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[PJRT][IFRT] Update PJRT, IFRT, and Py executable getters to return PjRtLayouts
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Add new overload of `GraphToFunctionDef()` that can consume the graph.
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Add new overload of `GraphToFunctionDef()` that can consume the graph.
Previously, we copied all of the node data from the input subgraph to the `FunctionDef`. This change reduces the memory footprint of multi-device function instantiation by (where possible) `std::move`-ing the bulk of the node data from the input graph to newly created `NodeDef` members of the resulting `FunctionDef` protocol buffer.
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Increase memory for ASAN test.
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Increase memory for ASAN test.
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PR_kwDOArmXAs5qSGVm
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[xla:gpu] Simplify the search for operand slices in DynamicAddressComputationFusion
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Add constants for non-batching cost
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Add constants for non-batching cost
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[xla:gpu] Support DUS in DynamicAddressComputationFusion
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Fix api generation?
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Fix api generation?
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Strip memory space from xla layout inside `PjRtLayout`. PJRT tracks memory space as a different field.
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Strip memory space from xla layout inside `PjRtLayout`. PJRT tracks memory space as a different field.
Stripping is done by setting the memory space to default memory space
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| 2,198,494,916 |
PR_kwDOArmXAs5qRgWb
| 64,084 |
Migrate to new header
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Migrate to new header
No functional change is intended, just a refactoring.
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PR_kwDOArmXAs5qRe_g
| 64,083 |
Integrate LLVM at llvm/llvm-project@407937036fa7
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Integrate LLVM at llvm/llvm-project@407937036fa7
Updates LLVM usage to match
[407937036fa7](https://github.com/llvm/llvm-project/commit/407937036fa7)
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PR_kwDOArmXAs5qReik
| 64,082 |
Fix typo in compile_mlir_util deprecation description.
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Fix typo in compile_mlir_util deprecation description.
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I_kwDOArmXAs6DChp0
| 64,081 |
slient overflow occurs in tf.range leading to incorrect result, here is a possible fix
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[
"Your analysis and solution seem valid. Open a PR if you'd like, otherwise I can handle it",
"I am afraid I don't have time in these few days, I would appreciate it if you can help me to open one.",
"Hi @QuantumCoder4 , @Aloqeely \r\n\r\nI acknowledge the issue of overflow and attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/7afd496e9453d4815948e8527be9d740/64081.ipynb) for reference.The proposed fix might work it seems. Please feel free to raise a PR.",
"Hi @Aloqeely ,\r\n\r\nI have a question. What happens if the value of delta =1 here keeping other two params same. It still might cause overflow ?",
"Line 83 in `sequence_ops.cc`:\n```python\nif (delta > 0) {\n OP_REQUIRES(\n context, start <= limit,\n errors::InvalidArgument(\n \"Requires start <= limit when delta > 0: \", start, \"/\", limit));\n }\n```\nIn that case, this if statement would get triggered since delta = 1, and then `start <= limit` would get evaluated to `false` so the function will raise an error",
"The proposed fix above won't work - it leads to different behavior. See the discussion in the (now closed) PR #64144.\r\n\r\nSomeone can pick that up if they like."
] | 2024-03-20T20:15:57 | 2024-04-03T14:59:33 | 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
_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?
Following the documentation https://www.tensorflow.org/api_docs/python/tf/range, tf.range should have consistent result with np.arange.
However, given the following inputs, tf.range outputs `tf.Tensor([1136033460], shape=(1,), dtype=int32)`, which is **incorrect**
The correct result is np.arange's output `[1136033460 -713794229]`.
I did some analysis and find the root cause lies in here:
https://github.com/tensorflow/tensorflow/blob/06e37b8d7f2ba0338efd0f588f068122414f0722/tensorflow/core/kernels/sequence_ops.cc#L100
When computing the `limit - start`, the actual value overflows.
To solve this problem, we can change the original code to a new one by applying the division first
origin:
```
Eigen::numext::ceil(Eigen::numext::abs((limit - start) / delta));
```
new:
```
Eigen::numext::ceil(Eigen::numext::abs(limit / delta - start / delta);
```
### Standalone code to reproduce the issue
```shell
import numpy as np
import tensorflow as tf
start = 1136033460
end = -2110457150
step = -1849827689
out = tf.range(start, end, step)
print(out)
out_np = np.arange(start, end, step)
print(out_np)
```
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5qRbPo
| 64,080 |
Adjust singleCompileOp to compile with old bridge.
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Adjust singleCompileOp to compile with old bridge.
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PR_kwDOArmXAs5qRWl_
| 64,079 |
Fix JAX PjRT Clients using XLA pins from 12/2023.
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Fix JAX PjRT Clients using XLA pins from 12/2023.
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PR_kwDOArmXAs5qRRmt
| 64,078 |
Move `tsl/python` to `xla/tsl/python`
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Move `tsl/python` to `xla/tsl/python`
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PR_kwDOArmXAs5qRFFN
| 64,077 |
[xla:gpu] Enable command buffers for FFI handlers registered as compatible
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[xla:gpu] Enable command buffers for FFI handlers registered as compatible
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I_kwDOArmXAs6DB8Rn
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Unexpected steps_per_epoch behavior in model.fit
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[
"@varshad18 Could you please double-check your calculation for steps_per_epoch. Kindly ensure it considers the total number of samples in your dataset and the batch size. \r\nIn order to expedite the trouble-shooting process, please provide a code snippet to reproduce the issue reported here. Thank you!",
"epochs = 300 \r\nBATCH_SIZE = 4\r\ntrain_size = trainImageTotalData\r\nvalid_size = validationImageTotalData\r\n\r\n# Calculate steps per epoch and validation steps\r\nsteps_per_epoch = train_size // BATCH_SIZE\r\nvalidation_steps = valid_size // BATCH_SIZE\r\n\r\n# Optionally, you can adjust steps_per_epoch and validation_steps based on whether your dataset is shuffled\r\n# If your dataset is shuffled during training, you might want to set steps_per_epoch to None\r\n# and let it automatically determine the number of steps based on the dataset size and batch size\r\n\r\n# hist = model.fit(\r\n# x=[trainNumericData, trainImagesSBData, trainImagesCBData, trainImagesWBData, trainImagesHBData,trainImagesLLData,trainImagesLBData, trainImagesUpLeftABData,trainImagesUpRightABData, trainImagesALeftLData, trainImagesARightLData], \r\n# y=trainAllRegressionData, \r\n# epochs=epochs,\r\n# validation_data=([validationNumericData, validationImagesSBData, validationImagesCBData, validationImagesWBData, validationImagesHBData, validationImagesLLData,validationImagesLBData, validationImagesUpLeftABData, validationImagesUpRightABData, validationImagesALeftLData, validationImagesARightLData], validationAllRegressionData),\r\n# steps_per_epoch=steps_per_epoch, # Set steps_per_epoch\r\n# validation_steps=validation_steps, # Set validation_steps\r\n# callbacks=[mc, tensorboard_callback]).history\r\n\r\n# Instead of manually setting steps_per_epoch and validation_steps, you can let it automatically determine based on the dataset size and batch size\r\nhist = model.fit(\r\n x=[trainNumericData, trainImagesSBData, trainImagesCBData, trainImagesWBData, trainImagesHBData,trainImagesLLData,trainImagesLBData, trainImagesUpLeftABData,trainImagesUpRightABData, trainImagesALeftLData, trainImagesARightLData], \r\n y=trainAllRegressionData, \r\n epochs=epochs,\r\n validation_data=([validationNumericData, validationImagesSBData, validationImagesCBData, validationImagesWBData, validationImagesHBData, validationImagesLLData,validationImagesLBData, validationImagesUpLeftABData, validationImagesUpRightABData, validationImagesALeftLData, validationImagesARightLData], validationAllRegressionData),\r\n callbacks=[mc, tensorboard_callback]).history\r\n",
"@varshad18 Could you please share the complete code in a notebook or gist to replicate the issue reported here?\r\nThank you!",
"@sushreebarsa I double-checked my calculation for steps_per_epoch and tried using the following formula:\r\n```\r\nBATCH_SIZE = 4\r\ntrain_size = trainImageTotalData\r\nvalid_size = validationImageTotalData\r\n\r\nprint(\"train size \" + str(train_size))\r\nprint(\"valid size \" + str(valid_size))\r\nprint(\"batch size \" + str(BATCH_SIZE))\r\nsteps_per_epoch = (train_size / BATCH_SIZE)\r\nvalidation_steps = (valid_size / BATCH_SIZE)\r\nprint(\"steps_per_epoch =\"+str(steps_per_epoch))\r\nprint(\"validation_steps =\"+str(validation_steps))\r\n\r\nsteps_per_epoch = math.ceil(steps_per_epoch)\r\n#steps_per_epoch=steps_per_epoch-1\r\nvalidation_steps = math.ceil(validation_steps)\r\n#validation_steps=validation_steps-1\r\nprint(\"steps_per_epoch =\"+str(steps_per_epoch))\r\nprint(\"validation_steps =\"+str(validation_steps))\r\n\r\n```\r\n> train size 714\r\n> valid size 89\r\n> batch size 4\r\n> steps_per_epoch =178.5\r\n> validation_steps =22.25\r\n> steps_per_epoch =179\r\n> validation_steps =23\r\n\r\nThis worked for me and is training for all 179 steps with no errors. But the most common approach is to simply exclude the last incomplete batch from training during an epoch and here if I try to exclude the last batch by `(steps_per_epoch-1)` I get an error as follows\r\n\r\n\r\n> KeyError: 'Failed to format this callback filepath: \"/content/drive/MyDrive/FashionBody/Regression/TrainingRun/300Run2.0/checkpoint-{epoch:02d}-{val_loss:.2f}.tf\". Reason: \\'val_loss\\''\r\n\r\nIs it okay to train 179 steps according to my train size? Or am I doing something wrong?",
"@varshad18 Could you please confirm if you are still using Keras 2 ? If so then please migrate to Keras 3 and follow this documentation [here](https://keras.io/guides/migrating_to_keras_3/). 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/64076\">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/64076\">No</a>\n"
] | 2024-03-20T18:59:59 | 2024-04-19T01:48:02 | 2024-04-19T01:47:54 |
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
Windows 11 x64
### Mobile device
_No response_
### Python version
3.10.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
According to [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit), under the Args of fit method:
- It mentions **_steps_per_epoch_** can have values as Integer or None.
- The default None is equal to the number of samples in your dataset divided by the batch size, or 1 if that cannot be determined.
So when I run the model.fit function without the steps_per_epoch assigned, and without the validation_steps, the model trains all epochs without error. But uses a very small number for steps_per_epoch.
## In my case:
- train size 714
- valid size 89
- batch size 4
- Steps_per_epoch must be =178 (using the formula train size // batch size)
- validation_steps must be=22, (using the formula valid size // batch size)
But it trains only for 23 steps each epoch.
## Questions
1. Why does it choose 23?
2. How does it use the data batches, as in does it select the first 23 batches only for every epoch or does it shuffle randomly?
3. Is all the data being trained?
4. On using steps_per_epoch and validation_steps running into [error](https://github.com/keras-team/keras/issues/19339#issue-2196808063)
### Standalone code to reproduce the issue
```shell
epochs = 300
BATCH_SIZE = 4
train_size = trainImageTotalData
valid_size = validationImageTotalData
steps_per_epoch = (train_size // BATCH_SIZE)-1
validation_steps = (valid_size // BATCH_SIZE)-1
hist = model.fit(
x=[trainNumericData, trainImagesSBData, trainImagesCBData, trainImagesWBData, trainImagesHBData,trainImagesLLData,trainImagesLBData, trainImagesUpLeftABData,trainImagesUpRightABData, trainImagesALeftLData, trainImagesARightLData], #x=trainImages, # images
y=trainAllRegressionData, #y=train["severity"], # severity target regression value
epochs=epochs,#steps_per_epoch=steps_per_epoch,
validation_data=([validationNumericData, validationImagesSBData, validationImagesCBData, validationImagesWBData, validationImagesHBData, validationImagesLLData,validationImagesLBData, validationImagesUpLeftABData, validationImagesUpRightABData, validationImagesALeftLData, validationImagesARightLData], validationAllRegressionData),
#validation_steps=validation_steps,
callbacks=[mc, tensorboard_callback]).history #callback for both checkpoints and tensorboard.
```
### Relevant log output
```shell
train size 714
valid size 89
batch size 4
steps_per_epoch =177
validation_steps =21
Steps per epoch for training is 177
[INFO] training model...
Epoch 1/10
23/23 [==============================] - ETA: 0s - loss: 5.9347
Epoch 1: val_loss improved from inf to 5.02212, saving model to /content/drive/MyDrive/FashionBody/Regression/TrainingRun/Run4/checkpoint-01-5.02.tf
23/23 [==============================] - 585s 8s/step - loss: 5.9347 - val_loss: 5.0221
Epoch 2/10
23/23 [==============================] - ETA: 0s - loss: 4.7514
Epoch 2: val_loss improved from 5.02212 to 5.01143, saving model to /content/drive/MyDrive/FashionBody/Regression/TrainingRun/Run4/checkpoint-02-5.01.tf
```
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Print out a warning if an instruction with misaligned sharding annotations.
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Print out a warning if an instruction with misaligned sharding annotations.
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Clean up some deps in flatbuffer_import, flatbuffer_export.
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Clean up some deps in flatbuffer_import, flatbuffer_export.
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Fix a test that fails when we enable small object optimization in swisstable.
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Fix a test that fails when we enable small object optimization in swisstable.
In this case, we don't call hash/eq on the first insertion with SOO so the crash doesn't take place on every insertion anymore.
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[MSA] Refactor AlternateMemoryBestFitHeap out of memory_space_assignment.
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[MSA] Refactor AlternateMemoryBestFitHeap out of memory_space_assignment.
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PR_kwDOArmXAs5qQs7a
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Minor changes that help debugability:
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Minor changes that help debugability:
1. Cast the printed memory budget into double to make it easy to compare it with the estimated minumum memory required, and thereby easy to compare.
2. Guard some checks when computing the memory lower bound within vlogs as the checks are often slow.
3. Increase the VLOG level for printing the memory usage of the solution as computing this also often takes a while.
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PR_kwDOArmXAs5qQr57
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PR #9896: Opaque types for NVTX domain and string handles
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PR #9896: Opaque types for NVTX domain and string handles
Imported from GitHub PR https://github.com/openxla/xla/pull/9896
This avoids having preprocessor checks on `GOOGLE_CUDA` inside some headers, which led to ODR violations.
Copybara import of the project:
--
22e65458993aea1a9b441e6c043f7fe315ff4cc3 by Olli Lupton <[email protected]>:
Opaque types for NVTX domain and string handles
This avoids having preprocessor checks on GOOGLE_CUDA inside some
headers, which led to ODR violations.
Merging this change closes #9896
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/9896 from olupton:opaque-nvtx 22e65458993aea1a9b441e6c043f7fe315ff4cc3
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Add debug logging when resharding may be needed.
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Add debug logging when resharding may be needed.
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PR #10747: [XLA:CPU][oneDNN][Bugfix] Fix FLOPs count for dot in oneDNN MatMul Rewriter
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PR #10747: [XLA:CPU][oneDNN][Bugfix] Fix FLOPs count for dot in oneDNN MatMul Rewriter
Imported from GitHub PR https://github.com/openxla/xla/pull/10747
This PR fixes a bug in MAC (multiply-accumulate) / FMA(fused-multiply-add) count in dot operation when B matrix is transposed.
Copybara import of the project:
--
d989fdba6beaff1f0deed1fdee80792f7ef03089 by mdfaijul <[email protected]>:
Fix MAC(Multiply Accumulate) count
Merging this change closes #10747
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10747 from Intel-tensorflow:amin/fix-mac-count d989fdba6beaff1f0deed1fdee80792f7ef03089
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Optimize XLA Compiler.
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Optimize XLA Compiler.
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Allow Shardonnay to use XLA targets.
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Allow Shardonnay to use XLA targets.
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PR_kwDOArmXAs5qQmXP
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PR #10730: [GPU] Reorganize compilation of cuDNN fusions.
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PR #10730: [GPU] Reorganize compilation of cuDNN fusions.
Imported from GitHub PR https://github.com/openxla/xla/pull/10730
As discussed recently this moves compiled cuDNN graphs from HLO attributes to a separate structure passed to GPU executable.
cuDNN backend is now explicitly disabled in deviceless compilation mode - in the GEMM fusion autotuner pass and in the GPU compiler.
Copybara import of the project:
--
e25ae7343bf08a88b0e917a10267d644dabde2d3 by Ilia Sergachev <[email protected]>:
[GPU] Reorganize compilation of cuDNN fusions.
Merging this change closes #10730
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10730 from openxla:cudnn_fusion_reorganize_compilation e25ae7343bf08a88b0e917a10267d644dabde2d3
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PR_kwDOArmXAs5qQLEe
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Bugfix, nit: do not use designated initializers for aggregate initialization.
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Bugfix, nit: do not use designated initializers for aggregate initialization.
Drive-by: Shard `onednn_softmax_test` test.
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PR_kwDOArmXAs5qQHDn
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Update documentation for using custom ops to recommend using the
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Update documentation for using custom ops to recommend using the
stable custom ops API.
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PR_kwDOArmXAs5qQDNH
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Update docs to make use of new API for adding a TfLiteRegistrationExternal to a MutableOpResolver.
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Update docs to make use of new API for adding a TfLiteRegistrationExternal to a MutableOpResolver.
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I_kwDOArmXAs6DAzzG
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Working code broke after deploying to new installation. ValueError: When using `stateful=True` in a RNN, the batch size must be static. Found dynamic batch size: sequence.shape=(None, xx, xx)
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"Hi **@summa-code** ,\r\n- Ensure that your input data has a static batch size. When using stateful=True, the batch size should not change between batches. You can achieve this by padding or truncating sequences to a fixed length.\r\n- When defining your LSTM layer, set stateful=True and specify the batch_input_shape parameter with a fixed batch size and sequence length.\r\nexample:\r\n```\r\nmodel.add(LSTM(units=64, batch_input_shape=(batch_size, sequence_length, input_dim), stateful=True))\r\n```\r\n- If you are using timeseries_dataset_from_array to prepare your data, make sure that all sequences have the same length. You can achieve this by setting the sequence_length parameter when creating the dataset.\r\nexample:\r\n```\r\ndataset = timeseries_dataset_from_array(data, targets, sequence_length=desired_length, batch_size=batch_size)\r\n```\r\n\r\nThank you!",
"So that was the working code, but after I updated to latest in fresh ubuntu install, \"batch_input_shape\" not a recognized attribute. \r\nThis is what I am getting\r\n\r\n```ValueError: Unrecognized keyword arguments passed to LSTM: {'batch_input_shape': (xxx, xxx, xxx)}```\r\n\r\nAnd yes, I am using sequence_length, please refer to timeseries example on this Tensorflow site.\r\nhttps://www.tensorflow.org/tutorials/structured_data/time_series",
"Facing the exact same issue with `tensorflow 2.16.1` and `Python 3.9.16`",
"Looks like either it is a bug or they got rid of the function, but either way, it is like a bug in how they configure the shape internally for statefulness. And also, there are no examples on how to use stateful LSTM in addition to lack of documentation on statefulness.",
"Anyone? ",
"Try using the [input layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/InputLayer) instead of batch_input_shape. \r\n\r\n`InputLayer(batch_shape=(1,None,4))`",
"If I add a input layer before LSTM layer, I get this,\r\n\r\nFile \"/.local/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py\", line 122, in error_handler\r\n raise e.with_traceback(filtered_tb) from None\r\n File \".local/lib/python3.11/site-packages/tensorflow/python/eager/execute.py\", line 53, in quick_execute\r\n tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\ntensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:\r\n\r\nDetected at node sequential_1/lstm_0_1/CudnnRNN defined at (most recent call last):\r\n\r\n File \"/.local/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\r\n\r\n File \"/.local/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py\", line 325, in fit\r\n\r\n File \"/.local/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py\", line 118, in one_step_on_iterator\r\n\r\n File \"/.local/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py\", line 106, in one_step_on_data\r\n\r\n File \"/.local/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py\", line 57, in train_step\r\n\r\n File \"/.local/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\r\n\r\n File \"/.local/lib/python3.11/site-packages/keras/src/layers/layer.py\", line 814, in __call__\r\n\r\n File \"/.local/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\r\n\r\n File \"/.local/lib/python3.11/site-packages/keras/src/ops/operation.py\", line 48, in __call__\r\n\r\n File \"/.local/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py\", line 156, in error_handler\r\n\r\n File \"/.local/lib/python3.11/site-packages/keras/src/models/sequential.py\", line 202, in call\r\n\r\n File \"/.local/lib/python3.11/site-packages/keras/src/models/functional.py\", line 194, in call\r\n\r\n File \"/.local/lib/python3.11/site-packages/keras/src/ops/function.py\", line 151, in _run_through_graph\r\n\r\n File \"/.local/lib/python3.11/site-packages/keras/src/models/functional.py\", line 578, in call\r\n\r\n File \"/.local/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\r\n\r\n File \"/.local/lib/python3.11/site-packages/keras/src/layers/layer.py\", line 814, in __call__\r\n\r\n File \"/.local/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\r\n\r\n File \"/.local/lib/python3.11/site-packages/keras/src/ops/operation.py\", line 48, in __call__\r\n\r\n File \"/.local/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py\", line 156, in error_handler\r\n\r\n File \"/.local/lib/python3.11/site-packages/keras/src/layers/rnn/lstm.py\", line 537, in call\r\n\r\n File \"/.local/lib/python3.11/site-packages/keras/src/layers/rnn/rnn.py\", line 398, in call\r\n\r\n File \"/.local/lib/python3.11/site-packages/keras/src/layers/rnn/lstm.py\", line 511, in inner_loop\r\n\r\n File \"/.local/lib/python3.11/site-packages/keras/src/backend/tensorflow/rnn.py\", line 823, in lstm\r\n\r\n File \"/.local/lib/python3.11/site-packages/keras/src/backend/tensorflow/rnn.py\", line 934, in _cudnn_lstm\r\n\r\nInvalid input_h shape: [1,512,48] [1,512,48]\r\n\t [[{{node sequential_1/lstm_0_1/CudnnRNN}}]] [Op:__inference_one_step_on_iterator_4048]\r\n",
"Hi @summa-code ,\r\n- Apologies for the dealy, In order to expedite the trouble-shooting process, please provide a code snippet to reproduce the issue reported here.\r\n\r\nThank you!\r\n "
] | 2024-03-20T16:50:17 | 2024-06-12T06:20:57 | null |
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
No
### OS platform and distribution
Ubuntu 22.04
### Mobile device
No
### Python version
3.10.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.4
### GPU model and memory
_No response_
### Current behavior?
So the existing code broke, with this error
ValueError: When using `stateful=True` in a RNN, the batch size must be static. Found dynamic batch size: sequence.shape=(None, XX, XX),
but when changed to (XX, XX, XX) I get this, ValueError: Input 0 of layer "lstm" is incompatible with the layer: expected ndim=3, found ndim=4. Full shape received: (None, 1, XX, XX)
If static shape is passed, not sure why additional dimension is added. Need help in resolving this. I tried both the release version and nightly version. The issue persists
And also the input is 3D tensor produced by timeseries_dataset_from_array function. Is there any example on training stateful LSTM using these 3D tensors? Not sure if this is a bug really. I am stuck.
### Standalone code to reproduce the issue
```shell
Dont have stand alone.
```
### Relevant log output
_No response_
|
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PR #10685: [ROCM] GPU profiling code refactor - seperarted rocm event tracer and…
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PR #10685: [ROCM] GPU profiling code refactor - seperarted rocm event tracer and…
Imported from GitHub PR https://github.com/openxla/xla/pull/10685
rocm profiler code refactored, Separated tracing and event collecting into rocm tracer (rocm_tracer.cc/h) and event collector (rocm_collector.cc/h).
Copybara import of the project:
--
9ada3c5629c208de530cf88beb934f401c8d99be by zahiqbal <[email protected]>:
[ROCM] GPU profiling code refactor - seperarted rocm event tracer and event collector.
Merging this change closes #10685
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10685 from ROCm:ci_rocm_profiler_code_refactor 9ada3c5629c208de530cf88beb934f401c8d99be
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PR_kwDOArmXAs5qPxnH
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Change pybind11_abseil `type_caster` `const_name` `Span` to `Sequence`.
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Change pybind11_abseil `type_caster` `const_name` `Span` to `Sequence`.
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graphcycles: extract NodeIO out of Node
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graphcycles: extract NodeIO out of Node
To improve cache locality when doing DFS.
name old time/op new time/op delta
BM_StressTest/2048 492ns ± 2% 470ns ± 1% -4.38% (p=0.008 n=5+5)
BM_StressTest/4096 499ns ± 2% 478ns ± 1% -4.07% (p=0.008 n=5+5)
BM_StressTest/32768 512ns ± 2% 494ns ± 1% -3.52% (p=0.016 n=5+4)
BM_StressTest/262144 558ns ± 2% 537ns ± 1% -3.77% (p=0.016 n=5+4)
BM_StressTest/1048576 621ns ± 2% 692ns ±23% ~ (p=0.690 n=5+5)
BM_ContractEdge/1000 104ns ± 2% 106ns ± 5% ~ (p=0.286 n=5+4)
BM_ContractEdge/10000 119ns ± 2% 119ns ± 1% ~ (p=0.651 n=5+5)
BM_IsReachableNonConst/10 9.76ns ±10% 9.17ns ±12% ~ (p=0.095 n=5+5)
BM_IsReachableNonConst/50 15.4ns ±11% 13.6ns ± 5% -11.41% (p=0.032 n=5+5)
BM_IsReachableNonConst/100 17.5ns ±13% 15.3ns ± 7% -12.66% (p=0.032 n=5+5)
BM_IsReachableNonConst/200 20.1ns ± 7% 18.1ns ± 9% -9.71% (p=0.016 n=5+5)
BM_IsReachableNonConst/1000 25.5ns ± 5% 22.7ns ± 3% -10.98% (p=0.008 n=5+5)
BM_IsReachableNonConst/30000 45.0ns ± 3% 39.2ns ± 3% -12.95% (p=0.008 n=5+5)
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Internal config change
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Internal config change
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| 2,197,948,973 |
PR_kwDOArmXAs5qPnmn
| 64,056 |
Allow shardonnay to use XLA targets.
|
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Allow shardonnay to use XLA targets.
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PR_kwDOArmXAs5qPlox
| 64,055 |
graphcycles_test: add BM_IsReachableNonConst
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graphcycles_test: add BM_IsReachableNonConst
name time/op
BM_StressTest/2048 489ns ± 5%
BM_StressTest/4096 501ns ± 6%
BM_StressTest/32768 521ns ± 4%
BM_StressTest/262144 575ns ±11%
BM_StressTest/1048576 614ns ± 2%
BM_ContractEdge/1000 129ns ± 2%
BM_ContractEdge/10000 148ns ± 1%
BM_IsReachableNonConst/10 12.2ns ± 7%
BM_IsReachableNonConst/50 19.9ns ±14%
BM_IsReachableNonConst/100 24.7ns ± 7%
BM_IsReachableNonConst/200 26.3ns ± 3%
BM_IsReachableNonConst/1000 37.3ns ± 5%
BM_IsReachableNonConst/30000 66.4ns ± 3%
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PR_kwDOArmXAs5qPlc6
| 64,054 |
graphcycles_test: use batch mode in benchmarks
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graphcycles_test: use batch mode in benchmarks
So that results are easier to interpret and compare.
Before:
----------------------------------------------------------------
Benchmark Time CPU Iterations
----------------------------------------------------------------
BM_StressTest/2048 989074 ns 988955 ns 707
BM_StressTest/4096 2043829 ns 2043653 ns 346
BM_StressTest/32768 17073324 ns 17071023 ns 40
BM_StressTest/262144 152278792 ns 152251000 ns 4
BM_StressTest/1048576 1403662723 ns 1397561773 ns 1
BM_ContractEdge/1000 122874 ns 122842 ns 5702
BM_ContractEdge/10000 1471461 ns 1471153 ns 477
After:
----------------------------------------------------------------
Benchmark Time CPU Iterations
----------------------------------------------------------------
BM_StressTest/2048 482 ns 482 ns 1394688
BM_StressTest/4096 494 ns 494 ns 1409024
BM_StressTest/32768 546 ns 546 ns 1409024
BM_StressTest/262144 568 ns 568 ns 1310720
BM_StressTest/1048576 1176 ns 1176 ns 1048576
BM_ContractEdge/1000 130 ns 130 ns 5271000
BM_ContractEdge/10000 150 ns 150 ns 4680000
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PR_kwDOArmXAs5qPlVg
| 64,053 |
graphcycles: keep nodes in a vector + move user_data out of it
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NONE
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graphcycles: keep nodes in a vector + move user_data out of it
This improves cache locality.
Note that user_data currently has no callers, so accessing it
quickly is not something we want to optimize for.
name old time/op new time/op delta
BM_StressTest/2048 474ns ± 1% 492ns ± 1% +3.63% (p=0.008 n=5+5)
BM_StressTest/4096 490ns ± 1% 497ns ± 1% +1.52% (p=0.008 n=5+5)
BM_StressTest/32768 504ns ± 1% 508ns ± 1% +0.86% (p=0.032 n=5+5)
BM_StressTest/262144 553ns ± 1% 557ns ± 3% ~ (p=0.548 n=5+5)
BM_StressTest/1048576 617ns ± 2% 616ns ± 2% ~ (p=0.690 n=5+5)
BM_ContractEdge/1000 121ns ± 2% 104ns ± 6% -13.56% (p=0.008 n=5+5)
BM_ContractEdge/10000 147ns ± 1% 119ns ± 1% -18.86% (p=0.008 n=5+5)
BM_IsReachableNonConst/10 10.4ns ±35% 9.3ns ± 6% ~ (p=0.310 n=5+5)
BM_IsReachableNonConst/50 15.8ns ±12% 15.4ns ±10% ~ (p=0.841 n=5+5)
BM_IsReachableNonConst/100 17.4ns ± 6% 17.5ns ±13% ~ (p=0.841 n=5+5)
BM_IsReachableNonConst/200 19.7ns ± 6% 19.5ns ± 6% ~ (p=0.690 n=5+5)
BM_IsReachableNonConst/1000 25.2ns ± 4% 24.9ns ± 6% ~ (p=0.690 n=5+5)
BM_IsReachableNonConst/30000 49.8ns ± 4% 44.3ns ± 5% -10.98% (p=0.008 n=5+5)
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PR_kwDOArmXAs5qPk1M
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graphcycles: drop Vec<>; use std::vector instead
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graphcycles: drop Vec<>; use std::vector instead
name old time/op new time/op delta
BM_StressTest/2048 487ns ± 1% 482ns ± 3% ~ (p=0.310 n=5+5)
BM_StressTest/4096 498ns ± 2% 491ns ± 0% ~ (p=0.111 n=5+4)
BM_StressTest/32768 523ns ± 1% 512ns ± 2% ~ (p=0.056 n=5+5)
BM_StressTest/262144 613ns ± 5% 588ns ±11% ~ (p=0.548 n=5+5)
BM_StressTest/1048576 758ns ± 3% 775ns ±26% ~ (p=1.000 n=5+5)
BM_ContractEdge/1000 127ns ± 3% 121ns ± 5% ~ (p=0.056 n=5+5)
BM_ContractEdge/10000 149ns ± 2% 146ns ± 1% -1.47% (p=0.032 n=5+5)
BM_IsReachableNonConst/10 12.1ns ± 9% 10.1ns ±11% -16.69% (p=0.016 n=5+5)
BM_IsReachableNonConst/50 20.6ns ±17% 14.7ns ±13% -29.00% (p=0.008 n=5+5)
BM_IsReachableNonConst/100 25.4ns ±20% 18.3ns ±13% -27.98% (p=0.008 n=5+5)
BM_IsReachableNonConst/200 30.6ns ±27% 20.1ns ± 6% -34.35% (p=0.008 n=5+5)
BM_IsReachableNonConst/1000 40.1ns ±27% 25.8ns ± 5% -35.71% (p=0.008 n=5+5)
BM_IsReachableNonConst/30000 67.1ns ± 2% 49.2ns ± 4% -26.77% (p=0.016 n=4+5)
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PR_kwDOArmXAs5qPdiT
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Update TFRT dependency to use revision
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Update TFRT dependency to use revision
http://github.com/tensorflow/runtime/commit/3d4d5024419cc94ad3898895ce43264105cb4620.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10636 from zhenying-liu:offloading/annotator 11148fe63588f58c85ab2530bda64807bf400476
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PR_kwDOArmXAs5qPY3p
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Open source IdentityPropogation pass
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PR_kwDOArmXAs5qPQw_
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Expose `.layout` on jax.Array. Also add checks in the AOT path to make sure that the input `Array`'s layout matches the layout given to `jax.jit`.
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Expose `.layout` on jax.Array. Also add checks in the AOT path to make sure that the input `Array`'s layout matches the layout given to `jax.jit`.
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PR_kwDOArmXAs5qO7IO
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Added clarification profiling pass that uses command buffers.
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Added clarification profiling pass that uses command buffers.
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PR_kwDOArmXAs5qOoKg
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Integrate Triton up to [92ac0aad](https://github.com/openai/triton/commits/92ac0aade352d7be626e8c608331ddebfe5f6fb2)
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NONE
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Integrate Triton up to [92ac0aad](https://github.com/openai/triton/commits/92ac0aade352d7be626e8c608331ddebfe5f6fb2)
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PR_kwDOArmXAs5qObbq
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PR #10722: Consolidate Unit Tests for GEMM Rewriter
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PR #10722: Consolidate Unit Tests for GEMM Rewriter
Imported from GitHub PR https://github.com/openxla/xla/pull/10722
Moves the unit tests in `xla/service/gpu/gemm_rewriter_test.cc` into `xla/service/gpu/tests/gemm_rewrite_test.cc`.
Copybara import of the project:
--
e3056a172238a256214d856cf3ad5cb44099e06a by Philipp Hack <[email protected]>:
Merge GEMM rewriter unit tests.
Merging this change closes #10722
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10722 from philipphack:u_merged_gemm_test_xla e3056a172238a256214d856cf3ad5cb44099e06a
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PR_kwDOArmXAs5qOZMN
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PR #10636: Offloading 1/3: Add annotation for copy-start/copy-done
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PR #10636: Offloading 1/3: Add annotation for copy-start/copy-done
Imported from GitHub PR https://github.com/openxla/xla/pull/10636
Add stream id in the backend_config of copy-start instruction. The stream id is obtained from hlo_query::NextChannelId().
The corresponding copy-done instruction which is the use of copy-start instruction will be traversed and added the stream id in the backend_config too. This part is automatically done by the subsequent AnnotateStreamAttributesForUsers() existing in the function.
The bool data member copy_start_done_ is used to differentiate copy-start/copy-done from other collective instructions and go through two different paths.
https://github.com/openxla/xla/pull/10450 is split and the current PR is the first 1 out of 3 PRs.
Copybara import of the project:
--
ff99c161a634b868d4265204d10d0b80adf2e772 by Jane Liu <[email protected]>:
Add annotation of stream id for copy-start and its use of copy-done instruction
--
64c746a5de6aa4c9370034ed712192dfd78e3a6e by Jane Liu <[email protected]>:
Enable the annotator for copy-start/copy-done in gpu compiler
--
7972b987e45cd165834483c4350e953094b5dbe8 by Jane Liu <[email protected]>:
Add the annotator pass after HLO rematerialization pass
--
2a9e317aac85e4de3f0ff5f1558885408ab4562d by Jane Liu <[email protected]>:
Add the dependency in BUILD
--
db5ed79f0ec90a1032252beff5108e4f01b61c4a by Jane Liu <[email protected]>:
Use a function to annotate copy-start and add description
--
b4b3932bfedfabc7f3e22a8766ecbc1fa1188402 by Jane Liu <[email protected]>:
remove the bool var copy_start from the StreamAttributeAnnotator class
--
11148fe63588f58c85ab2530bda64807bf400476 by Jane Liu <[email protected]>:
Fixes according to the code review
Merging this change closes #10636
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10636 from zhenying-liu:offloading/annotator 11148fe63588f58c85ab2530bda64807bf400476
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PR #10668: [XLA:GPU] Fix cuDNN FMHA rewriter sequence length checks and Bump up minimum flash attn cuDNN version to 8.9.4
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PR #10668: [XLA:GPU] Fix cuDNN FMHA rewriter sequence length checks and Bump up minimum flash attn cuDNN version to 8.9.4
Imported from GitHub PR https://github.com/openxla/xla/pull/10668
* If only seqlen_q or seqlen_kv is larger than 512, it will be lowered to fused attn instead of flash attn. This is incorrect as fused attn does not support this case. Add checks to lower to flash attn if one seqlen is larger than 512.
* While fixing the seqlen checks, also relax the seqlen/head dim constraints to support any seqlen % 2 == 0, head_dim <= 128 and head_dim % 8 = 0. (Since 8.9.4).
* Consolidate multiple get seqlen/head dim from fused attn/flash attn checks into one `getBHSD` function and rewrite both fused attn/flash attn checks to make it easier to understand.
* Bump up minimum cuDNN version require for flash attn from 8.9.3 to 8.9.4. (More seqlen/head dim support and cross attn support).
* Remove cross attn checks from rewriter and rewriter test since it is now default supported with 8.9.4.
* Add one testcase to cover the first bullet point.
Copybara import of the project:
--
bfdce45d669fe899dd845f497df783f014558384 by cjkkkk <[email protected]>:
rewrite seqlen checks
--
8a0e8b465cc1790953b1dc5f8740adfa7cf1124a by cjkkkk <[email protected]>:
return vector directly
--
72abf544d2f962c5554fe00ffea8d3167a9cde81 by cjkkkk <[email protected]>:
use struct for qkv_layout && add () for extra &&
Merging this change closes #10668
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10668 from Cjkkkk:rewrite_seqlen_checks 72abf544d2f962c5554fe00ffea8d3167a9cde81
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[IndexAnalysis] Add indexing map for DynamicUpdateSlice.
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[IndexAnalysis] Add indexing map for DynamicUpdateSlice.
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PR_kwDOArmXAs5qOJAQ
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Integrate LLVM at llvm/llvm-project@d93cfd8dab57
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Integrate LLVM at llvm/llvm-project@d93cfd8dab57
Updates LLVM usage to match
[d93cfd8dab57](https://github.com/llvm/llvm-project/commit/d93cfd8dab57)
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Add tflite_disable_mobile_test tag to mutable_op_resolver_utils_test.
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[XLA:GPU][IndexAnalysis] Add indexing map for gather.
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Remove missing `cuda.h` header from `mat_mul_op.cc`
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| 2,197,376,302 |
I_kwDOArmXAs6C-U0u
| 64,037 |
Add MetaGraphDef parser to C API to get SignatureDef (inputs / outputs) from saved model
|
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[
"related issue: #57802",
"@jagiella,\r\nThank you for the feature. The related issue https://github.com/tensorflow/tensorflow/issues/57802 is still in open state. I request you to take a look at the same issue where a similar feature has been proposed and it is still open and assigned to the develper. Also I request to follow the similar feature which has been proposed to have the updates on the similar issue. \r\n\r\nAnd I will try to re-check with the developer if there is any update on the same feature and will provide the progress on the same.\r\n\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"> This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.\r\n\r\nstill very relevant"
] | 2024-03-20T12:09:23 | 2024-04-03T09:28:13 | null |
NONE
| null | null | null |
### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.16
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Currently there is no way to retrieve the input / output operations / names for a given tensorflow/keras saved model. In c++ it exists.
### Standalone code to reproduce the issue
```shell
no code
```
### Relevant log output
_No response_
|
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I_kwDOArmXAs6C9xzr
| 64,036 |
How to open a Tensorflow <= 2.0 Legacy Model ?
|
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[
"Hi @asusdisciple ,\r\n\r\nWe are not supporting 1.x versions. For migration to Tf2.x please refer the attached guides [migrate_tf2](https://www.tensorflow.org/guide/migrate/migrate_tf2), [script](https://www.tensorflow.org/guide/migrate/upgrade) and other guides [here](https://www.tensorflow.org/guide/migrate).",
"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/64036\">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/64036\">No</a>\n"
] | 2024-03-20T10:54:00 | 2024-04-05T01:47:20 | 2024-04-05T01:47:17 |
NONE
| null | null | null |
So I have this really old model (2018) which I want to visualize in tensorboard. For that purpose I need to open/load it of course.
I have the following files:
checkpoint
model.data-00000-of-00001
model.index
model.meta
I have no model definition or anything else. I only find legacy approaches to open this with a session, which does not work anymore.
Is there a way to do it in tensorflow 2.0? I tried installing tensorflow 1.13 in a virtual environment but it's not working since a lot of dependencies on system side changed during the the years (cython, cuda, ...). Maybe you guys know a better way to do this.
Disclaimer: I have only a very very basic understanding of tensorflow, I really just want to visualize the model, no training or anything else.
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I_kwDOArmXAs6C9xm8
| 64,035 |
‘Val_loss’ is not generated and training is getting interrupted before the completion of the last epoch for a multi-input multi-output model. This is happening when my checkpoint directory is checkpoint_dir + "/checkpoint-{epoch:02d}.tf"
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[
"This is the [error](https://github.com/keras-team/keras/issues/19339#issue-2196808063) that we see when the checkpoint directory is checkpoint_dir + \"/checkpoint-{epoch:02d}-{val_loss:.2f}.tf\", ",
"@JuliaUReddy Thank you for raising this issue. \r\nIf the issue is the same as [this](https://github.com/keras-team/keras/issues/19339) one then we will track the issue in Keras repo. Meanwhile please ensure your checkpoint directory has enough space to store all checkpoints. If training is unexpectedly interrupted, the last checkpoint might be corrupted. Try deleting existing checkpoints and restarting training. Also try with the latest TF version?\r\nThank you!",
"@sushreebarsa its not the same issue as the issue #19339. Yes tried couple of times to by clearing out space in checkpoint directory but end up with the same issue.The main issue is that val_loss is not generated for all the epochs after the 1st epoch because of which there is an interruption. Yes tried with latest version of TF as well. Getting the same issue.",
"@JuliaUReddy You've set steps_per_epoch = 213 for training, indicating it expects 213 batches per epoch. Here the training gets interrupted due to \"Your input ran out of data\" warnings. This happens in both the first and ninth epoch. If your dataset is finite data then, use the repeat() method while building your dataset as follows for streaming continuous data;\r\n```\r\ndataset = your_dataset.repeat()\r\n```\r\nPlease let us know if that works?\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/64035\">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/64035\">No</a>\n"
] | 2024-03-20T10:53:43 | 2024-04-11T01:48:04 | 2024-04-11T01:48:00 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.12.0
### Custom code
Yes
### OS platform and distribution
MacOS Sonoma 14.1.2
### Mobile device
_No response_
### Python version
3.11.5
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I am trying to train a multi-input multi-output tensorflow model but the training gets interrupted(along with a warning) before the completion of the last epoch. Also after the 1st epoch, the val_loss is not getting generated for the rest of the following epochs.
This behavior is seen only when I give the checkpoint directory as checkpoint_dir + "/checkpoint-{epoch:02d}.tf" and if I give the directory as checkpoint_dir + "/checkpoint-{epoch:02d}-{val_loss:.2f}.tf", I get another another error mentioned here.
Both training and validation data are a list of numpy arrays and I am passing the validation data while training.
### Standalone code to reproduce the issue
```shell
def create_dataMLP(dim, regress=False):
# define our MLP network
model = Sequential()
model.add(Dense(16, input_dim=dim, activation="relu"))
model.add(Dense(8, activation="relu"))
# check to see if the regression node should be added
if regress:
model.add(Dense(1, activation="linear"))
# return our model
return model
Function to create ImageProcessing Branch imageCNN
def create_imageCNN(name):
model = tf.keras.Sequential([
# Explicitly define the input shape so the model can be properly
# loaded by the TFLiteConverter
tf.keras.layers.InputLayer(input_shape=IMAGE_SIZE + (3,)),
hub.KerasLayer(model_handle, trainable=do_fine_tuning, name=name),
])
return model
Function to create the multi-input and multi-out Model
def create_MultiInputMultiOutputRegressionModel():
# create the MLP and CNN models
#first create the dataMLP model
model_dataMLP = create_dataMLP(data_dimension)
print("DATA MLP model branch")
model_dataMLP.summary()
#next create the Neck imageCNN model
print("\n\n----------------------")
neck_model_imageCNN = create_imageCNN("Neck1")
print("IMAGE CNN model branch")
neck_model_imageCNN.summary()
print("\n\n\n\n----------------------")
#next create the SHOULDER imageCNN model
print("\n\n----------------------")
shoulder_model_imageCNN = create_imageCNN("Shoulder1")
print("IMAGE CNN model branch")
shoulder_model_imageCNN.summary()
print("\n\n\n\n----------------------")
#next create the CHEST imageCNN model
print("\n\n----------------------")
chest_model_imageCNN = create_imageCNN("Chest1")
print("IMAGE CNN model branch")
chest_model_imageCNN.summary()
print("\n\n\n\n----------------------")
#next create the WAIST imageCNN model
print("\n\n----------------------")
waist_model_imageCNN = create_imageCNN("Waist1")
print("IMAGE CNN model branch")
waist_model_imageCNN.summary()
print("\n\n\n\n----------------------")
#next create the HIPS imageCNN model
print("\n\n----------------------")
hips_model_imageCNN = create_imageCNN("Hips1")
print("IMAGE CNN model branch")
hips_model_imageCNN.summary()
print("\n\n\n\n----------------------")
#next create the LEGS imageCNN model
print("\n\n----------------------")
legs_model_imageCNN = create_imageCNN("Legs1")
print("IMAGE CNN model branch")
legs_model_imageCNN.summary()
print("\n\n\n\n----------------------")
#next create the UpperArmLeft_Breadth imageCNN model
print("\n\n----------------------")
Uparm_left_model_imageCNN = create_imageCNN("UpArmLeft1")
print("IMAGE CNN model branch")
Uparm_left_model_imageCNN.summary()
print("\n\n\n\n----------------------")
#next create the UpperArmRight_Breadth imageCNN model
print("\n\n----------------------")
Uparm_right_model_imageCNN = create_imageCNN("UpArmRight1")
print("IMAGE CNN model branch")
Uparm_right_model_imageCNN.summary()
print("\n\n\n\n----------------------")
#next create the ARM_Left_Length imageCNN model
print("\n\n----------------------")
arm_left_length_model_imageCNN = create_imageCNN("ArmLeft1")
print("IMAGE CNN model branch")
arm_left_length_model_imageCNN.summary()
print("\n\n\n\n----------------------")
#next create the ARM_Right_Length imageCNN model
print("\n\n----------------------")
arm_right_length_model_imageCNN = create_imageCNN("ArmRight1")
print("IMAGE CNN model branch")
arm_right_length_model_imageCNN.summary()
print("\n\n\n\n----------------------")
# create the input to our final set of layers as the *output* of both
# the mode_dataMLP and ALL THE multiple model_imageCNNs
combinedInput = concatenate([model_dataMLP.output, neck_model_imageCNN.output, shoulder_model_imageCNN.output, chest_model_imageCNN.output, waist_model_imageCNN.output, hips_model_imageCNN.output, legs_model_imageCNN.output, Uparm_left_model_imageCNN.output, Uparm_right_model_imageCNN.output, arm_left_length_model_imageCNN.output, arm_right_length_model_imageCNN.output])
#CREATE the fully connected network after the concatenation
x = Dense(16, activation="relu")(combinedInput) # was 8
x =tf.keras.layers.Dropout(rate=0.2)(x)
x = Dense(output_dimension, activation="linear", name="pred")(x)
model = Model(inputs=[model_dataMLP.input, neck_model_imageCNN.input, shoulder_model_imageCNN.input, chest_model_imageCNN.input, waist_model_imageCNN.input, hips_model_imageCNN.input, legs_model_imageCNN.input, Uparm_left_model_imageCNN.input, Uparm_right_model_imageCNN.input, arm_left_length_model_imageCNN.input, arm_right_length_model_imageCNN.input], outputs=x)
print("COMPLETE Multi-Input Multi-Output Model")
model.summary()
return model
Model compile and setting up checkpoint directory
def compileSetupModel(model, checkpoint_dir = "/content/drive/MyDrive/ObjectDetection/SmartFashion/Regression/TrainingRun/Run4"):
#from tensorflow.keras.losses import MeanAbsoluteError, MeanAbsolutePercentageErr
print("Contents of trainingRun directory ")
!ls $checkpoint_dir
#setup the loss as MeanAbsoluteError() so will do regression
model.compile(
optimizer=tf.keras.optimizers.SGD(learning_rate=0.0005, momentum=0.9),
loss = tf.keras.losses.MeanAbsoluteError())
checkpoint_dir = checkpoint_dir + "/checkpoint-{epoch:02d}.tf"
mc = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_dir, save_best_only=True, verbose=1, save_freq='epoch')
#callback for tensorboard
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
return mc, tensorboard_callback
mc, tensorboard_callback = compileSetupModel(model)
Model fit
checkpoint_dir = "/content/drive/MyDrive/ObjectDetection/SmartFashion/Regression/TrainingRun/Run1"
epochs = 10 #should be more like 1000
train_size = len(trainImagesNDData)
valid_size = len(validationImagesNDData)
steps_per_epoch = (train_size // BATCH_SIZE) - 1
validation_steps = (valid_size // BATCH_SIZE) - 1
hist = model.fit(
x=[trainNumericData, trainImagesNDData, trainImagesSBData, trainImagesCBData, trainImagesWBData, trainImagesHBData,trainImagesLLBData, trainImagesUpLeftABData,trainImagesUpRightABData, trainImagesALeftLData, trainImagesARightLData], #x=trainImages, # images
y=trainAllRegressionData, #y=train["severity"], # severity target regression value
epochs=epochs, steps_per_epoch=steps_per_epoch,
validation_data=([validationNumericData, validationImagesNDData, validationImagesSBData, validationImagesCBData, validationImagesWBData, validationImagesHBData, validationImagesLLBData, validationImagesUpLeftABData, validationImagesUpRightABData, validationImagesALeftLData, validationImagesARightLData], validationAllRegressionData),
validation_steps=validation_steps,
callbacks=[mc, tensorboard_callback]).history #callback for both checkpoints and tensorboard.
```
### Relevant log output
```shell
steps_per_epoch =213
validation_steps =25
Steps per epoch for training is 213
[INFO] training model...
Epoch 1/10
213/213 [==============================] - ETA: 0s - loss: 2.6573WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 25 batches). You may need to use the repeat() function when building your dataset.
Epoch 1: val_loss improved from inf to 2.23204, saving model to /content/drive/MyDrive/ObjectDetection/SmartFashion/Regression/TrainingRun/Run4/checkpoint-01.tf
213/213 [==============================] - 654s 1s/step - loss: 2.6573 - val_loss: 2.2320
Epoch 2/10
213/213 [==============================] - ETA: 0s - loss: 2.1445WARNING:tensorflow:Can save best model only with val_loss available, skipping.
213/213 [==============================] - 115s 541ms/step - loss: 2.1445
Epoch 3/10
213/213 [==============================] - ETA: 0s - loss: 1.8726WARNING:tensorflow:Can save best model only with val_loss available, skipping.
213/213 [==============================] - 115s 541ms/step - loss: 1.8726
Epoch 4/10
213/213 [==============================] - ETA: 0s - loss: 1.7671WARNING:tensorflow:Can save best model only with val_loss available, skipping.
213/213 [==============================] - 115s 542ms/step - loss: 1.7671
Epoch 5/10
213/213 [==============================] - ETA: 0s - loss: 1.6374WARNING:tensorflow:Can save best model only with val_loss available, skipping.
213/213 [==============================] - 115s 541ms/step - loss: 1.6374
Epoch 6/10
213/213 [==============================] - ETA: 0s - loss: 1.5755WARNING:tensorflow:Can save best model only with val_loss available, skipping.
213/213 [==============================] - 115s 541ms/step - loss: 1.5755
Epoch 7/10
213/213 [==============================] - ETA: 0s - loss: 1.5525WARNING:tensorflow:Can save best model only with val_loss available, skipping.
213/213 [==============================] - 115s 541ms/step - loss: 1.5525
Epoch 8/10
213/213 [==============================] - ETA: 0s - loss: 1.5087WARNING:tensorflow:Can save best model only with val_loss available, skipping.
213/213 [==============================] - 115s 541ms/step - loss: 1.5087
Epoch 9/10
16/213 [=>............................] - ETA: 1:22 - loss: 1.6039WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 2130 batches). You may need to use the repeat() function when building your dataset.
WARNING:tensorflow:Can save best model only with val_loss available, skipping.
213/213 [==============================] - 34s 156ms/step - loss: 1.6039
```
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PR_kwDOArmXAs5qNGsk
| 64,034 |
Sync the declaration and the tests of RegisterFunctionDefForSubgraphs with the definition
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Sync the declaration and the tests of RegisterFunctionDefForSubgraphs with the definition
This caused a linking error, which blocks many XLA commits
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I_kwDOArmXAs6C9eFc
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Upgrading to tensorflow 2.15.1 from 2.14.1 produces pylint bug: E1101: Module 'tensorflow' has no 'keras' member
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[
"Ok",
"I'm experiencing the same issue",
"Same issue here, using WSL: Ubuntu-22.04 in remote VSCode",
"Hi **@nathanwbailey** ,\r\n- Could you use following import statement in your files that requires in keras.\r\n```\r\nfrom tensorflow.keras import keras\r\n```\r\nThis will import the Keras module correctly and should resolve the pylint error.\r\n- Ensure that you are using the latest version of Pylint. \r\n```\r\npip install pylint --upgrade\r\n```\r\n- Instead of importing Keras from TensorFlow (import tensorflow.keras as keras), you can directly use tensorflow.keras for your Keras-related code. This can sometimes lead to clearer imports and avoid potential linting issues.\r\n\r\nThank you!",
"> Hi **@nathanwbailey** ,\r\n> \r\n> * Could you use following import statement in your files that requires in keras.\r\n> \r\n> ```\r\n> from tensorflow.keras import keras\r\n> ```\r\n> \r\n\r\nGot the same error for this import \r\n```\r\nE0401: Unable to import 'tensorflow.keras' (import-error)\r\nE0611: No name 'keras' in module 'tensorflow' (no-name-in-module)\r\n```\r\nwith TensorFlow 2.15.1 and pylint 3.1.0.\r\n\r\n",
"Hi is there any update on this?",
"Hi @nathanwbailey ,\r\n\r\nThis seems due to PEP issue. As keras is standalone package there seems to be issue. Could you please confirm whether this is an issue with TF2.16v also.\r\n\r\nWe should use import statement: `from tensorflow import keras` instead of `from tensorflow.keras import keras`",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"Hi, I currently see the issue with from tensorflow import keras.\r\n\r\nWith tensorflow 2.16.1 I see the following similar error:\r\n\r\n`E0611: No name 'keras' in module 'tensorflow' (no-name-in-module)`"
] | 2024-03-20T10:13:05 | 2024-05-03T14:26:18 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15.1
### Custom code
No
### OS platform and distribution
Ubuntu 21.04
### Mobile device
_No response_
### Python version
3.9.18
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When importing keras I see the following pylint error:
```
import tensorflow as tf
keras_model = tf.keras.Sequential(
...
```
`E1101: Module 'tensorflow' has no 'keras' member (no-member)`
Expected behaviour: No linting error raised
Tested with pylint version 2.7.15 and latest (3.1.0)
This looks like it is down to the following line(s) removed from the __init__.py file
```
if _typing.TYPE_CHECKING:
from keras.api._v2 import keras
```
A temporary workaround is to use the `from keras.api._v2 import keras` import in the files that require keras
### Standalone code to reproduce the issue
```shell
<file_name.py>:
"""Test file for TF issue."""
import tensorflow as tf
keras_model = tf.keras.Sequential(
[
tf.keras.layers.LeakyReLU()
]
)
pylint <file_name>.py
```
### Relevant log output
```shell
pylint <file_name>.py
<file_name>.py:4:14: E1101: Module 'tensorflow' has no 'keras' member (no-member)
<file_name>.py:6:8: E1101: Module 'tensorflow' has no 'keras' member (no-member)
```
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PR_kwDOArmXAs5qM4Nv
| 64,032 |
PR #10489: [GPU] Fix command buffer support for cuDNN fusions.
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PR #10489: [GPU] Fix command buffer support for cuDNN fusions.
Imported from GitHub PR https://github.com/openxla/xla/pull/10489
CuDnnCmd is constructed before DnnGraph in CuDnnThunk is initialized so CuDnnCmd has to get unique_ptr\<DnnGraph\>& instead of DnnGraph& at initialization.
Accordingly cuDNN thunks have to be initialized before command buffer ones to initialize graphs before they get captured.
Test CommandBuffersAreSupported used to not demonstrate the use of command buffers because the corresponding command buffer call used to be inlined and no command buffers were created. This is now cleaned up and does work as expected with minimal CUDA graph size set to 1 with a flag.
Copybara import of the project:
--
8547c674f3e0858efca9763bed586f1d796184d7 by Ilia Sergachev <[email protected]>:
[GPU] Fix command buffer support for cuDNN fusions.
Merging this change closes #10489
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10489 from openxla:fix_cudnn_cmd_buffers 8547c674f3e0858efca9763bed586f1d796184d7
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Added command buffer object.
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Added command buffer object.
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Also verify whether instructions are allowed to change layouts.
If a test provides a instruction_can_change_layout_func, we should also use it
for the HloVerifier created inside the VerifiedHloModule.
Doing this exposed a bug in algebraic_simplifier where a simplification didn't
check whether it is running in layout sensitive mode (since it doesn't handle
layouts in any way, it should not be used in layout sensitive mode).
Also adjust HloVerifier to be not as strict regarding tiling and memory space
to make a test pass.
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Fixed typos in post_training_quant.ipynb
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This PR fixes some typos in post_training_quant.ipynb
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Explicitly depend on MLIR interface targets.
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Explicitly depend on MLIR interface targets.
Preparation for https://github.com/llvm/llvm-project/pull/85867.
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PR #10636: Offloading 1/3: Add annotation for copy-start/copy-done
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PR #10636: Offloading 1/3: Add annotation for copy-start/copy-done
Imported from GitHub PR https://github.com/openxla/xla/pull/10636
Add stream id in the backend_config of copy-start instruction. The stream id is obtained from hlo_query::NextChannelId().
The corresponding copy-done instruction which is the use of copy-start instruction will be traversed and added the stream id in the backend_config too. This part is automatically done by the subsequent AnnotateStreamAttributesForUsers() existing in the function.
The bool data member copy_start_done_ is used to differentiate copy-start/copy-done from other collective instructions and go through two different paths.
https://github.com/openxla/xla/pull/10450 is split and the current PR is the first 1 out of 3 PRs.
Copybara import of the project:
--
ff99c161a634b868d4265204d10d0b80adf2e772 by Jane Liu <[email protected]>:
Add annotation of stream id for copy-start and its use of copy-done instruction
--
64c746a5de6aa4c9370034ed712192dfd78e3a6e by Jane Liu <[email protected]>:
Enable the annotator for copy-start/copy-done in gpu compiler
--
7972b987e45cd165834483c4350e953094b5dbe8 by Jane Liu <[email protected]>:
Add the annotator pass after HLO rematerialization pass
--
2a9e317aac85e4de3f0ff5f1558885408ab4562d by Jane Liu <[email protected]>:
Add the dependency in BUILD
--
db5ed79f0ec90a1032252beff5108e4f01b61c4a by Jane Liu <[email protected]>:
Use a function to annotate copy-start and add description
--
b4b3932bfedfabc7f3e22a8766ecbc1fa1188402 by Jane Liu <[email protected]>:
remove the bool var copy_start from the StreamAttributeAnnotator class
--
11148fe63588f58c85ab2530bda64807bf400476 by Jane Liu <[email protected]>:
Fixes according to the code review
Merging this change closes #10636
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10636 from zhenying-liu:offloading/annotator 11148fe63588f58c85ab2530bda64807bf400476
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I_kwDOArmXAs6C8WRV
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Could not find variable Conv_Pool_7/kernel_7. This could mean that the variable has been deleted. In TF1, it can also mean the variable is uninitialized. Debug info: container=localhost, status error message=Container localhost does not exist. (Could not find resource: localhost/Conv_Pool_7/kernel_7)
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"@arpithavijay,\r\nCould you please provide complete standalone code to reproduce the issue which helps to debug the issue in an effective way. Also I suspect you are trying to execute the code which is in tensorflow 1.x version. For executing the v1.x code in tensorflow 2.6, you need to make some changes accordingly.\r\n\r\nAlso I tried to execute sample code of handwriting recognition code using cnn and I was able to get the required output. Kindly find the gist of it [here](https://colab.research.google.com/gist/kiransair/f93a3cfce17ec1058643bf46d6209d0d/mnist.ipynb)\r\n\r\nReference: https://github.com/tensorflow/tensorflow/issues/51141\r\n\r\nThank you!\r\n\r\n",
"the original code was in tf 1.x version, i modified the code to 2.6",
"[Uploading Tensorflow_problem.txt…]()\r\n",
"@arpithavijay,\r\nAFAIK some of the api's which were on the code still related to 1.x version which might be leading to the mentioned error. I tried to execute a sample code of handwriting recognition code using cnn and I was able to get the required output. Kindly find the gist of it [here](https://colab.research.google.com/gist/kiransair/f93a3cfce17ec1058643bf46d6209d0d/mnist.ipynb). Could you please re-check and try to convert to the latest tensorflow version and execute the code.\r\n\r\nMigration document of tensorflow 1.x to 2.x for the reference. https://www.tensorflow.org/guide/migrate\r\n 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/64025\">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/64025\">No</a>\n"
] | 2024-03-20T07:33:25 | 2024-04-17T01:47:54 | 2024-04-17T01:47:47 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.6
### Custom code
Yes
### OS platform and distribution
Windows
### 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?
Running handwriting recognition code using cnn and tensorflow, getting the below error : Could not find variable Conv_Pool_7/kernel_7. This could mean that the variable has been deleted. In TF1, it can also mean the variable is uninitialized. Debug info: container=localhost, status error message=Container localhost does not exist. (Could not find resource: localhost/Conv_Pool_7/kernel_7)
Code: def setupCNN(self):
cnnIn4d = self.inputImgs
num_layers = 7 # Number of convolution-pooling layers
# Define the parameters for each layer
layer_params = [
(5, 1, 64), # Conv_Pool_1
(5, 64, 128), # Conv_Pool_2
(3, 128, 128),# Conv_Pool_BN_3
(3, 128, 256),# Conv_4
(3, 256, 256),# Conv_Pool_5
(3, 256, 512),# Conv_Pool_BN_6
(3, 512, 512) # Conv_Pool_7
]
pool = cnnIn4d # Initial input to the first CNN layer
# Create layers using a loop
for i in range(num_layers):
with tf.name_scope(f'Conv_Pool_{i+1}'):
kernel = tf.Variable(tf.random.truncated_normal([layer_params[i][0], layer_params[i][0], layer_params[i][1], layer_params[i][2]], stddev=0.1), name=f'kernel_{i+1}')
conv = tf.nn.conv2d(pool, kernel, padding='SAME', strides=(1, 1, 1, 1))
learelu = tf.nn.leaky_relu(conv, alpha=0.01)
pool = tf.nn.max_pool2d(learelu, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1), padding='SAME')
self.cnnOut4d = pool
### Standalone code to reproduce the issue
```shell
def setupCNN(self):
cnnIn4d = self.inputImgs
num_layers = 7 # Number of convolution-pooling layers
# Define the parameters for each layer
layer_params = [
(5, 1, 64), # Conv_Pool_1
(5, 64, 128), # Conv_Pool_2
(3, 128, 128),# Conv_Pool_BN_3
(3, 128, 256),# Conv_4
(3, 256, 256),# Conv_Pool_5
(3, 256, 512),# Conv_Pool_BN_6
(3, 512, 512) # Conv_Pool_7
]
pool = cnnIn4d # Initial input to the first CNN layer
# Create layers using a loop
for i in range(num_layers):
with tf.name_scope(f'Conv_Pool_{i+1}'):
kernel = tf.Variable(tf.random.truncated_normal([layer_params[i][0], layer_params[i][0], layer_params[i][1], layer_params[i][2]], stddev=0.1), name=f'kernel_{i+1}')
conv = tf.nn.conv2d(pool, kernel, padding='SAME', strides=(1, 1, 1, 1))
learelu = tf.nn.leaky_relu(conv, alpha=0.01)
pool = tf.nn.max_pool2d(learelu, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1), padding='SAME')
self.cnnOut4d = pool
```
### Relevant log output
_No response_
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PR_kwDOArmXAs5qLm0B
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Also handle DynamicSlice in MoveCopyToUsers
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Also handle DynamicSlice in MoveCopyToUsers
While there, also fix a bug in the test for Slice.
The slice operand should have the same layout as the slice.
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Aborted (core dumped) in tf.math.unsorted_segment_mean
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"Hi @Zoeeeeey ,\r\n\r\nI tried on Colab and getting crash due to Low Memory resource.\r\n\r\n`2024-03-20 09:41:37.338368: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 14391421936 exceeds 10% of free system memory.`\r\n\r\nCould you please confirm your RAM size and also could you please test with latest TF version i.e TF2.16v.Since it is possible that few bugs might resolved in latest releases and thus saves our time. ",
"It seems there is integer overflow happening with UnsortedSegmentSum Op which internally being used by `tf.math.unsorted_segment_mean`. Repro code snippet below & reference [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/2e15ecd56d841426985cfa84f873efdd/64023.ipynb#scrollTo=_oF8wIpuQptx).\r\n\r\n```\r\ndata=tf.constant(value=np.random.randint(0,100,size=(3, 2)), shape=(3, 2), dtype=tf.int32)\r\nsegment_ids=3\r\nsegment_ids = tf.convert_to_tensor(segment_ids)\r\nnum_segments=3597855484\r\n\r\ntf.raw_ops.UnsortedSegmentSum(data=data, segment_ids=segment_ids, num_segments=num_segments)\r\n```",
"Hi, thank you for your explanation. \r\nIn addition, I upgraded the version of TensorFlow and made some attempts, and this issue showed some different behaviors. \r\nActually, I want to ask, does the code hint `Aborted (core dumped)` indicate that this is a bug in the current version?\r\n@SuryanarayanaY ",
"> Actually, I want to ask, does the code hint `Aborted (core dumped)` indicate that this is a bug in the current version?\r\n\r\nThis must be a check fail that needs to be fixed with proper exception.\r\n\r\nAnyways the overflow problem as mentioned in above [comment](https://github.com/tensorflow/tensorflow/issues/64023#issuecomment-2009366453) might needs fix.",
"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/64023\">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/64023\">No</a>\n"
] | 2024-03-20T06:45:42 | 2024-05-10T16:52:15 | 2024-05-10T16:52:11 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
tf 2.14
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
7.5.0
### CUDA/cuDNN version
12.0
### GPU model and memory
_No response_
### Current behavior?
Under specific input, tf.math.unsorted_segment_mean encounters "Aborted (core dumped)".
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
data=tf.constant(value=np.random.randint(0,100,size=(3, 2)), shape=(3, 2), dtype=tf.int32)
segment_ids=3
num_segments=3597855484
tf.math.unsorted_segment_mean(data=data, segment_ids=segment_ids, num_segments=num_segments)
```
### Relevant log output
```shell
2024-03-20 06:38:50.497225: F ./tensorflow/core/util/gpu_launch_config.h:129] Check failed: work_element_count >= 0 (0 vs. -697111812)
Aborted (core dumped)
```
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PR #10668: [XLA:GPU] Fix cuDNN FMHA rewriter sequence length checks and Bump up minimum flash attn cuDNN version to 8.9.4
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PR #10668: [XLA:GPU] Fix cuDNN FMHA rewriter sequence length checks and Bump up minimum flash attn cuDNN version to 8.9.4
Imported from GitHub PR https://github.com/openxla/xla/pull/10668
* If only seqlen_q or seqlen_kv is larger than 512, it will be lowered to fused attn instead of flash attn. This is incorrect as fused attn does not support this case. Add checks to lower to flash attn if one seqlen is larger than 512.
* While fixing the seqlen checks, also relax the seqlen/head dim constraints to support any seqlen % 2 == 0, head_dim <= 128 and head_dim % 8 = 0. (Since 8.9.4).
* Consolidate multiple get seqlen/head dim from fused attn/flash attn checks into one `getBHSD` function and rewrite both fused attn/flash attn checks to make it easier to understand.
* Bump up minimum cuDNN version require for flash attn from 8.9.3 to 8.9.4. (More seqlen/head dim support and cross attn support).
* Remove cross attn checks from rewriter and rewriter test since it is now default supported with 8.9.4.
* Add one testcase to cover the first bullet point.
Copybara import of the project:
--
bfdce45d669fe899dd845f497df783f014558384 by cjkkkk <[email protected]>:
rewrite seqlen checks
--
8a0e8b465cc1790953b1dc5f8740adfa7cf1124a by cjkkkk <[email protected]>:
return vector directly
--
72abf544d2f962c5554fe00ffea8d3167a9cde81 by cjkkkk <[email protected]>:
use struct for qkv_layout && add () for extra &&
Merging this change closes #10668
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10668 from Cjkkkk:rewrite_seqlen_checks 72abf544d2f962c5554fe00ffea8d3167a9cde81
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PR_kwDOArmXAs5qLcBk
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Fix typo in [png_io.h]
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"> In the future, can you fix multiple typos in same file/directory per PR? There's a significant CI cost and we're trying to clamp that down as much as possible\r\n\r\nThank you for your review and answer. \r\nYes, I will try to fix multiple problems next time."
] | 2024-03-20T06:07:04 | 2024-03-21T16:25:36 | 2024-03-21T16:25:36 |
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fix all typo in `png_io.h`
information has no plural
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PR_kwDOArmXAs5qLXZ7
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Integrate StableHLO at openxla/stablehlo@ec4ec784
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Integrate StableHLO at openxla/stablehlo@ec4ec784
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I_kwDOArmXAs6C7jVN
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android.support.v13.app does not exist while building TfLiteCameraDemo
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"@yerriswa You are using an older version of TF so please consider using the latest version of the TensorFlow Lite Support Library, which is built with AndroidX compatibility. The error messages \"package android.support.v13.app does not exist\" and \"package FragmentCompat does not exist\" point to the usage of deprecated libraries from the Android Support Library.\r\n\r\nThank you!",
"I have cloned from https://github.com/tensorflow/tensorflow.git link and the branch is master.\r\nI guess this is the latest. Please let me know if any other latest code is available.",
"@yerriswa Since you've built the TensorFlow Lite library successfully, please try updating the TfLiteCameraDemo project to use modern libraries. Also kindly replace the deprecated android.support libraries with their AndroidX counterparts.\r\nThank you!",
"@sushreebarsa Thanks for your response, I wil try to update from my side and let you know if I need any help in upgrading the TfLiteCameraDemo project.",
"@yerriswa Could you please let us know if the issue has been resolved so that we could close the ticket?\r\nIf not please update us on the same?\r\nThank you! ",
"@sushreebarsa, The android.support.v13.app framework was used only for the dynamic permision request which was not very critical for our application. I have commented as of now and went ahead with other development activites.\r\nLet you know once I update this code base in future.",
"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/64019\">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/64019\">No</a>\n"
] | 2024-03-20T04:36:46 | 2024-06-07T01:51:09 | 2024-06-07T01:51:06 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.8.0
### Custom code
No
### OS platform and distribution
ubuntu 20.04
### Mobile device
container ubuntu 20.04
### Python version
3.11.8
### Bazel version
6.5.0
### GCC/compiler version
9.4.0
### CUDA/cuDNN version
NA
### GPU model and memory
_No response_
### Current behavior?
Trying to build TfLiteCameraDemo.apk using below command
Cmd:bazel build -c opt //tensorflow/lite/java/demo/app/src/main:TfLiteCameraDemo
Error: error: package android.support.v13.app does not exist
Please let me know the steps to resolve above issue.
### Standalone code to reproduce the issue
```shell
Steps followed:
1. Created container using the file "https://www.tensorflow.org/lite/android/lite_build" in the link (Guide==> Build for Android ==> You can download the Docker file [here])
2. Executed below sdkmanager command in the container
sdkmanager \
"build-tools;${ANDROID_BUILD_TOOLS_VERSION}" \
"platform-tools" \
"platforms;android-${ANDROID_API_LEVEL}"
3. successfully Configured WORKSPACE (RCOM ==> NO, CUDA ==> NO)
4. configured all the below variables
export ANDROID_NDK_HOME="/usr/local/android/android-ndk-r25b"
export ANDROID_NDK_API_LEVEL="26"
export ANDROID_BUILD_TOOLS_VERSION="30.0.3"
export ANDROID_SDK_API_LEVEL="30"
export ANDROID_SDK_HOME="/usr/local/android/android-sdk-linux"
5. Successfully able to build the complete tensorflow-lite library in the .aar format using below command
bazel build -c opt --cxxopt=--std=c++17 --config=android_arm64 \
--fat_apk_cpu=x86,x86_64,arm64-v8a,armeabi-v7a \
--define=android_dexmerger_tool=d8_dexmerger \
--define=android_incremental_dexing_tool=d8_dexbuilder \
//tensorflow/lite/java:tensorflow-lite
6. After the tensorflow-lite successfull build, tried building only TfLiteCameraDemo.apk using below command
Cmd: bazel build -c opt //tensorflow/lite/java/demo/app/src/main:TfLiteCameraDemo
Got struck with Error:
error: package android.support.v13.app does not exist
```
### Relevant log output
```shell
ERROR: /host_dir/tensorflow/tensorflow/lite/java/demo/app/src/main/BUILD:9:15: Building tensorflow/lite/java/demo/app/src/main/libTfLiteCameraDemo.jar (7 source files) failed: (Exit 1): java failed: error executing command (from target //tensorflow/lite/java/demo/app/src/main:TfLiteCameraDemo) external/remotejdk11_linux/bin/java -XX:-CompactStrings '--add-exports=jdk.compiler/com.sun.tools.javac.api=ALL-UNNAMED' '--add-exports=jdk.compiler/com.sun.tools.javac.main=ALL-UNNAMED' ... (remaining 18 arguments skipped)
tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java:64: error: package android.support.v13.app does not exist
import android.support.v13.app.FragmentCompat;
^
tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java:76: error: package FragmentCompat does not exist
implements FragmentCompat.OnRequestPermissionsResultCallback {
^
tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java:615: error: cannot find symbol
FragmentCompat.requestPermissions(this, getRequiredPermissions(), PERMISSIONS_REQUEST_CODE);
^
symbol: variable FragmentCompat
location: class Camera2BasicFragment
tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java:158: error: [strict] Using type androidx.annotation.NonNull from an indirect dependency (TOOL_INFO: "bazel-out/android-armeabi-v7a-opt/bin/external/maven/v1/https/maven.google.com/androidx/annotation/annotation/1.1.0/header_annotation-1.1.0.jar").
public void onOpened(@NonNull CameraDevice currentCameraDevice) {
^
Target //tensorflow/lite/java/demo/app/src/main:TfLiteCameraDemo failed to build
Use --verbose_failures to see the command lines of failed build steps.
INFO: Elapsed time: 8.829s, Critical Path: 4.20s
INFO: 47 processes: 16 internal, 31 local.
FAILED: Build did NOT complete successfully
```
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Fix a use after free error in LowerToIfrtRestoreVariable pass
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Fix a use after free error in LowerToIfrtRestoreVariable pass
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rollback
#tf-data Support global shuffle for the skip dataset.
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PR #10722: Consolidate Unit Tests for GEMM Rewriter
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PR #10722: Consolidate Unit Tests for GEMM Rewriter
Imported from GitHub PR https://github.com/openxla/xla/pull/10722
Moves the unit tests in `xla/service/gpu/gemm_rewriter_test.cc` into `xla/service/gpu/tests/gemm_rewrite_test.cc`.
Copybara import of the project:
--
e3056a172238a256214d856cf3ad5cb44099e06a by Philipp Hack <[email protected]>:
Merge GEMM rewriter unit tests.
Merging this change closes #10722
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10722 from philipphack:u_merged_gemm_test_xla e3056a172238a256214d856cf3ad5cb44099e06a
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Relax verification of ShardingParam so that a dimension can be sharded over multiple axis.
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Relax verification of ShardingParam so that a dimension can be sharded over multiple axis.
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#tf-data Fix asan failure.
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#tf-data Fix asan failure.
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Update implicit broadcast output shape element type to match operand.
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Update implicit broadcast output shape element type to match operand.
Some binary ops return types whose element type differs from the operand element types (e.g. Ne). While broadcasting, it should use the element type of the operand instead of the inferred type.
Before:
```
HloModule UnboundedBinaryOpTest_8.15, entry_computation_layout={(f32[?,10]{1,0}, f32[1]{0})->pred[?,10]{1,0}}
ENTRY %UnboundedBinaryOpTest_8.15 (lhs.1: f32[?,10], rhs.2: f32[1]) -> pred[?,10] {
%constant.3 = s32[1]{0} constant({1})
%lhs.1 = f32[?,10]{1,0} parameter(0)
%get-dimension-size.4 = s32[] get-dimension-size(f32[?,10]{1,0} %lhs.1), dimensions={0}
%reshape.5 = s32[1]{0} reshape(s32[] %get-dimension-size.4)
%constant.6 = s32[1]{0} constant({10})
%concatenate.9 = s32[2]{0} concatenate(s32[1]{0} %reshape.5, s32[1]{0} %constant.6), dimensions={0}
%constant.7 = s32[1]{0} constant({1})
%constant.8 = s32[1]{0} constant({1})
%concatenate.10 = s32[2]{0} concatenate(s32[1]{0} %constant.7, s32[1]{0} %constant.8), dimensions={0}
%maximum.11 = s32[2]{0} maximum(s32[2]{0} %concatenate.9, s32[2]{0} %concatenate.10)
%custom-call.12 = pred[?,10]{1,0} custom-call(f32[?,10]{1,0} %lhs.1, s32[2]{0} %maximum.11), custom_call_target="mhlo.dynamic_broadcast_in_dim", backend_config={broadcast_dimensions=[0,1]}
%rhs.2 = f32[1]{0} parameter(1)
%custom-call.13 = pred[?,10]{1,0} custom-call(f32[1]{0} %rhs.2, s32[2]{0} %maximum.11), custom_call_target="mhlo.dynamic_broadcast_in_dim", backend_config={broadcast_dimensions=[1]}
ROOT %compare.14 = pred[?,10]{1,0} compare(pred[?,10]{1,0} %custom-call.12, pred[?,10]{1,0} %custom-call.13), direction=NE
}
```
After:
```
HloModule UnboundedBinaryOpTest_8.15, entry_computation_layout={(f32[?,10]{1,0}, f32[1]{0})->pred[?,10]{1,0}}
ENTRY %UnboundedBinaryOpTest_8.15 (lhs.1: f32[?,10], rhs.2: f32[1]) -> pred[?,10] {
%constant.3 = s32[1]{0} constant({1})
%lhs.1 = f32[?,10]{1,0} parameter(0)
%get-dimension-size.4 = s32[] get-dimension-size(f32[?,10]{1,0} %lhs.1), dimensions={0}
%reshape.5 = s32[1]{0} reshape(s32[] %get-dimension-size.4)
%constant.6 = s32[1]{0} constant({10})
%concatenate.9 = s32[2]{0} concatenate(s32[1]{0} %reshape.5, s32[1]{0} %constant.6), dimensions={0}
%constant.7 = s32[1]{0} constant({1})
%constant.8 = s32[1]{0} constant({1})
%concatenate.10 = s32[2]{0} concatenate(s32[1]{0} %constant.7, s32[1]{0} %constant.8), dimensions={0}
%maximum.11 = s32[2]{0} maximum(s32[2]{0} %concatenate.9, s32[2]{0} %concatenate.10)
%custom-call.12 = f32[?,10]{1,0} custom-call(f32[?,10]{1,0} %lhs.1, s32[2]{0} %maximum.11), custom_call_target="mhlo.dynamic_broadcast_in_dim", backend_config={broadcast_dimensions=[0,1]}
%rhs.2 = f32[1]{0} parameter(1)
%custom-call.13 = f32[?,10]{1,0} custom-call(f32[1]{0} %rhs.2, s32[2]{0} %maximum.11), custom_call_target="mhlo.dynamic_broadcast_in_dim", backend_config={broadcast_dimensions=[1]}
ROOT %compare.14 = pred[?,10]{1,0} compare(f32[?,10]{1,0} %custom-call.12, f32[?,10]{1,0} %custom-call.13), direction=NE
}
```
Notice the difference in `mhlo.dynamic_broadcast_in_dim` custom call return type `pred[?,10]` vs `f32[?,10]`. The HLO after the change correctly returns unchanged element type from the operand.
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PR #10536: Size 1 Dimensions in Layer Norm Fusion
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PR #10536: Size 1 Dimensions in Layer Norm Fusion
Imported from GitHub PR https://github.com/openxla/xla/pull/10536
Enables the fusion of layer norm patterns with degenerate input dimensions of size 1 and with additional optional type conversions or reshapes adding or removing degenerate dimensions.
Copybara import of the project:
--
54e247d2bf7104358549f0fc31019c36cfb5ce9b by Philipp Hack <[email protected]>:
Layer norm fusion for inputs with degenerate dimensions.
--
7d766bf95049e0ea7fba30c7b6262dac553c94ed by Philipp Hack <[email protected]>:
Layer norm fusion for inputs with degenerate dimensions.
--
14075f1714dcfec83efe13b8903a34caa4b39c01 by Philipp Hack <[email protected]>:
Layer norm fusion for inputs with degenerate dimensions.
Merging this change closes #10536
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10536 from philipphack:u_layer_size1_xla 14075f1714dcfec83efe13b8903a34caa4b39c01
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Flag Reserved GPU HBM Size in Pathways.
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Flag Reserved GPU HBM Size in Pathways.
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Public API changes for forthcoming PjRt GPU client with remote devices fix
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Public API changes for forthcoming PjRt GPU client with remote devices fix
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#tf-data Fix asan failure.
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[xla:gpu][NFC] Simplify HloPredicateIsOp
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[xla:gpu][NFC] Simplify HloPredicateIsOp
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Relax test tolerance for Pow, which was failing on some platforms.
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PR_kwDOArmXAs5qJ-Z_
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Change tf.IfrtLoadVariableOp to receive tensor future from
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Change tf.IfrtLoadVariableOp to receive tensor future from
IfrtRestoreTensorRegistry instead of from resource manager.
Correspondingly, IfrtLoadVariableRegistry is changed to
store a future of array.
This enables tfrt+ifrt to restore tensor asynchronously
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[xla:gpu][NFC] Misc fixes to DynamicAddressComputationFusion
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[xla:gpu][NFC] Misc fixes to DynamicAddressComputationFusion
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10536 from philipphack:u_layer_size1_xla 14075f1714dcfec83efe13b8903a34caa4b39c01
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PR_kwDOArmXAs5qJ6lX
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Integrate LLVM at llvm/llvm-project@252e2551eab9
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Integrate LLVM at llvm/llvm-project@252e2551eab9
Updates LLVM usage to match
[252e2551eab9](https://github.com/llvm/llvm-project/commit/252e2551eab9)
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Allow MaybeSavedModelDirectory to read via custom env.
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Allow MaybeSavedModelDirectory to read via custom env.
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Deserialize MLRT Bytecode path in AOT GPU
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Deserialize MLRT Bytecode path in AOT GPU
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Add tensor shape check for ADD & MUL.
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Add tensor shape check for ADD & MUL.
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Replaces XNN_FLAG_KEEP_DIM --> XNN_FLAG_REDUCE_DIM to maintain existing behaviour
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10684 from openxla:cudnn_fusion_more_op_types 210f8aaae3206264379c9f1ff5f70f24ffca4a14
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PR_kwDOArmXAs5qJlFF
| 63,998 |
Optimize XLA Compiler.
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Optimize XLA Compiler.
This results in 2% improvement on some representative large model compilation benchmarks.
1. `MayUseOperandValue(position.index, user)`, `(user->IsRoot() && root_positions.contains(user)` is computed once per <position, user> instead of once per <position, user, operand>. We don't need to pass `i` to `MayUseOperandValue`. We can instead bring the `CHECK` out at the call site.
2. Use `emplace_back` instead of `push_back` and `std::move`. Why: https://godbolt.org/z/sT54Ef88h
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[xla:gpu] Move the collective-permute-decomposer and Send-Recv pipeliner to
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[xla:gpu] Move the collective-permute-decomposer and Send-Recv pipeliner to
pre-fusion.
Previously, the Send-Recv pipeliner is invoked as a post-fusion pass. This is a
problem for two reasons. The loop bound analysis in the pipeliner can't
recognize loop bound in fusioned computation; performing loop pipelining before
fusion can enable better fusion decision.
The Send-Recv pipeliner relies on the collective-permute-decomposer to generate
Send-Recv operations. We now change the collective-permute-decomposer to
process collective-permute operations instead of collective-permute-operations
and move this pass along with the Send-Recv pass to pre-fusion.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10489 from openxla:fix_cudnn_cmd_buffers 8547c674f3e0858efca9763bed586f1d796184d7
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Call unbounded broadcast only when necessary.
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Call unbounded broadcast only when necessary.
Here's a before & after HLO for `max(f32[?,10], f32[?,10])` where implicit broadcasting is not necessary:
Before:
```
HloModule UnboundedBinaryOpTest_6.17, entry_computation_layout={(f32[?,10]{1,0}, f32[?,10]{1,0})->f32[?,10]{1,0}}
ENTRY %UnboundedBinaryOpTest_6.17 (lhs.1: f32[?,10], rhs.2: f32[?,10]) -> f32[?,10] {
%constant.3 = s32[1]{0} constant({1})
%constant.7 = s32[1]{0} constant({1})
%lhs.1 = f32[?,10]{1,0} parameter(0)
%get-dimension-size.4 = s32[] get-dimension-size(f32[?,10]{1,0} %lhs.1), dimensions={0}
%reshape.5 = s32[1]{0} reshape(s32[] %get-dimension-size.4)
%constant.6 = s32[1]{0} constant({10})
%concatenate.11 = s32[2]{0} concatenate(s32[1]{0} %reshape.5, s32[1]{0} %constant.6), dimensions={0}
%rhs.2 = f32[?,10]{1,0} parameter(1)
%get-dimension-size.8 = s32[] get-dimension-size(f32[?,10]{1,0} %rhs.2), dimensions={0}
%reshape.9 = s32[1]{0} reshape(s32[] %get-dimension-size.8)
%constant.10 = s32[1]{0} constant({10})
%concatenate.12 = s32[2]{0} concatenate(s32[1]{0} %reshape.9, s32[1]{0} %constant.10), dimensions={0}
%maximum.13 = s32[2]{0} maximum(s32[2]{0} %concatenate.11, s32[2]{0} %concatenate.12)
%custom-call.14 = f32[?,10]{1,0} custom-call(f32[?,10]{1,0} %lhs.1, s32[2]{0} %maximum.13), custom_call_target="mhlo.dynamic_broadcast_in_dim", backend_config={broadcast_dimensions=[0,1]}
%custom-call.15 = f32[?,10]{1,0} custom-call(f32[?,10]{1,0} %rhs.2, s32[2]{0} %maximum.13), custom_call_target="mhlo.dynamic_broadcast_in_dim", backend_config={broadcast_dimensions=[0,1]}
ROOT %maximum.16 = f32[?,10]{1,0} maximum(f32[?,10]{1,0} %custom-call.14, f32[?,10]{1,0} %custom-call.15)
}
```
After:
```
HloModule UnboundedBinaryOpTest_6.4, entry_computation_layout={(f32[?,10]{1,0}, f32[?,10]{1,0})->f32[?,10]{1,0}}
ENTRY %UnboundedBinaryOpTest_6.4 (lhs.1: f32[?,10], rhs.2: f32[?,10]) -> f32[?,10] {
%lhs.1 = f32[?,10]{1,0} parameter(0)
%rhs.2 = f32[?,10]{1,0} parameter(1)
ROOT %maximum.3 = f32[?,10]{1,0} maximum(f32[?,10]{1,0} %lhs.1, f32[?,10]{1,0} %rhs.2)
}
```
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10631 from Intel-tensorflow:yimei/fix_cpu_bf16_support 9b4f9136f1976b4432e1a60f2deb4a17c150dd3f
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PR_kwDOArmXAs5qJcU7
| 63,995 |
Update the Configure.py to set CLANG as compiler
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"yes, I ran configure.py on my end and it works fine"
] | 2024-03-19T21:23:03 | 2024-04-02T07:12:18 | 2024-04-02T07:12:18 |
CONTRIBUTOR
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Update configure.py to set Clang as the preferred compiler to build TensorFlow CPU wheels on the Windows Platform starting with TF 2.16.1
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lite: code clean up
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"repos_url": "https://api.github.com/users/copybara-service%5Bbot%5D/repos",
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"received_events_url": "https://api.github.com/users/copybara-service%5Bbot%5D/received_events",
"type": "Bot",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null |
[] | 2024-03-19T21:10:10 | 2024-03-20T01:34:34 | 2024-03-20T01:34:32 |
NONE
| null | true |
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|
lite: code clean up
|
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https://api.github.com/repos/tensorflow/tensorflow/issues/63993
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https://api.github.com/repos/tensorflow/tensorflow
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https://github.com/tensorflow/tensorflow/pull/63993
| 2,196,022,416 |
PR_kwDOArmXAs5qJWB6
| 63,993 |
All subgraphs share a mutex. Subgraphs and Delegates are not thread safe.
|
{
"login": "copybara-service[bot]",
"id": 56741989,
"node_id": "MDM6Qm90NTY3NDE5ODk=",
"avatar_url": "https://avatars.githubusercontent.com/in/44061?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/copybara-service%5Bbot%5D",
"html_url": "https://github.com/apps/copybara-service",
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"following_url": "https://api.github.com/users/copybara-service%5Bbot%5D/following{/other_user}",
"gists_url": "https://api.github.com/users/copybara-service%5Bbot%5D/gists{/gist_id}",
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"subscriptions_url": "https://api.github.com/users/copybara-service%5Bbot%5D/subscriptions",
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"received_events_url": "https://api.github.com/users/copybara-service%5Bbot%5D/received_events",
"type": "Bot",
"site_admin": false
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|
[] |
closed
| false | null |
[] | null |
[] | 2024-03-19T21:05:31 | 2024-03-19T22:56:01 | 2024-03-19T22:56:00 |
NONE
| null | true |
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All subgraphs share a mutex. Subgraphs and Delegates are not thread safe.
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