /home/export/best_model_d0_198_0.807/ssd_tfexport/`
The content of the .config file is
```
##### SSD with EfficientNet-b0 + BiFPN feature extractor,
##### shared box predictor and focal loss (a.k.a EfficientDet-d0).
##### See EfficientDet, Tan et al, https://arxiv.org/abs/1911.09070
##### See Lin et al, https://arxiv.org/abs/1708.02002
##### Trained on COCO, initialized from an EfficientNet-b0 checkpoint.
#####
##### Train on TPU-8
model {
ssd {
inplace_batchnorm_update: true
freeze_batchnorm: false
num_classes: 1
add_background_class: false
box_coder {
faster_rcnn_box_coder {
y_scale: 10.0
x_scale: 10.0
height_scale: 5.0
width_scale: 5.0
}
}
matcher {
argmax_matcher {
matched_threshold: 0.5
unmatched_threshold: 0.5
ignore_thresholds: false
negatives_lower_than_unmatched: true
force_match_for_each_row: true
use_matmul_gather: true
}
}
similarity_calculator {
iou_similarity {
}
}
encode_background_as_zeros: true
anchor_generator {
multiscale_anchor_generator {
min_level: 3
max_level: 7
anchor_scale: 4.0
aspect_ratios: [1.0, 2.0, 0.5]
scales_per_octave: 3
}
}
image_resizer {
fixed_shape_resizer {
height: 512
width: 512
}
}
box_predictor {
weight_shared_convolutional_box_predictor {
depth: 64
class_prediction_bias_init: -4.6
conv_hyperparams {
force_use_bias: true
activation: SWISH
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
random_normal_initializer {
stddev: 0.01
mean: 0.0
}
}
batch_norm {
scale: true
decay: 0.99
epsilon: 0.001
}
}
num_layers_before_predictor: 3
kernel_size: 3
use_depthwise: true
}
}
feature_extractor {
type: 'ssd_efficientnet-b0_bifpn_keras'
bifpn {
min_level: 3
max_level: 7
num_iterations: 3
num_filters: 64
}
conv_hyperparams {
force_use_bias: true
activation: SWISH
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
truncated_normal_initializer {
stddev: 0.03
mean: 0.0
}
}
batch_norm {
scale: true,
decay: 0.99,
epsilon: 0.001,
}
}
}
loss {
classification_loss {
weighted_sigmoid_focal {
alpha: 0.25
gamma: 1.5
}
}
localization_loss {
weighted_smooth_l1 {
}
}
classification_weight: 1.0
localization_weight: 1.0
}
normalize_loss_by_num_matches: true
normalize_loc_loss_by_codesize: true
post_processing {
batch_non_max_suppression {
score_threshold: 1e-8
iou_threshold: 0.5
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SIGMOID
}
}
}
train_config: {
fine_tune_checkpoint: "/home/EfficientDet-tensorflow2-main/checkpoints"
fine_tune_checkpoint_version: V2
fine_tune_checkpoint_type: "detection"
batch_size: 8
sync_replicas: true
startup_delay_steps: 0
replicas_to_aggregate: 8
use_bfloat16: true
num_steps: 300000
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_scale_crop_and_pad_to_square {
output_size: 512
scale_min: 0.1
scale_max: 2.0
}
}
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: 1e-06
total_steps: 300000
warmup_learning_rate: 1e-6
warmup_steps: 2500
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
}
train_input_reader: {
label_map_path: "/home/EfficientDet-tensorflow2-main/dataset/pothole.names"
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/train2017-?????-of-00256.tfrecord"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1;
}
eval_input_reader: {
label_map_path: "/home/EfficientDet-tensorflow2-main/dataset/pothole.names"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/val2017-?????-of-00032.tfrecord"
}
}
```
Error I got was,
```
raise AssertionError(
AssertionError: Found 312 Python objects that were not bound to checkpointed values, likely due to changes in the Python program. Showing 10 of 312 unmatched objects: [<tf.Variable 'stack_2/block_0/depthwise_bn/beta:0' shape=(144,) dtype=float32, numpy=
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
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0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)>, <tf.Variable 'stack_4/block_2/depthwise_bn/moving_mean:0' shape=(672,) dtype=float32, numpy=
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
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0.42710254, -0.7122543 ],
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...,
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0.03987575, -0.01499438],
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0.04738962, -0.02136967],
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[ 0.09303389, -0.3610404 , 0.2815081 , ..., 0.19071342,
0.09110802, -0.12748355],
...,
[ 0.27226162, -0.09749246, 0.07736962, ..., 0.03416192,
0.1695876 , -0.2153568 ],
[ 0.17506738, -0.18321092, 0.35481608, ..., 0.07643802,
0.1920081 , -0.05615106],
[-0.148925 , 0.00492021, -0.12710334, ..., -0.3465367 ,
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```
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I_kwDOArmXAs5_tT8D
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[
"@Dibyajyoti227 Could you try to replace `keep_aspect_ratio_resizer` with `fixed_shape_resizer` and use latest TF version. Kindly let us know if it helps?\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/62991\">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/62991\">No</a>\n"
] | 2024-02-19T15:07:26 | 2024-03-10T01:47:58 | 2024-03-10T01:47:54 |
NONE
| null | null | null |
I am using the latest TensorFlow Model Garden release and TensorFlow 2.
I checked to make sure that this issue has not already been filed.
1. The entire URL of the file you are using
https://github.com/tensorflow/models/tree/master/research/object_detection/configs/tf2/ssd_efficientdet_d0_512x512_coco17_tpu-8.config
2. Describe the bug
While trying to create tflite graph using
`python3 object_detection/export_tflite_graph_tf2.py --pipeline_config_path ssd_efficientdet_d0_512x512_coco17_tpu-8.config --trained_checkpoint_dir /checkpoints --output_directory /ssd_tfexport`
I am getting error,
` File ".local/lib/python3.8/site-packages/object_detection/export_tflite_graph_lib_tf2.py", line 100, in _process_config raise ValueError( ValueError: Only fixed_shape_resizeris supported with tflite. Found keep_aspect_ratio_resizer`
I checked the .config file and looks like the keep_aspect_ratio_resizer is mentioned there. How do I get rid of this error and successfully create tflite graph for tflite conversion?
6. System information
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 20.04
TensorFlow installed from (source or binary):pip install
TensorFlow version (use command below):2.13.1
Python version:3.8
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