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"This issue was also reproduced in latest tf 2.16.1",
"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/65289\">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/65289\">No</a>\n",
"Sorry. It's a misunderstanding."
] | 2024-04-09T06:43:34 | 2024-04-09T07:07:01 | 2024-04-09T07:01:34 |
MEMBER
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.11
### Custom code
No
### OS platform and distribution
CentOS 8
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
gcc 9.2.1
### CUDA/cuDNN version
cuda 12.3 / cudnn 8.9
### GPU model and memory
NVIDIA A100 40 GB
### Current behavior?
<del>
Softplus produces outputs for inputs by rule: softplus(x) = log(exp(x) + 1). In my understanding, it will get 0 if the input element is 0. But in the following code snippet, it shows different result.
Is it a numerical bug or an expected behavior?
I'm glad to help to fix it if it is a bug.
</del>
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
a = tf.constant(
[[0.1,0.1,0,0],
[0.1,0,0,0]],
dtype=tf.float32,
)
print(a)
b = tf.math.softplus(a)
print(b)
```
### Relevant log output
```shell
The standard output gets:
'''
tf.Tensor(
[[0.1 0.1 0. 0. ]
[0.1 0. 0. 0. ]], shape=(2, 4), dtype=float32)
tf.Tensor(
[[0.7443967 0.7443967 0.6931472 0.6931472]
[0.7443967 0.6931472 0.6931472 0.6931472]], shape=(2, 4), dtype=float32)
'''
But print(b) expects:
tf.Tensor(
[[0.7443967 0.7443967 0. 0.]
[0.7443967 0. 0. 0.]], shape=(2, 4), dtype=float32)
```
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Use signature_name as the function name for the entry function for an inference function
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Use signature_name as the function name for the entry function for an inference function
This makes the debugging info more informative in TFRT serving.
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__dict__ error when saving a model using tf.saved_model.save(model, modelSavedPath)
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### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16.1
### Custom code
No
### OS platform and distribution
Windows 10
### Mobile device
_No response_
### Python version
3.12.2
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I was training MobileNetV3 on a custom dataset and tried to save it as a SavedModel because I get errors when converting .keras files to .tflite. So I retrained my model and to save the model I used `tf.saved_model.save(model, modelSavedPath)`. After the code was done, it displayed this error: `TypeError: this __dict__ descriptor does not support '_DictWrapper' objects`
### Standalone code to reproduce the issue
```shell
# transfer learning using MobileNet-V3 large
import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.applications import MobileNetV3Large
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.optimizers import Adam
trainPath = "C:/Users/User/Desktop/CDD/CDataset/train"
ValidPath = "C:/Users/User/Desktop/CDD/CDataset/validate"
trainGenerator = ImageDataGenerator(
rotation_range=15 , width_shift_range=0.1,
height_shift_range=0.1, brightness_range=(0, 0.2)).flow_from_directory(trainPath, target_size=(320,320), batch_size=32)
ValidGenerator = ImageDataGenerator(
rotation_range=15 , width_shift_range=0.1,
height_shift_range=0.1, brightness_range=(0, 0.2)).flow_from_directory(ValidPath, target_size=(320,320), batch_size=32)
# Build the model
baseModel = MobileNetV3Large(weights= "imagenet", include_top=False)
x = baseModel.output
x = GlobalAveragePooling2D()(x)
x = Dense(512, activation='relu')(x)
x = Dense(256, activation='relu')(x)
x = Dense(128, activation='relu')(x)
predictionLayer = Dense(3, activation='softmax')(x)
model = Model(inputs=baseModel.input , outputs=predictionLayer)
print(model.summary())
# freeze the layers of the MobileNetV3 (already trained)
for layer in model.layers[:-5]:
layer.trainable = False
# Compile
optimizer = Adam(learning_rate = 0.0001)
model.compile(loss= "categorical_crossentropy", optimizer=optimizer , metrics=['accuracy','precision','recall'])
# train
model.fit(trainGenerator, validation_data=ValidGenerator, epochs=6)
modelSavedPath = "C:/Users/User/Desktop/CDD/SavedModels"
# model.save(modelSavedPath)
tf.saved_model.save(model, modelSavedPath)
```
### Relevant log output
```shell
Traceback (most recent call last):
File "c:\Users\User\Downloads\thesis\thesis\2BuildModel.py", line 52, in <module>
tf.saved_model.save(model, modelSavedPath)
File "C:\Users\User\AppData\Local\Programs\Python\Python312\Lib\site-packages\tensorflow\python\saved_model\save.py", line 1392, in save
save_and_return_nodes(obj, export_dir, signatures, options)
File "C:\Users\User\AppData\Local\Programs\Python\Python312\Lib\site-packages\tensorflow\python\saved_model\save.py", line 1427, in save_and_return_nodes
_build_meta_graph(obj, signatures, options, meta_graph_def))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\AppData\Local\Programs\Python\Python312\Lib\site-packages\tensorflow\python\saved_model\save.py", line 1642, in _build_meta_graph
return _build_meta_graph_impl(obj, signatures, options, meta_graph_def)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\AppData\Local\Programs\Python\Python312\Lib\site-packages\tensorflow\python\saved_model\save.py", line 1564, in _build_meta_graph_impl
saveable_view = _SaveableView(augmented_graph_view, options)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\AppData\Local\Programs\Python\Python312\Lib\site-packages\tensorflow\python\saved_model\save.py", line 285, in __init__
checkpoint_util.objects_ids_and_slot_variables_and_paths(
File "C:\Users\User\AppData\Local\Programs\Python\Python312\Lib\site-packages\tensorflow\python\checkpoint\util.py", line 160, in
objects_ids_and_slot_variables_and_paths
trackable_objects, node_paths = graph_view.breadth_first_traversal()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\AppData\Local\Programs\Python\Python312\Lib\site-packages\tensorflow\python\checkpoint\graph_view.py", line 124, in breadth_first_traversal
return self._breadth_first_traversal()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\AppData\Local\Programs\Python\Python312\Lib\site-packages\tensorflow\python\saved_model\save.py", line 156, in _breadth_first_traversal
super(_AugmentedGraphView, self)._breadth_first_traversal())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\AppData\Local\Programs\Python\Python312\Lib\site-packages\tensorflow\python\checkpoint\graph_view.py", line 128, in _breadth_first_traversal
return super(ObjectGraphView, self)._descendants_with_paths()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\AppData\Local\Programs\Python\Python312\Lib\site-packages\tensorflow\python\checkpoint\trackable_view.py", line 111, in _descendants_with_paths
for name, dependency in self.children(current_trackable).items():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\AppData\Local\Programs\Python\Python312\Lib\site-packages\tensorflow\python\checkpoint\graph_view.py", line 97, in children
for name, ref in self.list_children(obj, **kwargs):
File "C:\Users\User\AppData\Local\Programs\Python\Python312\Lib\site-packages\tensorflow\python\saved_model\save.py", line 190, in list_children
for name, child in super(_AugmentedGraphView, self).list_children(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\AppData\Local\Programs\Python\Python312\Lib\site-packages\tensorflow\python\checkpoint\graph_view.py", line 75, in list_children
for name, ref in super(ObjectGraphView,
^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\AppData\Local\Programs\Python\Python312\Lib\site-packages\tensorflow\python\checkpoint\trackable_view.py", line 85, in children
ref = converter.convert_to_trackable(ref, parent=obj)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\AppData\Local\Programs\Python\Python312\Lib\site-packages\tensorflow\python\trackable\converter.py", line 31,
in convert_to_trackable
if (tensor_util.is_tf_type(obj) and
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\AppData\Local\Programs\Python\Python312\Lib\site-packages\tensorflow\python\framework\tensor_util.py", line 1156, in is_tf_type
return isinstance(x, tf_type_classes)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\AppData\Local\Programs\Python\Python312\Lib\typing.py", line 1871, in __instancecheck__
val = getattr_static(instance, attr)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\AppData\Local\Programs\Python\Python312\Lib\inspect.py", line 1839, in getattr_static
instance_result = _check_instance(obj, attr)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\User\AppData\Local\Programs\Python\Python312\Lib\inspect.py", line 1793, in _check_instance
instance_dict = object.__getattribute__(obj, "__dict__")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: this __dict__ descriptor does not support '_DictWrapper' objects
```
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Adding support for dot to LayoutCanonicalizer pass.
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[XLA:GPU] NFC: Create GpuAlgebraicSimplifier
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[XLA:GPU] NFC: Create GpuAlgebraicSimplifier
This class implements ShouldStrengthReduceDotToReduce so that we can control the which dot to strength-reduce for GPU. Currently we only strength-reduce vector-vector dots, but we should implement a heuristic by analyzing performance data.
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Move Dnn-specific functions into GpuExecutors instead of StreamExecutor.
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Move Blas caching into the GpuExecutor, the only StreamExecutor that does them.
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Move Blas caching into the GpuExecutor, the only StreamExecutor that does them.
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Increase default logging for MLIR embedding rewrite passes
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Increase default logging for MLIR embedding rewrite passes
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Move Fft caching into the GpuExecutor, the only StreamExecutor that does them.
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Move Fft caching into the GpuExecutor, the only StreamExecutor that does them.
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PR_kwDOArmXAs5sEXdd
| 65,271 |
Move Dnn caching into the GpuExecutor, the only StreamExecutor that does them.
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Move Dnn caching into the GpuExecutor, the only StreamExecutor that does them.
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Use different mutexes for different data structures to fix a deadlock issue.
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I_kwDOArmXAs6FDEhB
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Tensorflow does not recognice GPU no matter what
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[
"@6CRIPT,\r\nI suspect you are trying to install the every tensorflow version on CUDA 11.5 only which is not compatible. Could you please uninstall the installed libraries and try to install CUDA 12.3 and cuDNN 8.9 for the tensorflow v2.16.1 and check the below process.\r\n```python\r\n- Make sure cuda and cudnn are installed correctly.\r\n- Make sure $Env:CUDA_PATH is given correctly\r\n- Make sure $Env:LD_LIBRARY_PATH is given correctly\r\n- Make sure $Env:TF_CUDA_PATHS is given correctly\r\n- Use pip install tensorflow[and-cuda] instead of pip install tensorflow. Because extensions, such as nvidia-cuda-* and nvidia-cudnn-* from pip(Pypi) are also needed.\r\n```\r\nhttps://github.com/tensorflow/tensorflow/issues/63341\r\nhttps://github.com/tensorflow/tensorflow/issues/63948\r\n\r\nThank you!",
"Hi, thanks for your reply.\r\nAs you can see on my nvidia-smi output I was using CUDA 12.4 I will retry the entire process anyways, any updates will be posted :p ",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"Hi I finally solved it. It was all about installing exactly the acurrate versions. Thanks for your responses ! :D\r\n\r\nI have uploaded a video to youtube explaining the process:\r\nhttps://www.youtube.com/watch?v=iIYHfCh1rmU&t=5s",
"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/65269\">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/65269\">No</a>\n"
] | 2024-04-08T22:10:46 | 2024-04-18T10:47:08 | 2024-04-18T10:47:05 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.16.1 or 2.12.0 or 2.10.0
### Custom code
Yes
### OS platform and distribution
Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.9.19
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
CUDA 11.5
### GPU model and memory
i7 12700k, 32 GB RAM
### Current behavior?
So I am trying in all the ways that are written on the internet to tensorflow detect my GPU. I am currently using a conda enviroment on WSL2. I was following this guide: https://medium.com/@mass.thanapol/tensorflow-with-gpu-on-linux-or-wsl2-10b02fd19924 and then this: https://www.tensorflow.org/install/pip?hl=es-419#windows-wsl2 I have spent like 15 hours of trying tensorflow detect my GPU. I really really really need help. I am using it to my final project of CS. Thanks in advice.
### Standalone code to reproduce the issue
```shell
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
```
### Relevant log output
```shell
First, the nvidia-smi output:
(tfg_linux) cesar@CesarPC:~$ nvidia-smi
Tue Apr 9 00:05:08 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.73.01 Driver Version: 552.12 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 4060 On | 00000000:01:00.0 On | N/A |
| 0% 31C P8 N/A / 115W | 1015MiB / 8188MiB | 4% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| 0 N/A N/A 237281 C /python3.9 N/A |
+-----------------------------------------------------------------------------------------+
And this the output of nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Thu_Nov_18_09:45:30_PST_2021
Cuda compilation tools, release 11.5, V11.5.119
Build cuda_11.5.r11.5/compiler.30672275_0
and for the code:
(tfg_linux) cesar@CesarPC:~$ python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
2024-04-09 00:06:41.417626: 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`.
2024-04-09 00:06:41.445626: 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.
2024-04-09 00:06:41.819579: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-04-09 00:06:42.269409: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] 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.
2024-04-09 00:06:42.281813: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
[]
Also if I execute from this guide https://www.tensorflow.org/install/gpu?hl=es-419 the following line:
sudo apt install ./libnvinfer7_7.1.3-1+cuda11.0_amd64.deb
I got:
sudo apt install ./libnvinfer7_7.1.3-1+cuda11.0_amd64.deb
Reading package lists... Done
Building dependency tree... Done
Reading state information... Done
Note, selecting 'libnvinfer7' instead of './libnvinfer7_7.1.3-1+cuda11.0_amd64.deb'
Some packages could not be installed. This may mean that you have
requested an impossible situation or if you are using the unstable
distribution that some required packages have not yet been created
or been moved out of Incoming.
The following information may help to resolve the situation:
The following packages have unmet dependencies:
libnvinfer7 : Depends: libcublas-11-0 but it is not installable
Depends: cuda-cudart-11-0 but it is not installable
E: Unable to correct problems, you have held broken packages.
```
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Integrate ifrt serving with core selection. This currently only support sinlge-core cases. For SPMD, we still relies on Default deivce assignment for now.
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[pjrt] Add PjRtFuture<void> specialization for stateless futures
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[pjrt] Add PjRtFuture<void> specialization for stateless futures
PjRtFuture<void> (or PjRtFuture<>) is an implicit wrapper for absl::Status that will become available at some point in the future and will signal completion of an async event.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11205 from ROCm:ci_link_break_20240404 8ee9b5e90d4e1a3ca7734bbe1ef73140e4d879dd
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PR_kwDOArmXAs5sDsVw
| 65,266 |
[tsl:concurrency] Specify Isa/DynCast/Cast semantics for indirect async values
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[tsl:concurrency] Specify Isa/DynCast/Cast semantics for indirect async values
Reverts e664ade8a0063f2800f27df3e4c96358e376c5cf
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| 65,265 |
Add the mixed_priority_policy attr to the fallback kernel and make the fallback kernel pass the policy and low priority params to the batch resource.
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Add the mixed_priority_policy attr to the fallback kernel and make the fallback kernel pass the policy and low priority params to the batch resource.
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PR_kwDOArmXAs5sDl5J
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Add the composite lowering pass for aten.avg_pool2d
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Add the composite lowering pass for aten.avg_pool2d
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PR_kwDOArmXAs5sDeze
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Make StreamExecutor::SynchronousMemcpy return an absl::Status rather than a bool.
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NONE
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Make StreamExecutor::SynchronousMemcpy return an absl::Status rather than a bool.
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PR_kwDOArmXAs5sDeYc
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Remove StreamExecutorInterface::GetUnderlyingExecutor function.
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NONE
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Remove StreamExecutorInterface::GetUnderlyingExecutor function.
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PR_kwDOArmXAs5sDanU
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Make most StreamExecutor functions complete pass-throughs to the StreamExecutorInterface in preparation for having it inherit from the Interface class.
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Make most StreamExecutor functions complete pass-throughs to the StreamExecutorInterface in preparation for having it inherit from the Interface class.
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PR_kwDOArmXAs5sDYFN
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Eliminate StreamExecutor::live_stream_count_ as StreamExecutors are never destroyed, and the destructor is never run.
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Eliminate StreamExecutor::live_stream_count_ as StreamExecutors are never destroyed, and the destructor is never run.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11205 from ROCm:ci_link_break_20240404 8ee9b5e90d4e1a3ca7734bbe1ef73140e4d879dd
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PR_kwDOArmXAs5sDWFy
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Reverts e664ade8a0063f2800f27df3e4c96358e376c5cf
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[] | 2024-04-08T19:55:22 | 2024-04-08T22:40:34 | 2024-04-08T22:40:34 |
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Reverts e664ade8a0063f2800f27df3e4c96358e376c5cf
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| 2,231,973,115 |
PR_kwDOArmXAs5sDVc8
| 65,258 |
PR #10485: [XLA:CPU][oneDNN] Scratchpad support to MatMul
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[] | 2024-04-08T19:53:17 | 2024-04-09T11:27:08 | 2024-04-09T11:27:07 |
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PR #10485: [XLA:CPU][oneDNN] Scratchpad support to MatMul
Imported from GitHub PR https://github.com/openxla/xla/pull/10485
This PR enables user provided scratch memory to MatMul. It utilizes buffer assigner to allocate scratch buffers. The following is the HLO rewrites.
```
Input HLO:
result = result_shape custom-call(args), custom_call_target="__onednn$matmul", ...
Output HLO:
tuple = (result_shape, scratch_shape) custom-call(args), custom_call_target="__onednn$matmul", ...
result_from_tuple = result_shape get-tuple-element(tuple), index=0
```
Besides enabling scratch memory, this PR refactors the MatMul rewrites and kernel implementation in order to reuse common code. It also introduces the following template function so that similar features can be easily used for other oneDNN primitives.
```
template <typename PrimDesc>
std::unique_ptr<PrimDesc> CreateOneDnnPrimDesc(HloInstruction*);
```
Copybara import of the project:
--
830ace45111c7fa9d7b03ec2018e6c51d0052e81 by mdfaijul <[email protected]>:
Scratch support to matmul
--
a6e652633d7fc8eb65b76d1e18c18bd0629e7f47 by mdfaijul <[email protected]>:
Address review comments.
--
d6cb1f2e9618c6874dd28af13ba43c11bc7071b5 by mdfaijul <[email protected]>:
Use TF_ASSIGN_OR_RETURN
Merging this change closes #10485
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10485 from Intel-tensorflow:amin/matmul-scratch d6cb1f2e9618c6874dd28af13ba43c11bc7071b5
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PR_kwDOArmXAs5sDUit
| 65,257 |
Move `tsl/BUILD`, `tsl.bzl`, and `tsl.default.bzl` to XLA
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Move `tsl/BUILD`, `tsl.bzl`, and `tsl.default.bzl` to XLA
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I_kwDOArmXAs6FCFRD
| 65,256 |
Infinity loop when try to save model with input_signatures on funtion decorator
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[
"For simplify the bug reprodution follow the code where the inputs is two simple dict of tensors and one int tensor.\r\nThis code do the same as issue code and reproduce the error.\r\n\r\n`@tf.keras.saving.register_keras_serializable(package=\"MyLayers\")`\r\n\r\n class BruteForce3(tf.keras.layers.Layer):\r\n\r\n def __init__(self, model, k=5, **kwargs):\r\n super().__init__(**kwargs)\r\n self.model = model\r\n self.k = k\r\n \r\n @tf.function(input_signature=[data_ds.element_spec, candidaster.element_spec, tf.TensorSpec([], dtype=tf.int32)])\r\n # @tf.function\r\n def call(self, queries, candidates, ks):\r\n \r\n standard_brute = tfrs.layers.factorized_top_k.BruteForce(self.model.query_model)\r\n \r\n candidates = [candidates]\r\n \r\n parse_one = pd.DataFrame.from_dict(candidates).to_dict(orient='list')\r\n candidates = tf.data.Dataset.from_tensor_slices(parse_one)\r\n \r\n standard_brute.index_from_dataset(tf.data.Dataset.zip(candidates.batch(2).map(lambda x: x['experience.jobTitle']), \r\n candidates.batch(2).map(self.model.candidate_model)\r\n ))\r\n scores, ids = standard_brute(queries, ks)\r\n \r\n return scores, ids`\r\n\r\nand save model:\r\n\r\n `custom = BruteForce3(final_model)\r\n \r\n for l in data_ds.take(1):\r\n scores, jobs = custom(l, cand[0], 2)\r\n \r\n print(scores, jobs)\r\n \r\n tf.saved_model.save(custom, \"./serveModels/exportedMode36\")`\r\n\r\nwhen i save the model without function decorator and inputs_signatures i have the follow error on calling loaded model:\r\n`ValueError: Found zero restored functions for caller function.`\r\nand if i serve with tensorflow serving i have the follow response from server endpoint:\r\n`\"error\": \"Serving signature name: \\\"serving_default\\\" not found in signature def\"`\r\n",
"Hi @Chm-vinicius ,\r\n\r\nCould you please minimal code snippet for reproducing the issue? Thanks!\r\n\r\nIt seems the code contains tensorflow recommender layers which we are not supporting here at TF repo. I can see this is already reported in concerned repo. \r\n\r\nIf you can able to reproduce the issue with TF/Keras please submit the code snippet for same. Thanks!\r\n\r\n",
"Hi @SuryanarayanaY,\r\n\r\nThanks for your response!\r\nUnfortunately due to NDA contract i cant share more than that, also we still in permantly contact with a google engineers to find and solve this issue.\r\nWhen the solution are reached i certainly share her."
] | 2024-04-08T19:12:39 | 2024-04-22T23:03:52 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15.1
### Custom code
Yes
### OS platform and distribution
Linux; Windows
### Mobile device
_No response_
### Python version
3.9.5
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I have a custom BruteForce method, it's almost a copy of tfrs.layers.factorized_top_k.BruteForce differing on inputs as candidates are one variable input like the queries on original method. I was abble to save model and load without any issues, i can up the tensorflow serve as well, but when i call the served endpoint I receive the message "Serving signature name: \"serving_default\" not found in signature def", when i try to setup the input signatures on call function, be it list of tensors dict or dataset of tensors dict, the kernel get in infinity loop and dont output anything, the issue occurs on windows and on gcloud vertex AI workbranch with a linux os env.
On the code bellow candidaster and data_ds is a datasets of tensors dicts;
The model are two towers recommendation model were "self.model.query_model" and "self.model.candidate_model" are concatenation of embeddings and some normalizations
### Standalone code to reproduce the issue
```shell
@tf.keras.saving.register_keras_serializable(package="MyLayers")
class BruteForce2(tf.keras.layers.Layer):
"""Brute force retrieval."""
def __init__(self, model, k=5, **kwargs):
super().__init__(**kwargs)
self.model = model
self.k = k
self._k = k
self._candidates = None
def _compute_score(self, queries: tf.Tensor,
candidates: tf.Tensor) -> tf.Tensor:
print(queries, candidates)
return tf.matmul(queries, candidates, transpose_b=True)
def _topk_ds(self, data):
df = data.copy()
df = {key: np.array(value)[:,tf.newaxis] for key, value in df.items()}
ds = tf.data.Dataset.from_tensor_slices((dict(df)))
ds.prefetch(data.size)
return ds
positions_inputs = {
'key_1': tf.TensorSpec(shape=(1,), dtype=tf.string),
'key_2': tf.TensorSpec(shape=(1,), dtype=tf.string),
'key_3': tf.TensorSpec(shape=(1,), dtype=tf.string),
'key_4': tf.TensorSpec(shape=(1,), dtype=tf.string),
'key_5': tf.TensorSpec(shape=(1,), dtype=tf.string),
'key_6': tf.TensorSpec(shape=(1,), dtype=tf.string),
'key_7': tf.TensorSpec(shape=(1,), dtype=tf.string)
}
@tf.function(input_signature=[positions_inputs, [candidaster.element_spec], tf.TensorSpec(shape=(), dtype=tf.int32)])
def _encode(self, queries, raw_candidates, k):
# data = pd.DataFrame(raw_candidates)
# data.head()
# returnment = self._topk_ds(data)
return queries, raw_candidates, k
# @tf.function(input_signature=[
# positions_inputs,
# tf.TensorSpec(shape=[None], dtype=tf.int64),
# tf.TensorSpec(shape=(), dtype=tf.int64)
# ])
@tf.function(input_signature=[positions_inputs, [candidaster.element_spec], tf.TensorSpec(shape=(), dtype=tf.int32)])
def call(self, queries: Union[tf.Tensor, Dict[Text, tf.Tensor]], candidates_raw: List[tf.Tensor], k: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:
# queries, candidates_raw, k = self._encode(queries, candidates_raw, k)
# candidates_raw = self._topk_ds(pd.DataFrame(candidates_raw))
# candidates_raw = self._encode(candidates_raw)
parse_one = pd.DataFrame.from_dict(candidates_raw).to_dict(orient='list')
candidates_raw = tf.data.Dataset.from_tensor_slices(parse_one)
candidates = tf.data.Dataset.zip(candidates_raw.batch(1).map(lambda x: x['experience.jobTitle']),
candidates_raw.batch(1).map(self.model.candidate_model)
)
spec = candidates.element_spec
if isinstance(spec, tuple):
identifiers_and_candidates = list(candidates)
candidates = tf.concat(
[embeddings for _, embeddings in identifiers_and_candidates],
axis=0
)
identifiers = tf.concat(
[identifiers for identifiers, _ in identifiers_and_candidates],
axis=0
)
else:
candidates = tf.concat(list(candidates), axis=0)
identifiers = None
# self._candidates = candidates
if identifiers is None:
identifiers = tf.range(candidates.shape[0])
if tf.rank(candidates) != 2:
raise ValueError(
f"The candidates tensor must be 2D (got {candidates.shape}).")
if candidates.shape[0] != identifiers.shape[0]:
raise ValueError(
"The candidates and identifiers tensors must have the same number of rows "
f"(got {candidates.shape[0]} candidates rows and {identifiers.shape[0]} "
"identifier rows). "
)
# We need any value that has the correct dtype.
identifiers_initial_value = tf.zeros((), dtype=identifiers.dtype)
self._identifiers = self.add_weight(
name="identifiers",
dtype=identifiers.dtype,
shape=identifiers.shape,
initializer=tf.keras.initializers.Constant(
value=identifiers_initial_value),
trainable=False)
self._candidates = self.add_weight(
name="candidates",
dtype=candidates.dtype,
shape=candidates.shape,
initializer=tf.keras.initializers.Zeros(),
trainable=False)
self._identifiers.assign(identifiers)
self._candidates.assign(candidates)
# self._reset_tf_function_cache()
k = k if k is not None else self._k
if self._candidates is None:
raise ValueError("The `index` method must be called first to "
"create the retrieval index.")
if self.model.query_model is not None:
queries = self.model.query_model(queries)
scores = self._compute_score(queries, self._candidates)
values, indices = tf.math.top_k(scores, k=k)
return values, tf.gather(self._identifiers, indices)
# save model
custom_index = BruteForce2(final_model, k=5)
for l in data_ds.take(1):
# scores, chose = custom_index(l, mapped_candidates, 2)
# custom_index.call = tf.function(custom_index.call, input_signature=[positions_inputs, [candidaster.element_spec], tf.TensorSpec(shape=(), dtype=tf.int32)])
# print(scores, chose)
custom_index.model.task_retrieval = tfrs.tasks.Retrieval()
signatures = {"serving_default": custom_index.call.get_concrete_function()}
tf.saved_model.save(custom_index, "./serveModels/exportedMode19", signatures=signatures)
```
### Relevant log output
_No response_
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Add metadata matcher for unit testing.
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Add metadata matcher for unit testing.
This can be used to test if metadata is being propagated properly in hlo pass unit tests.
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[pjrt] NFC: Add use-declared move constructor/assignment to PjRtFuture
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[pjrt] NFC: Add use-declared move constructor/assignment to PjRtFuture
User-declared move constructor/assignment implicitly deletes copy constructor/assignment. It is deleted today anyway because AsyncValueRef is not copyable, but if that will change we don't want PjRtFuture to become copyable because OnReady API assumes that value moved-out to the callback.
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Inject TpuCompiler pass to IfrtBackendCompiler
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Inject TpuCompiler pass to IfrtBackendCompiler
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Remove Stream::SetPriority functions from Stream.
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Remove Stream::SetPriority functions from Stream.
They don't work after the stream is initialized in GpuStream (the only Stream implementation to make use of the priority). Instead, move the parameter to Stream::Initialize, which is the only place it's actually used.
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[tsl:concurrency] NFC: Fix warnings in tsl/concurrency folder
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[tsl:concurrency] NFC: Fix warnings in tsl/concurrency folder
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KV Cache takes a third argument for input positions
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KV Cache takes a third argument for input positions
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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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[tsl:concurrency] NFC: Use port::Aligned(Malloc|Free) instead of custom ones
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[tsl:concurrency] NFC: Use port::Aligned(Malloc|Free) instead of custom ones
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[tsl:concurrency] Specify casting rules for ErrorAsyncValue
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[tsl:concurrency] Specify casting rules for ErrorAsyncValue
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[tsl:concurrency] NFC: Add tests for various types of AndThen callbacks to AsyncValuePtr and AsyncValueRef
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[tsl:concurrency] NFC: Add tests for various types of AndThen callbacks to AsyncValuePtr and AsyncValueRef
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Move StreamImplementation creation into Stream::Initialize, and eliminate bool allocated_ member that's now unnecessary.
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Move StreamImplementation creation into Stream::Initialize, and eliminate bool allocated_ member that's now unnecessary.
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PR_kwDOArmXAs5sCmSe
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[pjrt] Add PjRtFuture<T>::Pomise::SetError API and OnReady callbacks to handle errors
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[pjrt] Add PjRtFuture<T>::Pomise::SetError API and OnReady callbacks to handle errors
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PR_kwDOArmXAs5sClVs
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Add support for the PartialReduce custom call op in auto-sharding.
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Add support for the PartialReduce custom call op in auto-sharding.
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PR_kwDOArmXAs5sCjkK
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tf.Tensor supports Garbage collection.
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tf.Tensor supports Garbage collection.
Unit tests added. Specificly cycles from EagerTensor._handle_data and EagerTensor.shape were not tracked in the old behavior.
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PR_kwDOArmXAs5sCYKx
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[XLA:GPU] Add RTVar support to SymbolicTile
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[XLA:GPU] Add RTVar support to SymbolicTile
This is neccesary for codegen of operations with dynamic offsets, such as dynamic-{update-}slice, gather, scatter...
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PR_kwDOArmXAs5sCXi5
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PR #10164: [XLA:CPU] Improve Trace emitter
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PR #10164: [XLA:CPU] Improve Trace emitter
Imported from GitHub PR https://github.com/openxla/xla/pull/10164
This PR improves tracing a bit.
Without this PR, you would see operations in trace view as :
This way , it s difficult to see a) target of custom call b) can't group operations based on type of hlo operation and hence makes op list huge
This PR addresses these problems and now you should see tensorboard trace view as :
Copybara import of the project:
--
8cd6363890107ce7af2ed3630d577ad4d20a745b by Sachin Muradi <[email protected]>:
Update Trace emitter
--
a2813ad7439da8934b98b12623193c3b120eb9ca by Sachin Muradi <[email protected]>:
more update
Merging this change closes #10164
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10164 from Intel-tensorflow:sachin/tracemeupdate a2813ad7439da8934b98b12623193c3b120eb9ca
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Semantic Segmentation using Deep Learning By Aman Mehta
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"Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/65239/checks?check_run_id=23576758217) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.",
"**Project Description: Semantic Segmentation using Deep Learning**\r\n\r\n**Overview:**\r\nSemantic segmentation is a computer vision task that involves assigning semantic labels to each pixel in an image, enabling precise understanding and analysis of its contents. This project aims to develop a robust deep learning model capable of accurately segmenting objects and scenes within images.\r\n\r\n**Dataset Selection:**\r\nThe project begins with the selection of a suitable dataset tailored for semantic segmentation tasks. Datasets such as Pascal VOC, Cityscapes, or COCO provide labeled images with pixel-level annotations, facilitating model training and evaluation.\r\n\r\n**Data Preprocessing:**\r\nBefore training the model, extensive preprocessing is necessary to prepare the dataset. This involves tasks such as resizing images to a consistent resolution, normalizing pixel values to a standard range, and generating corresponding segmentation masks for training samples.\r\n\r\n**Model Architecture:**\r\nThe core of the project lies in designing an effective neural network architecture capable of performing semantic segmentation. Common architectures like U-Net, Fully Convolutional Networks (FCNs), or DeepLab are well-suited for this task. These architectures typically consist of an encoder-decoder structure with skip connections to capture both local and global context information.\r\n\r\n**Model Training:**\r\nOnce the model architecture is defined, it is trained using the prepared dataset. Training involves optimizing the model parameters using techniques like stochastic gradient descent (SGD) or Adam optimization. Additionally, data augmentation techniques such as random rotation, flipping, and scaling are employed to improve the model's generalization ability.\r\n\r\n**Evaluation:**\r\nThe trained model's performance is evaluated using appropriate metrics such as Intersection over Union (IoU), Pixel Accuracy, or Mean Intersection over Union (mIoU). This evaluation helps gauge the model's accuracy and generalization ability on unseen data.\r\n\r\n**Inference:**\r\nAfter training and evaluation, the model is deployed to perform semantic segmentation on new images. The inference pipeline involves feeding input images to the trained model and obtaining segmentation maps as output. Visualization techniques are used to interpret and analyze the segmentation results, enabling insights into the model's performance.\r\n\r\n**Conclusion:**\r\nSemantic segmentation using deep learning is a challenging yet rewarding task with diverse applications across various domains. By undertaking this project, participants gain valuable experience in designing, training, and deploying deep learning models for real-world image analysis tasks. Moreover, the project contributes to advancing the field of computer vision by developing models capable of understanding and interpreting visual information at a pixel level.",
"**Project Description: Semantic Segmentation using Deep Learning**\r\n\r\n**Overview:**\r\nSemantic segmentation is a computer vision task that involves assigning semantic labels to each pixel in an image, enabling precise understanding and analysis of its contents. This project aims to develop a robust deep learning model capable of accurately segmenting objects and scenes within images.\r\n\r\n**Dataset Selection:**\r\nThe project begins with the selection of a suitable dataset tailored for semantic segmentation tasks. Datasets such as Pascal VOC, Cityscapes, or COCO provide labeled images with pixel-level annotations, facilitating model training and evaluation.\r\n\r\n**Data Preprocessing:**\r\nBefore training the model, extensive preprocessing is necessary to prepare the dataset. This involves tasks such as resizing images to a consistent resolution, normalizing pixel values to a standard range, and generating corresponding segmentation masks for training samples.\r\n\r\n**Model Architecture:**\r\nThe core of the project lies in designing an effective neural network architecture capable of performing semantic segmentation. Common architectures like U-Net, Fully Convolutional Networks (FCNs), or DeepLab are well-suited for this task. These architectures typically consist of an encoder-decoder structure with skip connections to capture both local and global context information.\r\n\r\n**Model Training:**\r\nOnce the model architecture is defined, it is trained using the prepared dataset. Training involves optimizing the model parameters using techniques like stochastic gradient descent (SGD) or Adam optimization. Additionally, data augmentation techniques such as random rotation, flipping, and scaling are employed to improve the model's generalization ability.\r\n\r\n**Evaluation:**\r\nThe trained model's performance is evaluated using appropriate metrics such as Intersection over Union (IoU), Pixel Accuracy, or Mean Intersection over Union (mIoU). This evaluation helps gauge the model's accuracy and generalization ability on unseen data.\r\n\r\n**Inference:**\r\nAfter training and evaluation, the model is deployed to perform semantic segmentation on new images. The inference pipeline involves feeding input images to the trained model and obtaining segmentation maps as output. Visualization techniques are used to interpret and analyze the segmentation results, enabling insights into the model's performance.\r\n\r\n**Conclusion:**\r\nSemantic segmentation using deep learning is a challenging yet rewarding task with diverse applications across various domains. By undertaking this project, participants gain valuable experience in designing, training, and deploying deep learning models for real-world image analysis tasks. Moreover, the project contributes to advancing the field of computer vision by developing models capable of understanding and interpreting visual information at a pixel level.",
"Hi @AmanMehta199816 Can you please sign CLA. Thank you!"
] | 2024-04-08T16:57:31 | 2024-04-10T14:58:01 | 2024-04-10T14:58:00 |
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**Project Description: Semantic Segmentation using Deep Learning By Aman Mehta**
**Overview:**
Semantic segmentation is a computer vision task that involves assigning semantic labels to each pixel in an image, enabling precise understanding and analysis of its contents. This project aims to develop a robust deep learning model capable of accurately segmenting objects and scenes within images.
**Dataset Selection:**
The project begins with the selection of a suitable dataset tailored for semantic segmentation tasks. Datasets such as Pascal VOC, Cityscapes, or COCO provide labeled images with pixel-level annotations, facilitating model training and evaluation.
**Data Preprocessing:**
Before training the model, extensive preprocessing is necessary to prepare the dataset. This involves tasks such as resizing images to a consistent resolution, normalizing pixel values to a standard range, and generating corresponding segmentation masks for training samples.
**Model Architecture:**
The core of the project lies in designing an effective neural network architecture capable of performing semantic segmentation. Common architectures like U-Net, Fully Convolutional Networks (FCNs), or DeepLab are well-suited for this task. These architectures typically consist of an encoder-decoder structure with skip connections to capture both local and global context information.
**Model Training:**
Once the model architecture is defined, it is trained using the prepared dataset. Training involves optimizing the model parameters using techniques like stochastic gradient descent (SGD) or Adam optimization. Additionally, data augmentation techniques such as random rotation, flipping, and scaling are employed to improve the model's generalization ability.
**Evaluation:**
The trained model's performance is evaluated using appropriate metrics such as Intersection over Union (IoU), Pixel Accuracy, or Mean Intersection over Union (mIoU). This evaluation helps gauge the model's accuracy and generalization ability on unseen data.
**Inference:**
After training and evaluation, the model is deployed to perform semantic segmentation on new images. The inference pipeline involves feeding input images to the trained model and obtaining segmentation maps as output. Visualization techniques are used to interpret and analyze the segmentation results, enabling insights into the model's performance.
**Conclusion:**
Semantic segmentation using deep learning is a challenging yet rewarding task with diverse applications across various domains. By undertaking this project, participants gain valuable experience in designing, training, and deploying deep learning models for real-world image analysis tasks. Moreover, the project contributes to advancing the field of computer vision by developing models capable of understanding and interpreting visual information at a pixel level.
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PR_kwDOArmXAs5sCR1v
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Fix NCCL 2.21.5 compatibility issues
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Fix NCCL 2.21.5 compatibility issues
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ValueError: Invalid dtype: str768
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[
"@maxlelyonais Please make sure the string data uses the same encoding as the rest of your code?\r\n Could you provide the access to the colab gist you have provided?\r\nThank you!",
"Sorry for the delay, yes here is the colab gist: https://colab.research.google.com/gist/maxlelyonais/28bd9f80a71a55ec9513950c057cf5c6/clasificacion_de_spam.ipynb",
"I had a similar issue when I upgraded to Tensorflow 2.16.1, Python 3.12.2. In my case, I triggered the error `\"ValueError: Invalid dtype: str39968\"` when trying to run the 'fit' function for a very similar RNN with string data (dtype <U1249). For a quick workaround, the fit function works when setting the dtype to a generic 'object' (i.e. `x_train = x_train.astype(object)`. In Tensorflow 2.15.0 , Python 3.9.16, I didn't have the same issue.",
"@maxlelyonais Could you please have a look at the workaround provided by @nlbrown62 and let us know?\r\nThank you!",
"It works perfectly, May I know why using 'object' works?",
"@maxlelyonais object dtype in TensorFlow acts as a catch-all for various data types, including strings of any length.\r\nThank you!"
] | 2024-04-08T15:28:07 | 2024-04-20T18:22:45 | 2024-04-20T18:22:45 |
NONE
| null | null | null |
### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.17.0-dev20240407
### Custom code
No
### OS platform and distribution
Google Colab
### Mobile device
_No response_
### Python version
Python 3.10.12
### Bazel version
None
### GCC/compiler version
None
### CUDA/cuDNN version
_No response_
### GPU model and memory
None
### Current behavior?
Tensorflow doesn't support arrays where the dtype is numpy.str_.
pred_text = "how are you doing today?"
pred_text = np.array([[pred_text]])
It returns ValueError: Invalid dtype: str768 when I use it to train/predict with my model:
model = tf.keras.Sequential([
encoder,
tf.keras.layers.Embedding(
input_dim=len(encoder.get_vocabulary())+1,
output_dim=64,
mask_zero=True),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
model.compile(loss=tf.keras.losses.BinaryCrossentropy(),
optimizer=tf.keras.optimizers.Adam(1e-4),
metrics=['accuracy'])
The problem is that the data is stored in a 2D array numpy.array([[]]), so converting each data to str or int slows down the process.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1ymbplqo6wK1ZUVn-vP8jnBMsh6dccgZP#scrollTo=DhMNlE-AOzH9
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5sBiy7
| 65,236 |
[XLA:GPU][IndexAnalysis] Get rid of LaTeX in indexing.md.
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[XLA:GPU][IndexAnalysis] Get rid of LaTeX in indexing.md.
It is rendered differently in github.com, openxla.org.
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PR_kwDOArmXAs5sA_rQ
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[XLA:GPU][Coalescing] Add coalescing for ops with runtime vars.
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[XLA:GPU][Coalescing] Add coalescing for ops with runtime vars.
Add tests for gather and dynamic slice.
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PR_kwDOArmXAs5sA-i8
| 65,234 |
Expand RequestCost with ability to record metrics.
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Expand RequestCost with ability to record metrics.
Similar to the request Duration costs adds ability to record request metrics.
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I_kwDOArmXAs6E_iYC
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Unknown: JIT Compilation Failed
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[
"Hi **@firmanserdana** ,\r\n\r\nSorry for the delay, I reproduced the code shared but facing different error .Could you please share the colab gist with all the dependencies to analyze more of it. Here i providing [gist](https://colab.sandbox.google.com/gist/Venkat6871/85f4e2a69f97050869bd81592f1ca5c7/65233_2-16-1_gpu.ipynb) for reference.\r\n\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/65233\">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/65233\">No</a>\n"
] | 2024-04-08T13:36:07 | 2024-05-01T01:49:40 | 2024-05-01T01:49:35 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
Endeavouros latest - archlinux
### Mobile device
_No response_
### Python version
3.9-3.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.3 - 12.4/8.9
### GPU model and memory
_No response_
### Current behavior?
Somehow JIT failed to run on tuning process, not sure what it is, I am still new here using GPU based TF
CPU based works okay, using TF-Metal on m1 mac also works
### Standalone code to reproduce the issue
```shell
from keras.models import Sequential
from keras.layers import LSTM, Dense, Dropout
from keras_tuner import RandomSearch
from keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard, CSVLogger
from sklearn.metrics import r2_score
from sklearn.preprocessing import StandardScaler
import numpy as np
import matplotlib.pyplot as plt
# rest of your code
# Normalize the data
scaler = StandardScaler()
train_tables_windows = scaler.fit_transform(train_tables_windows).astype('float32')
test_tables_windows = scaler.transform(test_tables_windows).astype('float32')
# Define the model
def build_model(hp):
model = Sequential()
model.add(LSTM(units=hp.Int('units', min_value=32, max_value=512, step=32), return_sequences=True, input_shape=(128, 1)))
model.add(Dropout(0.2))
model.add(LSTM(units=hp.Int('units', min_value=32, max_value=512, step=32)))
model.add(Dropout(0.2))
model.add(Dense(10))
model.compile(loss='mse', optimizer='adam')
return model
# Define the tuner
tuner = RandomSearch(
build_model,
objective='val_loss',
max_trials=5,
executions_per_trial=3,
directory='my_dir',
project_name='helloworld')
# Define callbacks
early_stopping = EarlyStopping(monitor='val_loss', patience=20)
model_checkpoint = ModelCheckpoint('best_model.keras', monitor='val_loss', save_best_only=True)
tensorboard = TensorBoard(log_dir=os.path.join('logs'))
csv_logger = CSVLogger('training.log')
callbacks = [early_stopping, model_checkpoint, tensorboard, csv_logger]
# Fit the model
tuner.search(train_tables_windows, train_labels_windows, epochs=50, validation_split=0.2, callbacks=callbacks)
# Get the optimal hyperparameters
best_hps=tuner.get_best_hyperparameters(num_trials=1)[0]
# Build the model with the optimal hyperparameters and train it on the data
model = tuner.hypermodel.build(best_hps)
# Fit the model
model.fit(train_tables_windows, train_labels_windows, epochs=50, batch_size=32, callbacks=callbacks)
# Evaluate the model
mse = model.evaluate(test_tables_windows, test_labels_windows)
predictions = model.predict(test_tables_windows)
print('Mean Squared Error:', mse)
# Calculate R2 score
r2 = r2_score(test_labels_windows, predictions)
print('R2:', r2)
# Plot the predicted angles against the true angles
for i in range(10):
plt.figure(figsize=(16,3))
plt.plot(predictions[1:200,i], label='Predicted')
plt.plot(np.array(test_labels_windows.iloc[1:200, i]), label='True')
plt.xlabel('Time')
plt.ylabel('Angle')
plt.title('Predicted vs True Angles of Joint {}'.format(i))
plt.legend()
plt.show()
```
### Relevant log output
```shell
Trial 2 Complete [00h 00m 02s]
Best val_loss So Far: None
Total elapsed time: 00h 00m 03s
Search: Running Trial #3
Value |Best Value So Far |Hyperparameter
192 |128 |units
Epoch 1/50
/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras/src/layers/rnn/rnn.py:204: 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__(**kwargs)
2024-04-08 15:31:39.985374: W tensorflow/core/framework/op_kernel.cc:1827] UNKNOWN: JIT compilation failed.
2024-04-08 15:31:39.985401: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: UNKNOWN: JIT compilation failed.
[[{{function_node __inference_one_step_on_data_15483}}{{node adam/Pow_5}}]]
2024-04-08 15:31:39.985408: I tensorflow/core/framework/local_rendezvous.cc:426] Local rendezvous send item cancelled. Key hash: 2708995372197654331
2024-04-08 15:31:39.985417: W tensorflow/core/framework/op_kernel.cc:1827] UNKNOWN: JIT compilation failed.
2024-04-08 15:31:39.985424: W tensorflow/core/framework/op_kernel.cc:1827] UNKNOWN: JIT compilation failed.
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Traceback (most recent call last):
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras_tuner/src/engine/base_tuner.py", line 274, in _try_run_and_update_trial
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras_tuner/src/engine/base_tuner.py", line 239, in _run_and_update_trial
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras_tuner/src/engine/tuner.py", line 314, in run_trial
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras_tuner/src/engine/tuner.py", line 233, in _build_and_fit_model
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras_tuner/src/engine/hypermodel.py", line 149, in fit
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras/src/utils/traceback_utils.py", line 122, in error_handler
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/tensorflow/python/eager/execute.py", line 53, in quick_execute
tensorflow.python.framework.errors_impl.UnknownError: Graph execution error:
Detected at node adam/Pow_5 defined at (most recent call last):
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/runpy.py", line 197, in _run_module_as_main
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/runpy.py", line 87, in _run_code
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/ipykernel_launcher.py", line 18, in <module>
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/traitlets/config/application.py", line 1075, in launch_instance
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/ipykernel/kernelapp.py", line 739, in start
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/tornado/platform/asyncio.py", line 205, in start
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/asyncio/base_events.py", line 601, in run_forever
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/asyncio/base_events.py", line 1905, in _run_once
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/asyncio/events.py", line 80, in _run
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/ipykernel/kernelbase.py", line 545, in dispatch_queue
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/ipykernel/kernelbase.py", line 534, in process_one
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/ipykernel/kernelbase.py", line 437, in dispatch_shell
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/ipykernel/ipkernel.py", line 359, in execute_request
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/ipykernel/kernelbase.py", line 778, in execute_request
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/ipykernel/ipkernel.py", line 446, in do_execute
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/ipykernel/zmqshell.py", line 549, in run_cell
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3048, in run_cell
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3103, in _run_cell
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/IPython/core/async_helpers.py", line 129, in _pseudo_sync_runner
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3308, in run_cell_async
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3490, in run_ast_nodes
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3550, in run_code
File "/tmp/ipykernel_726232/2121198222.py", line 49, in <module>
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras_tuner/src/engine/base_tuner.py", line 234, in search
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras_tuner/src/engine/base_tuner.py", line 274, in _try_run_and_update_trial
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras_tuner/src/engine/base_tuner.py", line 239, in _run_and_update_trial
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras_tuner/src/engine/tuner.py", line 314, in run_trial
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras_tuner/src/engine/tuner.py", line 233, in _build_and_fit_model
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras_tuner/src/engine/hypermodel.py", line 149, in fit
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras/src/utils/traceback_utils.py", line 117, in error_handler
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras/src/backend/tensorflow/trainer.py", line 325, in fit
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras/src/backend/tensorflow/trainer.py", line 118, in one_step_on_iterator
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras/src/backend/tensorflow/trainer.py", line 106, in one_step_on_data
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras/src/backend/tensorflow/trainer.py", line 73, in train_step
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras/src/optimizers/base_optimizer.py", line 269, in apply_gradients
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras/src/optimizers/base_optimizer.py", line 330, in apply
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras/src/optimizers/base_optimizer.py", line 380, in _backend_apply_gradients
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras/src/backend/tensorflow/optimizer.py", line 117, in _backend_update_step
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras/src/backend/tensorflow/optimizer.py", line 131, in _distributed_tf_update_step
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras/src/backend/tensorflow/optimizer.py", line 128, in apply_grad_to_update_var
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras/src/optimizers/adam.py", line 119, in update_step
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras/src/ops/numpy.py", line 5649, in power
File "/home/firep1/Documents/gitworks/phd/ReWire/intracortical-decoding/.conda/lib/python3.9/site-packages/keras/src/backend/tensorflow/numpy.py", line 1886, in power
JIT compilation failed.
[[{{node adam/Pow_5}}]] [Op:__inference_one_step_on_iterator_15546]
```
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I_kwDOArmXAs6E_DxH
| 65,232 |
Upgrading TFLite package (from TF 2.8 to Play Services)
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[
"Hi @tgpsantos, I don't have all the answers but here are some of my thoughts:\r\n\r\n> If Interpreter was actually replaced by InterpreterApi, what's the new way to get the getSignatureKeys from this Play Services library?\r\n\r\nI don't believe there is a new way... is there something you can't do with the new InterpreterApi, that you needed that method previously that you can't do now?\r\n\r\n> I was using Interpreter before so it seems natural to update everything for InterpreterApi, but is it advised against in any way?\r\n\r\nI would update everything and that is expected. Let us know if anything goes wrong.\r\n\r\nHi @arfaian, can you please take a look. Thanks."
] | 2024-04-08T12:45:39 | 2024-04-10T18:07:34 | null |
NONE
| null | null | null |
**System information**
- TensorFlow Lite in Play Services SDK version (found in `build.gradle`):
```
implementation 'com.google.android.gms:play-services-tflite-java:16.1.0'
implementation 'com.google.android.gms:play-services-tflite-gpu:16.1.0'
```
Hi guys,
I'm currently upgrading our tensorflow-lite package from 2.8 to the new Play Services bundle. However, I've come across into a significantly different API compared to what we have right now and some of the documentation.
Here are my doubts
- is version from Play services actually the most up-to-date? last update to tflite-java 16.1.0 seems to be from [may 2023](https://maven.google.com/web/index.html?q=tflite#com.google.android.gms:play-services-tflite-java:16.1.0)
- what's the reason for `[com.google.android](com.google.android.gms:play-services-tflite-java)` library and distribution to be different from `org.tensorflow:tensorflow-lite` ?
- if play services is the most up to date, there seems to be some inconsistencies in the documentation? For instance, I can't find the [Interpreter](https://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/Interpreter) anymore now. It seems to have been replaced by InterpreterApi, but it's unclear why we still see both in documentation
- If `Interpreter` was actually replaced by `InterpreterApi`, what's the new way to get the `getSignatureKeys` from this Play Services library?
- I was using Interpreter before so it seems natural to update everything for InterpreterApi, but is it advised against in any way?
- Is `[CompatList](https://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/gpu/CompatibilityList)` deprecated / no need to call `getBestOptionsForThisDevice` anymore?
- `compatList.isDelegateSupportedOnThisDevice` replaced by `TfLiteGpu.isGpuDelegateAvailable` ?
Thanks for the help!
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PR_kwDOArmXAs5sAS1G
| 65,231 |
Remove unecessary shape checks for FC.
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The shape can be inferred by XNNPack
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Fix conditions for `tfl.batch_matmul` legalization and e2e tests.
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Fix conditions for `tfl.batch_matmul` legalization and e2e tests.
This CL also:
* Adds TFLite op check to `QuantizationConfig` preset tests in ODML.
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TensorFlow lite compilation error under Ubuntu 22.04.4 LTS
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[
"1-Clone the TensorFlow Lite repository.\r\n2-Build the library using this command: `bazel build -c opt //tensorflow/lite/c:libtensorflowlite_c.so`.\r\n3-Copy **libtensorflowlite_c.so** to your folder from `bazel-bin/tensorflow/lite/c/`.\r\n4-Compile: `gcc -o hello_tf hello_tf.c -I. -Llib/tensorflow/lite -ltensorflowlite_c`.\r\n5-Copy and paste your dynamic library to **/usr/lib**: `sudo cp lib/tensorflow/lite/libtensorflowlite_c.so /usr/lib`.\r\n6-Almost done: `./hello_tf`.",
"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/65222\">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/65222\">No</a>\n"
] | 2024-04-08T09:08:13 | 2024-04-08T09:55:26 | 2024-04-08T09:55:22 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.10.0baz
### Custom code
Yes
### OS platform and distribution
Ubuntu 22.04.4 LTS
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
bazel 6.5.0
### GCC/compiler version
gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I built the tflite library using bazel and i'm just trying to test if it work but can't compile
### Standalone code to reproduce the issue
```shell
#include <stdio.h>
#include "tensorflow/lite/c/c_api.h"
int main() {
printf("Hello from TensorFlow C library version %s\n", TfLiteExtensionApisVersion());
return 0;
}
```
### Relevant log output
```shell
gcc -o hello_tf hello_tf.c -I. -Llib/tensorflow/lite -ltensorflowlite
/usr/bin/ld: /tmp/ccynBol8.o: in function `main':
hello_tf.c:(.text+0x9): undefined reference to `TfLiteExtensionApisVersion'
collect2: error: ld returned 1 exit status
```
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Fix conditions for `tfl.batch_matmul` legalization and e2e tests.
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Fix conditions for `tfl.batch_matmul` legalization and e2e tests.
This CL also:
* Adds TFLite op check to `QuantizationConfig` preset tests in ODML.
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I_kwDOArmXAs6E8czv
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[Bazel CI] Bazel build error: Target '//tensorflow/tools/pip_package:build_pip_package' contains an error with Bazel@HEAD
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[
"/cc [@learning-to-play](https://github.com/learning-to-play), [@vam-google](https://github.com/vam-google), [@chsigg](https://github.com/chsigg)"
] | 2024-04-08T07:12:43 | 2024-04-10T05:50:22 | null |
NONE
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CI: https://buildkite.com/bazel/bazel-at-head-plus-disabled/builds/2004#018eb729-e4a7-46e0-b68c-4b90fcac20cc
Platform: Ubuntu
Logs:
```
ERROR: [0m/var/lib/buildkite-agent/builds/bk-docker-f8l9/bazel-org-repo-root/tensorflow/tensorflow/python/BUILD:465:27: //tensorflow/python:_pywrap_tfcompile.so: no such attribute 'licenses' in 'cc_shared_library' rule
ERROR: [0m/var/lib/buildkite-agent/builds/bk-docker-f8l9/bazel-org-repo-root/tensorflow/tensorflow/python/BUILD:513:27: //tensorflow/python:_pywrap_py_exception_registry.so: no such attribute 'licenses' in 'cc_shared_library' rule
ERROR: [0m/var/lib/buildkite-agent/builds/bk-docker-f8l9/bazel-org-repo-root/tensorflow/tensorflow/python/BUILD:1189:27: //tensorflow/python:_pywrap_tfe.so: no such attribute 'licenses' in 'cc_shared_library' rule
ERROR: [0m/var/lib/buildkite-agent/builds/bk-docker-f8l9/bazel-org-repo-root/tensorflow/tensorflow/tools/pip_package/BUILD:255:10: Target '//tensorflow/python:distributed_framework_test_lib' contains an error and its package is in error and referenced by '//tensorflow/tools/pip_package:build_pip_package'
ERROR: [0mAnalysis of target '//tensorflow/tools/pip_package:build_pip_package' failed; build aborted: Analysis failed
```
Culprit:
Steps:
```
git clone https://github.com/tensorflow/tensorflow
git reset f12154def538a7093598c870be9dbb374b86bbe3 --hard
export USE_BAZEL_VERSION=3f9d80c35d88a280c2d53682f3d201b4733a3fff
bazel build //tensorflow/tools/pip_package:build_pip_package
```
CC Greenteam @meteorcloudy
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PR_kwDOArmXAs5r-CCE
| 65,219 |
Add CalibrationStatisticsSaver op definition
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Add CalibrationStatisticsSaver op definition
This op is designed to replace the CalibrationSingleton by aggregating and saving the statistics to files. It is stateful and is designed to run on CPU. This op has no output so we can easily lift it to outer function when required.
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Second cl of LayoutCanonicalizer that handles convolution op
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| 65,216 |
Enable per-channel weight-only quantization
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Enable per-channel weight-only quantization
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Add proper `QuantizationSpec` for `WeightOnlyPtq`.
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Add proper `QuantizationSpec` for `WeightOnlyPtq`.
References to `*Preset` should be eliminated after `ExpandPresets`.
Apply proper `QuantizationSpec` changes to carry all information necessary for quantization passes.
Follow-up changes will include the following:
- Removal of `StaticRangePtqPreset` usage in quantization passes
- Removal of preset flag usages in pipeline and quantization passes
- Moving `stablehlo_op_quant_spec` to the beginning of the pipeline, so that individual op quantization spec information can be used to create proper `QuantizationMethod` per target quantizable block.
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I_kwDOArmXAs6E7Mnp
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A checker is needed for inputs of Conv layers.
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[
"Hi **@Shuo-Sun20** ,\r\n\r\nI tried to run your code on colab using TF-nightly and faced the same issue. Please find the [gist ](https://colab.sandbox.google.com/gist/Venkat6871/58720b9ca973bb8a9670b3d00fdd6e52/65214_tf-nightly.ipynb)here for reference.\r\n\r\nThank you!",
"Hi **@Shuo-Sun20** ,\r\n- I tried your code on windows and facing the same issue. Here i am adding screenshot for reference .\r\n\r\nThank you!",
"Hi **@Shuo-Sun20** ,\r\n- Please post this issue on [keras-team/keras repo.](https://github.com/keras-team/keras/issues)\r\nTo know more see;\r\n[https://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999](https://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999).\r\n\r\nThank you!"
] | 2024-04-08T02:07:11 | 2024-06-12T06:32:18 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
WIndows10 & Linux
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
As disscussed in tensorflow/tensorflow#64396, it should be considered as an abnormal input when kernel_size > input_size, while currently the Conv layers will silently generate an unexpected output, therefore maybe a checker is needed here.
The code to reproduce this issue:
```
from keras.layers import Conv2D
import numpy as np
x=np.random.rand(1,2,2,1)
l=Conv2D(1,3,(1,1),'valid','channels_last', [1,1],1, 'linear', True)
print(l(x).shape)
print(l.compute_output_shape(x.shape))
```
In my opinion, this code should not generate an output, a warning or error is expected since the kernel(3x3) is larger than input (2x2).
### Standalone code to reproduce the issue
```shell
from keras.layers import Conv2D
import numpy as np
x=np.random.rand(1,2,2,1)
l=Conv2D(1,3,(1,1),'valid','channels_last', [1,1],1, 'linear', True)
print(l(x).shape)
print(l.compute_output_shape(x.shape))
```
### Relevant log output
```shell
Currently in windows, the output is
(1, 2, 2, 1)
(1, 0, 0, 1)
And in Linux the output is
(1, 0, 0, 1)
(1, 0, 0, 1)
The inconsistant is discussed in tensorflow/tensorflow#64396. This issue is to report the need of checker for abnormal inputs of Conv layers.
```
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Updated the `serialize_first_arg_tensor` function in the Node class to improve handling of various types of first arguments passed to layers.
>> - If the first argument is a Keras tensor, it is now serialized with additional metadata including the layer name, node index, and tensor index.
>> - For other types such as NumPy arrays, tensors, integers, floats, booleans, and strings, they are directly serialized without additional metadata.
>> - Added a ValueError raise statement for invalid first arguments, ensuring that only supported types are accepted.
>> - Updated the `map_arguments` method documentation to reflect the changes in `serialize_first_arg_tensor`.
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"This PR adds a missing docstring to the is_keras_tensor function in the Node class. The is_keras_tensor function is used to check whether an object is a Keras tensor by checking if it has a _keras_history attribute. However, it lacked a docstring explaining its purpose and usage. This change improves code readability and documentation clarity."
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[xla:gpu] Fix the dimension order propagation for kPad
Previously trivial fragments are not handled because we didn't support matrices with trivial dimensions.
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"Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/65209/checks?check_run_id=23538781372) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.",
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In the Auto Sharding solver output, populates the times where peak memory was observed & the subsequent constraints were enforced.
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In the Auto Sharding solver output, populates the times where peak memory was observed & the subsequent constraints were enforced.
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Remove deprecated code from JAX lowering and compilation
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ERROR: @local_config_cuda//:enable_cuda :: Error loading option @local_config_cuda//:enable_cuda: 'NoneType' value has no field or method 'replace'
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"@ensound1,\r\nThe error seems to be a bad path. Can you please try to edit the file **\"tensorflow/third_party/gpus/cuda_configure.bzl\"**, line 1400 to identify what path the code is looking for and then validate that it exists? I also suspect that the problem comes from the global environment, have you added the MVSC_tool to your path? and if so check if you have the cl.exe file inside it. \r\n\r\nCould you try **bazel clean --expunge** followed by bazel sync. Also try to set the **BAZEL_SH** environment variable, restarting the pc and run the cmd with Administrator rights. Thank you!",
"if i change at line 100\r\ndef _get_msvc_compiler(repository_ctx):\r\n vc_path = find_vc_path(repository_ctx)\r\n print(\"vc_path: \", vc_path\")\r\n return find_msvc_tool(repository_ctx, vc_path, \"cl.exe\").replace(\"\\\\\", \"/\")\r\ni get C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\VC\\Tools\\MSVC\\14.29.30133\\bin\\HostX64\\x64\r\nwhich exist and points to cl.exe. If i simply write cl it executes, so globally it is also set.\r\n\r\nIf i run bazel sync i get a bunch of errors... the first 2 are\r\n\r\n\r\n\r\nC:\\Users\\aless\\dev\\tensorflow>bazel sync\r\nINFO: Repository build_bazel_rules_swift_local_config instantiated at:\r\n C:/users/aless/dev/tensorflow/WORKSPACE:23:14: in <toplevel>\r\n C:/users/aless/dev/tensorflow/tensorflow/workspace0.bzl:125:29: in workspace\r\n C:/users/aless/_bazel_aless/ozqc7q5z/external/build_bazel_rules_swift/swift/repositories.bzl:123:11: in swift_rules_dependencies\r\n C:/users/aless/_bazel_aless/ozqc7q5z/external/build_bazel_rules_swift/swift/repositories.bzl:32:18: in _maybe\r\nRepository rule swift_autoconfiguration defined at:\r\n C:/users/aless/_bazel_aless/ozqc7q5z/external/build_bazel_rules_swift/swift/internal/swift_autoconfiguration.bzl:302:42: in <toplevel>\r\nERROR: An error occurred during the fetch of repository 'build_bazel_rules_swift_local_config':\r\n Traceback (most recent call last):\r\n File \"C:/users/aless/_bazel_aless/ozqc7q5z/external/build_bazel_rules_swift/swift/internal/swift_autoconfiguration.bzl\", line 300, column 32, in _swift_autoconfiguration_impl\r\n _create_linux_toolchain(repository_ctx)\r\n File \"C:/users/aless/_bazel_aless/ozqc7q5z/external/build_bazel_rules_swift/swift/internal/swift_autoconfiguration.bzl\", line 212, column 13, in _create_linux_toolchain\r\n fail(\"ERROR: rules_swift uses Bazel's CROSSTOOL to link, but Swift \" +\r\nError in fail: ERROR: rules_swift uses Bazel's CROSSTOOL to link, but Swift requires that the driver used is clang. Please set `CC=clang` in your environment before invoking Bazel.\r\nERROR: C:/users/aless/dev/tensorflow/WORKSPACE:23:14: fetching swift_autoconfiguration rule //external:build_bazel_rules_swift_local_config: Traceback (most recent call last):\r\n File \"C:/users/aless/_bazel_aless/ozqc7q5z/external/build_bazel_rules_swift/swift/internal/swift_autoconfiguration.bzl\", line 300, column 32, in _swift_autoconfiguration_impl\r\n _create_linux_toolchain(repository_ctx)\r\n File \"C:/users/aless/_bazel_aless/ozqc7q5z/external/build_bazel_rules_swift/swift/internal/swift_autoconfiguration.bzl\", line 212, column 13, in _create_linux_toolchain\r\n fail(\"ERROR: rules_swift uses Bazel's CROSSTOOL to link, but Swift \" +\r\nError in fail: ERROR: rules_swift uses Bazel's CROSSTOOL to link, but Swift requires that the driver used is clang. Please set `CC=clang` in your environment before invoking Bazel.\r\nINFO: Repository sigbuild-r2.10-python3.7_config_nccl instantiated at:\r\n C:/users/aless/dev/tensorflow/WORKSPACE:15:14: in <toplevel>\r\n C:/users/aless/dev/tensorflow/tensorflow/workspace2.bzl:879:19: in workspace\r\n C:/users/aless/dev/tensorflow/tensorflow/workspace2.bzl:86:27: in _tf_toolchains\r\n C:/users/aless/dev/tensorflow/tensorflow/tools/toolchains/remote_config/configs.bzl:257:24: in initialize_rbe_configs\r\n C:/users/aless/dev/tensorflow/tensorflow/tools/toolchains/remote_config/rbe_config.bzl:184:30: in sigbuild_tf_configs\r\nRepository rule remote_nccl_configure defined at:\r\n C:/users/aless/dev/tensorflow/third_party/nccl/nccl_configure.bzl:134:40: in <toplevel>\r\nERROR: An error occurred during the fetch of repository 'sigbuild-r2.10-python3.7_config_nccl':\r\n Traceback (most recent call last):\r\n File \"C:/users/aless/dev/tensorflow/third_party/nccl/nccl_configure.bzl\", line 77, column 35, in _create_local_nccl_repository\r\n cuda_config = find_cuda_config(repository_ctx, find_cuda_config_path, [\"cuda\"])\r\n File \"C:/users/aless/dev/tensorflow/third_party/gpus/cuda_configure.bzl\", line 649, column 41, in find_cuda_config\r\n exec_result = _exec_find_cuda_config(repository_ctx, script_path, cuda_libraries)\r\n File \"C:/users/aless/dev/tensorflow/third_party/gpus/cuda_configure.bzl\", line 643, column 19, in _exec_find_cuda_config\r\n return execute(repository_ctx, [python_bin, \"-c\", decompress_and_execute_cmd])\r\n File \"C:/users/aless/dev/tensorflow/third_party/remote_config/common.bzl\", line 230, column 13, in execute\r\n fail(\r\nError in fail: Repository command failed\r\njava.io.IOException: ERROR: src/main/native/windows/process.cc(202): CreateProcessW(\"C:\\users\\aless\\_bazel_aless\\ozqc7q5z\\external\\sigbuild-r2.10-python3.7_config_nccl\\usr\\bin\\python3\" -c \"from zlib import decompress;from base64 import b64decode;from os import system;script = decompress(b64decode('eJzdPGtz48aR3/krJtjbEqilIMmXcuV4kVOytHvWRZG2JO76UhLDjIAhBS8I8ACQEpPKf093zwODAUC9LHvLqvKaAKZ7unv6Oa837ChbrPN4dluyb/b2/4uNbgUbibTI8g9JdscOl+VtlhcBO0wSdoHNCnYhCpGvRBT03vTesNM4hOYiYss0EjkrAf5wwUP4n/oyYJ9FXsRZyr4J9piPDTz1yev/N2BYZ0s252uWZiVbFgJQxAWbxolg4j4Ui5LFKQuz+SKJeRoKdheXt9SNQgJk(...)): Impossibile trovare il file specificato.\r\n (error: 2)\r\n\r\ni dont really understand why i read linux while im on windows",
"@ensound1,\r\nI suspect there are some discrepancies happening because of multiple environments in the system. Could you please try to create the virtual environment and try to follow the steps for the installation. Also Have you tried **--config=nccl** command during Tensorflow build run. Thank you!"
] | 2024-04-06T21:35:50 | 2024-06-10T15:42:28 | null |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.10.0
### Custom code
No
### OS platform and distribution
windows 11
### Mobile device
_No response_
### Python version
3.9.12
### Bazel version
5.1.1 (bazelisk)
### GCC/compiler version
msvc 2019
### CUDA/cuDNN version
11.1 8.1
### GPU model and memory
RTX4090 32GB
### Current behavior?
I was trying to build tensorflow for c++ api use on visual studio, with support for gpu. CUDA/cuDNN/TensorRT. It seems there is a problem with finding the path for the compiler. But if i run cl on console it finds it and its correctly added to path
### Standalone code to reproduce the issue
```shell
git clone https://github.com/tensorflow/tensorflow.git
cd tensorflow
git checkout v2.10.0
python .\configure.py
bazel clean --expunge
bazel build //tensorflow:tensorflow_cc.dll //tensorflow:tensorflow_cc.lib
```
### Relevant log output
```shell
C:\Users\aless\dev\tensorflow>bazel build //tensorflow:tensorflow_cc.dll //tensorflow:tensorflow_framework.dll
INFO: Options provided by the client:
Inherited 'common' options: --isatty=1 --terminal_columns=120
INFO: Reading rc options for 'build' from c:\users\aless\dev\tensorflow\.bazelrc:
Inherited 'common' options: --experimental_repo_remote_exec
INFO: Options provided by the client:
'build' options: --python_path=C:/Users/aless/AppData/Local/Programs/Python/Python39/python.exe
INFO: Reading rc options for 'build' from c:\users\aless\dev\tensorflow\.bazelrc:
'build' options: --define framework_shared_object=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false
INFO: Reading rc options for 'build' from c:\users\aless\dev\tensorflow\.tf_configure.bazelrc:
'build' options: --action_env PYTHON_BIN_PATH=C:/Users/aless/AppData/Local/Programs/Python/Python39/python.exe --action_env PYTHON_LIB_PATH=C:/Users/aless/AppData/Local/Programs/Python/Python39/lib/site-packages --python_path=C:/Users/aless/AppData/Local/Programs/Python/Python39/python.exe --config=tensorrt --action_env CUDA_TOOLKIT_PATH=C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.1 --action_env TF_CUDA_COMPUTE_CAPABILITIES=8.9 --config=cuda --copt=/d2ReducedOptimizeHugeFunctions --host_copt=/d2ReducedOptimizeHugeFunctions --define=override_eigen_strong_inline=true
INFO: Reading rc options for 'build' from c:\users\aless\dev\tensorflow\.bazelrc:
'build' options: --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/common,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils
INFO: Found applicable config definition build:short_logs in file c:\users\aless\dev\tensorflow\.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file c:\users\aless\dev\tensorflow\.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:tensorrt in file c:\users\aless\dev\tensorflow\.bazelrc: --repo_env TF_NEED_TENSORRT=1
INFO: Found applicable config definition build:cuda in file c:\users\aless\dev\tensorflow\.bazelrc: --repo_env TF_NEED_CUDA=1 --crosstool_top=@local_config_cuda//crosstool:toolchain --@local_config_cuda//:enable_cuda
INFO: Found applicable config definition build:windows in file c:\users\aless\dev\tensorflow\.bazelrc: --copt=/W0 --host_copt=/W0 --copt=/Zc:__cplusplus --host_copt=/Zc:__cplusplus --copt=/D_USE_MATH_DEFINES --host_copt=/D_USE_MATH_DEFINES --features=compiler_param_file --copt=/d2ReducedOptimizeHugeFunctions --host_copt=/d2ReducedOptimizeHugeFunctions --cxxopt=/std:c++17 --host_cxxopt=/std:c++17 --config=monolithic --copt=-DWIN32_LEAN_AND_MEAN --host_copt=-DWIN32_LEAN_AND_MEAN --copt=-DNOGDI --host_copt=-DNOGDI --copt=/experimental:preprocessor --host_copt=/experimental:preprocessor --linkopt=/DEBUG --host_linkopt=/DEBUG --linkopt=/OPT:REF --host_linkopt=/OPT:REF --linkopt=/OPT:ICF --host_linkopt=/OPT:ICF --verbose_failures --features=compiler_param_file --distinct_host_configuration=false
INFO: Found applicable config definition build:monolithic in file c:\users\aless\dev\tensorflow\.bazelrc: --define framework_shared_object=false --experimental_link_static_libraries_once=false
INFO: Repository local_config_cuda instantiated at:
C:/users/aless/dev/tensorflow/WORKSPACE:15:14: in <toplevel>
C:/users/aless/dev/tensorflow/tensorflow/workspace2.bzl:879:19: in workspace
C:/users/aless/dev/tensorflow/tensorflow/workspace2.bzl:91:19: in _tf_toolchains
Repository rule cuda_configure defined at:
C:/users/aless/dev/tensorflow/third_party/gpus/cuda_configure.bzl:1453:33: in <toplevel>
ERROR: An error occurred during the fetch of repository 'local_config_cuda':
Traceback (most recent call last):
File "C:/users/aless/dev/tensorflow/third_party/gpus/cuda_configure.bzl", line 1406, column 38, in _cuda_autoconf_impl
_create_local_cuda_repository(repository_ctx)
File "C:/users/aless/dev/tensorflow/third_party/gpus/cuda_configure.bzl", line 1184, column 17, in _create_local_cuda_repository
cc = find_cc(repository_ctx)
File "C:/users/aless/dev/tensorflow/third_party/gpus/cuda_configure.bzl", line 216, column 34, in find_cc
return _get_msvc_compiler(repository_ctx)
File "C:/users/aless/dev/tensorflow/third_party/gpus/cuda_configure.bzl", line 133, column 61, in _get_msvc_compiler
return find_msvc_tool(repository_ctx, vc_path, "cl.exe").replace("\\", "/")
Error: 'NoneType' value has no field or method 'replace'
ERROR: C:/users/aless/dev/tensorflow/WORKSPACE:15:14: fetching cuda_configure rule //external:local_config_cuda: Traceback (most recent call last):
File "C:/users/aless/dev/tensorflow/third_party/gpus/cuda_configure.bzl", line 1406, column 38, in _cuda_autoconf_impl
_create_local_cuda_repository(repository_ctx)
File "C:/users/aless/dev/tensorflow/third_party/gpus/cuda_configure.bzl", line 1184, column 17, in _create_local_cuda_repository
cc = find_cc(repository_ctx)
File "C:/users/aless/dev/tensorflow/third_party/gpus/cuda_configure.bzl", line 216, column 34, in find_cc
return _get_msvc_compiler(repository_ctx)
File "C:/users/aless/dev/tensorflow/third_party/gpus/cuda_configure.bzl", line 133, column 61, in _get_msvc_compiler
return find_msvc_tool(repository_ctx, vc_path, "cl.exe").replace("\\", "/")
Error: 'NoneType' value has no field or method 'replace'
INFO: Found applicable config definition build:cuda in file c:\users\aless\dev\tensorflow\.bazelrc: --repo_env TF_NEED_CUDA=1 --crosstool_top=@local_config_cuda//crosstool:toolchain --@local_config_cuda//:enable_cuda
ERROR: @local_config_cuda//:enable_cuda :: Error loading option @local_config_cuda//:enable_cuda: 'NoneType' value has no field or method 'replace'
```
|
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| 2,229,428,084 |
PR_kwDOArmXAs5r6uyC
| 65,194 |
Directly use absl::Status instead of xla::Status now that they're the same thing.
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[] | 2024-04-06T21:22:51 | 2024-04-10T18:19:57 | 2024-04-10T18:19:56 |
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Directly use absl::Status instead of xla::Status now that they're the same thing.
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PR_kwDOArmXAs5r6mFX
| 65,193 |
[tsl:concurrency] Add LLVM-style type casting to AsyncValueRef<T>
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[tsl:concurrency] Add LLVM-style type casting to AsyncValueRef<T>
+ cleanup ref_count header and consistently use DerivedFrom helper
Coming-next: for rvalue references we can do Cast and DynCast by moving RCReference and save wasted AddRef/DropRef
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PR_kwDOArmXAs5r6mE8
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[tsl:concurrency] Add helper functioms to block/await on AsyncValueRef and AsyncValuePtr
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[tsl:concurrency] Add helper functioms to block/await on AsyncValueRef and AsyncValuePtr
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