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Change camera lens
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"@Yaze104 If the code directly sets the camera zoom or FOV using CameraX's setZoomRatio() or similar methods, you'd need to adjust the value to 1.0 for a 1x zoom.\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."
] | 2024-03-27T16:00:10 | 2024-04-17T01:47:43 | 2024-04-17T01:47:42 |
NONE
| null | null | null |
**System information**
Hello, In this url https://github.com/tensorflow/examples/tree/master/lite/examples/pose_estimation/android project, i can use success run the code, but the cameras lens is x5, I just wanna change camera lens to x1, so how can i modify the code?
Should I change CameraSource.kt or something else?
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Build kernel for all numbertypes for ApproximateEqual Op
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"Hi @rohan100jain Can you please review this PR ? Thank you!",
"Hi @rohan100jain Can you please review this PR ? Thank you!",
"Hi @rohan100jain Can you please review this PR ? Thank you!",
"Hi @rohan100jain Can you please review this PR ? Thank you!"
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https://github.com/tensorflow/tensorflow/blob/dadeddbce13b7d422c73ed872e386c56dd41a478/tensorflow/core/ops/math_ops.cc#L781-L783
But Kernel built for `float` and `double`. Proposing a fix for same.
Might Fix #64256 .
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Calling submodels in train_step throws ValueError when saving and loading error.
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[
"@hubgrim I am able to get a different error while reproducing the issue reported here. Kindly check the [gist](https://colab.research.google.com/gist/sushreebarsa/8965a0e01764c86c092f2a2d6d86959f/64576.ipynb) for reference. \r\nThe .keras extension typically represents the recommended Keras v3 format. If your model was saved in a different format (e.g., HDF5 - .h5), you'll need to use the appropriate loading function as follows;\r\n```\r\nloaded_model = tf.keras.models.load_model('custom_model.h5') # For HDF5 format\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.",
"> @hubgrim I am able to get a different error while reproducing the issue reported here. Kindly check the [gist](https://colab.research.google.com/gist/sushreebarsa/8965a0e01764c86c092f2a2d6d86959f/64576.ipynb) for reference. The .keras extension typically represents the recommended Keras v3 format. If your model was saved in a different format (e.g., HDF5 - .h5), you'll need to use the appropriate loading function as follows;\r\n> \r\n> ```\r\n> loaded_model = tf.keras.models.load_model('custom_model.h5') # For HDF5 format\r\n> ```\r\n> \r\n> Thank you!\r\n\r\nHm, I don't know why this error differs or why the saving format would be an issue, since I use the .keras extension when loading and saving. The lines are right next to each other.",
"@hubgrim While the .keras extension suggests a Keras model, there may be compatibility issues between the version of Keras you used to save the model and the version you're using to load it. Even small version bumps can sometimes cause errors. Please ensure the version compatibility.\r\nThank you!",
"The error arises even when saving/loading within the same script. I did not save the model at some point and reloaded it at some other time, but within the same call of the program. Both the model, that provides .save(), and load_model() were imported from tensorflow.keras.models so I don't see how this could be rooted in version incompatibility."
] | 2024-03-27T09:16:13 | 2024-04-17T09:59:18 | null |
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### 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
Linux Ubuntu 23.10
### 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?
When saving and loading a nested model I got this error.
```ValueError: Layer 'dense_3' expected 0 variables, but received 2 variables during loading. Expected: []```
Which was weird, because there was no dense_3 layer.
Calling the submodels by themselves over self.net1 and self.net2 **inside** the train_step would throw the error, but having them called by the call method of the higher model and returning their respective values does not throw an error.
How this could lead to this error messages is beyond me.
I managed to build the following minimal example:
### Standalone code to reproduce the issue
```shell
keras
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, Activation, Input, BatchNormalization, Dropout, Flatten, Identity
args = {"activation": "relu",
"batch_norm": True}
@keras.saving.register_keras_serializable()
class CustomModel1(Model):
def __init__(self):
super().__init__()
self.dense = Dense(32)
def call(self, inputs):
x = self.dense(inputs)
return x
@keras.saving.register_keras_serializable()
class CustomModel2(Model):
def __init__(self):
super().__init__()
self.dense = Dense(32)
def call(self, inputs):
x = self.dense(inputs)
return x
@keras.saving.register_keras_serializable()
class CustomModel3(Model):
def __init__(self):
super().__init__()
self.net1 = CustomModel1()
self.net2 = CustomModel2()
def call(self, inputs):
z = self.net1(inputs)
x = self.net2(z)
return z, x
def train_step(self, data):
x, y = data
with tf.GradientTape() as tape:
# z, y_pred = self(x) # this fixes it instead
y_pred = self.net2(self.net1(x)) # this line throws the error
loss = self.compiled_loss(y, y_pred)
trainable_vars = self.trainable_weights
gradients = tape.gradient(loss, trainable_vars)
self.optimizer.apply_gradients(zip(gradients, trainable_vars))
self.compiled_metrics.update_state(y, y_pred)
return {m.name: m.result() for m in self.metrics}
# Instantiate the model
model = CustomModel3()
# Compile the model
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# Create some dummy data for training
x_train = np.random.random((1000, 32))
y_train = np.random.randint(10, size=(1000,))
# Train the model for one epoch
model.fit(x_train, y_train, epochs=1)
# Save the model
model.save('custom_model.keras', save_format='keras')
# Load the model again
loaded_model = tf.keras.models.load_model('custom_model.keras')
# Generate some sample data for prediction
x_sample = np.random.random((10, 32)) # Assuming 10 samples with 32 features each
# Make predictions using the loaded model
predictions = loaded_model.predict(x_sample)
print(predictions)
# Print the predictions
print(model.summary())
```
### Relevant log output
```shell
/home/georg/anaconda3/envs/vae/bin/python3.11 /home/georg/Python Projects/vae/tests/test_saving_basic.py
2024-03-27 10:02:21.156169: 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: SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
32/32 [==============================] - 1s 2ms/step - loss: 9.2085 - accuracy: 0.0400
/home/georg/anaconda3/envs/vae/lib/python3.11/site-packages/keras/src/saving/saving_api.py:164: UserWarning: You are saving a model that has not yet been built. It might not contain any weights yet. Consider building the model first by calling it on some data.
saving_lib.save_model(model, filepath)
WARNING:absl:Skipping variable loading for optimizer 'Adam', because it has 1 variables whereas the saved optimizer has 9 variables.
Traceback (most recent call last):
File "/home/georg/Python Projects/vae/tests/test_saving_basic.py", line 79, in <module>
loaded_model = tf.keras.models.load_model('custom_model.keras')
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/georg/anaconda3/envs/vae/lib/python3.11/site-packages/keras/src/saving/saving_api.py", line 254, in load_model
return saving_lib.load_model(
^^^^^^^^^^^^^^^^^^^^^^
File "/home/georg/anaconda3/envs/vae/lib/python3.11/site-packages/keras/src/saving/saving_lib.py", line 281, in load_model
raise e
File "/home/georg/anaconda3/envs/vae/lib/python3.11/site-packages/keras/src/saving/saving_lib.py", line 269, in load_model
_load_state(
File "/home/georg/anaconda3/envs/vae/lib/python3.11/site-packages/keras/src/saving/saving_lib.py", line 457, in _load_state
_load_state(
File "/home/georg/anaconda3/envs/vae/lib/python3.11/site-packages/keras/src/saving/saving_lib.py", line 466, in _load_state
_load_container_state(
File "/home/georg/anaconda3/envs/vae/lib/python3.11/site-packages/keras/src/saving/saving_lib.py", line 534, in _load_container_state
_load_state(
File "/home/georg/anaconda3/envs/vae/lib/python3.11/site-packages/keras/src/saving/saving_lib.py", line 435, in _load_state
trackable.load_own_variables(weights_store.get(inner_path))
File "/home/georg/anaconda3/envs/vae/lib/python3.11/site-packages/keras/src/engine/base_layer.py", line 3531, in load_own_variables
raise ValueError(
ValueError: Layer 'dense_3' expected 0 variables, but received 2 variables during loading. Expected: []
Process finished with exit code 1
```
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Add output-to-input convolution indexing map.
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Add output-to-input convolution indexing map.
Reuse the existing ReduceWindowOp code to generate the indexing map, then remap it to convolution dimensions (taken from ConvolutionDimensionNumbers).
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I_kwDOArmXAs6DvZL9
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could not find valid device for node when call tf.acos
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[
"Hi **@shiwenou** ,\r\nThe tf.acos operation in tensorflow supports float and double data types but not an integer data types. When you pass an integer type tensor to tf.acos, it raises an error because there is no valid device registered for that operation with integer data types.\r\n Here i am providing [gist](https://colab.sandbox.google.com/gist/Venkat6871/7abac947112d0a06a517fa42562e963e/64574_2-15-v.ipynb) for your reference. Please go through it once.\r\nThank you!",
"> Hi **@shiwenou** , The tf.acos operation in tensorflow supports float and double data types but not an integer data types. When you pass an integer type tensor to tf.acos, it raises an error because there is no valid device registered for that operation with integer data types. Here i am providing [gist](https://colab.sandbox.google.com/gist/Venkat6871/7abac947112d0a06a517fa42562e963e/64574_2-15-v.ipynb) for your reference. Please go through it once. Thank you!\r\n\r\nThanks for the answer. I found that in higher versions of tf it will warn that abs does not support integer type tensor. It's just that the version I'm using is too low.\r\nThank you!",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/64574\">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/64574\">No</a>\n"
] | 2024-03-27T09:04:37 | 2024-03-31T11:54:33 | 2024-03-31T11:54:30 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
tf 2.1
### Custom code
Yes
### OS platform and distribution
win11 10.0.22635
### Mobile device
_No response_
### Python version
3.7.16
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When I was using tf.abs to process my data, I realized that it wasn't working properly. After my review, I realized that if I input a integer type tensor, an error occurs, tensorflow.python.framework.errors_impl.NotFoundError: could not find valid device for node.
However, if I input a tensor of float type, it works fine.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
x1 = tf.constant([-1,0,1])
print(tf.acos(x1))
```
### Relevant log output
```shell
raceback (most recent call last):
File "~\test.py", line 3, in <module>
print(tf.acos(x1))
File "~\Anaconda3\envs\tf2.1\lib\site-packages\tensorflow_core\python\ops\gen_math_ops.py", line 192, in acos
_ops.raise_from_not_ok_status(e, name)
File "~\Anaconda3\envs\tf2.1\lib\site-packages\tensorflow_core\python\framework\ops.py", line 6606, in raise_from_not_ok_status
six.raise_from(core._status_to_exception(e.code, message), None)
File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.NotFoundError: Could not find valid device for node.
Node:{{node Acos}}
All kernels registered for op Acos :
device='CPU'; T in [DT_FLOAT]
device='CPU'; T in [DT_DOUBLE]
[Op:Acos]
```
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Internal package group change
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Internal package group change
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Safely cast float to int16
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Safely cast float to int16
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"@KnSun99,\r\nCan you run and post the output of `python -m pip list` and` python -c 'import tensorflow'` run from the same environment and also the steps followed? I will also try to install with the same environment details and provide the info. Thank you!",
"OK，\r\n\r\n(DLC) hhh@hhh49:~$ python -m pip list\r\nPackage Version\r\n----------------------------- ------------\r\nabsl-py 1.4.0\r\naiohttp 3.9.3\r\naiosignal 1.2.0\r\nalabaster 0.7.12\r\narrow 1.2.3\r\nastroid 2.14.2\r\nasttokens 2.0.5\r\nastunparse 1.6.3\r\natomicwrites 1.4.0\r\nattrs 23.1.0\r\nautopep8 2.0.4\r\nBabel 2.11.0\r\nbeautifulsoup4 4.12.2\r\nbinaryornot 0.4.4\r\nblack 23.11.0\r\nbleach 4.1.0\r\nblinker 1.6.2\r\nBrotli 1.0.9\r\ncached-property 1.5.2\r\ncachetools 4.2.2\r\ncertifi 2024.2.2\r\ncffi 1.16.0\r\nchardet 4.0.0\r\ncharset-normalizer 2.0.4\r\nclick 8.1.7\r\ncloudpickle 2.2.1\r\ncolorama 0.4.6\r\ncomm 0.2.1\r\ncontourpy 1.2.0\r\ncookiecutter 2.6.0\r\ncryptography 42.0.5\r\ncycler 0.12.1\r\ndebugpy 1.6.7\r\ndecorator 5.1.1\r\ndefusedxml 0.7.1\r\ndiff-match-patch 20200713\r\ndill 0.3.7\r\ndlclibrary 0.0.6\r\ndocstring-to-markdown 0.11\r\ndocutils 0.18.1\r\nexecuting 0.8.3\r\nfastjsonschema 2.16.2\r\nfilelock 3.13.3\r\nfilterpy 1.4.5\r\nflake8 7.0.0\r\nflatbuffers 24.3.25\r\nfonttools 4.50.0\r\nfrozenlist 1.4.0\r\nfsspec 2024.3.1\r\ngast 0.5.3\r\ngoogle-auth 2.6.0\r\ngoogle-auth-oauthlib 0.5.2\r\ngoogle-pasta 0.2.0\r\ngrpcio 1.59.3\r\nh5py 3.10.0\r\nhuggingface-hub 0.22.0\r\nidna 3.4\r\nimageio 2.34.0\r\nimageio-ffmpeg 0.4.9\r\nimagesize 1.4.1\r\nimgaug 0.4.0\r\nimportlib-metadata 7.0.1\r\ninflection 0.5.1\r\nintel-openmp 2024.0.3\r\nintervaltree 3.1.0\r\nipykernel 6.29.3\r\nipython 8.20.0\r\nisort 5.9.3\r\njaraco.classes 3.2.1\r\njedi 0.18.1\r\njeepney 0.7.1\r\njellyfish 1.0.1\r\nJinja2 3.1.3\r\njoblib 1.3.2\r\njsonschema 4.19.2\r\njsonschema-specifications 2023.7.1\r\njupyter_client 8.6.0\r\njupyter_core 5.5.0\r\njupyterlab-pygments 0.2.2\r\nkeras 2.15.0\r\nkeyring 24.3.1\r\nkiwisolver 1.4.5\r\nlazy_loader 0.3\r\nlazy-object-proxy 1.6.0\r\nlibclang 18.1.1\r\nllvmlite 0.42.0\r\nMarkdown 3.4.1\r\nmarkdown-it-py 2.2.0\r\nMarkupSafe 2.1.3\r\nmatplotlib 3.8.3\r\nmatplotlib-inline 0.1.6\r\nmccabe 0.7.0\r\nmdurl 0.1.0\r\nmistune 2.0.4\r\nml-dtypes 0.2.0\r\nmore-itertools 10.1.0\r\nmsgpack 1.0.8\r\nmsgpack-numpy 0.4.8\r\nmultidict 6.0.4\r\nmypy-extensions 1.0.0\r\nnamex 0.0.7\r\nnbclient 0.8.0\r\nnbconvert 7.10.0\r\nnbformat 5.9.2\r\nnest-asyncio 1.6.0\r\nnetworkx 3.2.1\r\nnumba 0.59.1\r\nnumexpr 2.8.7\r\nnumpy 1.26.4\r\nnumpydoc 1.5.0\r\noauthlib 3.2.2\r\nopencv-python 4.9.0.80\r\nopt-einsum 3.3.0\r\noptree 0.11.0\r\npackaging 23.2\r\npandas 2.2.1\r\npandocfilters 1.5.0\r\nparso 0.8.3\r\npathspec 0.10.3\r\npatsy 0.5.6\r\npexpect 4.8.0\r\npickleshare 0.7.5\r\npillow 10.2.0\r\npip 23.3.1\r\nplatformdirs 3.10.0\r\npluggy 1.0.0\r\nply 3.11\r\nprompt-toolkit 3.0.43\r\nprotobuf 4.24.4\r\npsutil 5.9.0\r\nptyprocess 0.7.0\r\npure-eval 0.2.2\r\npy-cpuinfo 9.0.0\r\npyasn1 0.4.8\r\npyasn1-modules 0.2.8\r\npycodestyle 2.11.1\r\npycparser 2.21\r\npydocstyle 6.3.0\r\npyflakes 3.2.0\r\nPygments 2.15.1\r\nPyJWT 2.4.0\r\npylint 2.16.2\r\npylint-venv 3.0.3\r\npyls-spyder 0.4.0\r\npyOpenSSL 24.0.0\r\npyparsing 3.1.2\r\nPyQt5 5.15.10\r\nPyQt5-sip 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\"/home/hhh/Software/Miniconda3/envs/DLC/lib/python3.11/site-packages/tensorflow/__init__.py\", line 40, in <module>\r\n from tensorflow.python import pywrap_tensorflow # pylint: disable=unused-import\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/hhh/Software/Miniconda3/envs/DLC/lib/python3.11/site-packages/tensorflow/python/pywrap_tensorflow.py\", line 34, in <module>\r\n self_check.preload_check()\r\n File \"/home/hhh/Software/Miniconda3/envs/DLC/lib/python3.11/site-packages/tensorflow/python/platform/self_check.py\", line 63, in preload_check\r\n from tensorflow.python.platform import _pywrap_cpu_feature_guard\r\nImportError: /home/hhh/Software/Miniconda3/envs/DLC/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2: undefined symbol: _ZTIN6snappy4SinkE\r\n\r\n",
"@KnSun99,\r\nI tried to install the tensorflow v2.15 with the same specifications & in the same environment(Ubuntu 22.04) and was able to install and execute the tensorflow import without any issue. Kindly find the screenshot for the reference.\r\nAlso please try to create a new virtual environment and install the tensorflow according to the compatible tested [configurations](https://www.tensorflow.org/install/source#gpu).\r\n\r\n\r\n\r\n \r\n",
"OK，I‘ll try.\r\nBut the cuda version required by tf2.15.0 is not in configurations.\r\n\r\n\r\nCan you share your pip list and conda list information?\r\n\r\nThanks for your reply!\r\n\r\n",
"@KnSun99,\r\nI guess you are looking for the other language document which has not been updated for a long time. I will check with the developer team on the same. I request to have a look at this official document for the tested build configurations.\r\nhttps://www.tensorflow.org/install/source#gpu\r\n\r\nThank you!",
"OK.\r\nThanks for your reply.",
"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/64569\">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/64569\">No</a>\n"
] | 2024-03-27T05:00:45 | 2024-04-08T02:11:40 | 2024-04-08T02:11:36 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
tf 2.15.0
### Custom code
Yes
### OS platform and distribution
Ubuntu 22.04.4
### Mobile device
_No response_
### Python version
3.11.8
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
cuda-version 12.3; cudnn 8.9.7.29
### GPU model and memory
RTX 4090, 24G
### Current behavior?
In the conda environment, use the following command to install tensorflow:
conda install conda-forge::tensorflow-gpu
Run: import tensorflow as tf
The following error is reported:
File ~/Software/Miniconda3/envs/DLC/lib/python3.11/site-packages/tensorflow/python/platform/self_check.py:63 in preload_check
from tensorflow.python.platform import _pywrap_cpu_feature_guard
ImportError: /home/hhh/Software/Miniconda3/envs/DLC/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2: undefined symbol: _ZTIN6snappy4SinkE
How can I solve it?
###
If you use the following command to install tensorflow:
$ pip install tensorflow==2.16.1
How should I configure cudatoolkit, cudnn, etc., in a conda environment?
Because there are many conda environments, cuda and cudnn are expected to be installed in the conda environment.
When cuda is installed in the system, conflicts between different conda environments are feared.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
Traceback (most recent call last):
Cell In[1], line 1
import tensorflow as tf
File ~/Software/Miniconda3/envs/DLC/lib/python3.11/site-packages/tensorflow/__init__.py:40
from tensorflow.python import pywrap_tensorflow # pylint: disable=unused-import
File ~/Software/Miniconda3/envs/DLC/lib/python3.11/site-packages/tensorflow/python/pywrap_tensorflow.py:34
self_check.preload_check()
File ~/Software/Miniconda3/envs/DLC/lib/python3.11/site-packages/tensorflow/python/platform/self_check.py:63 in preload_check
from tensorflow.python.platform import _pywrap_cpu_feature_guard
ImportError: /home/hhh/Software/Miniconda3/envs/DLC/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2: undefined symbol: _ZTIN6snappy4SinkE
```
### Relevant log output
_No response_
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PR_kwDOArmXAs5q4Jwd
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Allow optional `stablehlo.broadcast_in_dim` op.
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Allow optional `stablehlo.broadcast_in_dim` op.
When deferring `stablehlo.transpose` of the lhs of `stablehlo.add`, the rhs may have a `constant->broadcast_in_dim` pattern.
Allow the pattern `DeferActivationTransposeForAddOp` to handle such a case.
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Additional overflow test for sobol_sample
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Additional overflow test for `sobol_sample` operation, inspired by https://github.com/tensorflow/tensorflow/issues/64149. This test checks an `InvalidArgument` error is indeed raised if `num_results` + `skip` exceeds signed integer maximum.
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Integrate StableHLO at openxla/stablehlo@94c30ae6
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Integrate StableHLO at openxla/stablehlo@94c30ae6
Reverts changelist 506335783
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PR_kwDOArmXAs5q3aKE
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Guard host transfers inside pure_callbacks from deadlocking the TPU.
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Guard host transfers inside pure_callbacks from deadlocking the TPU.
Also fix python/callback.cc to not swallow errors in numpy conversions.
Reverts changelist 506335783
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PR_kwDOArmXAs5q3XnZ
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Implement a pass to merge requantize+dequantize for StableHLO Quantizer
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Implement a pass to merge requantize+dequantize for StableHLO Quantizer
This pass will convert req+dq pattern in the quantized fusion to dq+relu(6).
Also add an option in PipelineConfig to enable this pass. This option is defaulted to false for ODML pipeline. But enabled by server.
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PR_kwDOArmXAs5q3TFH
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Change default calibration options. Populate both calibration method and calibration parameters.
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Change default calibration options. Populate both calibration method and calibration parameters.
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PR_kwDOArmXAs5q3PVv
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[xla] Excluding check for users with a token type.
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[xla] Excluding check for users with a token type.
Add a test.
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PR_kwDOArmXAs5q3OLZ
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Replace `RemoteTensorHandle` with `TensorProto` for scalars in an `EnqueueRequest` except for `DT_RESOURCE`
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Replace `RemoteTensorHandle` with `TensorProto` for scalars in an `EnqueueRequest` except for `DT_RESOURCE`
Reverts changelist 506335783
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Change the default gpu loop value to 1
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Change the default gpu loop value to 1
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PR_kwDOArmXAs5q3F_u
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Added a virtual function (`CanPropagateShardingToOperands`) to `CustomCallShardingHelper` that can be used to specify whether a custom call can propagate sharding from/to its operands.
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Added a virtual function (`CanPropagateShardingToOperands`) to `CustomCallShardingHelper` that can be used to specify whether a custom call can propagate sharding from/to its operands.
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PR_kwDOArmXAs5q3EWI
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PR #10668: [XLA:GPU] Fix cuDNN FMHA rewriter sequence length checks and Bump up minimum flash attn cuDNN version to 8.9.4
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PR #10668: [XLA:GPU] Fix cuDNN FMHA rewriter sequence length checks and Bump up minimum flash attn cuDNN version to 8.9.4
Imported from GitHub PR https://github.com/openxla/xla/pull/10668
* If only seqlen_q or seqlen_kv is larger than 512, it will be lowered to fused attn instead of flash attn. This is incorrect as fused attn does not support this case. Add checks to lower to flash attn if one seqlen is larger than 512.
* While fixing the seqlen checks, also relax the seqlen/head dim constraints to support any seqlen % 2 == 0, head_dim <= 128 and head_dim % 8 = 0. (Since 8.9.4).
* Consolidate multiple get seqlen/head dim from fused attn/flash attn checks into one `getBHSD` function and rewrite both fused attn/flash attn checks to make it easier to understand.
* Bump up minimum cuDNN version require for flash attn from 8.9.3 to 8.9.4. (More seqlen/head dim support and cross attn support).
* Remove cross attn checks from rewriter and rewriter test since it is now default supported with 8.9.4.
* Add one testcase to cover the first bullet point.
Copybara import of the project:
--
bfdce45d669fe899dd845f497df783f014558384 by cjkkkk <[email protected]>:
rewrite seqlen checks
--
8a0e8b465cc1790953b1dc5f8740adfa7cf1124a by cjkkkk <[email protected]>:
return vector directly
--
72abf544d2f962c5554fe00ffea8d3167a9cde81 by cjkkkk <[email protected]>:
use struct for qkv_layout && add () for extra &&
--
0cac35422315180fcc9b04372916e061ebe17754 by cjkkkk <[email protected]>:
fix head dim check with cuDNN < 8.9.6
Merging this change closes #10668
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10668 from Cjkkkk:rewrite_seqlen_checks 0cac35422315180fcc9b04372916e061ebe17754
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PR_kwDOArmXAs5q3BnE
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#tf-data Add type annotations for `distributed_save`.
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#tf-data Add type annotations for `distributed_save`.
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PR_kwDOArmXAs5q3Bb4
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Fix a use-after-free issue, change a non-nullable pointer argument to a reference and get rid of an unused function argument.
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PR_kwDOArmXAs5q2813
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[XLA:GPU] Deprecate Triton codegen before Ampere.
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[XLA:GPU] Deprecate Triton codegen before Ampere.
Unfortunately, upstream Triton has decided to drop support for NVIDIA GPUs
below Ampere, so we bump the GPU version requirements for using Triton.
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[xla] hlo_computation: drop instruction_indices_
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[xla] hlo_computation: drop instruction_indices_
It's redundant given that we have HloInstruction.index_in_parent_.
There is only one caller (a TF_RET_CHECK) that needs it; I think
we can survive without that check.
This reduces the size of the struct from 176 to 152 bytes.
While at it, move to_be_deleted_ up in the struct so that
instructions_, instruction_count_ and to_be_deleted_ are now
contiguous.
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Error with loading a .pb model for prediction: Op type not registered 'DecodeProtoSparseV4'
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[
"Hi @shabnamso ,\r\n\r\nActually the mentioned Op DecodeProtoSparseV4 is not exists in latest Official TF binaries. It might be possible that this particular Op is a user created Op. Tensorflow provides the flexibility to users to create their own Op and gradient for same. You can refer this [guide](https://www.tensorflow.org/guide/create_op) on same.\r\n\r\nI believe you are trying to reload a model that is trained on custom built TF binary with latest Official release. May be you have to use same binary wheel upon which the model was trained initially. Unfortunately we can't provide support for custom ops of users.\r\n\r\nThanks!",
"Hi @SuryanarayanaY\r\n\r\nThe model was trained by Vertex AI. I downloaded the model to my local machine.",
"Hi @shabnamso , Could you please share the model details. You mean the model is a pretrained model of Vertex AI ?",
"Hi @SuryanarayanaY \r\nI trained the model on vertex AI using my data and the files and folders of the model were downloaded to local machine. The saved_model.pb was automatically in the folder /predict/001. ",
"Hi @shabnamso .\r\n\r\nHave you used any pre-trained/readily available model from VertexAI.If so which one? VertexAI can host user built models also if I am not wrong? ",
"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/64553\">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/64553\">No</a>\n"
] | 2024-03-26T22:20:28 | 2024-04-17T01:47:51 | 2024-04-17T01:47:43 |
NONE
| null | null | null |
### Issue type
Performance
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
latest version
### Custom code
Yes
### OS platform and distribution
Windows
### 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?
Encountered an issue while trying to load a trained .pb model using tf.saved_model.load. I'm receiving a RuntimeError stating that the Op type not registered 'DecodeProtoSparseV4'. Here are the details:
Error Message:
RuntimeError: Op type not registered 'DecodeProtoSparseV4' in binary running on [Machine's Name]. Make sure the Op and Kernel are registered in the binary running in this process. Note that if you are loading a saved graph which used ops from tf.contrib, accessing should be done before importing the graph, as contrib ops are lazily registered when the module is first accessed.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
model_directory = 'C:\Users\...\predict\001'
model = tf.saved_model.load(model_directory)
```
### Relevant log output
_No response_
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[
"@sunitha8687 You are using an older TF version 2.8 which is not actively supported so could you please try to upgrade to the latest and follow the tested build configurations [here](https://www.tensorflow.org/install/source#tested_build_configurations). Thank you!",
"Thanks for your response. I checked the tested build configurations for Linux from your link, looks like 2.8 and 2.10 supports python 3.7to 3.10. \r\nJust to give you some background context here: I am building a docker image in local and then pushing it to the artifactory. This also uses amazoncoretto:17 as base image. So my local system is Mac os, but anyway i specify the platform to be linux/amd64 when i build docker image. The EMR cluster also uses amazon linux os. \r\n\r\nIn this case i look for linux right ?\r\n\r\n",
"@sunitha8687 Since your tested build configurations support Python 3.7 to 3.10, please update the base image in your Dockerfile. Kindly rebuild your Docker image locally with the updated base image and let us know?\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/64552\">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/64552\">No</a>\n"
] | 2024-03-26T22:19:28 | 2024-04-19T01:47:48 | 2024-04-19T01:47:45 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.8.0
### Custom code
Yes
### OS platform and distribution
Linux/amd64
### Mobile device
EMR cluster
### Python version
3.9.16
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I am running a python script(udf from pyspark) from emr node that is launched using a custom docker image. I have python 3.9 installed and tensorflow 2.8.0 installed. When running this particular python script as a spark-submit job, I have an error with tenosrflow PyCMethod_New.
<img width="1170" alt="Screenshot 2024-03-26 at 23 16 33" src="https://github.com/tensorflow/tensorflow/assets/44788173/d08cc361-aa6c-4284-ae32-87037a4475c5">
### Standalone code to reproduce the issue
```shell
Below is the simple python code that I am testing as a spark-submit job that uses docker as runtime environment and error is thrown while importing this package itself.
from pyspark.sql import SparkSession
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType
import sys
sys.path.append("/home/ec2-user/venv/lib/python3.9/site-packages")
import zooperetl
import tensorflow as tf
# Initialize Spark session
spark = SparkSession.builder.appName("MySparkJob").getOrCreate()
# Create a sample DataFrame
data = [("zooperetl",), ("numpy",), ("tensorflow",)]
columns = ["Package"]
df = spark.createDataFrame(data, columns)
# Define a custom UDF to import a library inside udf.
def get_tensorflow_version():
return tf.__version__
# Register the UDF
get_tf_version_udf = udf(get_tensorflow_version, StringType())
# Apply the UDF to create a new column with TensorFlow version
df_with_tf_version = df.withColumn("tensorflow_version", get_tf_version_udf())
# Show the DataFrame with the added column
df_with_tf_version.show()
# Submit the Spark job to the EMR cluster
# Replace 's3://your-bucket/your-script.py' with your actual script location
# spark-submit --master yarn --deploy-mode client s3://your-bucket/your-script.py
```
### Relevant log output
_No response_
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Add machine attributes to topology
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Add machine attributes to topology
(so Jax persistent cache won't reuse code from incompatible machines)
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PR_kwDOArmXAs5q2wfW
| 64,550 |
Make the computation of the memory budget lower bound more efficient. Specifically:
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Make the computation of the memory budget lower bound more efficient. Specifically:
1. Iterate over AliasAnalysis::buffers() rather than the live ranges as the latter has too many duplicates
2. Turn a Shape object into a const reference
3. Pull out a conditional to exit early when possible.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10344 from Intel-tensorflow:kanvi/bmm+mul+add 9c1fee304a6cfed55c0c869d1930641d942cf93f
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Remove unnecessary GraphExportConfig flags. They are only evaluated once and are never reset.
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`tf.cast` does not preserve requested precision for Python types of float64/int64/complex128 etc
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[
"Hi @jonas-eschle ,\r\nI tried to run your code on Colab using TF v2.16.1, nightly faced same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/49b254b03e3fa872fd70b442c510d55f/64548_2-16-1-nightly.ipynb) here for reference.\r\n\r\nThank you!",
"Hi @jonas-eschle ,\r\n\r\nI have tested the given code snippet with TF2.15v. \r\n\r\nFor 1st case(casting of float64 to float32 and checking both same or not) the assertion fails with `InvalidArgumentError` contrary to your reported behaviour.This is expected behaviour for this case. Could you please verify this again ?\r\n\r\n`\r\nInvalidArgumentError: cannot compute Equal as input # 1 (zero-based) was expected to be a float tensor but is a double tensor [Op:Equal] name: \r\n`\r\n\r\n For 2nd case (casting of float64 value to float64 and checking both same or not) the assertion fails but it should not fail IMO. Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/a39ed4a9d0438737815a498e26b32ee0/64548.ipynb) for reference.",
"True, the equal fails. The equal was meant to show that the numbers are actually the same, I adjusted the script to include the conversion for the equal to work (note that the `cast32_as64` was truncated, as it was a python float64 -> cast to tf float32 -> cast to tf float64, while the other one, python float64 -> tf float64, should not be truncated. These should not be equal)\r\n\r\n\r\n",
"maybe, just to be clear @SuryanarayanaY , this isn't a 2.15 only issue, it appears in all versions up to the nigthlies and probably has existed for years",
"Hi @jonas-eschle ,\r\n\r\nI have checked the [documentation](https://www.tensorflow.org/api_docs/python/tf/cast#:~:text=Note%20this%20operation,non%2Dtensor%20inputs.) and found below note regarding casting the python floats to higher precision can lead to loss of precision. This is expected behaviour which is also documented.\r\n\r\n> Note this operation can lead to a loss of precision when converting native Python float and complex variables to [tf.float64](https://www.tensorflow.org/api_docs/python/tf#float64) or [tf.complex128](https://www.tensorflow.org/api_docs/python/tf#complex128) tensors, since the input is first converted to the float32 data type and then widened. It is recommended to use [tf.convert_to_tensor](https://www.tensorflow.org/api_docs/python/tf/convert_to_tensor) instead of [tf.cast](https://www.tensorflow.org/api_docs/python/tf/cast) for any non-tensor inputs.",
"I see, this is true, although highly unexpected behavior IMHO. I do not quite understand why https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/math_ops.py?rgh-link-date=2024-03-26T22%3A03%3A17Z#L1019 doesn't just take the dtype argument though, the comment seems not clear to me.\r\n\r\nI think it's unexpected and should either a) fail if a Python type is given or b) converted to the tensor type first (the comment suggests that things could go wrong, but then, what does a user expect? It will obviously be converted to a tensor?), but doing things silently \"wrong\" seems not the ideal way? Especially since the conversion to a float32 is a bit random (i.e. why 32, could be 64, or 16?)\r\n\r\nFeel free to close or keep open for \"fix\", I would regard it as a design issue and there is a \"fix\" comment there, so why not do it right",
"Hi @jonas-eschle ,\r\n\r\nIf you want to modify the comment in docs please feel free to create a PR. The source is [here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/math_ops.py#L942).",
"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/64548\">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/64548\">No</a>\n"
] | 2024-03-26T22:03:17 | 2024-04-19T01:47:51 | 2024-04-19T01:47:46 |
CONTRIBUTOR
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
any new version, up to develop
### Custom code
No
### Current behavior?
`tf.cast` truncates the precision of non-TF (python, numpy,...) types where the requested conversion is higher than the default of `convert_to_tensor`, i.e. `float64`, `int64`, `complex128`.
Example:
Casting a numpy array with float64 (or a python float) with `tf.cast` returns a tf.Tensor float64 _with effective float32 precision_ (see example below)
The reason is in https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/math_ops.py#L1006:
if it's not a TF type, it converts it to the default type returned by `convert_to_tensor`, which is for floats (as an example) float32. Later on, it up-casts (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/math_ops.py#L1019), but the precision was already lost at this point.
### proposed solution
The comment suggests that providing `dtype` could convert things that are inconvertible. However, isn't this happening now already? And wouldn't that fail later on in https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/math_ops.py#L1019 in the cast?
I do not see any downside of using `dtype=dtype`.
If you agree, I can make the change and see if the tests still pass.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
print(tf.__version__)
oneplus = 1 + 1e-15 # this 1.0 in float32 representation, but correctly represented in float64
oneplus_cast32 = tf.cast(oneplus, dtype=tf.float32) # 1.0, as expected
oneplus_cast64 = tf.cast(oneplus, dtype=tf.float64) # 1.0, but should be larger
oneplus_converted64 = tf.convert_to_tensor(oneplus, dtype=tf.float64)
oneplus_cast32_as64 = tf.cast(oneplus_cast32, dtype=tf.float64) # casting just for the equal below to work
# note that it is truncated due to being a float32 that is represented in a float64
# we can check that it's wrong
assert oneplus_cast32_as64 == oneplus_cast64, "This must fail!" # works, but should not
assert oneplus_converted64 == oneplus_cast64, "This should work" # fails, but should work
```
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[xla:gpu] Make kernel arguments deduplication optional
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[xla:gpu] Make kernel arguments deduplication optional
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PR_kwDOArmXAs5q2ojh
| 64,546 |
Disable the import tensorflow check for TF-TPU
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Disable the import tensorflow check for TF-TPU
For TF-TPU, import tensorflow step loads libtpu along with it, which in turn try to connect with TPU drivers. When these whls are built on a CPU only machine which doesn't have drivers, the build fails. We add an env variable to disable the import tensorflow check temporarily until we come up with a neat solution.
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PR_kwDOArmXAs5q2oK6
| 64,545 |
Replace TODO bug, to track investigation and fix for histogram percentile calibration method for StableHLO Quantizer.
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Replace TODO bug, to track investigation and fix for histogram percentile calibration method for StableHLO Quantizer.
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PR_kwDOArmXAs5q2jd-
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[XLA:Python] Add python function to convert `xla::LiteralProto` into a tuple-tree of `numpy.ndarray`.
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[XLA:Python] Add python function to convert `xla::LiteralProto` into a tuple-tree of `numpy.ndarray`.
This is intended for internal debugging use. It cannot be used on OSS because the relevant protobufs are not part of the public API. (Though it must not break the OSS build, naturally.)
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PR_kwDOArmXAs5q2jV8
| 64,543 |
Differentiate DNS resolution and cert errors for metadata vs. external requests
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Differentiate DNS resolution and cert errors for metadata vs. external requests
The goal of this CL is to improve behavior on GCS file requests from within TensorFlow, which often fail due to transient DNS resolution failures.
This change provides more granular error handling, distinguishing between non-retriable errors (metadata server DNS, certificate) and potentially transient network issues (other DNS failures) when deciding when to retry. Resolution failures for URIs like "metadata" or "metadata.google.internal" result in a FailedPrecondition error, since a retry is very likely to still fail.
Non-metadata DNS failures trigger an Unavailable error, indicating potential for resolution on retry.
Certificate validation errors (CURLE_SSL_CACERT_BADFILE) result in a FailedPrecondition error.
Other errors default to Unavailable for the possibility of retries.
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PR_kwDOArmXAs5q1zXS
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[tsl] Implement `tsl::NullTerminatedMessage()` in terms of `absl::StatusMessageAsCStr()`
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[tsl] Implement `tsl::NullTerminatedMessage()` in terms of `absl::StatusMessageAsCStr()`
* This allows TF to outsource correct acquisition of a null-terminated C-string of the status message to the Abseil library itself
* The previous implementation happened to work currently, but depended on internal implementation details of `absl::Status::message()` (namely, that when the message isn't empty, the returned `absl::string_view` is backed by a null-terminated `std::string`)
* Unfortunately we currently have to duplicate the definition depending on the platform due to linker-issues in the Windows build
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PR_kwDOArmXAs5q1y6s
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[tsl] Mark `tsl::NullTerminatedMessage()` with `ABSL_DEPRECATE_AND_INLINE`
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Automated Code Change
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shlo_ref: Add doc links for contributing & iOS
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This PR adds links to the main tensorflow contributing guideline to the SHLO doc and highlights the expectation that clang-format is to be used. It also adds some rudimentary instructions for building targets for iOS, but testing is still left as a todo.
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#shlo_ref Add `minimum` op.
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#shlo_ref Add `minimum` op.
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#shlo_ref Add `maximum` op.
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#shlo_ref Add `maximum` op.
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PR_kwDOArmXAs5q1QG8
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Run xla triton hopper tests on demand on TAP
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PR #10858: [NVIDIA GPU] Enforce first collective permute of collective matmul to always run at the top of loop.
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PR #10858: [NVIDIA GPU] Enforce first collective permute of collective matmul to always run at the top of loop.
Imported from GitHub PR https://github.com/openxla/xla/pull/10858
This is to port collective matmul related changes from this pr(https://github.com/openxla/xla/pull/10316).
We'd need the first collective permute of a collective matmul loop always run at the beginning of the loop.
We set the force_earliest_schedule flag for that instruction to achieve so.
Force this schedule order would give about additional 3% speedup on top of existing collective matmul for GPT-3 530B model.
Copybara import of the project:
--
eb822e1493c0b0b51438f4b7153df6f9dfaa9276 by TJ Xu <[email protected]>:
Enforce first collective permute of collective matmul to always run at
the top of loop.
Merging this change closes #10858
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10858 from Tixxx:tixxx/fixed_schedule_collective_matmul eb822e1493c0b0b51438f4b7153df6f9dfaa9276
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[XLA:GPU] Fix Triton emitter for nested reducer fusions.
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[XLA:GPU] Fix Triton emitter for nested reducer fusions.
This should not have any reason to happen in principle, but the fusion passes
currently sometimes produce fusions inside reduction computations---causing
us to have to codegen a fusion inside our Triton fusion.
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Models only a subset of memory segments that are sufficiently different from one another, skipping those that are "similar enough."
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Models only a subset of memory segments that are sufficiently different from one another, skipping those that are "similar enough."
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PR_kwDOArmXAs5q1JJl
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In general, avoid the suffix on StatusOr.
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In general, avoid the suffix on StatusOr.
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PR_kwDOArmXAs5q1Go8
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Integrate StableHLO at openxla/stablehlo@f4459e76
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Integrate StableHLO at openxla/stablehlo@f4459e76
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PR_kwDOArmXAs5q1Fdi
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Internal config change
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PR_kwDOArmXAs5q0_oK
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[XLA:GPU] Add fusion into Triton Softmax in PriorityFusion pass.
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[XLA:GPU] Add fusion into Triton Softmax in PriorityFusion pass.
This feature is under development and will likely remain unstable until we finish work on SymbolicTileAnalysis.
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PR_kwDOArmXAs5q04qy
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Update github xla contacts
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Update github xla contacts
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I_kwDOArmXAs6DqA2x
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Problem with Tensorflow 2.16.1 and gpu
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"Hi @Altrastorique ,\r\n\r\nAfter creating Conda environment please use pip to install TF package. For GPU package please use pip install `tensorflow[and-cuda]` that will install all the required compatible cuda/cudnn packages. Please ensure compatible cuda driver installed manually. Please check and come back if still having problem. Thanks.",
"Okay, but when I run this in the annaconda power shell I have this: `WARNING: tensorflow 2.13.1 does not provide the extra 'and-cuda'`\r\nSo when I run this code:\r\n ```\r\nimport tensorflow as tf\r\ntf.test.gpu_device_name()\r\n\r\nprint(tf.config.list_physical_devices('GPU'))\r\n```\r\nI have: `[ ]`\r\n\r\nThe same happen when I run it in my terminal.",
"> Okay, but when I run this in the annaconda power shell I have this: `WARNING: tensorflow 2.13.1 does not provide the extra 'and-cuda'`\r\n\r\nThe 'and-cuda' package introduced in TF2.14 onwards. You are trying for Tf2.13v as warning states.Please upgrade to higher version.",
"The tensorflow version >2.13 are not available on the anaconda powershell. So I had to use the terminal. But I still have this error when installing tensorflow[and-cuda]\r\n```\r\nERROR: Could not find a version that satisfies the requirement nvidia-nccl-cu12==2.19.3; extra == \"and-cuda\" (from tensorflow[and-cuda]) (from versions: 0.0.1.dev5)\r\nERROR: No matching distribution found for nvidia-nccl-cu12==2.19.3; extra == \"and-cuda\"\r\n\r\n```\r\nWhen trying the 2.14 version I had this:\r\n```\r\npy -m pip install tensorflow==2.14\r\nERROR: Could not find a version that satisfies the requirement tensorflow==2.14 (from versions: 2.16.0rc0, 2.16.1)\r\nERROR: No matching distribution found for tensorflow==2.14\r\n```",
"Hi @Altrastorique ,\r\n\r\nIf you are using Windows OS cuda package is not available for windows from TF2.11 onwards. Please refer the [caution](https://www.tensorflow.org/install/pip#windows-native:~:text=Caution%3A%20TensorFlow%202.10%20was%20the%20last%20TensorFlow%20release%20that%20supported%20GPU%20on%20native%2DWindows.%20Starting%20with%20TensorFlow%202.11%2C%20you%20will%20need%20to%20install%20TensorFlow%20in%20WSL2%2C%20or%20install%20tensorflow%20or%20tensorflow%2Dcpu%20and%2C%20optionally%2C%20try%20the%20TensorFlow%2DDirectML%2DPlugin) on same in documentation.\r\n\r\nIf you want to use GPU then you need to install WSL2. Please refer the instructions for wsl2 [here](https://www.tensorflow.org/install/pip#windows-wsl2).",
"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/64525\">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/64525\">No</a>\n",
"I had same troble \r\n\r\n Could not find a version that satisfies the requirement nvidia-nccl-cu12==2.19.3; extra == \"and-cuda\" (from tensorflow[and-cuda]) (from versions: 0.0.1.dev5)",
"> I had same troble\r\n> \r\n> Could not find a version that satisfies the requirement nvidia-nccl-cu12==2.19.3; extra == \"and-cuda\" (from tensorflow[and-cuda]) (from versions: 0.0.1.dev5)\r\n\r\nsame here as well. WSL2 is installed with all the prereqs for 2.16.1 - https://www.tensorflow.org/install/source#gpu"
] | 2024-03-26T17:10:15 | 2024-06-03T01:55:09 | 2024-05-03T01:48:46 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.16.1
### Custom code
Yes
### OS platform and distribution
Windows 11
### Mobile device
_No response_
### Python version
3.12 and also tried with 3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
RTX 3070
### Current behavior?
Hello, I was trying to make TensorFlow work with my GPU, but the CUDA installation crashed every time. So I tried using Anaconda, but they don't have CUDA version 12.3 and CuDNN version 8.9. So I succeeded using the Anaconda PowerShell and downloading TensorFlow-GPU 2.10.
I heard that TensorFlow-GPU is an old version we should not use.
Does someone have any idea which could help me?
Thank you very much!
### Standalone code to reproduce the issue
```shell
(base) C:\Users\User>conda create -n tensorflow python=3.12
(base) C:\Users\User>conda activate tensorflow
(tensorflow) C:\Users\User>conda install -c conda-forge cudatoolkit=12.3 cudnn=8.9
```
### Relevant log output
```shell
Channels:
- conda-forge
- defaults
Platform: win-64
Collecting package metadata (repodata.json): done
Solving environment: failed
PackagesNotFoundError: The following packages are not available from current channels:
- cudatoolkit=12.3*
Current channels:
- https://conda.anaconda.org/conda-forge
- defaults
To search for alternate channels that may provide the conda package you're
looking for, navigate to
https://anaconda.org
and use the search bar at the top of the page.
```
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PR_kwDOArmXAs5q0fjy
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PR #10649: [ROCm] Triton in XLA for ROCm - ir_emitter_triton related changes.
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PR #10649: [ROCm] Triton in XLA for ROCm - ir_emitter_triton related changes.
Imported from GitHub PR https://github.com/openxla/xla/pull/10649
Second commit of the series for enabling Triton in XLA for ROCm.
Copybara import of the project:
--
02a1077a50386462f22f97d3db7903037164d92f by Zoran Jovanovic <[email protected]>:
[ROCm] Triton in XLA for ROCm - ir_emitter_triton related changes.
Merging this change closes #10649
Reverts changelist 506335783
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10649 from ROCm:rocm_triton_backend_3 02a1077a50386462f22f97d3db7903037164d92f
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PR_kwDOArmXAs5q0dKG
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PR #10376: Fix expm1 inaccuracies on complex inputs with small absolute values. Add Cosm1.
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PR #10376: Fix expm1 inaccuracies on complex inputs with small absolute values. Add Cosm1.
Imported from GitHub PR https://github.com/openxla/xla/pull/10376
As in the title.
Tests and improvement reports are in https://github.com/google/jax/pull/20144.
Accuracy tests are enabled in https://github.com/google/jax/pull/20436
Copybara import of the project:
--
42e222a436787c52adf453c8c0c39125b010e2b2 by Pearu Peterson <[email protected]>:
Fix expm1 inaccuracies on complex inputs with small absolute values. Add Cosm1.
Fix expm1(x+yi) when x is large and y is zero.
Merging this change closes #10376
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10376 from pearu:pearu/expm1 42e222a436787c52adf453c8c0c39125b010e2b2
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PR_kwDOArmXAs5q0Vpj
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Move sparsecore passes under transforms/sparsecore.
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Move sparsecore passes under transforms/sparsecore.
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PR #10954: [ROCM] fix 'Invalid plugin kind specified: DNN' error
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PR #10954: [ROCM] fix 'Invalid plugin kind specified: DNN' error
Imported from GitHub PR https://github.com/openxla/xla/pull/10954
This pr removes "Invalid plugin kind specified: DNN" error/warning often seen on ROCm platform.
@xla-rotation : would you take a look please?
Copybara import of the project:
--
b2c8d601a9aa1bcd7f6a6d9d7bb8c1b4eda93f8a by Ruturaj4 <[email protected]>:
fix 'Invalid plugin kind specified: DNN' error
Merging this change closes #10954
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10954 from ROCm:ci_initialize_miopen_fix b2c8d601a9aa1bcd7f6a6d9d7bb8c1b4eda93f8a
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PR_kwDOArmXAs5q0L9p
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[TF-XLA:CPU][oneDNN] Disable grappler remapper fusions on TF->XLA CPU JIT compilation
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"@cantonios Thanks for the review. I have addressed the comments."
] | 2024-03-26T16:15:19 | 2024-04-24T04:53:05 | 2024-04-24T04:53:04 |
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This PR disables remapper fusions on CPU, when TF model is set for XLA JIT compilation. TF fused ops cannot be translated into XLA instructions. The fusions are carried out by XLA CPU compiler passes.
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[XLA:GPU] Move Tiling Complexity heuristic to EmitMatMul.
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[XLA:GPU] Move Tiling Complexity heuristic to EmitMatMul.
Currently the heuristic lives in the wrong place. It requires to pass fusion_kind to TritonWrapper.
The heuristic is used only for GEMMs, so it should move either up (to TritonFusion::Emit), or down (to EmitMatMul). I chose EmitMatMul because it requires less changes to the current infrastructure.
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no matching function for call to 'MakeOneDnnStream'
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"@waarmond Could you please try with the following command and let us know;\r\n```\r\nbazel build --repo_env=TF_PYTHON_VERSION=3.10 //tensorflow/tools/pip_package:build_pip_package --repo_env=WHEEL_NAME=tf_nightly\r\n\r\n```\r\nThank you!\r\n",
"Duplicate of https://github.com/tensorflow/tensorflow/issues/63360",
"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/64518\">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/64518\">No</a>\n"
] | 2024-03-26T15:36:51 | 2024-03-27T08:23:18 | 2024-03-27T08:23:15 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16.1+ git
### Custom code
No
### OS platform and distribution
ArchLinux
### Mobile device
_No response_
### Python version
3.10
### Bazel version
6.1.0
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
```
ERROR: [.. bazel cache ..]/local_xla/xla/service/cpu/BUILD:1600:11: Compiling xla/service/cpu/onednn_matmul.cc failed: (Exit 1): clang-17 failed: error executing command (from target @local_xla//xla/service/cpu:onednn_matmul) /usr/bin/clang-17 -U_FORTIFY_SOURCE -fstack-protector -Wall -Wthread-safety -Wself-assign -Wunused-but-set-parameter -Wno-free-nonheap-object -fcolor-diagnostics -fno-omit-frame-pointer -g0 -O2 ... (remaining 220 arguments skipped)
external/local_xla/xla/service/cpu/onednn_matmul.cc:186:24: error: no matching function for call to 'MakeOneDnnStream'
186 | auto onednn_stream = MakeOneDnnStream(cpu_engine, thread_pool.get());
| ^~~~~~~~~~~~~~~~
external/local_xla/xla/service/cpu/onednn_matmul.cc:146:14: note: candidate function not viable: no known conversion from 'pointer' (aka 'tsl::OneDnnThreadPool *') to 'dnnl::threadpool_interop::threadpool_iface *' for 2nd argument
146 | dnnl::stream MakeOneDnnStream(
| ^
147 | const dnnl::engine& cpu_engine,
148 | dnnl::threadpool_interop::threadpool_iface* thread_pool) {
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
external/local_xla/xla/service/cpu/onednn_matmul.cc:322:24: error: no matching function for call to 'MakeOneDnnStream'
322 | auto onednn_stream = MakeOneDnnStream(cpu_engine, thread_pool.get());
| ^~~~~~~~~~~~~~~~
external/local_xla/xla/service/cpu/onednn_matmul.cc:146:14: note: candidate function not viable: no known conversion from 'pointer' (aka 'tsl::OneDnnThreadPool *') to 'dnnl::threadpool_interop::threadpool_iface *' for 2nd argument
146 | dnnl::stream MakeOneDnnStream(
| ^
147 | const dnnl::engine& cpu_engine,
148 | dnnl::threadpool_interop::threadpool_iface* thread_pool) {
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 errors generated.
```
Not fixed by https://github.com/tensorflow/tensorflow/pull/64421 ENABLE_ONEDNN_V3
I've updated from onednn 3.1.1 to 3.4 to no avail.
### Standalone code to reproduce the issue
same as in 63298
```shell
bazel build --repo_env=TF_PYTHON_VERSION=3.10 //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tf_nightly
```
### Relevant log output
_No response_
|
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Add basic support for folding constant RTVars
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Add basic support for folding constant RTVars
This allows turning dynamic HLO operands into a index expressions when the HLO yields some constant value.
As a first step this is adding support for the HLO ops `constant` and `iota`.
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PR_kwDOArmXAs5qziha
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hlo_dfs_reachability_test: use batch mode in Build benchmark
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hlo_dfs_reachability_test: use batch mode in Build benchmark
- Before:
----------------------------------------------------------------------------
Benchmark Time CPU Iterations
----------------------------------------------------------------------------
BM_HloDfsReachabilityBuild/1 143 ns 143 ns 10314343
BM_HloDfsReachabilityBuild/64 1776 ns 1776 ns 783254
BM_HloDfsReachabilityBuild/128 3565 ns 3564 ns 391936
BM_HloDfsReachabilityBuild/256 7446 ns 7445 ns 189334
BM_HloDfsReachabilityBuild/512 15044 ns 15041 ns 92625
BM_HloDfsReachabilityBuild/4096 158882 ns 158865 ns 8625
BM_HloDfsReachabilityBuild/32768 1709590 ns 1709401 ns 825
BM_HloDfsReachabilityBuild/262144 37142558 ns 37136529 ns 38
- After:
----------------------------------------------------------------------------
Benchmark Time CPU Iterations
----------------------------------------------------------------------------
BM_HloDfsReachabilityBuild/1 135 ns 135 ns 10330052
BM_HloDfsReachabilityBuild/64 27.8 ns 27.8 ns 50131200
BM_HloDfsReachabilityBuild/128 28.7 ns 28.7 ns 47522432
BM_HloDfsReachabilityBuild/256 29.2 ns 29.2 ns 47567104
BM_HloDfsReachabilityBuild/512 29.9 ns 29.9 ns 46650880
BM_HloDfsReachabilityBuild/4096 41.1 ns 41.1 ns 36945920
BM_HloDfsReachabilityBuild/32768 59.5 ns 59.5 ns 23035904
BM_HloDfsReachabilityBuild/262144 154 ns 154 ns 8912896
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10954 from ROCm:ci_initialize_miopen_fix b2c8d601a9aa1bcd7f6a6d9d7bb8c1b4eda93f8a
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I_kwDOArmXAs6DolR6
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Cannot create TFLite model with TF 2.16
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[
"Had same issue, switched to 2.15 temporarily. But can confirm the issue.",
"> Had same issue, switched to 2.15 temporarily. But can confirm the issue.\r\n\r\nIt seems TFLite converter is not compatible with Keras 3. A workaround is to use [ExportArchive](https://keras.io/api/models/model_saving_apis/export/) to convert Keras model to saved model at first.",
"Hi @LaurentBerger,\r\n\r\nI have reproduced the code with TF 2.15 and TF 2.16. With 2.15 it's working fine as expected. As you mentioned, it's getting crashed with TF2.16.1 at TFLite conversion . Right now, TF2.16 has an issue with Keras 3.0. As a workaround install Keras 2 as follows. \r\n`pip install -U tf_keras # Keras 2`\r\n`import os`\r\n`os.environ[\"TF_USE_LEGACY_KERAS\"] = \"1\" `. Here is the [gist](https://colab.sandbox.google.com/gist/LakshmiKalaKadali/03c5ccb6bdfca1c6f774e41f429e8085/tflite_64515_2-16.ipynb) with TF 2.16 and keras2 workaround. Please let us know if problem still persists.\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."
] | 2024-03-26T14:40:45 | 2024-04-19T01:47:48 | 2024-04-19T01:47:47 |
NONE
| null | null | null |
### 1. System information
Windows 11
Installed with pip
tensorboard 2.16.2
tensorboard-data-server 0.7.2
tensorboard-plugin-wit 1.8.1
tensorboardX 2.6.2.2
tensorflow 2.16.1
tensorflow-addons 0.22.0
tensorflow-cpu 2.10.0
tensorflow-datasets 4.9.3
tensorflow-estimator 2.12.0
tensorflow-intel 2.16.1
tensorflow-io-gcs-filesystem 0.31.0
tensorflow-metadata 1.14.0- TensorFlow library (version, if pip package or github SHA, if built from source):
### 2. Code
```
import tensorflow as tf
def lenet5():
model = tf.keras.Sequential()
couche0 = tf.keras.layers.Conv2D(6, kernel_size=(3, 3), activation='relu', input_shape=(28, 28,1))
couche1 = tf.keras.layers.MaxPooling2D((2, 2))
couche2 = tf.keras.layers.Conv2D(16, activation='relu',kernel_size=(3, 3))
couche3 = tf.keras.layers.MaxPooling2D((2, 2))
couche4 = tf.keras.layers.Flatten()
couche5 = tf.keras.layers.Dense(120, activation='relu')
couche6 = tf.keras.layers.Dense(84, activation='relu')
couche7 = tf.keras.layers.Dense(10, activation='softmax', name="output")
model.add(tf.keras.Input(shape=( 28,28, 1), name="digit", dtype=tf.float32))
model.add(couche0)
model.add(couche1)
model.add(couche2)
model.add(couche3)
model.add(couche4)
model.add(couche5)
model.add(couche6)
model.add(couche7)
return model
model = lenet5()
model.compile(optimizer='adam',loss=tf.keras.losses.categorical_crossentropy,metrics=['CategoricalAccuracy'])
model.load_weights("mnist_0000062_.weights.h5")
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_types = [tf.float16]
tflite_model = converter.convert()
# Save the converted model to a file
temp_model_file = "lenet5.tflite"
with open(temp_model_file,'wb') as f:
f.write(tflite_model)
```
model
[mnist_0000062_.weights.zip](https://github.com/tensorflow/tensorflow/files/14760093/mnist_0000062_.weights.zip)
Results
```
2024-03-26 15:34:04.151254: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-03-26 15:34:04.523991: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
C:\Users\laurent\AppData\Roaming\Python\Python310\site-packages\keras\src\layers\convolutional\base_conv.py:99: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
super().__init__(
2024-03-26 15:34:05.413502: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
C:\Users\laurent\AppData\Roaming\Python\Python310\site-packages\keras\src\saving\saving_lib.py:396: UserWarning: Skipping variable loading for optimizer 'adam', because it has 2 variables whereas the saved optimizer has 21 variables.
trackable.load_own_variables(weights_store.get(inner_path))
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
W0000 00:00:1711463645.723617 20680 tf_tfl_flatbuffer_helpers.cc:390] Ignored output_format.
W0000 00:00:1711463645.723804 20680 tf_tfl_flatbuffer_helpers.cc:393] Ignored drop_control_dependency.
2024-03-26 15:34:05.724291: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: C:\Users\laurent\AppData\Local\Temp\tmp7oi815am
2024-03-26 15:34:05.724845: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve }
2024-03-26 15:34:05.724946: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: C:\Users\laurent\AppData\Local\Temp\tmp7oi815am
2024-03-26 15:34:05.730047: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled
2024-03-26 15:34:05.730441: I tensorflow/cc/saved_model/loader.cc:234] Restoring SavedModel bundle.
2024-03-26 15:34:05.744601: I tensorflow/cc/saved_model/loader.cc:218] Running initialization op on SavedModel bundle at path: C:\Users\laurent\AppData\Local\Temp\tmp7oi815am
2024-03-26 15:34:05.748741: I tensorflow/cc/saved_model/loader.cc:317] SavedModel load for tags { serve }; Status: success: OK. Took 24452 microseconds.
2024-03-26 15:34:05.755730: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
loc(fused["ReadVariableOp:", callsite("sequential_1/conv2d_1/Reshape/ReadVariableOp@__inference_serving_default_174"("C:\\data\\mnist\\test_tf2_lite_16.py":34:1) at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\tensorflow\\lite\\python\\lite.py":1175:1 at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\tensorflow\\lite\\python\\lite.py":1129:1 at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\tensorflow\\lite\\python\\lite.py":1636:1 at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\tensorflow\\lite\\python\\lite.py":1614:1 at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\tensorflow\\lite\\python\\convert_phase.py":205:1 at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\tensorflow\\lite\\python\\lite.py":1537:1 at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\keras\\src\\backend\\tensorflow\\layer.py":57:1 at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\keras\\src\\backend\\tensorflow\\layer.py":119:1 at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\keras\\src\\utils\\traceback_utils.py":118:1 at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\keras\\src\\layers\\layer.py":816:1 at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\keras\\src\\utils\\traceback_utils.py":118:1 at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\keras\\src\\ops\\operation.py":42:1 at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\keras\\src\\utils\\traceback_utils.py":157:1 at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\keras\\src\\models\\sequential.py":203:1 at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\keras\\src\\models\\functional.py":188:1 at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\keras\\src\\ops\\function.py":153:1 at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\keras\\src\\models\\functional.py":572:1 at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\keras\\src\\utils\\traceback_utils.py":118:1 at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\keras\\src\\layers\\layer.py":816:1 at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\keras\\src\\utils\\traceback_utils.py":118:1 at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\keras\\src\\ops\\operation.py":42:1 at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\keras\\src\\utils\\traceback_utils.py":157:1 at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py":227:1 at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\keras\\src\\ops\\numpy.py":4499:1 at callsite("C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\keras\\src\\backend\\tensorflow\\numpy.py":1378:1 at "C:\\Users\\laurent\\AppData\\Roaming\\Python\\Python310\\site-packages\\keras\\src\\backend\\tensorflow\\core.py":65:1))))))))))))))))))))))))))]): error: missing attribute 'value'
LLVM ERROR: Failed to infer result type(s).
```
### 5. (optional) Any other info / logs
When I use same code with windows WSL2 and
```
tensorboard 2.15.2
tensorboard-data-server 0.7.0
tensorflow 2.15.1
tensorflow-addons 0.22.0
tensorflow-datasets 4.9.3
tensorflow-estimator 2.15.0
tensorflow-io-gcs-filesystem 0.32.0
tensorflow-metadata 1.14.0
tensorrt 8.5.3.1
```
No error
```
2024-03-26 15:33:28.524995: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-03-26 15:33:28.543576: 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
2024-03-26 15:33:28.543609: 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
2024-03-26 15:33:28.544176: 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
2024-03-26 15:33:28.547183: 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 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-03-26 15:33:28.888368: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-03-26 15:33:29.290515: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-03-26 15:33:29.307882: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-03-26 15:33:29.307936: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-03-26 15:33:29.311209: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-03-26 15:33:29.311242: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-03-26 15:33:29.311360: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-03-26 15:33:30.078224: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-03-26 15:33:30.078277: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-03-26 15:33:30.078295: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2022] Could not identify NUMA node of platform GPU id 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2024-03-26 15:33:30.078369: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-03-26 15:33:30.078420: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 21596 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:01:00.0, compute capability: 8.6
WARNING:absl:Skipping variable loading for optimizer 'Adam', because it has 1 variables whereas the saved optimizer has 21 variables.
2024-03-26 15:33:31.110652: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:378] Ignored output_format.
2024-03-26 15:33:31.110679: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:381] Ignored drop_control_dependency.
2024-03-26 15:33:31.110980: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: /tmp/tmpuo61349g
2024-03-26 15:33:31.111933: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve }
2024-03-26 15:33:31.111958: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: /tmp/tmpuo61349g
2024-03-26 15:33:31.115567: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled
2024-03-26 15:33:31.115928: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle.
2024-03-26 15:33:31.130666: I tensorflow/cc/saved_model/loader.cc:217] Running initialization op on SavedModel bundle at path: /tmp/tmpuo61349g
2024-03-26 15:33:31.134896: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 23916 microseconds.
2024-03-26 15:33:31.142410: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
Summary on the non-converted ops:
---------------------------------
* Accepted dialects: tfl, builtin, func
* Non-Converted Ops: 11, Total Ops 33, % non-converted = 33.33 %
* 11 ARITH ops
- arith.constant: 11 occurrences (f16: 10, i32: 1)
(f32: 2)
(f32: 10)
(f32: 3)
(f32: 2)
(f32: 1)
(f32: 1)
Traceback (most recent call last):
File "/mnt/c/data/mnist/test_tf2_lite_16.py", line 36, in <module>
temp_model_file = name_base + ".tflite"
NameError: name 'name_base' is not defined
laurent@traimaocv-i9:/mnt/c/data/mnist$ python3 test_tf2_lite_16.py
2024-03-26 15:33:44.430741: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-03-26 15:33:44.448582: 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
2024-03-26 15:33:44.448619: 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
2024-03-26 15:33:44.449139: 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
2024-03-26 15:33:44.452013: 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 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-03-26 15:33:44.784147: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-03-26 15:33:45.183602: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-03-26 15:33:45.201469: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-03-26 15:33:45.201526: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-03-26 15:33:45.204504: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-03-26 15:33:45.204544: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-03-26 15:33:45.204611: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-03-26 15:33:45.986890: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-03-26 15:33:45.986944: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-03-26 15:33:45.986961: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2022] Could not identify NUMA node of platform GPU id 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2024-03-26 15:33:45.986996: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-03-26 15:33:45.987037: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 21596 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:01:00.0, compute capability: 8.6
WARNING:absl:Skipping variable loading for optimizer 'Adam', because it has 1 variables whereas the saved optimizer has 21 variables.
2024-03-26 15:33:47.029778: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:378] Ignored output_format.
2024-03-26 15:33:47.029813: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:381] Ignored drop_control_dependency.
2024-03-26 15:33:47.030097: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: /tmp/tmpz3jzsy93
2024-03-26 15:33:47.030795: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve }
2024-03-26 15:33:47.030815: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: /tmp/tmpz3jzsy93
2024-03-26 15:33:47.034126: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled
2024-03-26 15:33:47.034483: I tensorflow/cc/saved_model/loader.cc:233] Restoring SavedModel bundle.
2024-03-26 15:33:47.049229: I tensorflow/cc/saved_model/loader.cc:217] Running initialization op on SavedModel bundle at path: /tmp/tmpz3jzsy93
2024-03-26 15:33:47.053484: I tensorflow/cc/saved_model/loader.cc:316] SavedModel load for tags { serve }; Status: success: OK. Took 23388 microseconds.
2024-03-26 15:33:47.060119: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
Summary on the non-converted ops:
---------------------------------
* Accepted dialects: tfl, builtin, func
* Non-Converted Ops: 11, Total Ops 33, % non-converted = 33.33 %
* 11 ARITH ops
- arith.constant: 11 occurrences (f16: 10, i32: 1)
```
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PR_kwDOArmXAs5qzWGC
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Integrate LLVM at llvm/llvm-project@3cf169ca160e
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Integrate LLVM at llvm/llvm-project@3cf169ca160e
Updates LLVM usage to match
[3cf169ca160e](https://github.com/llvm/llvm-project/commit/3cf169ca160e)
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Add the result sharding to reshapes added in `ReshapeWithCorrectRepresentationAndSharding`.
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Add the result sharding to reshapes added in `ReshapeWithCorrectRepresentationAndSharding`.
If `ConvertMlirHloToHlo` is applied after sharding propagation, then it's important that the added reshapes will have the same sharding as that of the corresponding function outputs.
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Convert S8 to BF16 in one step without going though F32.
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Convert S8 to BF16 in one step without going though F32.
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[Triton] Modify disabled mixed-precision mmav2 swizzling to be enabled with different configuration.
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[Triton] Modify disabled mixed-precision mmav2 swizzling to be enabled with different configuration.
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[XLA:GPU][IndexAnalysis] Check if the scatter is canonicalized when computing thread indexing.
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[XLA:GPU][IndexAnalysis] Check if the scatter is canonicalized when computing thread indexing.
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[XLA:GPU][CoalescingAnalysis] Put the TODOs in the code.
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[XLA:GPU][CoalescingAnalysis] Put the TODOs in the code.
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[XLA:GPU] Add GpuHloCostAnalysis option to estimate Triton Softmax fusions.
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[XLA:GPU] Add GpuHloCostAnalysis option to estimate Triton Softmax fusions.
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protobuf fatal error in Tensorflow 2.16.1 on macos M1
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[
"@asapsmc,\r\nThank you for the issue. Could you please provide the steps you have followed to install the tensorflow 2.16.1 on macos and allow some time to debug the issue. Thank you!",
"I'm using miniforge, so it was `conda install -c apple tensorflow-deps`, then activate and then `python -m pip install tensorflow`followed by `python -m pip install tensorflow-metal`.",
"Hi,\r\nI understand that you are using GPU to build your model. This is an issue with the `tensorflow-metal` library, as it only supports `tensorflow` up to version 2.14. Therefore, it seems that we need to wait for a new version of the `tensorflow-metal` library. For now, if you want to use `tensorFlow` >2.14, you will need to uninstall` tensorflow-metal` or refrain from using the GPU.",
"Thanks for your help. Now I define tensorflow==2.14 and everything works. ",
"@asapsmc,\r\nAFAIK the version of tensorflow-metal==1.1.0 must be along with tensorflow-macos==2.14.0. So, If you use \"pip install tensorflow-macos\" alone. You will get 2.15.0 or the latest. So you must install the version which tensorflow-metal is supported. 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/64507\">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/64507\">No</a>\n"
] | 2024-03-26T12:58:34 | 2024-04-12T01:47:37 | 2024-04-12T01:47:34 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.16.1
### Custom code
No
### OS platform and distribution
macOS Sonoma 14.4
### Mobile device
_No response_
### Python version
3.10.14
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I'm trying to run a test snippet, and I get protobuf FATAL error `[libprotobuf FATAL google/protobuf/message_lite.cc:353] CHECK failed: target + size == res:
libc++abi: terminating due to uncaught exception of type google::protobuf::FatalException: CHECK failed: target + size == res: `
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import tensorflow_datasets as tfds
print("TensorFlow version:", tf.__version__)
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
tf.config.list_physical_devices('GPU')
(ds_train, ds_test), ds_info = tfds.load(
'mnist',
split=['train', 'test'],
shuffle_files=True,
as_supervised=True,
with_info=True,
)
def normalize_img(image, label):
"""Normalizes images: `uint8` -> `float32`."""
return tf.cast(image, tf.float32) / 255., label
batch_size = 128
ds_train = ds_train.map(
normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE)
ds_train = ds_train.cache()
ds_train = ds_train.shuffle(ds_info.splits['train'].num_examples)
ds_train = ds_train.batch(batch_size)
ds_train = ds_train.prefetch(tf.data.experimental.AUTOTUNE)
ds_test = ds_test.map(
normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE)
ds_test = ds_test.batch(batch_size)
ds_test = ds_test.cache()
ds_test = ds_test.prefetch(tf.data.experimental.AUTOTUNE)
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(32, kernel_size=(3, 3),
activation='relu'),
tf.keras.layers.Conv2D(64, kernel_size=(3, 3),
activation='relu'),
tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
# tf.keras.layers.Dropout(0.25),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
# tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(
loss='sparse_categorical_crossentropy',
optimizer=tf.keras.optimizers.Adam(0.001),
metrics=['accuracy'],
)
model.fit(
ds_train,
epochs=12,
validation_data=ds_test,
)
```
### Relevant log output
```shell
TensorFlow version: 2.16.1
Num GPUs Available: 1
2024-03-26 12:50:25.667311: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1
2024-03-26 12:50:25.667392: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 16.00 GB
2024-03-26 12:50:25.667406: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 5.33 GB
2024-03-26 12:50:25.667714: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2024-03-26 12:50:25.667759: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)
Epoch 1/12
2024-03-26 12:50:27.299359: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:117] Plugin optimizer for device_type GPU is enabled.
[libprotobuf FATAL google/protobuf/message_lite.cc:353] CHECK failed: target + size == res:
libc++abi: terminating due to uncaught exception of type google::protobuf::FatalException: CHECK failed: target + size == res:
```
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Update TFRT dependency to use revision
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Update TFRT dependency to use revision
http://github.com/tensorflow/runtime/commit/8436259aebb7f551bb010d615a734e94a996ba07.
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[XLA:GPU] Implement EmitDot in the new MLIR emitters
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[XLA:GPU] Implement EmitDot in the new MLIR emitters
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[XLA:GPU] Remove FusionMergerTriton pass.
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[XLA:GPU] Remove FusionMergerTriton pass.
This was never enabled, and is being replaced by the more principled
solution using SymbolicTileAnalysis and priority fusion.
Update an imprecise test that was supposed to test this (but apparently
wasn't).
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Integrate LLVM at llvm/llvm-project@fa3d789df15b
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Integrate LLVM at llvm/llvm-project@fa3d789df15b
Updates LLVM usage to match
[fa3d789df15b](https://github.com/llvm/llvm-project/commit/fa3d789df15b)
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[XLA:GPU][IndexAnalysis] Rename GetDefaultThreadIdToOutputIndexingMap to GetDefaultThreadIdIndexingMap.
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[XLA:GPU][IndexAnalysis] Rename GetDefaultThreadIdToOutputIndexingMap to GetDefaultThreadIdIndexingMap.
The "output" part was a bit confusing. We use this function for threadId->input
mapping as well.
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[XLA:GPU] Add threadId mappings to the legacy emitters and add tests to the new ones.
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[XLA:GPU] Add threadId mappings to the legacy emitters and add tests to the new ones.
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[XLA:GPU] Expand test coverage for SymbolicTile derivation.
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[XLA:GPU] Expand test coverage for SymbolicTile derivation.
Added tests to cover failure paths for mod, floordiv, and non-0 offset across
untiled dimensions.
Also contains some cosmetic/style changes.
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Check that zero points of FullyConnected filter is 0 for int4 quantization
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Integrate LLVM at llvm/llvm-project@e6f63a942a45
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Integrate LLVM at llvm/llvm-project@e6f63a942a45
Updates LLVM usage to match
[e6f63a942a45](https://github.com/llvm/llvm-project/commit/e6f63a942a45)
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