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Uses intervals instead of a liveness matrix to construct all memory constraints.
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Integrate LLVM at llvm/llvm-project@21ef187654c8
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Integrate LLVM at llvm/llvm-project@21ef187654c8
Updates LLVM usage to match
[21ef187654c8](https://github.com/llvm/llvm-project/commit/21ef187654c8)
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Update //tensorflow/lite/swift:Tests size to medium.
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Update //tensorflow/lite/swift:Tests size to medium.
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[oneDNN] Code cleanup: unsupport blocked format for fused batchnorm op
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"@penpornk Hi, please help to review this and another PRs. Once they are approved/merged, I have another one (big cleanup) for TF 2.17. Thanks a lot!",
"Hi @penpornk Can you please assist on above [comments](https://github.com/tensorflow/tensorflow/pull/66388#issuecomment-2103579140) from @gzmkl. Thank you!",
"> Hi @gzmkl Can you please assist on above [comments](https://github.com/tensorflow/tensorflow/pull/66388#issuecomment-2103579140) from @gzmkl. Thank you!\r\n\r\nHi, that are comments from myself! Right now, we are planning to remove a lot of very old code which is N/A forever. ",
"> > Hi @gzmkl Can you please assist on above [comments](https://github.com/tensorflow/tensorflow/pull/66388#issuecomment-2103579140) from @gzmkl. Thank you!\r\n> \r\n> Hi, that are comments from myself! Right now, we are planning to remove a lot of very old code which is N/A forever.\r\n\r\nHi @gzmkl My apologies for the typo in my above comment. Thank you very much for the update. ",
"Hi @penpornk Any update on this PR? Please. Thank you!"
] | 2024-04-24T18:25:54 | 2024-06-07T17:08:01 | null |
CONTRIBUTOR
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We will not support blocked format for MKL kernel operations.
This PR does related code cleanup of the FusedBatchNorm op and related tests.
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Update users of `tsl/concurrency` to use `xla/tsl/concurrency`
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[] | 2024-04-24T18:22:34 | 2024-04-24T22:14:37 | 2024-04-24T22:14:36 |
NONE
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Update users of `tsl/concurrency` to use `xla/tsl/concurrency`
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PR_kwDOArmXAs5torO9
| 66,386 |
Reverts 21f6e08a3f39591c1915410f4c256c9456e36d80
|
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[] | 2024-04-24T18:16:27 | 2024-04-24T18:23:04 | 2024-04-24T18:23:01 |
NONE
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Reverts 21f6e08a3f39591c1915410f4c256c9456e36d80
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PR_kwDOArmXAs5toq7v
| 66,385 |
Reverts 21f6e08a3f39591c1915410f4c256c9456e36d80
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[] | 2024-04-24T18:15:31 | 2024-04-24T18:59:11 | 2024-04-24T18:59:08 |
NONE
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Reverts 21f6e08a3f39591c1915410f4c256c9456e36d80
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PR_kwDOArmXAs5top2u
| 66,384 |
Reverts 21f6e08a3f39591c1915410f4c256c9456e36d80
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[] | 2024-04-24T18:12:22 | 2024-04-24T19:07:52 | 2024-04-24T19:07:52 |
NONE
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Reverts 21f6e08a3f39591c1915410f4c256c9456e36d80
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| 2,261,892,420 |
PR_kwDOArmXAs5too4f
| 66,383 |
Support reductions that require atomics.
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[] | 2024-04-24T18:09:49 | 2024-04-24T21:44:14 | 2024-04-24T21:44:13 |
NONE
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Support reductions that require atomics.
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PR_kwDOArmXAs5toovJ
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[tsl:concurrency] Disable ambiguous AndThen callback for AsyncValueRef<absl::Status>
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NONE
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[tsl:concurrency] Disable ambiguous AndThen callback for AsyncValueRef<absl::Status>
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Exception encountered: Unrecognized keyword arguments: ['batch_shape']
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"My thinking process onto this Error is probably due to multiple issues. I can lend a few ideas to the situation and anyone is free to correct me, if I'm wrong about it. \n\nFirst, I would check to see if the TenserFlow compiler is updated to the versions you're using. Maybe it could cause these issues. As it did suggest rebuild TenserFlow with appropriate compiler. \n\nSecond, I would double check to see if the versions are even compatible. That could be causing issues.\n",
"Hi @MuhammadBilal848 ,\r\n\r\nSince TF2.16 uses Keras3 by default.In Keras3 saving to the TF SavedModel format via model.save() is no longer supported in Keras 3. Please refer to [migration](https://keras.io/guides/migrating_to_keras_3/) guide for some more details.\r\n\r\n",
"So I trained another just to check and used `model.export(\"FOLDER_NAME\")` instead `model.save(\"model.h5\")`.\r\nGot this as output,\r\n\r\n\r\n and the folder is saved with assets , variables , pb file and fingerprint:\r\n\r\n\r\n\r\nI load the model using `tf.keras.layers.TFSMLayer(\"FOLDER_NAME\", call_endpoint=\"serving_default\")`\r\n\r\nIt worked. @SuryanarayanaY Thank you 🖤",
"> Hi @MuhammadBilal848 ,\r\n> \r\n> Since TF2.16 uses Keras3 by default.In Keras3 saving to the TF SavedModel format via model.save() is no longer supported in Keras 3. Please refer to [migration](https://keras.io/guides/migrating_to_keras_3/) guide for some more details.\r\n\r\nAlso could you tell me how can I convert the pd model to h5? I want to convert the model to tflite.\r\n\r\nI tried using this:\r\n\r\n\r\nGot:\r\n```\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\r\nW0000 00:00:1714042377.283703 4504 tf_tfl_flatbuffer_helpers.cc:390] Ignored output_format.\r\nW0000 00:00:1714042377.284215 4504 tf_tfl_flatbuffer_helpers.cc:393] Ignored drop_control_dependency.\r\n2024-04-25 15:52:57.285827: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: C:\\Users\\Bilal\\AppData\\Local\\Temp\\tmphhpbtz_x\r\n2024-04-25 15:52:57.287069: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve }\r\n2024-04-25 15:52:57.287242: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: C:\\Users\\Bilal\\AppData\\Local\\Temp\\tmphhpbtz_x\r\n2024-04-25 15:52:57.300472: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled\r\n2024-04-25 15:52:57.306249: I tensorflow/cc/saved_model/loader.cc:234] Restoring SavedModel bundle.\r\n2024-04-25 15:52:57.378809: I tensorflow/cc/saved_model/loader.cc:218] Running initialization op on SavedModel bundle at path: C:\\Users\\Bilal\\AppData\\Local\\Temp\\tmphhpbtz_x\r\n2024-04-25 15:52:57.391327: I tensorflow/cc/saved_model/loader.cc:317] SavedModel load for tags { serve }; Status: success: OK. Took 105497 microseconds.\r\n2024-04-25 15:52:57.412351: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\r\nTraceback (most recent call last):\r\n File \"F:\\Projects\\Trigger Word Detection\\converter.py\", line 10, in <module>\r\n tflite_model = converter.convert()\r\n File \"F:\\Projects\\Trigger Word Detection\\twd\\lib\\site-packages\\tensorflow\\lite\\python\\lite.py\", line 1175, in wrapper\r\n return self._convert_and_export_metrics(convert_func, *args, **kwargs)\r\n File \"F:\\Projects\\Trigger Word Detection\\twd\\lib\\site-packages\\tensorflow\\lite\\python\\lite.py\", line 1129, in _convert_and_export_metrics\r\n result = convert_func(self, *args, **kwargs)\r\n File \"F:\\Projects\\Trigger Word Detection\\twd\\lib\\site-packages\\tensorflow\\lite\\python\\lite.py\", line 1636, in convert\r\n saved_model_convert_result = self._convert_as_saved_model()\r\n File \"F:\\Projects\\Trigger Word Detection\\twd\\lib\\site-packages\\tensorflow\\lite\\python\\lite.py\", line 1617, in _convert_as_saved_model\r\n return super(TFLiteKerasModelConverterV2, self).convert(\r\n File \"F:\\Projects\\Trigger Word Detection\\twd\\lib\\site-packages\\tensorflow\\lite\\python\\lite.py\", line 1407, in convert\r\n result = _convert_graphdef(\r\n File \"F:\\Projects\\Trigger Word Detection\\twd\\lib\\site-packages\\tensorflow\\lite\\python\\convert_phase.py\", line 212, in wrapper\r\n raise converter_error from None # Re-throws the exception.\r\n File \"F:\\Projects\\Trigger Word Detection\\twd\\lib\\site-packages\\tensorflow\\lite\\python\\convert_phase.py\", line 205, in wrapper\r\n return func(*args, **kwargs)\r\n File \"F:\\Projects\\Trigger Word Detection\\twd\\lib\\site-packages\\tensorflow\\lite\\python\\convert.py\", line 995, in convert_graphdef\r\n data = convert(\r\n File \"F:\\Projects\\Trigger Word Detection\\twd\\lib\\site-packages\\tensorflow\\lite\\python\\convert.py\", line 367, in convert\r\n raise converter_error\r\ntensorflow.lite.python.convert_phase.ConverterError: Could not translate MLIR to FlatBuffer.\r\n```",
"> > Hi @MuhammadBilal848 ,\r\n> > Since TF2.16 uses Keras3 by default.In Keras3 saving to the TF SavedModel format via model.save() is no longer supported in Keras 3. Please refer to [migration](https://keras.io/guides/migrating_to_keras_3/) guide for some more details.\r\n> \r\n> Also could you tell me how can I convert the pd model to h5? I want to convert the model to tflite.\r\n> \r\n> I tried using this: \r\n> \r\n> Got:\r\n> \r\n> ```\r\n> To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n> WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\r\n> W0000 00:00:1714042377.283703 4504 tf_tfl_flatbuffer_helpers.cc:390] Ignored output_format.\r\n> W0000 00:00:1714042377.284215 4504 tf_tfl_flatbuffer_helpers.cc:393] Ignored drop_control_dependency.\r\n> 2024-04-25 15:52:57.285827: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: C:\\Users\\Bilal\\AppData\\Local\\Temp\\tmphhpbtz_x\r\n> 2024-04-25 15:52:57.287069: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve }\r\n> 2024-04-25 15:52:57.287242: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: C:\\Users\\Bilal\\AppData\\Local\\Temp\\tmphhpbtz_x\r\n> 2024-04-25 15:52:57.300472: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled\r\n> 2024-04-25 15:52:57.306249: I tensorflow/cc/saved_model/loader.cc:234] Restoring SavedModel bundle.\r\n> 2024-04-25 15:52:57.378809: I tensorflow/cc/saved_model/loader.cc:218] Running initialization op on SavedModel bundle at path: C:\\Users\\Bilal\\AppData\\Local\\Temp\\tmphhpbtz_x\r\n> 2024-04-25 15:52:57.391327: I tensorflow/cc/saved_model/loader.cc:317] SavedModel load for tags { serve }; Status: success: OK. Took 105497 microseconds.\r\n> 2024-04-25 15:52:57.412351: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\r\n> Traceback (most recent call last):\r\n> File \"F:\\Projects\\Trigger Word Detection\\converter.py\", line 10, in <module>\r\n> tflite_model = converter.convert()\r\n> File \"F:\\Projects\\Trigger Word Detection\\twd\\lib\\site-packages\\tensorflow\\lite\\python\\lite.py\", line 1175, in wrapper\r\n> return self._convert_and_export_metrics(convert_func, *args, **kwargs)\r\n> File \"F:\\Projects\\Trigger Word Detection\\twd\\lib\\site-packages\\tensorflow\\lite\\python\\lite.py\", line 1129, in _convert_and_export_metrics\r\n> result = convert_func(self, *args, **kwargs)\r\n> File \"F:\\Projects\\Trigger Word Detection\\twd\\lib\\site-packages\\tensorflow\\lite\\python\\lite.py\", line 1636, in convert\r\n> saved_model_convert_result = self._convert_as_saved_model()\r\n> File \"F:\\Projects\\Trigger Word Detection\\twd\\lib\\site-packages\\tensorflow\\lite\\python\\lite.py\", line 1617, in _convert_as_saved_model\r\n> return super(TFLiteKerasModelConverterV2, self).convert(\r\n> File \"F:\\Projects\\Trigger Word Detection\\twd\\lib\\site-packages\\tensorflow\\lite\\python\\lite.py\", line 1407, in convert\r\n> result = _convert_graphdef(\r\n> File \"F:\\Projects\\Trigger Word Detection\\twd\\lib\\site-packages\\tensorflow\\lite\\python\\convert_phase.py\", line 212, in wrapper\r\n> raise converter_error from None # Re-throws the exception.\r\n> File \"F:\\Projects\\Trigger Word Detection\\twd\\lib\\site-packages\\tensorflow\\lite\\python\\convert_phase.py\", line 205, in wrapper\r\n> return func(*args, **kwargs)\r\n> File \"F:\\Projects\\Trigger Word Detection\\twd\\lib\\site-packages\\tensorflow\\lite\\python\\convert.py\", line 995, in convert_graphdef\r\n> data = convert(\r\n> File \"F:\\Projects\\Trigger Word Detection\\twd\\lib\\site-packages\\tensorflow\\lite\\python\\convert.py\", line 367, in convert\r\n> raise converter_error\r\n> tensorflow.lite.python.convert_phase.ConverterError: Could not translate MLIR to FlatBuffer.\r\n> ```\r\n\r\nI tried this method and it worked:\r\n\r\n",
"Hi @MuhammadBilal848 ,\r\n\r\nThanks for confirmation and happy that it worked. Could you please mark this issue as closed. Thanks!",
"Hello.. After upgrading the following packages, the original problem of loading the h5 file was seemingly solved.\r\n!pip install --upgrade tensorflow\r\n!pip install --upgrade keras\r\n\r\nOn Google Colab.."
] | 2024-04-24T18:03:58 | 2024-05-19T04:10:46 | 2024-04-29T09:34:47 |
NONE
| null | null | null |
I tried this on tf version **2.16.1** & **2.13.0** on python **3.10** and **3.8** respectively. My model is not that big it is under an MB, but this should not cause problem.
**CODE**:
import tensorflow as tf
h5_model = tf.keras.models.load_model('helfen_1.h5')
converter = tf.lite.TFLiteConverter.from_keras_model(h5_model)
tflite_model = converter.convert()
with open('converted_model.tflite', 'wb') as f:
f.write(tflite_model)
**ERROR**:
2024-04-24 23:00:17.022540: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: SSE SSE2 SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
Traceback (most recent call last):
File "t.py", line 3, in <module>
h5_model = tf.keras.models.load_model('helfen_1.h5')
File "f:\Projects\Conv\convert\lib\site-packages\keras\src\saving\saving_api.py", line 238, in load_model
return legacy_sm_saving_lib.load_model(
File "f:\Projects\Conv\convert\lib\site-packages\keras\src\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "f:\Projects\Conv\convert\lib\site-packages\keras\src\engine\base_layer.py", line 870, in from_config
raise TypeError(
TypeError: Error when deserializing class 'InputLayer' using config={'batch_shape': [None, 50], 'dtype': 'float32', 'sparse': False, 'name': 'input_layer_10'}.
Exception encountered: Unrecognized keyword arguments: ['batch_shape']
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PR_kwDOArmXAs5tolAg
| 66,380 |
Add RBE toolchains for Clang on Windows.
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PR_kwDOArmXAs5tohWs
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Reverts 51a4843aff53901e1ee08b7623b59cf0dcf2a43a
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Reverts 51a4843aff53901e1ee08b7623b59cf0dcf2a43a
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Defines an alternate interface for the Memory Term Reducer that uses primitive intervals instead of liveness matrices.
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Defines an alternate interface for the Memory Term Reducer that uses primitive intervals instead of liveness matrices.
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PR_kwDOArmXAs5toPui
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Fix execution engine after https://github.com/llvm/llvm-project/commit/7da63426ac5d9719038842c30ca2a644620be071.
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Fix execution engine after https://github.com/llvm/llvm-project/commit/7da63426ac5d9719038842c30ca2a644620be071.
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Remove unnecessary epilogue restrictions and corner cases.
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NONE
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Remove unnecessary epilogue restrictions and corner cases.
- Allow injected values to be roots of epilogues
- Allow n-ary values to be injected
- Always generate an epilogue function if it's requested.
- Simplify EmitEpilogue interface to hide indexes and calling convention
details from the caller.
This simplifies the logic in the reduction emitter somewhat.
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PR_kwDOArmXAs5toFNr
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Reverts 92304fecd216e522deef31adefa1a17b441b507d
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Reverts 92304fecd216e522deef31adefa1a17b441b507d
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I_kwDOArmXAs6Gzi5j
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TF-Keras mixed precision training leads to autograph errors
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[
"@lgeiger I was not able to replicate the issue on colab using TF [v2.16.1](https://colab.research.google.com/gist/sushreebarsa/60005f6240f6632694003f18c99fdf96/66374.ipynb#scrollTo=CWYFhjxzepie) and tf-[nightly](https://colab.research.google.com/gist/sushreebarsa/6f4d7c4abfaa6ef4d951b7ab2251de4b/66374.ipynb) as well. Please have a look at the attached gists and confirm the same?\r\nThank you!",
"@sushreebarsa As mentioned in the issue this is an issue with TF-Keras, I added `os.environ[\"TF_USE_LEGACY_KERAS\"] = \"1\"` to the example code as well to match the colab which should enable you to reproduce this issue.\r\n",
"Fixed in #67189",
"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/66374\">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/66374\">No</a>\n"
] | 2024-04-24T16:05:35 | 2024-05-14T07:22:08 | 2024-05-10T11:05:23 |
CONTRIBUTOR
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.16.1, 2.17.0.dev20240423
### Custom code
No
### OS platform and distribution
Linux, Colab
### Mobile device
_No response_
### Python version
3.10, 3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.3 / 8.9.7.29
### GPU model and memory
NVIDIA Tesla T4
### Current behavior?
Since TF version 2.16 mixed precision training is fails to compile with autograph and throws warnings when running a minimal mixed precision training example:
```python
import os
os.environ["TF_USE_LEGACY_KERAS"] = "1"
import tensorflow as tf
from tensorflow import keras
keras.mixed_precision.set_global_policy('mixed_float16')
inputs = keras.Input(shape=(784,))
x = keras.layers.Dense(10)(inputs)
outputs = keras.layers.Activation('softmax', dtype='float32')(x)
model = keras.Model(inputs=inputs, outputs=outputs)
model.compile(loss='sparse_categorical_crossentropy', optimizer=keras.optimizers.RMSprop())
(x_train, y_train), _ = keras.datasets.mnist.load_data()
x_train = x_train.reshape(60000, 784).astype('float32') / 255
_ = model.fit(x_train, y_train, batch_size=128, epochs=1, steps_per_epoch=1, verbose=0)
```
TF Autograph doesn't transform the `create_autocast_variable` function and throws the following warnings:
```
WARNING:tensorflow:AutoGraph could not transform <function create_autocast_variable at 0x7a263a673400> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: <gast.gast.Expr object at 0x7a25b0be2e00>
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
```
Autograph should be able to transform the function and should not throw any warnings like it did in TF 2.15.
Unfortunately we're currently unable to upgrade to Keras 3 due to other issues, so it would be good to be able to get this patched in TF-Keras as well.
I'm not sure if this issue is caused by Autograph or by TF-Keras.
### Standalone code to reproduce the issue
See the following notebooks: This [wasn't an issue in TF 2.15](https://colab.research.google.com/drive/1iW1V_a7vXYvZ0wjAwlbNYz2ORjaFWCeW?usp=sharing), but [fails in TF 2.16](https://colab.research.google.com/drive/1Zzz-ydhptsvjFAs8B4TMAo20jqA2Y07Z?usp=sharing) and still [fails in TF Nightly](https://colab.research.google.com/drive/16qW1wbEIEFJ_4H7TM8XE5RzuQIdCRTZS?usp=sharing).
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5tnaVz
| 66,373 |
Don't try to patch pywrap_calibrator.so, as it was removed.
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Don't try to patch pywrap_calibrator.so, as it was removed.
Follow-up to
https://github.com/tensorflow/tensorflow/commit/c8ee77626915c7a5ed3be88bce0f12b1a9ef2c6f
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PR_kwDOArmXAs5tm_Q0
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FC bias is per channel quantized when per channel quantization is used
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NONE
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FC bias is per channel quantized when per channel quantization is used
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PR_kwDOArmXAs5tm3_h
| 66,371 |
Reverts d9417b25e86151823b3be5aa3f57b3b35f303fd5
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Reverts d9417b25e86151823b3be5aa3f57b3b35f303fd5
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PR_kwDOArmXAs5tmqGb
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Manually importing bazel changes to build Triton AMD backend from https://github.com/openxla/triton/pull/7/files
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Manually importing bazel changes to build Triton AMD backend from https://github.com/openxla/triton/pull/7/files
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PR_kwDOArmXAs5tmaSX
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Add an api to get all op names of tflite model
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Add an api to get all op names of tflite model
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#shlo_ref Fix OSS.
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#shlo_ref Fix OSS.
- Use the correct absl::BitGen.
- Fix the definition of `std::common_type` for `shlo_ref::F16`
- Fix building `shlo_ref::F16` from values that can be converted to `float`.
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Allow to fuse multi-output fusion producers into consumers.
It was already possible to sibling-fuse two multi-output fusions. This worked
as treating tuples that are fused into a computation in a special way, and uses
the assumption that there is no (direct or indirect) user of the tuple inside
the "target" fusion. Now we add logic to detect which of the fusion outputs
is still needed as fusion output of the merged fusion, and fuse the tuple
elements one by one.
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Automated Code Change
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PR_kwDOArmXAs5tk8tq
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Integrate LLVM at llvm/llvm-project@b3ca9c30dedf
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Integrate LLVM at llvm/llvm-project@b3ca9c30dedf
Updates LLVM usage to match
[b3ca9c30dedf](https://github.com/llvm/llvm-project/commit/b3ca9c30dedf)
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[xla] memory_placement: drop number of users' restriction on to_host custom calls
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[xla] memory_placement: drop number of users' restriction on to_host custom calls
There is no good reason to place this restriction.
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PR #11784: [ROCm] Add Dropout support based on MIOpen API
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PR #11784: [ROCm] Add Dropout support based on MIOpen API
Imported from GitHub PR https://github.com/openxla/xla/pull/11784
Copybara import of the project:
--
fc334e480802d2686907ff720a97697e51177690 by mmakevic <[email protected]>:
Add dropout descriptor for rocm
--
52cf3ad49e70b3ecf632d8bf83283a068fbb58d0 by mmakevic <[email protected]>:
Use SetRNNDescriptor version 2
Merging this change closes #11784
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11784 from ROCm:ci_add_dropout_layer 52cf3ad49e70b3ecf632d8bf83283a068fbb58d0
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change the embedding API to enable the new sparse core preprocessing ops
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change the embedding API to enable the new sparse core preprocessing ops
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PR_kwDOArmXAs5tklMy
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[SparseCore Embedding] Add SparseCoreEmbeddingConfig to TPUEmbeddingV2.
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[SparseCore Embedding] Add SparseCoreEmbeddingConfig to TPUEmbeddingV2.
Reverts changelist 443785169
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Running the same model in TF and TFLiteMicro produces different outputs
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"hi @kratmanski-logi ,\r\n\r\nCan you check this [link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/layers/core.py) and let me know if this is the one you were looking for.",
"Hi,\r\nI checked your link, the core.py seem to have a Dense class reference, however it does not tell me how exactly 8x8bit multiplication (data X weights), adding 32bit bias, and rescaling is happening, on the same detail as in link I provided for Micro side (that line 65 for fully connected). My model is fixed point 8x8 but data X weights, 32bit bias, and since fixed point there is rescaling/zero centering before passing to the next layer. I need to see the reference fixed point computation. Thanks",
"Hi @kratmanski-logi ,\r\n\r\nCan you please check [this](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/quantized_matmul_op.cc) and [this link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/quantization_utils.cc#L20) and let me know if this is the one you were looking for.",
"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.",
"Thank you for your reply, Sawantkumar, I will check and get back to you"
] | 2024-04-24T08:08:54 | 2024-06-04T06:47:32 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
latest
### Custom code
No
### OS platform and distribution
both Linux and Windows
### Mobile device
_No response_
### Python version
latest
### Bazel version
_No response_
### GCC/compiler version
latest
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I have trained float point tf model and converted it to 8bit .tflite model. I run this .tflite model on the same test input in two environments, both on the same PC with linux (no ):
1) in tensorflow, installed as typically in linux via "pip install tensorflow" and then imported via "import tensorflow as tf" in python
2) in gcc in linux, after converting .tflite to flat buffer, where the C reference kernels are in https://github.com/tensorflow/tflite-micro/tree/main/tensorflow/lite/micro/kernels
Problem: Running the same .tflite model/same input in these two different environments produces different outputs. Given that the (very small and simple) model is already heavily quantized, I am expecting to get bit-exact output, whereas I observe the output difference that is beyond some sort of rounding error. In the process of debugging, I narrowed down to one of the kernels that produces not-bit-exact result: Fully_Connected. The source code for this kernel under environment (2) lives here (line 65):
https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/kernels/internal/reference/fully_connected.h
Main Question: Can you point me to the source code of fully_connected computations for the environment (1) so I can compare the computations to the ones for the environment (2) and figure out myself where the output difference comes from? I understand that in (2) python calls precompiled C/C++ kernels, and I would like to see the source code of fully_connected (and other standard kernels such as conv2d) that was used for pre-compiling.
Note: I think the following code is being used in environment (1), however it is still not at the deep level where i can clearly see the matrix multiplication -> add bias -> rescale sequence of fully_connected operations that I can compare with the source code above for the environment (2)
### Standalone code to reproduce the issue
```shell
https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/kernels/internal/reference/fully_connected.h
https://github.com/sourcecode369/tensorflow-1/blob/master/tensorflow/lite/kernels/cpu_backend_gemm_ruy.h
```
### Relevant log output
_No response_
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Support reusing calibration data if exists
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Support reusing calibration data if exists
Calibration is the most time-consuming step in quantization. This cl will help avoiding it in case of users already quantized it before and the changes in configuration if have doesn't affect the calibration results.
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PR #11760: [gpu] Change the implementation of GetFusionRoots to a simpler one
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PR #11760: [gpu] Change the implementation of GetFusionRoots to a simpler one
Imported from GitHub PR https://github.com/openxla/xla/pull/11760
Following the tuple -> get-tuple-element sequence for getting roots and remove allowing a duplicate root in case it is encountered twice in a row in the recursive search.
Copybara import of the project:
--
56c10695a2ff6937b67259b7d5037187e043bbcb by Shraiysh Vaishay <[email protected]>:
[gpu] Change the implementation of GetFusionRoots to a simpler one
There is no apparent requirement for this check. If the CI tests fail,
then I will revert this and work on a sophisticated implementation of
the function.
Merging this change closes #11760
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11760 from shraiysh:getfusionroots_fix 56c10695a2ff6937b67259b7d5037187e043bbcb
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How to turn off mlir during tensorflow2.13 compilation？
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[
"@s1mplezpc,\r\nCould you please provide the sequence of the steps you followed and also provide the complete error log which helps to debug the issue in an effective way. Thank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"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/66348\">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/66348\">No</a>\n"
] | 2024-04-24T02:59:10 | 2024-05-10T01:49:19 | 2024-05-10T01:49:16 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
r2.13-rocm-edhanced-57
### Custom code
Yes
### OS platform and distribution
ubuntu 18.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
.I want to turn off mlir, during compilation
### Standalone code to reproduce the issue
```shell
disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable
```
### Relevant log output
_No response_
|
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Why doesn't `The calling iterator did not fully read the dataset being cached.` appear on Google Colab?
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[
"Hi @miticollo ,\r\n\r\nColab usually filters Info and Warning logs. That's why you are not able to see it. The warning descriptions states if you are caching and then applying take() or batch() method. If your dataset is large caching the whole dataset might be a problem and can leads to truncation of dataset. This is just a warning to inform the user about possible data truncation.\r\n\r\nSince tf.data.Dataset is a pipeline object you can first batching the dataset and then caching.\r\n\r\n```\r\ndataset = tf.data.Dataset.from_tensor_slices((X_valid, y_valid))\r\ndataset = dataset.map(lambda x, y: (x, y), num_parallel_calls=tf.data.AUTOTUNE)\r\ndataset = dataset.batch(32)\r\ndataset = dataset.cache()\r\ndataset = dataset.prefetch(buffer_size=tf.data.AUTOTUNE)\r\ndataset = dataset.apply(tf.data.experimental.prefetch_to_device(device=\"/gpu:0\"))\r\n```",
"I tried to reduce dataset to 64 bytes and 70 bytes (I hope I did it correctly).\r\nThen I run the notebook different times and I noticed that the warning sometimes appears and sometimes not. \r\nI don't know why, could be an issue?\r\n\r\nAnyway these are outputs from 6 **sequential** runs (every time I turned off and then on the IPython runtime):\r\n- [1](https://colab.research.google.com/drive/10jdLHyeteRgy8QhRT2VLtH00qBWyY8ZA?usp=sharing): the warning appears twice.\r\n- [2](https://colab.research.google.com/drive/1MMNejSvhORnsG6pm71LbuWwbKwcR9mpU?usp=sharing): the warning appears only in the second case.\r\n- [3](https://colab.research.google.com/drive/19y-rRfu3R5ehWXgliYuxKGxbZYMqybAq?usp=sharing): see case 2.\r\n- [4](https://colab.research.google.com/drive/1FJSWWAdb2AK5oVdKCkVjmouUJBqcMyFE?usp=sharing): see case 2.\r\n- [5](https://colab.research.google.com/drive/1Lo_XIqAsgiyZorDXTlCXWXwC3dLm_vGa?usp=sharing): see case 2.\r\n- [6](https://colab.research.google.com/drive/1Jqja1rHOuDZ5IXwYkGSkhzlIHlhDHDAF?usp=sharing): see case 1.",
"It seems to working as intended. Cache() operator will collect the data from upstream into an in-memory cache as the downstream iterator is pulling the data elements. As the user iterates through the full dataset, all of it will get processed and loaded into the in-memory cache.\r\n\r\nThe Colab version is running in eager mode and is waiting on further user inputs while holding the partial dataset in the cache. When running the notebook in a shell, it is evaluating the full session and given that the code only asks for a single element, it hasn't iterated through the full dataset. The warning is just informing the user that the cached data (i.e. data read so far will get discarded) and the iterators reset to the beginning.\r\n\r\nIf you were to replace:\r\n```\r\nx_batch, y_batch = next(iter(dataset))\r\n```\r\nwith:\r\n```\r\nfor (x_batch, y_batch) in dataset:\r\n pass\r\n```\r\nYou shouldn't see this warning in the shell execution.\r\n"
] | 2024-04-24T01:32:40 | 2024-05-02T08:43:58 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
v2.16.1-0-g5bc9d26649c 2.16.1
### Custom code
Yes
### OS platform and distribution
Linux Manjaro
### Mobile device
_No response_
### Python version
3.11.8
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
CUDA Version: 12.4 (from `optirun nvidia-smi`)
### GPU model and memory
_No response_
### Current behavior?
[Here](https://colab.research.google.com/drive/1f3E2HyZ4bBNx-EHOxi3cLoIBBRdVCZZz?usp=sharing) the output running the notebook on my laptop.
Below you can find another Colab link that shows that the warning (see **Relevant log output**) doesn't appear on Google Colab.
I don't know if this is an issue. Maybe my laptop is not quite powerful or maybe the culprit is the Python version: Colab uses Python 3.10 while I tested the notebook on Python 3.11.8.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1Ns5P1aPDcFjuoPohSDO567rPL1jluSun?usp=sharing
```
### Relevant log output
```shell
W tensorflow/core/kernels/data/cache_dataset_ops.cc:858] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
```
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PR_kwDOArmXAs5tib88
| 66,346 |
Integrate LLVM at llvm/llvm-project@688c10d23630
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Integrate LLVM at llvm/llvm-project@688c10d23630
Updates LLVM usage to match
[688c10d23630](https://github.com/llvm/llvm-project/commit/688c10d23630)
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[pjrt] Add a run time check to guarantee that only one unique future created from a promise for move-only types
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[pjrt] Add a run time check to guarantee that only one unique future created from a promise for move-only types
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[pjrt] Remove error-prone PjRtFuture constructor taking AynsValueRef directly
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[pjrt] Remove error-prone PjRtFuture constructor taking AynsValueRef directly
For move-only types it's impossible to guarantee correct PjRtFuture::Await and OnReady semantics if we allow constructing futures from async values directly
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[pjrt] Add OnReady overload for unique (move-only) futures
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[XLA:GPU][MLIR-based emitters] Add verifier for apply_indexing op.
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[XLA:GPU][MLIR-based emitters] Add verifier for apply_indexing op.
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Add macros to disable tests on GRM
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NONE
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Add macros to disable tests on GRM
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Add `IsPredeterminedError` to annotate an error buffer which doesn't have any events on the device.
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NONE
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Add `IsPredeterminedError` to annotate an error buffer which doesn't have any events on the device.
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Stabilize flaky test & tolerances
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Stabilize flaky test & tolerances
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PR_kwDOArmXAs5tiFKu
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adding support for Composite ops in TFLite flatbuffer schema
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adding support for Composite ops in TFLite flatbuffer schema
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Integrate LLVM at llvm/llvm-project@f426be195a08
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Integrate LLVM at llvm/llvm-project@f426be195a08
Updates LLVM usage to match
[f426be195a08](https://github.com/llvm/llvm-project/commit/f426be195a08)
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Support reduction side outputs.
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[pjrt] Add support for awaiting on move-only (unique) PjRtFutures
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XProf GPU: Cache CUPTI activity buffer during tracing, process them after tracing is stopped.
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Add a new op to convert the sorted coo tensor into sparse core CSR wrapped COO format.
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Add a new op to convert the sorted coo tensor into sparse core CSR wrapped COO format.
Reverts 2ba594da18e8d016494ec91a9d2e973991a4ce72
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11721 from inailuig:mpicollectives_pytype 3924cc0fbbb63e9503f38a59aede3b8e817b17fa
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PR_kwDOArmXAs5thx6M
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#tf-data Support save/load for cache dataset random access iterators.
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#tf-data Support save/load for cache dataset random access iterators.
Moved `IteratorRandomAccessCache` from the iterator to the dataset so
the cache is preserved after iterator restoration.
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[PJRT] Fork the GPU client out of `xla.cc` and adjust some test imports.
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[PJRT] Fork the GPU client out of `xla.cc` and adjust some test imports.
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PR_kwDOArmXAs5thlP1
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Reverts 2ba594da18e8d016494ec91a9d2e973991a4ce72
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NONE
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Reverts 2ba594da18e8d016494ec91a9d2e973991a4ce72
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PR_kwDOArmXAs5thjno
| 66,329 |
[XLA] Avoid crash in LatencyHidingScheduler when it fails to schedule a node and improve error message.
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NONE
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[XLA] Avoid crash in LatencyHidingScheduler when it fails to schedule a node and improve error message.
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| 2,259,767,262 |
PR_kwDOArmXAs5thebn
| 66,328 |
Update the comment and error message.
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NONE
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Update the comment and error message.
The selector doesn't have to be round-robin to reach the comment or error.
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PR_kwDOArmXAs5thb7M
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[XLA:GPU][MLIR-based emitters] Add xla_gpu.apply_indexing op.
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[XLA:GPU][MLIR-based emitters] Add xla_gpu.apply_indexing op.
This first CL just adds printer/parser.
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Make ThreadpoolEventCollector instance static to prevent access after deletions.
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Make ThreadpoolEventCollector instance static to prevent access after deletions.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12896 from openxla:cleanup 32ded36419a4a1b2177a6374846497a8bb458ac9
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PR_kwDOArmXAs5thUjP
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Add a SortListOfSparseCoreCooTensors op.
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Add a SortListOfSparseCoreCooTensors op.
Reverts 04aa434c0e5a011e980c9b42f4da489834b054de
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Python bindings for tensorflow to stablehlo tooling
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Python bindings for tensorflow to stablehlo tooling
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Remove unused `requires-gpu` tag from `tf_gpu_test_tags`
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Remove unused `requires-gpu` tag from `tf_gpu_test_tags`
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[tsl:concurrency] Fix a potential data race when emplacig into all_allocated_elements_ vector
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[tsl:concurrency] Fix a potential data race when emplacig into all_allocated_elements_ vector
Reverts 04aa434c0e5a011e980c9b42f4da489834b054de
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PR_kwDOArmXAs5tg2w6
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[XLA] Fix an infinite loop in MSA.
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[XLA] Fix an infinite loop in MSA.
The bug was due to use time definitions being different in AddRequiredAssignment
vs AllocateAllocationAssignment.
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Refactor memory_kind and layout utils to take a HloModuleProto
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Refactor memory_kind and layout utils to take a HloModuleProto
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PR_kwDOArmXAs5tg02Y
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Add donation attr to ReshardOp and pass that verifies donations are set correctly.
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Add donation attr to ReshardOp and pass that verifies donations are set correctly.
The pass verifies that:
1) No array is donated more than once.
2) If an array that is an input to the IR program is donated, then it must have the ifrt.donated attribute set.
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Re-introduced EuclideanNorm TF-XLA kernel.
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NONE
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Re-introduced EuclideanNorm TF-XLA kernel.
Originally PR #41916
Reverts changelist 326343156
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[tsl:concurrency] Add AsyncValueRef::FlatMap to be able to use functors returning async values
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[tsl:concurrency] Add AsyncValueRef::FlatMap to be able to use functors returning async values
Reverts 04aa434c0e5a011e980c9b42f4da489834b054de
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Remove call to StreamExecutor::implementation in rocm_executor.cc now that all StreamExecutors inherit from StreamExecutorInterface.
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Remove call to StreamExecutor::implementation in rocm_executor.cc now that all StreamExecutors inherit from StreamExecutorInterface.
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Align tensorflow-tpu and libtpu versions for release consistency
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Align tensorflow-tpu and libtpu versions for release consistency
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[pjrt] Make PjRtFuture<T> for move-only T also move-only type
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NONE
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[pjrt] Make PjRtFuture<T> for move-only T also move-only type
More CLs will follow to use rvalue qualified methods for OnReady and Await.
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Move tsl/concurrency to xla/tsl/concurrency
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NONE
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Move tsl/concurrency to xla/tsl/concurrency
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PR_kwDOArmXAs5tgTjZ
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Add platform_version to GpuTopology.
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Add platform_version to GpuTopology.
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Update GpuTopologyProto to include multi-host information.
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Update GpuTopologyProto to include multi-host information.
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[XLA:GPU] Avoid segfault in serializing autotune results.
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NONE
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[XLA:GPU] Avoid segfault in serializing autotune results.
In some cases, the debug options reference may become invalid in
GpuCompiler::RunHloPasses, at which point serializing segfaults upon access to
the debug options.
The reason for the invalid reference is not clear, as all methods operate on
unique_ptr::get(), not on raw pointers that would invalidate it, but taking a
copy of a modest-sized variable is not the end of the world from a performance
perspective even if it's not ideal.
A never-used call to GetAutotuneConfig is also removed from RunHloPasses.
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Add Adreno 750 to GPU info
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Add Adreno 750 to GPU info
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Issue 65632
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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/66308/checks?check_run_id=24164887942) 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.",
"This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.",
"Hi @bmoroch Can you please check the above [comment](https://github.com/tensorflow/tensorflow/pull/66308#pullrequestreview-2051218939) from @mihaimaruseac and keep us posted? Thank you!"
] | 2024-04-23T17:11:10 | 2024-06-01T11:04:15 | 2024-06-01T11:04:10 |
NONE
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Added fix for issue #65632: Check failed in tf.strings.unsorted_segment_join when the rank of inputs is larger than 1 and the value of num_segments is too large.
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[pjrt] Add OnReady overload for unique (move-only) futures
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[pjrt] Add OnReady overload for unique (move-only) futures
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This CL adds support for handling the SPMDFullToShardShape and SPMDShardToFullShape custom calls in auto-sharding.
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This CL adds support for handling the SPMDFullToShardShape and SPMDShardToFullShape custom calls in auto-sharding.
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[tsl:concurrency] NFC: Add a benchmark for AsyncValuePtr<T>::Map(executor, ...)
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[tsl:concurrency] NFC: Add a benchmark for AsyncValuePtr<T>::Map(executor, ...)
---------------------------------------------------------------------
Benchmark Time CPU Iterations
---------------------------------------------------------------------
BM_MapIntToFloat 38.3 ns 38.3 ns 18232592
BM_MapIntToFloatOnExecutor 53.1 ns 53.1 ns 13204614
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[tsl:concurrency] Correctly form a strong reference to async values when running on executor
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[tsl:concurrency] Correctly form a strong reference to async values when running on executor
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PR #11603: [ROCm] Configure pjrt gpu plugin for rocm
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PR #11603: [ROCm] Configure pjrt gpu plugin for rocm
Imported from GitHub PR https://github.com/openxla/xla/pull/11603
Configure PJRT GPU plugin so it can be built for ROCm as well.
Copybara import of the project:
--
a1c8bcb4be41dc56899118d44bf604a2723a3c56 by mmakevic <[email protected]>:
Configure pjrt gpu plugin for rocm
--
9ca24357f52c53febb474c798c67d0b8dda586ee by mmakevic <[email protected]>:
Change platform name defining
Merging this change closes #11603
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11603 from ROCm:rocm_gpu_plugin 9ca24357f52c53febb474c798c67d0b8dda586ee
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I_kwDOArmXAs6GqRP6
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I think we should use a separate api token named for this view. We may have additional clients of the APIs in the future, and we should be able to make the decision to limit one of the other of these endpoints from those additional clients.
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"@Darknight1500 Could you provide more context on the issue reported here?\r\nThank you!",
"since the spam captured me by @mention and I'm one of the datatracker's lead maintainers, I'll confirm for you that this is simply a cut-paste from the datatracker's issue tracker and is not related to tensorflow at all. I recommend handling this as you would handle any other spam issues.",
"@rjsparks Thank you for your response here. Closing this issue for now."
] | 2024-04-23T15:53:14 | 2024-05-08T04:46:33 | 2024-05-08T04:46:33 |
NONE
| null | null | null |
I think we should use a separate api token named for this view. We may have additional clients of the APIs in the future, and we should be able to make the decision to limit one of the other of these endpoints from those additional clients.
_Originally posted by @rjsparks in https://github.com/ietf-tools/datatracker/pull/7291#issuecomment-2037572500_
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PR #11313: Use CHECK-DAG to validate low and high padding mlir
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PR #11313: Use CHECK-DAG to validate low and high padding mlir
Imported from GitHub PR https://github.com/openxla/xla/pull/11313
Copybara import of the project:
--
98ee3b456f62a9404bfaf976ab9a2d25415293cc by Harsha HS <[email protected]>:
Distinguish order of padding operation between CUDA and ROCm
--
6f318827a4f2f31938349e24d292966fc6b95b97 by Harsha HS <[email protected]>:
Use CHECK-DAG to check for low and high padding mlir
--
8a3da357c20a865dc91f090d6e600ade492c3e7f by Harsha HS <[email protected]>:
Add -no_gpu tag to the test, so that it is picked by CI
--
f13a4003aedb5a005c3b0668c474da30b46fe5e2 by Harsha HS <[email protected]>:
Revert "Add -no_gpu tag to the test, so that it is picked by CI"
This reverts commit 8a3da357c20a865dc91f090d6e600ade492c3e7f.
--
9fca062f688dca198e232b1cfcd04eba3a8d99d2 by Harsha HS <[email protected]>:
Remove tags as it is covered by CPU CI
Merging this change closes #11313
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11313 from ROCm:ci_fix_elemental_hlo2mlir_test_20240408 9fca062f688dca198e232b1cfcd04eba3a8d99d2
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[stream_executor:host] Add HostExecutionEngine
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] | 2024-04-23T15:45:32 | 2024-05-07T19:35:53 | 2024-05-07T19:35:50 |
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[stream_executor:host] Add HostExecutionEngine
This is a part of the ongoing effort https://github.com/openxla/xla/issues/7234
Add:
- RuntimeExecutionEngine class which compiles LLVM IR into kernel. This is a simplified copy-paste from runtime/execution_engine
- HostExecutionEngine virtual base class which contains JIT-compiled function.
- LlvmExecutionEngine, which uses two classes above to compile LLVM IR and store result.
- Dummy CppExecutionEngine for kernels written in C++ and precompiled together with XLA.
- HostKernelTest.LlvmAddition test which verifies only compilation (for now).
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shlo_ref: Add convolution op and unit tests
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"Hi @LokeshReddyOVS-MCW This PR is in draft, any update on this? Please. Thank you!"
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- Adds initial version of convolution op
- Adds unit tests for convolution op
CC: @rascani @qukhan
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#shlo_ref Inline the definitions of arithmetic operators for the F16 shim.
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[] | 2024-04-23T14:03:42 | 2024-04-23T15:48:04 | 2024-04-23T15:48:03 |
NONE
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#shlo_ref Inline the definitions of arithmetic operators for the F16 shim.
This would fail to compile because:
> On a friend function template declaration, default template arguments are allowed only if the declaration is a definition, and no other declarations of this function appear in this translation unit.
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I_kwDOArmXAs6GpHHX
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Error in computation = RQAComputation.create(settings, verbose=True) when I use HPC. However, I do not encounter any error when I use my personal laptop for the same code and installing the same package : from pyrqa.computation import RQAComputation
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[] | 2024-04-23T13:43:26 | 2024-04-23T15:21:42 | 2024-04-23T15:21:42 |
NONE
| null | null | null |
Traceback (most recent call last):
File "/home/n/nnq2/PhD_env/RPs/RPs.py", line 139, in
computation = RQAComputation.create(settings,
File "/alice-home/3/n/nnq2/PhD_env/py38_example/lib64/python3.9/site-packages/pyrqa/computation.py", line 98, in create
return RQAEngine(settings,
File "/alice-home/3/n/nnq2/PhD_env/py38_example/lib64/python3.9/site-packages/pyrqa/variants/rqa/radius/engine.py", line 87, in init
self.__initialise()
File "/alice-home/3/n/nnq2/PhD_env/py38_example/lib64/python3.9/site-packages/pyrqa/variants/rqa/radius/engine.py", line 93, in __initialise
self.validate_opencl()
File "/alice-home/3/n/nnq2/PhD_env/py38_example/lib64/python3.9/site-packages/pyrqa/variants/engine.py", line 80, in validate_opencl
self.opencl = OpenCL(verbose=self.verbose)
File "/alice-home/3/n/nnq2/PhD_env/py38_example/lib64/python3.9/site-packages/pyrqa/opencl.py", line 77, in init
self.__initialise()
File "/alice-home/3/n/nnq2/PhD_env/py38_example/lib64/python3.9/site-packages/pyrqa/opencl.py", line 91, in __initialise
self.create_environment()
File "/alice-home/3/n/nnq2/PhD_env/py38_example/lib64/python3.9/site-packages/pyrqa/opencl.py", line 277, in create_environment
self.set_platform()
File "/alice-home/3/n/nnq2/PhD_env/py38_example/lib64/python3.9/site-packages/pyrqa/opencl.py", line 386, in set_platform
platforms = cl.get_platforms()
pyopencl._cl.LogicError: clGetPlatformIDs failed: PLATFORM_NOT_FOUND_KHR
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`libnum` has build errors on MacOS on both Arm and x86 CPUs, hence removed from TensorFlow build.
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`libnum` has build errors on MacOS on both Arm and x86 CPUs, hence removed from TensorFlow build.
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Fix HostOffloader's assumption that an "annotation" always has users.
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[] | 2024-04-23T12:29:22 | 2024-05-03T19:38:16 | 2024-05-03T19:38:14 |
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Fix HostOffloader's assumption that an "annotation" always has users.
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