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[
"@sondosaabed,\r\nI suspect that this is due to **Keras 3** which uses jit compile by default, and **Keras 2** doesn't (user needs to explicitly enable jit), and the warning message is probably from jit/xla. I request you to take a look at this [issue](https://github.com/keras-team/keras/issues/19081) where a similar issue/warning has been proposed and it is still open. Also I request to follow the similar issue which has been proposed to have the updates on the similar issue. Thank you!",
"> @sondosaabed, I suspect that this is due to **Keras 3** which uses jit compile by default, and **Keras 2** doesn't (user needs to explicitly enable jit), and the warning message is probably from jit/xla. I request you to take a look at this [issue](https://github.com/keras-team/keras/issues/19081) where a similar issue/warning has been proposed and it is still open. Also I request to follow the similar issue which has been proposed to have the updates on the similar issue. Thank you!\r\n\r\nThank you for your reply! I suggest they make a better warning message so as a user I could easily understand what this is and how to fix it. I will check out the jit/xla ! \r\nThank you for indicating that issue I'll follow up and see if it's affecting my training. I'm gonna close this issue with this...",
"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/66589\">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/66589\">No</a>\n"
] | 2024-04-28T14:54:55 | 2024-04-29T10:14:13 | 2024-04-29T10:14:10 |
NONE
| null | null | null |
### Issue type
Others
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15
### Custom code
Yes
### OS platform and distribution
Kaggle notebook
### Mobile device
_No response_
### Python version
3.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
What does this warning indicate and how does it affect the training
### Standalone code to reproduce the issue
```shell
I have included an image in the upper cell. I see a warning when training
```
### Relevant log output
_No response_
|
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ImportError: DLL load failed while importing _pywrap_tf2
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[
"Hi @kotKolil ,\r\n\r\nIt seems you are using tensorflow 2.16.1 which is a GPU package. GPU support not available for Windows OS since TF2.11v. Please refer the doc [source](https://www.tensorflow.org/install/pip#windows-native) for same.\r\n\r\nEither you need to install WSL2 for enabling GPU support or use tensorflow_cpu package. For more details please refer to the documentation attached.",
"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/66588\">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/66588\">No</a>\n"
] | 2024-04-28T13:35:54 | 2024-04-29T19:35:26 | 2024-04-29T19:35:22 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16.1
### Custom code
Yes
### OS platform and distribution
windows 10
### Mobile device
_No response_
### Python version
3.12.1
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
not have
### GPU model and memory
Intel UHD Graphics
### Current behavior?
Hello evereone! I tried to run this code snippet:
```
import cv2
from keras.applications.vgg16 import VGG16, preprocess_input
from keras.preprocessing import image
import numpy as np
model = VGG16(weights='imagenet', include_top=False)
async def detect_objects(image_path):
img = await cv2.imread(image_path)
img = await cv2.resize(img, (224, 224))
x = await image.img_to_array(img)
x = await np.expand_dims(x, axis=0)
x = await preprocess_input(x)
features = await model.predict(x)
return features
```
with this libraries, installed via pip. List is here: https://pastebin.com/jM46gp9T
But i get this error:
**from tensorflow.python.platform import _pywrap_tf2
ImportError: DLL load failed while importing _pywrap_tf2: Произошел сбой в программе инициализации библиотеки динамической компоновки (DLL).**
### Standalone code to reproduce the issue
```shell
my project is here:
https://github.com/kotKolil/-Strange-Library-of-Sir.-Tom-/tree/master/asynchronous%20web%20server%20for%20the%20thing%20recognition%20API
```
### Relevant log output
```shell
Traceback (most recent call last):
File "D:\strange library of Sir Tom\asynchronous web server for the thing recognition API\main.py", line 3, in <module>
from recg import *
File "D:\strange library of Sir Tom\asynchronous web server for the thing recognition API\recg.py", line 2, in <module>
from keras.applications.vgg16 import VGG16, preprocess_input
File "C:\Users\Tom Fox\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\__init__.py", line 10, in <module>
from keras.api import * # noqa: F403
^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Tom Fox\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\api\__init__.py", line 7, in <module>
from keras.api import activations
File "C:\Users\Tom Fox\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\api\activations\__init__.py", line 7, in <module>
from keras.src.activations import deserialize
File "C:\Users\Tom Fox\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\__init__.py", line 1, in <module>
from keras.src import activations
File "C:\Users\Tom Fox\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\activations\__init__.py", line 3, in <module>
from keras.src.activations.activations import elu
File "C:\Users\Tom Fox\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\activations\activations.py", line 1, in <module>
from keras.src import backend
File "C:\Users\Tom Fox\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\backend\__init__.py", line 9, in <module>
from keras.src.backend.common.dtypes import result_type
File "C:\Users\Tom Fox\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\backend\common\__init__.py", line 2, in <module>
from keras.src.backend.common.dtypes import result_type
File "C:\Users\Tom Fox\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\backend\common\dtypes.py", line 5, in <module>
from keras.src.backend.common.variables import standardize_dtype
File "C:\Users\Tom Fox\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\backend\common\variables.py", line 10, in <module>
from keras.src.utils.module_utils import tensorflow as tf
File "C:\Users\Tom Fox\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\utils\__init__.py", line 12, in <module>
from keras.src.utils.model_visualization import model_to_dot
File "C:\Users\Tom Fox\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\utils\model_visualization.py", line 6, in <module>
from keras.src import tree
File "C:\Users\Tom Fox\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\tree\__init__.py", line 1, in <module>
from keras.src.tree.tree_api import assert_same_structure
File "C:\Users\Tom Fox\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\tree\tree_api.py", line 6, in <module>
from keras.src.tree import optree_impl as tree_impl
File "C:\Users\Tom Fox\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\tree\optree_impl.py", line 17, in <module>
from tensorflow.python.trackable.data_structures import ListWrapper
File "C:\Users\Tom Fox\AppData\Local\Programs\Python\Python312\Lib\site-packages\tensorflow\__init__.py", line 42, in <module>
from tensorflow.python import tf2 as _tf2
File "C:\Users\Tom Fox\AppData\Local\Programs\Python\Python312\Lib\site-packages\tensorflow\python\tf2.py", line 21, in <module>
from tensorflow.python.platform import _pywrap_tf2
ImportError: DLL load failed while importing _pywrap_tf2: Произошел сбой в программе инициализации библиотеки динамической компоновки (DLL).
```
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[
"@rkazants I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/620bf43013e6accbbad6b39ca6446670/66583.ipynb#scrollTo=QScJQq8WYQsk). Could you consider using tf.strings.split instead. It offers an optional max_split parameter to limit the number of splits: https://www.tensorflow.org/api_docs/python/tf/strings/split\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.",
"Hi @sushreebarsa,\r\n\r\nplease remove stale label because the issue is not fixed.\r\n\r\nBest regards,\r\nRoman"
] | 2024-04-28T09:40:56 | 2024-05-15T05:49:10 | null |
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| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16.1
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
tf.raw_ops.StringSplitV2 does not limit a number of splits by max_split attribute.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
tf.raw_ops.StringSplitV2(input=['one<>'], sep='<>', maxsplit=1) # outputs two splits 'one' and '' instead of just one split
tf.raw_ops.StringSplitV2(input=['one<>two'], sep='<>', maxsplit=1) # outputs two splits 'one' and 'two' instead of just one split
```
### Relevant log output
```shell
>>> tf.raw_ops.StringSplitV2(input=['one<>'], sep='<>', maxsplit=1)
StringSplitV2(indices=<tf.Tensor: shape=(2, 2), dtype=int64, numpy=
array([[0, 0],
[0, 1]])>, values=<tf.Tensor: shape=(2,), dtype=string, numpy=array([b'one', b''], dtype=object)>, shape=<tf.Tensor: shape=(2,), dtype=int64, numpy=array([1, 2])>)
>>> tf.raw_ops.StringSplitV2(input=['one<>two'], sep='<>', maxsplit=1)
StringSplitV2(indices=<tf.Tensor: shape=(2, 2), dtype=int64, numpy=
array([[0, 0],
[0, 1]])>, values=<tf.Tensor: shape=(2,), dtype=string, numpy=array([b'one', b'two'], dtype=object)>, shape=<tf.Tensor: shape=(2,), dtype=int64, numpy=array([1, 2])>)
```
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Sharding custom calls cannot be hoisted individually in `WhileLoopInvariantCodeMotion`.
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Sharding custom calls cannot be hoisted individually in `WhileLoopInvariantCodeMotion`.
A sharding custom call annotates sharding to its operand. If we move a single sharding custom call out of the while body, the sharding annotation may not take effect as expected.
Taking the follow while body as an example,
```
body {
p_body = (f32[2], f32[2], s32[]) parameter(0)
gte.0 = f32[2] get-tuple-element(p_body), index=0
gte.1 = f32[2] get-tuple-element(p_body), index=1
sharding.0 = f32[2] custom-call(gte.0), custom_call_target="Sharding", sharding={devices=[2]<=[2]}
sharding.1 = f32[2] custom-call(gte.1), custom_call_target="Sharding", sharding={replicated}
add.0 = f32[2] add(sharding.0, sharding.1)
gte.2 = s32[] get-tuple-element(p_body), index=2
const = s32[] constant(1)
add.1 = s32[] add(gte.2, const)
ROOT root = (f32[2], f32[2], s32[]) tuple(gte.0, add.0, add.1)
}
```
Before this cl, `WhileLoopInvariantCodeMotion` moves `sharding.0` out of the body and keeps `sharding.1` in the body. With this cl, `WhileLoopInvariantCodeMotion` does not modify this body.
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Nitpick: fix bullet point formatting in docs for `Tout` arg of `tf.py_function`
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Nitpick: fix bullet point formatting in docs for `Tout` arg of `tf.py_function`
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Please update the links on documentation page, pointing to the new location - moved to /src
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[
"@MarkDaoust , Could you please look into this. Thanks!",
"Fix inflight.\r\n",
"@EonosLovos Here is the fix commit https://github.com/tensorflow/tensorflow/commit/ca59b909c82d254f3dd20bb2acef4485347f31bf\r\n@MarkDaoust , When does the change reflect in the webpage?",
"Probably Thursday."
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Hello!
Thank you for all the awesome things you have done!
I was browsing tf.keras documentation pages, for example
https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dropout
and noticed that the pages do not find the lines in source code as expected, instead the 404 is being shown in the middle of github page where source code would be supposed to be.
in order for the links to work as expected, it seems that after branch number and keras another folder, /src should be added.
I mean:
https://github.com/keras-team/keras/tree/v3.3.3/keras/layers/regularization/dropout.py#L6-L76
becomes
https://github.com/keras-team/keras/blob/v3.3.3/keras/src/layers/regularization/dropout.py#L6-L76
h_ttps://github.com/keras-team/keras/blob/v3.3.3/keras/ **src/** layers/regularization/dropout.py#L6-L76
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PR #10495: Enforce same input/output layout for offloading ops
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PR #10495: Enforce same input/output layout for offloading ops
Imported from GitHub PR https://github.com/openxla/xla/pull/10495
This patch makes sure that the host offloader will not introduce layout mismatches
after removing the offloading custom calls by constraining the layout assignment
to assign the same layout for the custom call's input and output.
Copybara import of the project:
--
ce6aeac3ac5445ab014d36c92a181dbe7afc35e7 by Jaroslav Sevcik <[email protected]>:
Enforce same input/output layout for offloading ops
Merging this change closes #10495
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10495 from jaro-sevcik:host-offload-call-layout-fix ce6aeac3ac5445ab014d36c92a181dbe7afc35e7
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Skip graph export for newly added TF MLIR functions if backend_compiler is not specified.
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Skip graph export for newly added TF MLIR functions if backend_compiler is not specified.
All the needed function defs are already in the function library if no special backend_compiler is used.
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TFLite Multipose model input error for android
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[
"Hi @Raaed-Ilham ,\r\n\r\nI will replicate the issue and i will get back to you .",
"Hi @Raaed-Ilham ,\r\n\r\nDid you check out [this](https://github.com/tensorflow/examples/tree/master/lite/examples/pose_estimation) . This example\r\nuses the movenet thunder model similar to yours.",
"y\r\n\r\n> Hi @Raaed-Ilham ,\r\n> \r\n> Did you check out [this](https://github.com/tensorflow/examples/tree/master/lite/examples/pose_estimation) . This example uses the movenet thunder model similar to yours.\r\n\r\nYes that was what I initially tried but I cant build the sample app due to a gradle build issue which is a whole other issue\r\nsame error for someone who tried building the sample app for another classifier\r\n#62823 \r\n\r\nwill look into it a bit more then but it uses the same model so it shouldnt be an issue I have compared the code as well.",
"Hi @Raaed-Ilham ,\r\n\r\nTry with the gradle version 7.3.3 and let me know if you were able to build the project.",
"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/66564\">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/66564\">No</a>\n"
] | 2024-04-27T14:54:55 | 2024-06-07T01:51:06 | 2024-06-07T01:51:03 |
NONE
| null | null | null |
I have been trying to work with the movenet multipose lightning model. The model gives the following error
` java.lang.IllegalArgumentException: Cannot copy to a TensorFlowLite tensor (serving_default_input:0) with 3 bytes from a Java Buffer with 122880 bytes.`
The model, downloaded from the official tfhub/kaggle page [](https://www.kaggle.com/models/google/movenet/tfLite),
requests inputs of (1,1,1,3) of Datatype.UINT8
However in the documentation on the model page it asks for inputs in the form of 1*H*W*3 where h and w are multiples of 32
ideally 160,256 for example.
I have opened this issue on recommendation from @sushreebarsa after viewing another user with a similar issue given here:
[](https://github.com/tensorflow/tensorflow/issues/53127)
#53127
Currently if 1,1 are given as H and W it results in a bunch of random points on the left hand side of the phone screen which do not seem to move with the user. Any changes to the input size of the image and model only change the latter java buffer size, the tensor image size always remains at 3 bytes in the error messages.
Please let me know if its possible to resolve this issue or if theres something wrong with the model itself. If it is an error with the model could converting the the original model to tflite version with different input parameters be possible somehow.
Relevant code given below
```
imageProcessor = new ImageProcessor.Builder().add(new ResizeOp(160,256, ResizeOp.ResizeMethod.BILINEAR)).build();
private AutoModel1 model;
try {
model = AutoModel1.newInstance(this);
}
catch (IOException e) {
}
public void onSurfaceTextureUpdated(@NonNull SurfaceTexture p0) {
bitmap = textureView.getBitmap();
TensorImage tensorImage = new TensorImage(DataType.UINT8);
tensorImage.load(bitmap);
tensorImage = imageProcessor.process(tensorImage);
int[] shape = new int[] {1,160,256,3};
TensorBuffer inputFeature0 = TensorBuffer.createFixedSize(shape,DataType.UINT8);
// TensorBuffer inputFeature0 = TensorBuffer.createDynamic(DataType.UINT8);
Log.d("size of input buffer", String.valueOf(inputFeature0.getFlatSize()));
inputFeature0.loadBuffer(tensorImage.getBuffer());
Log.d("Buffer created", "true");
AutoModel1.Outputs outputs = model.process(inputFeature0);
Log.d("Model processed", "outputs created");
TensorBuffer temp = outputs.getOutputFeature0AsTensorBuffer();
float[] outputFeature0 = outputs.getOutputFeature0AsTensorBuffer().getFloatArray();
Bitmap mutable = bitmap.copy(Bitmap.Config.ARGB_8888, true);
Canvas canvas = new Canvas(mutable);
int h = bitmap.getHeight();
int w = bitmap.getWidth();
int x = 0;
Log.d("output__", String.valueOf(outputFeature0.length));
while (x <= 51) {
float score_val = outputFeature0[x+2];
Log.d("score values", String.valueOf(Math.abs(score_val)));
//if (outputFeature0[x+2] > 0.3f) {
canvas.drawCircle(Math.abs(outputFeature0[x+1] * w), Math.abs(outputFeature0[x] * h), 10f, paint);
//}
x += 3;
}
imageView.setImageBitmap(mutable);
}
```
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PR #61632: Allow merging compute-copy streams
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PR #61632: Allow merging compute-copy streams
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/61632
This PR works as a part of the whole Multi-Stream feature in TF, which is proposed in #61185.
Allow merging the host_to_device/device_to_host/device_to_device data copy streams into the compute stream in one stream group. This is useful to reduce the overhead caused by GPU stream synchronization, especially when data transfers are frequent. Another benefit is, for host_to_device copy, merging streams allows early scheduling of subsequent ops, doesn't have to wait until the data copy is really finished.
As a part of the multi-stream feature, it can help multi-stream reach a much higher throughput. Taking our proto models as an example, the original model inference throughput is **1524** samples/second, and **2229** samples/ second with multi-stream, and **2471** samples/second further with stream-merging.
However, stream-merging can also be used separately. We got inference throughput gain from **1028** samples/second to **1187** samples/second by enabling stream-merging.
Please refer to the 'Performance' part in our [document](https://docs.google.com/document/d/1yL3lWk_iFKqLTyekkuaiKXZ78I0lPmD5kM1fghHRs4Y/edit?usp=sharing) for detailed and more experiment results.
Copybara import of the project:
--
9e51f38c3a4e31de89be520c4bc5315abf30cd4b by Robin Zhang <[email protected]>:
Allow merging compute-copy streams
--
a45967f67b026fc9f6b314e4e6581578da430629 by Robin Zhang <[email protected]>:
Improve coding style
--
ccae79bb3156219c1bf7010d146194ae38887806 by Robin Zhang <[email protected]>:
Rename stream_merge_options_
--
332e1fef23cd09b0972b7b6d7520bf5507afbda6 by Robin Zhang <[email protected]>:
Put stream checking out of callback
--
4a0c7892f9ed9ac110af63ac6832d813743b7cc7 by Robin Zhang <[email protected]>:
Move StreamMergeOptions to Experimental
--
efe56d7143c2b11f87e478cabeab894e264b4314 by Robin Zhang <[email protected]>:
add some comments
Merging this change closes #61632
Reverts changelist 525613555
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/61632 from buptzyb:multistream-streammerge 5aabb58431e56868c1f462ec3851785132d4cd47
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[pjrt] Disable non-absl::StatusOr<T> PjRtFutures
Before switching all PjRtFutures to implicit absl::StatusOr make sure that all users use explicit StatusOr payloads.
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[pjrt] NFC: Always use concrete async value in PjRtFuture<T>
We plan to make absl::StatusOr<T> an implicit payload of PjRtFuture<T> and for consistency always use AsyncValueRef with concrete payload instead of relying on AsyncValue error semantics.
PjRtFuture can't be constructed from async values passed from a user (only from a promise) so we can safely ignore the error bit as we never use it.
This is a first CL in preparation for making absl::StatusOr implicit in PjRtFuture<T>.
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[ifrt] NFC: Use PjRtFuture<absl::StatusOr<T>> in ifrt
In preparation for making PjRtFuture holding absl::StatusOr<T> payload add StatusOr to all existing PjRtFuture<T>. It will be removed later when all users will use absl::StatusOr wrappers.
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Clean up HloInputOutputAliasConfig
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Clean up HloInputOutputAliasConfig
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I_kwDOArmXAs6HGyml
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is_installed check for tensorflow-cpu failed as 'spec is None'
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[
"Hi @earlytalk ,\r\n\r\nAs per the log what i understood is the system is checking for `tensorflow-cpu` package which not exists. If you have installed tensorflow package which is by default GPU package. You might need to install `tensorflow-cpu` and check.\r\n\r\nAlso If your OS is windows then GPU support not available for TF versions>=2.11v. Either you need to install `tensorflow-cpu` or install WSL2 for enabling GPU. Please check the attached [documentation](https://www.tensorflow.org/install/pip#windows-native) for more details.",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"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/66545\">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/66545\">No</a>\n"
] | 2024-04-27T01:45:31 | 2024-05-15T01:49:45 | 2024-05-15T01:49:42 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16.1
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
stable diffusion start have this issue
### Standalone code to reproduce the issue
```shell
Python 3.10.11 (tags/v3.10.11:7d4cc5a, Apr 5 2023, 00:38:17) [MSC v.1929 64 bit (AMD64)]
Version: v1.9.3
Commit hash: 1c0a0c4c26f78c32095ebc7f8af82f5c04fca8c0
current transparent-background 1.2.12
is_installed check for tensorflow-cpu failed as 'spec is None'
Installing requirements for easyphoto-webui
Installing requirements for tensorflow
loading WD14-tagger reqs from I:\AI\stable-diffusion-webui\extensions\stable-diffusion-webui-wd14-tagger\requirements.txt
Checking WD14-tagger requirements.
Launching Web UI with arguments: --xformers --disable-safe-unpickle --api
2024-04-27 09:32:57.827792: 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`.
2024-04-27 09:33:06.916852: 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`.
```
### Relevant log output
_No response_
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Ensure that all instructions which are inserted by HloRematerialization have .remat in their name.
Cloned computations are excluded from this check.
Reverts changelist 473051719
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#tf-data Add prefetching to WeightedFlatMap tests.
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NONE
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#tf-data Add prefetching to WeightedFlatMap tests.
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#tf-data Support save/load for flat_map dataset global shuffling.
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NONE
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#tf-data Support save/load for flat_map dataset global shuffling.
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PR_kwDOArmXAs5t4m6h
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Integrate LLVM at llvm/llvm-project@c49b74a4e6ff
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Integrate LLVM at llvm/llvm-project@c49b74a4e6ff
Updates LLVM usage to match
[c49b74a4e6ff](https://github.com/llvm/llvm-project/commit/c49b74a4e6ff)
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PR_kwDOArmXAs5t4bxr
| 66,539 |
Enable scatter_test on all targets
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Enable scatter_test on all targets
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PR_kwDOArmXAs5t4Qd8
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#tf-data Support global shuffle for the `flat_map` dataset.
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NONE
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#tf-data Support global shuffle for the `flat_map` dataset.
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PR_kwDOArmXAs5t4PLk
| 66,537 |
Replace `absl::make_unique_for_overwrite` with `std::make_unique`
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NONE
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Replace `absl::make_unique_for_overwrite` with `std::make_unique`
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PR_kwDOArmXAs5t4ORy
| 66,536 |
Enable some previously disabled tests
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NONE
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Enable some previously disabled tests
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I_kwDOArmXAs6HFutR
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Suspected Corner Case in XLA Compilation - vectorized_sum, conditional, scatter_nd_update Complains about Dynamic Shape when we should Know it
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[
"@stellarpower I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/e45c255aa960d3dac97d3965924c2607/tensorflow-if-and-scatter-bug.ipynb#scrollTo=u6Z0yO9F16TV). Could you please confirm the results?\r\nAlso please try without vectorization? We will analyze if a non-vectorized version compiles correctly?\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/66535\">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/66535\">No</a>\n"
] | 2024-04-26T20:00:08 | 2024-05-23T01:48:52 | 2024-05-23T01:48:48 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.15.0; nightly
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I seem to have hit what I think is a really weird corner-case in XLA compiling. My code may have a problem in it, this is the first time I'm using XLA, but, I seem to need a particular combination of factors for the bug to reproduce.
The compiler is complaining about not being able to operate using dynamic shapes, although these should be known (or inferred) by the graph compiler. I don't know if some errors that could have come up are elided away as the optimisations are applied, but reproducing it required a combination of a conditional, a vectorised map, and a matrix generated using reduce_sum, as well as passing these between functions. Change some or all of these elements to try and pin the bug down and the code compiled fine.
I'm working on an implementation of Soft Dynamic Time Warping as a loss function. For LSTM-format tensors (sequences in batch, timesteps in sequence, elements of feature vector), the basics of the algorithm are we compute a distance matrix using some metric across all timesteps of the predictions and the ground truth. Then we calculate a loss matrix iteratively, using this distance matrix, using two for loops.
In order to help parallelisation, we can use `vectorized_map`, as each sequence in the batch is independent. The issue appears to be coming out of this vectorisation, and on using a distance matrix constructed using a suitable expression (Euclidean distance is used as an example) - change one of those two things (just hardcode to calculate on one sequence in the batch and do not loop, or make the distance matrix using e.g. `tf.ones`), and the error did not reproduce. The actual line at fault is a conditional assignment within the loops - if we have infinity in the loss matrix for the current `(i, j)` pair, we need to change that to minus infinity. The compiler spits out a long trace (because of the vectorisation, it's repeating itself) that there is a reshape operation happening inside the translation of the `scatter_nd_update` for which the shape is dynamic. This is particularly confusing given that I know all I am doing is assigning a scalar element to a scalar value within a matrix at known indices - when operating eagerly a reshape would be totally unnecessary, it essentially amounts to one comparison and a memory access on CPU. I can paste the traceback if required, but maybe it's easier to see using the nightly package in the colab notebook.
I did try every possible way I could think of to inform the compiler that the shape of the tensors is known and fix the shape of intermediate values (we can infer all the required information from the shape of the ground truth/predictions; the rest is deterministic), as well as trying to force it to understand that the single element in the distance matrix and the scatter_nd_update are only making scalar changes. E.g. fixing the input_signature of the tf.function, hardcoding the shape as constants, etc.
I suspected the fact that the conditional branch is optionally assigning makes it not-very-functional, and this could be causing problems, but I have tried expanding it to a ternary-style if in a few ways and the same issue applies. Ultimately my only solution was to `scatter_nd_update` on every iteration with either the original value or the constant I need to replace it by - terrible for performance but I will just pray that the compiler can melt this away.
I hope this provides some level of explanation, but as I am struggling to work out what the real issue is here, and any attempts to isolate the problem failed, afraid this is the most minimal example I could come up with; I'm not really sure what is going on.
As an aside, I'm sure it's a work-in-progress, but the traceback I get form the XLA compiler made this really, really hard work. It makes the template errors from the rangev3 library look like a breeze, and dumping the intermediaries just was not feasible to look through and work out anything sensible. It took about two days and I've gone round and round all of my code, before I finally pinned down the actual failure site to be the if statement. Aside from general improvements, given that static shapes are required, if it would be possible to get a traceback of how these shapes have been worked out would probably go a long way (in the same way I can trace an error in a C++ template and modern compilers will walk back each time and tell me how they deduced the particular type they used).
I'd probably also add that at a high level, the behaviour between symbolic tensors and XLA has caused rather a lot of friction. When using symbolic tensors, often the shape is not available, so we have to write our code in such a way that lazy operations that understand symbolic templates can function fine. But then, when it comes to XLA, we absolutely do need the shape, and it's impossible for me to guide the graph (when I absolutely do know what shape I am expecting), because of all the None values. As a user-programmer it really feels like the two systems are fighting each other and whilst I'd like to see how XLA performs and compares, I could probably achieve this more easily and quickly trying to write something using the HLO directly or using SYCL and making a custom Op. In an ideal world, I'd quite like to see some sort of DSL that builds upon the HLO, targets XLA, and that could interoperate with TensorFlow, as doing this in python and then with the graph compiler before converting that to XLA is a lot of overhead.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1PrrUgSHfGjvTKAjoZtBQEJDk2ZwXuEoP?usp=drive_link
Please note that my own code is much more involved. I have tried to cut back an MRE and tested this locally, but there may be some changes between behaviour on this colab notebook and my local code - I've pasted it in and checked it quickly in colab but not picked it apart in the same way I have locally.
```
### Relevant log output
_No response_
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PR_kwDOArmXAs5t4I4r
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Add odml_converter to runlit.cfg and runlite.site.cfg
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Add odml_converter to runlit.cfg and runlite.site.cfg
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Set frontend attributes for copied instructions in layout assignment.
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Set metadata for new while instructions created in while_loop_simplifier.
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Replace `absl::make_unique_for_overwrite` with `std::make_unique`
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Replace `absl::make_unique_for_overwrite` with `std::make_unique`
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[xla:gpu] Fix a bug in GpuAlgebraicSimplifier
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[xla:gpu] Fix a bug in GpuAlgebraicSimplifier
I don't know what I was thinking
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#tf-data Do not log auto-scaler error messages by default.
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#tf-data Do not log auto-scaler error messages by default.
Otherwise this may clutter output logs.
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Adding MLIR debugging instrumentation to experimental MLIR Quantizer
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Move `tsl/framework` to `xla/tsl/framework`
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Verify flatbuffers passed from the python bindings.
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Verify flatbuffers passed from the python bindings.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11422 from Tixxx:tixxx/flag_clean_up 72020bef1368f514760cbfd6630c22d2348121c9
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Enable flatbuffer verifier for TFLite files bigger than 2GiB.
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NONE
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Enable flatbuffer verifier for TFLite files bigger than 2GiB.
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PR_kwDOArmXAs5t3aA9
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Reverts 92304fecd216e522deef31adefa1a17b441b507d
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Reverts 92304fecd216e522deef31adefa1a17b441b507d
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PR_kwDOArmXAs5t3VKm
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[xla] Test output to operands aliasing by updating the input in the custom call.
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NONE
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[xla] Test output to operands aliasing by updating the input in the custom call.
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PR_kwDOArmXAs5t3TRr
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Remove invalid quantized tests
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Remove invalid quantized tests
Quantized tensors have a constraint that `sizeof(storage_type) < sizeof(expressed_type)`. This CL removes quantized tensor tests that were quantizing from BF16/F16 to SI16 and BF16/F16/F32 to SI32.
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| 2,266,171,129 |
PR_kwDOArmXAs5t3Rba
| 66,521 |
Do not keep the bytecode that is only used for loading if lazy loading is enabled.
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NONE
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Do not keep the bytecode that is only used for loading if lazy loading is enabled.
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PR_kwDOArmXAs5t3N8K
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Remove tsl/concurrency now that it has mmoved to xla/tsl/concurrency
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Remove tsl/concurrency now that it has mmoved to xla/tsl/concurrency
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PR_kwDOArmXAs5t3LW4
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Reverts 92304fecd216e522deef31adefa1a17b441b507d
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NONE
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Reverts 92304fecd216e522deef31adefa1a17b441b507d
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PR_kwDOArmXAs5t3GlZ
| 66,518 |
Integrate LLVM at llvm/llvm-project@1728a56d0e66
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Integrate LLVM at llvm/llvm-project@1728a56d0e66
Updates LLVM usage to match
[1728a56d0e66](https://github.com/llvm/llvm-project/commit/1728a56d0e66)
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I_kwDOArmXAs6HEfcO
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Cannot find any way to install tensorflow<=2.15.0
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[
"What is ur python version?",
"3.11. I think I tried 3.10 at some point but it didn't seem to help.",
"Please have you try to use python version **3.9** for the installing of the tensorflow ",
"Hi **@peter-fm** ,\r\n\r\nI tried to run your code on colab using TF v2.15 with python version 3.10 and I am not faced any issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/c8b343e8bbea0c92991a54daa476df32/66517_2-15-v.ipynb) here for reference.\r\n\r\nThank you!",
"Thanks for verifying that it should be possible @Venkat6871. I think I'm getting a handle on it now. If I set up a brand new environment in docker and do `pip install \"tensorflow==2.15.0\"` it works. If I then try pip install `\"tensorflow[and-cuda]==2.15.0\"` it fails with:\r\n```\r\nERROR: Could not find a version that satisfies the requirement tensorrt-libs==8.6.1; extra == \"and-cuda\" (from tensorflow[and-cuda]) (from versions: 9.0.0.post11.dev1, 9.0.0.post12.dev1, 9.0.1.post11.dev4, 9.0.1.post12.dev4, 9.1.0.post11.dev4, 9.1.0.post12.dev4, 9.2.0.post11.dev5, 9.2.0.post12.dev5, 9.3.0.post11.dev1, 9.3.0.post12.dev1)\r\nERROR: No matching distribution found for tensorrt-libs==8.6.1; extra == \"and-cuda\"\r\n``` \r\nThen when I tried to reinstall just `\"tensorflow==2.15.0\"`, i.e. without cuda it is at that point I get the error. So I'm guessing it's something to do with my pip cache or it might be because I also have rye installed which might be breaking pip in someway. But at least I can now install <=2.15.0!",
"Hi **@peter-fm** ,\r\n\r\n- That's good to listen it is working for you, and I tried to run your code on colab using TF v2.15[cuda] with python version 3.10 and facing same issue. i am adding [gist](https://colab.sandbox.google.com/gist/Venkat6871/fa2637abd4c823908944155f520461e8/66517_2-15-cuda.ipynb) for your reference.\r\n\r\nThank you!",
"Hi @Venkat6871, yeah it looks like you're getting the same error in your gist:\r\n```\r\nCollecting tensorrt==8.6.1.post1 (from tensorflow[and-cuda]==2.15.0)\r\n Downloading tensorrt-8.6.1.post1.tar.gz (18 kB)\r\n Preparing metadata (setup.py) ... done\r\nCollecting tensorrt-bindings==8.6.1 (from tensorflow[and-cuda]==2.15.0)\r\n Downloading tensorrt_bindings-8.6.1-cp310-none-manylinux_2_17_x86_64.whl (979 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 979.4/979.4 kB 52.3 MB/s eta 0:00:00\r\nINFO: pip is looking at multiple versions of tensorflow[and-cuda] to determine which version is compatible with other requirements. This could take a while.\r\nERROR: Could not find a version that satisfies the requirement tensorrt-libs==8.6.1; extra == \"and-cuda\" (from tensorflow[and-cuda]) (from versions: 9.0.0.post11.dev1, 9.0.0.post12.dev1, 9.0.1.post11.dev4, 9.0.1.post12.dev4, 9.1.0.post11.dev4, 9.1.0.post12.dev4, 9.2.0.post11.dev5, 9.2.0.post12.dev5, 9.3.0.post11.dev1, 9.3.0.post12.dev1)\r\nERROR: No matching distribution found for tensorrt-libs==8.6.1; extra == \"and-cuda\"\r\n```\r\nIt turns out that your code works fine if you're not using a build tool since all the packages before `tensorrt` are all that are needed. But in my case my build tool won't let me install any of the packages because of that error. However, I found a work around. Since it works without `tensorrt`, if I delete my local cache and then install all the cuda packages in your gist, i.e. \r\n`\"nvidia-cublas-cu12==12.2.5.6\" \"nvidia-cuda-cupti-cu12==12.2.142\" \"nvidia-cuda-nvcc-cu12==12.2.140\" \"nvidia-cuda-nvrtc-cu12==12.2.140\" \"nvidia-cuda-runtime-cu12==12.2.140\" \"nvidia-cudnn-cu12==8.9.4.25\" \"nvidia-cufft-cu12==11.0.8.103\" \"nvidia-curand-cu12==10.3.3.141\" \"nvidia-cusolver-cu12==11.5.2.141\" \"nvidia-cusparse-cu12==12.1.2.141\" \"nvidia-nccl-cu12==2.16.5\" \"nvidia-nvjitlink-cu12==12.2.140\"`\r\nand ignore the`tensorrt` libraries, the gpu works perfectly ... it seems to even work with python 3.11.8 🥳 ",
"Hi **@peter-fm** ,\r\n- That's great listen it is working for you. Could you please confirm if this issue is resolved for you ? Please feel free to close the issue if it is resolved ? \r\n\r\nThank you!",
"I would say it isn't entirely resolved since it currently isn't possible to install `tensorflow[and-cuda]<=2.15.0` without experiencing the `ERROR: No matching distribution found for tensorrt-libs==8.6.1; extra == \"and-cuda\"`. For most people this error probably won't matter since you can still use tensorflow without it. But some build tools might not work with that error in place so it should probably be fixed. For example, does `and-cuda` really need to include`tensorrt-libs==8.6.1` by default?",
"Hi @peter-fm ,\r\n- Could you please confirm which version you need to use? And use that version directly instead of ```tensorflow[and-cuda]<=2.15.0```.\r\n--- Use the following code for your version\r\n```\r\ntensorflow[and-cuda]==2.15.0\r\n```\r\nThank you!",
"This might not be the problem, but if you are using zsh, you must enclose anything with square brackets in single quotes, or escape them with a backslash.\r\n\r\n```\r\nat 12:27:54 > pip install tensorflow[and-cuda]==2.14.1 \r\nzsh: no matches found: tensorflow[and-cuda]==2.14.1\r\n```\r\nWhereas, this works:\r\n```\r\nat 12:28:27 x pip install 'tensorflow[and-cuda]==2.14.1'\r\nLooking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\r\nRequirement already satisfied: tensorflow[and-cuda]==2.14.1 in ./venv-tf214/lib/python3.9/site-packages (2.14.1)\r\n```\r\n\r\nJust cost me an hour."
] | 2024-04-26T16:13:03 | 2024-06-05T14:46:44 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15.0
### Custom code
Yes
### OS platform and distribution
Ubuntu 22.04.3
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I want to install tensorflow in python and also then serve a model in a rust application. Since the only c++ bindings are for 2.15.0 (I cannot find any other compiled versions without building from source...which I tried and failed to do), I have to install tensorflow 2.15.0. I tried installing tensorflow 2.15.1 and training a model but the c++ bindings complain the model was trained using a version later than the c++ bindings (which is true)...so I assume I have to install tensorflow 2.15.0 or lower:
```
pip install "tensorflow==2.15.0"
ERROR: Could not find a version that satisfies the requirement tensorflow==2.15.0 (from versions: 2.16.0rc0, 2.16.1)
ERROR: No matching distribution found for tensorflow==2.15.0
```
or
```
pip install "tensorflow<=2.15.0"
ERROR: Could not find a version that satisfies the requirement tensorflow<=2.15.0 (from versions: 2.16.0rc0, 2.16.1)
ERROR: No matching distribution found for tensorflow<=2.15.0
```
I then tried to install 2.15 from source but since I don't have 3 phds I cannot decipher the reasons why it fails (after taking 4 hours to download, unzip and then compile).
I'm at a total loss at what to do... if there was just a wheel available for some version below 2.15.1 or just more compiled c++ bindings available for something other than 2.15.0 then I could not be wasting my time.
The problem is even worse if I try and use `and-cuda` (but one thing at a time?).
### Standalone code to reproduce the issue
```shell
pip install "tensorflow<=2.15.0"
```
### Relevant log output
```shell
ERROR: Could not find a version that satisfies the requirement tensorflow<=2.15.0 (from versions: 2.16.0rc0, 2.16.1)
ERROR: No matching distribution found for tensorflow<=2.15.0
```
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[xla:ffi] Fix a bug in the FFI buffer conversion.
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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/66514/checks?check_run_id=24304961574) 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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[xla:ffi] Fix a bug in the FFI buffer conversion.
The size of the buffer was not set correctly.
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Fixes the Memory Term Reducer to properly handle the case of invalid intervals in the input.
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Fix LHS input masking for sparse dots
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Integrate LLVM at llvm/llvm-project@0c6e1ca1c704
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Integrate LLVM at llvm/llvm-project@0c6e1ca1c704
Updates LLVM usage to match
[0c6e1ca1c704](https://github.com/llvm/llvm-project/commit/0c6e1ca1c704)
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Allow fusions for sparse dot metadata input
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Allow fusions for sparse dot metadata input
Note: metadata has the same shape as LHS, except for the contracting dimension size.
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Add dynamically quantized Transpose Convolution to XNNPACK delegate
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Fix sparse dot metadata loader
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Add HloFindIf to search through non-fusion computations (NFC).
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Add HloFindIf to search through non-fusion computations (NFC).
Use this in AddressComputationFusionRewriter. It (mis-)uses the HloFindIf for
fusions.
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[xla:ffi] Extend typed DeviceMemory API
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[xla:ffi] Extend typed DeviceMemory API
Add methods returning native data pointers to se::DeviceMemory<T> class. These methods aim to reduce code duplication (for example in typed FFI custom calls).
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[xla:ffi] Fix nested tuples CPU custom call tests
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[xla:ffi] Fix nested tuples CPU custom call tests
Nested tuples tests use FfiTupleRotate custom call, which works correctly only for scalar F32 buffers. Changed FfiTupleRotate signature to express that (use ranked 0 typed buffers instead of generic BufferBase).
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Add a check that HloComputationFusion is only created for fusion computations.
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Add a check that HloComputationFusion is only created for fusion computations.
The comment is outdated. But fix other wrong callers of FindNonTrivialHero.
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PR #11859: [XLA:GPU] Add cuDNN Flash Attention Padding Mask Generation & Skipping computation on padded tokens in XLA
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PR #11859: [XLA:GPU] Add cuDNN Flash Attention Padding Mask Generation & Skipping computation on padded tokens in XLA
Imported from GitHub PR https://github.com/openxla/xla/pull/11859
* Follow up PR on https://github.com/openxla/xla/pull/11449 and https://github.com/openxla/xla/pull/10232.
* Add cuDNN flash attention padding mask generation in XLA. cuDNN also supports skip computation at padded tokens.
* Add an e2e test to compare padding mask generation with pass padding mask as bias in cuDNN.
Copybara import of the project:
--
aab103097be5ab7b23573e605bb8e877e004f323 by cjkkkk <[email protected]>:
add var_seq
--
ba755d7c145a7c54a0cf939ea911d791f6d7f755 by cjkkkk <[email protected]>:
add uid
--
5da56ba30542d75c4e80847788b48df500934579 by cjkkkk <[email protected]>:
add var seq e2e test
--
3a109b8d26c3958557d70c5e0802b0b5e19cb2dc by cjkkkk <[email protected]>:
fix rebase error
Merging this change closes #11859
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11859 from Cjkkkk:var_seq 3a109b8d26c3958557d70c5e0802b0b5e19cb2dc
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PR #11514: Move cublasLT workspace allocation to buffer assignment
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PR #11514: Move cublasLT workspace allocation to buffer assignment
Imported from GitHub PR https://github.com/openxla/xla/pull/11514
Move cublasLT workspace allocation to buffer assignment to support command buffer lowering
Copybara import of the project:
--
7a448b05a54345c0cb15a47c9dc45558255c9656 by Shawn Wang <[email protected]>:
Move cublasLT workspace allocation to buffer assignment to support command buffer lowering
--
16e6efe960d92825bed0a0237a374f7980baf5d2 by Shawn Wang <[email protected]>:
set workspace size
Merging this change closes #11514
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11514 from shawnwang18:shawnw/cublaslt_command_buffer 16e6efe960d92825bed0a0237a374f7980baf5d2
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Fix deprecated `mlir::Type::dyn_cast` call.
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Fix deprecated `mlir::Type::dyn_cast` call.
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PR #10782: Offloading 2/3: generate multi-stream async copies using events on GPUs
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PR #10782: Offloading 2/3: generate multi-stream async copies using events on GPUs
Imported from GitHub PR https://github.com/openxla/xla/pull/10782
Emit asynchronous memory copies between hosts and devices using additional streams while the main stream does the computation. The async copies are guarded by RecordEvent() and WaitForEvent() created by the copy-start/copy-done thunks respectively. A hash table is utilized to map copy-start instructions to events. The corresponding events will be waited at copy-done and extracted from the hash table.
Copybara import of the project:
--
a9271ee9b99eb43a0a0806c8dbdfc7753aef4644 by Jane Liu <[email protected]>:
Add the multi-stream implementation for copy-start and copy-done.
Merging this change closes #10782
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10782 from zhenying-liu:async_copy a9271ee9b99eb43a0a0806c8dbdfc7753aef4644
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