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Add a pass to insert CalibrationStatisticsSaver op
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Add a pass to insert CalibrationStatisticsSaver op
As the op is stateful. Operations like If, IfRegion,... that contain CustomAggregator must be marked as stateful. If not, the CalibrationStatisticsSaver op might be skipped.
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Allow users of XLA to control TSL's repo name
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Allow users of XLA to control TSL's repo name
This will allow tf_runtime to depend on XLA, and still refer to TSL as local_tsl.
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PR_kwDOArmXAs5s2psW
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Reverts 9f8089490ad2ceb78e8ef02f17e941d70f093f41
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Reverts 9f8089490ad2ceb78e8ef02f17e941d70f093f41
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PR_kwDOArmXAs5s2lh3
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Fix float16 quantization for LSTM ops
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Fix float16 quantization for LSTM ops
This CL fixes 2 issues:
1. in Quantize pass, for float16 quant, the pass replaces the original op with an identical one. This causes infinite loop for lstm ops. This CL adds checks to avoid such situations.
2. in SplitMergedOperands pass, const stateful operands are duplicated. But for float16 quantization, stateful operands have const->dequantize pattern. This CL add duplication for such pattern.
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PR_kwDOArmXAs5s2lR5
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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/9fdfdeada1eb04e11e0db68461b5bd1dcdb02062.
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PR_kwDOArmXAs5s2lBX
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#tf-data Add a test for WeightedFlatMap checkpoints without shuffling.
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#tf-data Add a test for WeightedFlatMap checkpoints without shuffling.
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Remove unused functions & structs from device_memory.h
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Remove unused functions & structs from device_memory.h
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Fix JAX to TFLite public documentation
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Call XLA workspace files in TF
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Call XLA workspace files in TF
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Remove outside compilation in Non-replicated Phase1 Bridge
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Remove outside compilation in Non-replicated Phase1 Bridge
Remove the support as part of the deprecation of Pepperjack M1.
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#tf-data Add a range check for `index_flat_map`.
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#tf-data Add a range check for `index_flat_map`.
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out-of-bound index due to:
https://github.com/tensorflow/tensorflow/blob/69a908420c5c5b90027f23905cd842c76ca3955c/tensorflow/core/framework/tensor.cc#L1104
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Reenable test as the original change is rolled back
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Reenable test as the original change is rolled back
Reverts 94350b84b5deb780e6d3ae01f8f1a5c7ba71b302
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Update `tsl.bzl` with the knowledge that `distributed_runtime` is moved
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NONE
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Update `tsl.bzl` with the knowledge that `distributed_runtime` is moved
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Remove unused workflow
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NONE
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Remove unused workflow
This workflow is no longer relevant.
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#tf-data Fix the graph mode tests for index flat map.
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#tf-data Fix the graph mode tests for index flat map.
Otherwise, I'm getting this error:
```
Tensor("Const:0", shape=(4, 3), dtype=int64) must be from the same graph
as Tensor("TensorSliceDataset:0", shape=(), dtype=variant)
```
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11353 from jaro-sevcik:host-offloading-async-fusion-dynamic-slicing 905db6df8130300014728bf4eceb2ee2fc23d6e1
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Don't load `tf_runtime` in TSL
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Don't load `tf_runtime` in TSL
This will allow tf_runtime to depend on XLA in a followup change
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[XLA-oneDNN] Add missing BUILD dependencies for oneDNN matmul.
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[XLA-oneDNN] Add missing BUILD dependencies for oneDNN matmul.
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Remove unused workflow
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NONE
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Remove unused workflow
This workflow is no longer relevant.
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PR_kwDOArmXAs5s13IA
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Add folding for shlo div op and start a pass that canonicalizes and folds shlo within the odml converter.
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NONE
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Add folding for shlo div op and start a pass that canonicalizes and folds shlo within the odml converter.
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PR_kwDOArmXAs5s10P-
| 65,872 |
Set metadata and frontend attributes for instructions added in tpu_slice_normalizer HLO pass.
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Set metadata and frontend attributes for instructions added in tpu_slice_normalizer HLO pass.
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| 2,246,732,136 |
I_kwDOArmXAs6F6mlo
| 65,871 |
`Overflow` in `tf.raw_ops.SparseReduceSumSparse` when there are too large values in `input_shape`.
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[
"@xl1043237213,\r\nThank you for reporting the issue. I was able to reproduce the issue on tensorflow v2.15, v2.16 and tf-nightly. Kindly find the gist of it [here](https://colab.sandbox.google.com/gist/tilakrayal/608a78c05825ddeba08516a1893a5095/untitled1865.ipynb)."
] | 2024-04-16T19:18:25 | 2024-04-24T12:56:42 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`Overflow` in `tf.raw_ops.SparseReduceSumSparse` when there are too large values in `input_shape`. See the colab link below for details.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/17mqn3t23p9sb4e3stOsPvZBTHENqJYku?usp=sharing
```
### Relevant log output
```shell
Non-OK-status: InitDims(dim_sizes) status: INVALID_ARGUMENT: Encountered overflow when multiplying 1700000000000 with 6000000, result: -8246744073709551616
```
|
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PR_kwDOArmXAs5s1w7u
| 65,870 |
Reverts changelist 570504216
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Reverts changelist 570504216
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I_kwDOArmXAs6F6kdO
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`Check failed` in `tf.raw_ops.GatherV2` when the value of `axis` is too large.
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[
"Hi @xl1043237213 ,\r\n\r\nI have replicated the reported behaviour and attached screenshot below. Need to check the root cause.\r\n\r\n<img width=\"1506\" alt=\"Screenshot 2024-04-17 at 11 30 33 AM\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/3fdc52a6-80b8-41e3-af37-42bb4447840a\">\r\n",
"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/65869\">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/65869\">No</a>\n"
] | 2024-04-16T19:12:46 | 2024-05-29T00:01:52 | 2024-05-29T00:01:48 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`Check failed` in `tf.raw_ops.GatherV2` when the value of `axis` is too large.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1murNElV5aOKQRNFwvWhygfoy21__ycTX?usp=sharing
```
### Relevant log output
```shell
Check failed: d >= 0 (0 vs. -1)
```
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I_kwDOArmXAs6F6iSa
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`Assertion failure` in `tf.signal.inverse_stft` when the value of `frame_length` is too large.
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[
"@xl1043237213 I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/572a65b5de7f20dad771bc9947959a36/-6_tf_signal_inverse_stft.ipynb). Thank you!"
] | 2024-04-16T19:07:33 | 2024-04-25T07:27:42 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`Assertion failure` in `tf.signal.inverse_stft` when the value of `frame_length` is too large. See the colab link below for details.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1zrvFDj93eGx9bt3A5WbEqrcMTrqPcHT9?usp=sharing
```
### Relevant log output
```shell
Assertion failure
no zero-sized FFTs
```
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PR_kwDOArmXAs5s1uhz
| 65,867 |
#tf-data Fix the graph mode tests for index flat map.
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#tf-data Fix the graph mode tests for index flat map.
Otherwise, I'm getting this error:
```
Tensor("Const:0", shape=(4, 3), dtype=int64) must be from the same graph
as Tensor("TensorSliceDataset:0", shape=(), dtype=variant)
```
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| 2,246,689,623 |
I_kwDOArmXAs6F6cNX
| 65,866 |
`Check failed` in `tf.linalg.diag`
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[
"Hi **@xl1043237213** ,\r\n\r\nI tried to run your code on colab using TF v2.16.1, nightly and faced the same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/97cfca50397a45a2bf7a8538e62e5400/65866_2-16-1-nightly.ipynb) here for reference. \r\n\r\nThank you!"
] | 2024-04-16T18:52:12 | 2024-04-30T04:56:14 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`Check failed` in `tf.linalg.diag`. See the colab link below for details.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1NHx4GTMJVif01eWn4xcP3Z8H_wINYyce?usp=sharing
```
### Relevant log output
```shell
Check failed: d >= 0 (0 vs. -1)
```
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I_kwDOArmXAs6F6YGg
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`Overflow` in `tf.raw_ops.SparseReduceMaxSparse` when there are too large values in `input_shape`.
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"@xl1043237213,\r\nThank you for reporting the issue. I was able to reproduce the issue on tensorflow v2.15, v2.16 and tf-nightly. Kindly find the gist of it [here](https://colab.sandbox.google.com/gist/tilakrayal/99b080faded029711517f4dd9864ab27/untitled1866.ipynb).",
"Not a bug - the shape is too large and informing you of this.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/65865\">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/65865\">No</a>\n",
"> Not a bug - the shape is too large and informing you of this.\r\n\r\nCan you tell me in more detail why the issue is not considered a bug? `INVALID_ARGUMENT` is an error type defined by TensorFlow but causes the program to crash, should this be avoided?",
"> > Not a bug - the shape is too large and informing you of this.\r\n> \r\n> Can you tell me in more detail why the issue is not considered a bug? `INVALID_ARGUMENT` is an error type defined by TensorFlow but causes the program to crash, should this be avoided?\r\n\r\nThrowing an error is not a crash. The error is telling you that your input argument is invalid.",
"> > > Not a bug - the shape is too large and informing you of this.\r\n> > \r\n> > \r\n> > Can you tell me in more detail why the issue is not considered a bug? `INVALID_ARGUMENT` is an error type defined by TensorFlow but causes the program to crash, should this be avoided?\r\n> \r\n> Throwing an error is not a crash. The error is telling you that your input argument is invalid.\r\n\r\nError is thrown by C/C++ code, causing the Python interpreter to crash, and similarly `Check failed`. Are these errors not considered bugs?🤔",
"> > > > Not a bug - the shape is too large and informing you of this.\r\n> > > \r\n> > > \r\n> > > Can you tell me in more detail why the issue is not considered a bug? `INVALID_ARGUMENT` is an error type defined by TensorFlow but causes the program to crash, should this be avoided?\r\n> > \r\n> > \r\n> > Throwing an error is not a crash. The error is telling you that your input argument is invalid.\r\n> \r\n> Error is thrown by C/C++ code, causing the Python interpreter to crash\r\n\r\nThe interpreter does not crash - an exception is thrown, and you can catch it in python.\r\n\r\n>, and similarly `Check failed`. Are these errors not considered bugs?🤔\r\n\r\nWe don't consider those serious bugs, especially if you intentionally provide obviously invalid data with the sole intention of trying to trigger a crash.",
"Thanks for your patient answer. \r\n\r\nI'd like to know how to catch the exception. When you run the code in [colab](https://colab.research.google.com/drive/1XHTMWmfVRuKBorPLZyk6iUv29_AUr8HF?usp=sharing), the web page will throw error message \"Your session crashed for an unknown reason\". When you run the code using `subprocess`, you can get return code [-6](https://www-uxsup.csx.cam.ac.uk/courses/moved.Building/signals.pdf). I think the exception is thrown by C/C++ code but cannot be caught by the Python process. \r\n\r\nIt is true that this is an artificially constructed invalid input, but improper handling of the boundary case may affect the stability of the program execution😔",
"> Thanks for your patient answer.\r\n> \r\n> I'd like to know how to catch the exception. When you run the code in [colab](https://colab.research.google.com/drive/1XHTMWmfVRuKBorPLZyk6iUv29_AUr8HF?usp=sharing), the web page will throw error message \"Your session crashed for an unknown reason\". When you run the code using `subprocess`, you can get return code [-6](https://www-uxsup.csx.cam.ac.uk/courses/moved.Building/signals.pdf). I think the exception is thrown by C/C++ code but cannot be caught by the Python process.\r\n> \r\n> It is true that this is an artificially constructed invalid input, but improper handling of the boundary case may affect the stability of the program execution😔\r\n\r\nThanks, the error was not propagated correctly to python. It was a quick fix, so should now work.\r\n\r\nBut in general, we now deprioritize any artificial edge-case issues like this that are not encountered by real-world models."
] | 2024-04-16T18:40:34 | 2024-05-14T15:46:23 | 2024-05-09T20:11:19 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`Overflow` in `tf.raw_ops.SparseReduceMaxSparse` when there are too large values in `input_shape`. See the colab link below for details.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1XHTMWmfVRuKBorPLZyk6iUv29_AUr8HF?usp=sharing
```
### Relevant log output
```shell
Non-OK-status: InitDims(dim_sizes) status: INVALID_ARGUMENT: Encountered overflow when multiplying 2684354560 with 666666666666, result: -1
```
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I_kwDOArmXAs6F6VHD
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`Overflow` in `tf.compat.v1.nn.ctc_loss` and `tf.compat.v1.nn.ctc_loss_v2` when there are too large values in `labels`.
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[
"Hi @xl1043237213 ,\r\n\r\nI have replicated the reported behaviour and attached screenshot below. The overflow warning is expected but it follows a crash which might needs fix.\r\n\r\n<img width=\"1485\" alt=\"Screenshot 2024-04-17 at 11 38 46 AM\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/a7ec7fa2-780d-4d37-8f33-739bd678fb97\">\r\n"
] | 2024-04-16T18:32:12 | 2024-04-25T07:25:42 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`Overflow` in `tf.compat.v1.nn.ctc_loss` and `tf.compat.v1.nn.ctc_loss_v2` when the value of `labels` is too large. See the colab link below for details.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1zKblem8QIsfEA973gnXrY6NDLch6ysoa?usp=sharing
```
### Relevant log output
```shell
Non-OK-status: InitDims(dim_sizes) status: INVALID_ARGUMENT: Encountered overflow when multiplying 2 with 9143586203607084488, result: -159571666495382640
```
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PR_kwDOArmXAs5s1fG9
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#tf-data Support global shuffling for the memory cache dataset.
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#tf-data Support global shuffling for the memory cache dataset.
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[
"@xl1043237213 I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/1488ae46454a829d92a9c537f0cff564/-6_tf_raw_ops_tensorlistsplit.ipynb).\r\nThank you!"
] | 2024-04-16T18:14:07 | 2024-04-25T07:30:28 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`Check failed` in `tf.raw_ops.TensorListSplit` when the input of `element_shape` is [0, large number, large number] or [-1, large number, large number].
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1usBJTSd7ip0j6DZwQft28p4deOIcktE2?usp=sharing
```
### Relevant log output
```shell
Check failed: 0 <= new_num_elements (0 vs. -1)
```
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PR_kwDOArmXAs5s1UTM
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Reverts c6644112a83e6bc09b848978ae9a373cf8b7c9fe
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Reverts c6644112a83e6bc09b848978ae9a373cf8b7c9fe
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PR_kwDOArmXAs5s1MD6
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Disable sqrt conversions tests
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[] | 2024-04-16T17:27:29 | 2024-04-16T18:29:22 | 2024-04-16T18:29:21 |
NONE
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Disable sqrt conversions tests
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PR_kwDOArmXAs5s1Ks6
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Integrate LLVM at llvm/llvm-project@694c444b5bbb
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Integrate LLVM at llvm/llvm-project@694c444b5bbb
Updates LLVM usage to match
[694c444b5bbb](https://github.com/llvm/llvm-project/commit/694c444b5bbb)
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PR #11353: Implement async dynamic slicing
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PR #11353: Implement async dynamic slicing
Imported from GitHub PR https://github.com/openxla/xla/pull/11353
This implements async dynamic-slice and dynamic-update-slice for host memory offloading on GPU.
Since the emitter does not understand dynamic slicing instructions in async computation, we wrap them in a fusion node and mark them for execution on a different stream. This is all we need to execute the offloading of slices asynchronously.
Copybara import of the project:
--
905db6df8130300014728bf4eceb2ee2fc23d6e1 by Jaroslav Sevcik <[email protected]>:
Wrap async dynamic slicing into fusion
Merging this change closes #11353
Reverts changelist 592550538
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11353 from jaro-sevcik:host-offloading-async-fusion-dynamic-slicing 905db6df8130300014728bf4eceb2ee2fc23d6e1
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Integrate StableHLO at openxla/stablehlo@e81411ef
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Integrate StableHLO at openxla/stablehlo@e81411ef
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Remove unnecessary mentions of tf_runtime in visibilities
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Remove unnecessary mentions of tf_runtime in visibilities
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Define MLIR helper `StatusScopedDiagnosticHandler`.
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Define MLIR helper `StatusScopedDiagnosticHandler`.
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PR_kwDOArmXAs5s0_z2
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Set metadata and frontend attributes for new get-tuple-element instructions in multi-output-fusion pass.
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Set metadata and frontend attributes for new get-tuple-element instructions in multi-output-fusion pass.
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[oneDNN] Remove oneDNN eltment-wise ops in mkl_relu_op.cc
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[
"@penpornk Hi Penporn, all the listed element-wise ops are registered with MKL layout op label. Now oneDNN ops only support native format, thus Mkl tensors are not used any more in graph rewriting. \r\n\r\nAs part of code cleanup effort (hopefully reduce compilation time, and a little execution time w/o if/else), we remove thie implementation of mkl_relu_op.cc and related tests. In near future, I will submit a sequence of PRs to clean up all \"Mkl layout\" related implementation for all oneDNN ops, as well as lots of outdated \"layout\" comments in core/common_runtime/mkl_layout_pass.cc. \r\n\r\nThanks",
"Hi @penpornk Can you please review this PR ? Thank you!",
"@penpornk Hi Penporn, please help to review this PR. A follow-up PR will clean up all MKL layout propagation code (for instance, get rid of MKL tensor stuff) in core/common_runtime/mkl_layout_pass.cc (remove 1000+ lines of source code which is not functioning any more). THANKS! -GZ",
"Hi @penpornk Can you please review this PR ? Thank you!\r\n\r\n",
"Hi @penpornk Can you please review this PR ? Thank you!"
] | 2024-04-16T16:51:25 | 2024-06-07T17:06:29 | null |
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Remove the following oneDNN ops which are not supported since TF2.15 (all registered ops used to support MKL layout format):
Relu, ReluGrad, Relu6, Relu6Grad, Elu, EluGrad, Tanh, TanhGrad, LeakyRelu, LeakyReluGrad
Related C++ test cases are also removed
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Change `BuildGetTupleElementsForTupleResults` to allow for a non-tuple sharding on a tuple op, by using the same sharding for all get-tuple-elements.
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Change `BuildGetTupleElementsForTupleResults` to allow for a non-tuple sharding on a tuple op, by using the same sharding for all get-tuple-elements.
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PR_kwDOArmXAs5s04v9
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Integrate StableHLO at openxla/stablehlo@c979359b
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NONE
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Integrate StableHLO at openxla/stablehlo@c979359b
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KV Cache: add multi-signature test
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KV Cache: add multi-signature test
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PR_kwDOArmXAs5s0iYT
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Support sparse dot op in triton emitter
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Support sparse dot op in triton emitter
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[XLA] give Array an option to slice out of bounds.
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[XLA] give Array an option to slice out of bounds.
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PR_kwDOArmXAs5s0Xml
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Reverts a080cbad71e4c483a1851d802da2ea997bc4352f
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PR_kwDOArmXAs5s0Qui
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Integrate LLVM at llvm/llvm-project@694c444b5bbb
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Integrate LLVM at llvm/llvm-project@694c444b5bbb
Updates LLVM usage to match
[694c444b5bbb](https://github.com/llvm/llvm-project/commit/694c444b5bbb)
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PR_kwDOArmXAs5s0LUw
| 65,844 |
Refactor GEMM autotuner
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Refactor GEMM autotuner
The refactoring is trying to fix two issues:
1) Expose API for compiling and autotuning dot fusions - previously the implementation was hidden in an anonymous namespace and used a global object for storing the results (autotuning cache). This might be useful for modelling matmul performance.
2) Reduce code duplication - previously the filtering of the triton configs was done separately for default and exhaustive search. Some configs need to be adjusted to be correctly compiled by Triton, for example:
- when there are less elements in a tensor than the number of threads (happens for sparse dot metadata with small tiles);
- when compiling for Hopper with `wgmma` enabled (block_m or num_warps may need to be adjusted).
Notably, `kDefaultGemmTiling` could need such adjustments, so returning it as-is may result in fatal compilation errors. With this patch, the same config generator is used for all code paths.
There are a few more changes:
- do not handle reference implementation (cuBLAS) separately; instead, use it in the `Config` variant along with the other options (cuDNN, triton).
- do not repeat cuDNN conditions - put them in one place.
- use actual tile sizes and SM count for calculating the split-K upper bound (more accurate heuristic than a global limit).
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[xla:ffi] Add test case for tuple input to CPU custom call tests
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"Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/65843/checks?check_run_id=23883178682) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request."
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[xla:ffi] Add test case for tuple input to CPU custom call tests
Add a test case for handling tuple input to CPU custom call typed FFI API tests.
Implements #10056.
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It is unclear how a user should configure LD_LIBRARY_PATH after pip install tensorflow[and-cuda]
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[
"@jackhart We had a long discussion about it and figured it out: https://github.com/tensorflow/tensorflow/issues/63362\r\n\r\nIt's a path issue. Supposedly, there's a pull request where this gets fixed either in 2.16.2 or 2.17.",
"@jackhart it seems practically impossible for someone owning a PC with CUDA-enabled GPU to perform deep learning experiments with TensorFlow version 2.16.1 and utilize his GPU locally without manually performing some extra steps not included (until today) in the official TensorFlow documentation of the standard installation procedure of TensorFlow for Linux users with GPUs at least as a temporal fix! That's why I submitted the pull request in good faith and for the shake of all users as TensorFlow is \"_An Open Source Machine Learning Framework for Everyone_\".\n\nHope that the next patch version of TensorFlow will fix the bug as soon as possible!\n\nhttps://github.com/tensorflow/docs/pull/2299",
"I am having an incredibly frustrating experience. I followed the exact steps outlined here (https://www.tensorflow.org/install/pip), and despite my best efforts and hours of searching I keep getting \"Cannot dlopen some GPU libraries\" .. \"Skipping registering GPU devices...\".\r\n\r\nI keep reading something about the LD_LIBRARY_PATH however I cannot find an obvious fix especially as I am using a python venv! I miss the days when tf-gpu worked on windows. This is pushing me to pytorch because I am exhausted trying different commands and path configurations to no avail. Why is it so difficult to use my GPU with Tensorflow?",
"> I am having an incredibly frustrating experience. I followed the exact steps outlined here (https://www.tensorflow.org/install/pip), and despite my best efforts and hours of searching I keep getting \"Cannot dlopen some GPU libraries\" .. \"Skipping registering GPU devices...\".\n> \n> I keep reading something about the LD_LIBRARY_PATH however I cannot find an obvious fix especially as I am using a python venv! I miss the days when tf-gpu worked on windows. This is pushing me to pytorch because I am exhausted trying different commands and path configurations to no avail. Why is it so difficult to use my GPU with Tensorflow?\n\n@Edfrost321 a revised document addressing the issue of TensorFlow installation for Linux users with GPUs is pending review. Here is the link: https://github.com/sgkouzias/docs/blob/patch-1/site%2Fen%2Finstall%2Fpip.md \n\nI hope it helps!",
"Hi **@jackhart** ,\r\nSorry for the delay, discussion is going on belongs to this issue already [#63362](https://github.com/tensorflow/tensorflow/issues/63362) , [#2299](https://github.com/tensorflow/docs/pull/2299). Once we get update, let you know.\r\n\r\nThank you!",
"@sgkouzias \r\n\r\nThat's a huge help, thank you!",
"Thank you! Closing.",
"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/65842\">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/65842\">No</a>\n"
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NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
v2.16.1-0-g5bc9d26649c 2.16.1
### Custom code
No
### OS platform and distribution
Debian GNU/Linux 12 (bookworm)
### Mobile device
_No response_
### Python version
3.9.19
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
See below
### GPU model and memory
n/a
### Current behavior?
## Overview
It is unclear how a user should manage the CUDA dependencies installed with `pip install tensorflow[and-cuda]`.
LD_LIBRARY_PATH is used to load CUDA libs. However, the install/install docs do not tell the user how to add the installed CUDA libs to the LD_LIBRARY_PATH. I had to add every `venv/python*/site-packages/nvidia/<name>/lib` the LD_LIBRARY_PATH, since the linker does not recursively search a directory.
## Dependencies
* Container CUDA RuntimeVersion: 12.3
* CUDA Diver Version (what is linked by nvidia-smi - a nuance of containerized NVIDIA): 12.2
* Misc other NVIDIA dependencies installed with [and-cuda]: https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/tools/pip_package/setup.py#L174
## Final Note
I might be missing something obvious. But my impression is that the process required to use the pip installed CUDA dependencies is a bit involved and not documented. I would appreciate any insight your teams' expertise can provide here! Thank you!
### Standalone code to reproduce the issue
```shell
======================== Dockerfile ========================
FROM python:3.9
ENV NVARCH=x86_64
ENV NVIDIA_VISIBLE_DEVICES=all
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
ENV LD_LIBRARY_PATH /usr/local/nvidia/lib:/usr/local/nvidia/lib64:${LD_LIBRARY_PATH}
ENV NVIDIA_REQUIRE_CUDA "cuda>=11.6, arch<9,driver>=450"
WORKDIR /app
ENV PATH /app/venv/bin:${PATH}
RUN python3 -m venv /app/venv && \
pip install --upgrade pip setuptools wheel
RUN pip install tensorflow[and-cuda]
COPY entrypoint.sh /app/entrypoint.sh
ENTRYPOINT ["/app/entrypoint.sh"]
======================== entrypoint.sh ========================
#!/bin/bash
set -e
set -o xtrace
# Uncomment for solution.
# The LD_LIBRARY_PATH cannot have nested directories, so I link each pip installed nvidia lib.
#export LD_LIBRARY_PATH=$(find /app/venv/lib -type d -path "*/python*/site-packages/nvidia/*" -prune -exec printf ':%s/lib' {} +):${LD_LIBRARY_PATH}
LD_DEBUG=libs python -c "import tensorflow as tf; print(f'Devices List: {tf.config.list_physical_devices()}')"
======================== command line from host ========================
docker build -t tensorflow-test .
docker run --gpus all tensorflow-test:latest
```
### Relevant log output
```shell
# ======================== logs when LD_LIBRARY_PATH isn't properly set ========================
+ python -c 'import tensorflow as tf; print(f'\''Devices List: {tf.config.list_physical_devices()}'\'')'
2024-04-16 13:49:18.866268: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-04-16 13:49:20.778286: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-04-16 13:49:22.322541: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
Devices List: [PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]
# ======================== logs when LD_LIBRARY_PATH is properly set ========================
+ LD_LIBRARY_PATH=:/app/venv/lib/python3.9/site-packages/nvidia/cudnn/lib:/app/venv/lib/python3.9/site-packages/nvidia/cuda_cupti/lib:/app/venv/lib/python3.9/site-packages/nvidia/cufft/lib:/app/venv/lib/python3.9/site-packages/nvidia/nccl/lib:/app/venv/lib/python3.9/site-packages/nvidia/__pycache__/lib:/app/venv/lib/python3.9/site-packages/nvidia/cublas/lib:/app/venv/lib/python3.9/site-packages/nvidia/nvjitlink/lib:/app/venv/lib/python3.9/site-packages/nvidia/cuda_runtime/lib:/app/venv/lib/python3.9/site-packages/nvidia/cuda_nvrtc/lib:/app/venv/lib/python3.9/site-packages/nvidia/cusolver/lib:/app/venv/lib/python3.9/site-packages/nvidia/cuda_nvcc/lib:/app/venv/lib/python3.9/site-packages/nvidia/curand/lib:/app/venv/lib/python3.9/site-packages/nvidia/cusparse/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64:
+ python -c 'import tensorflow as tf; print(f'\''Devices List: {tf.config.list_physical_devices()}'\'')'
2024-04-16 13:50:41.598098: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-04-16 13:50:43.546095: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Devices List: [PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:1', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:2', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:3', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:4', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:5', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:6', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:7', device_type='GPU')]
```
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Add new RBE toolchains for Windows.
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Transfer `_quantization_method` attribute to the converted quantized `CallOp`.
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tf.custom_gradient silently fails when using all keyword arguments
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[
"Triage Notes:\r\nI was able to reproduce the issue on tensorflow v2.15, v2.16 and tf-nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/b43909cc45fce3162c36bd37c3af981d/untitled1863.ipynb).\r\n\r\nThe tf.custom_gradients are working as expected when z = bar(x, y) & z = bar(x=x, y=y) and it was failing with z = bar(x, y=y) by throwing the error.",
"@tilakrayal when running your gist, the gradient is NOT as expected when z = bar(x=x, y=y). the expected output should be the custom gradient (which is [[-2000], [-4000], [-6000]] for dz/dx), given the function is wrapped in tf.custom_gradient, but instead returns the gradient produced by autograd (which is [[-0.66], [-1.33], [-2.]]).\r\n\r\nThe issue is that the custom gradient is silently ignored when using keyword arguments for every parameter.",
"The decorator `@tf.custom_gradient` uses the input to deduce which arguments are differentiable inputs, and which arguments are non-differentiable parameters to the function. Positional arguments are considered differentiable, keyword arguments non-differentiable.\r\n\r\n> When passing all keyword arguments, the custom gradient is silently ignored and defaults to the autograd.\r\n\r\nIt thinks your function has no differentiable inputs, so simply forwards the upstream gradient and continues the computation.\r\n\r\n> When passing a mix of positional and keyword arguments, an exception is thrown.\r\n\r\nBecause you're returning multiple gradients when only the one non-keyword input is considered a differentiable input.\r\n\r\n---\r\n\r\nAs for what to do: we will not change the behavior of the `custom_gradient` decorator. You can update the documentation with a PR if you like to reflect this behavior.\r\n\r\n",
"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.",
"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/65839\">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/65839\">No</a>\n"
] | 2024-04-16T13:28:32 | 2024-04-25T15:08:30 | 2024-04-25T15:08:27 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.12
### Custom code
Yes
### OS platform and distribution
Amazon Linux 2
### Mobile device
_No response_
### Python version
3.8.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When decorating a function with tf.custom_gradient, calling that function results in unexpected behavior when passing all keyword arguments. When passing all keyword arguments, the custom gradient is silently ignored and defaults to the autograd. When passing a mix of positional and keyword arguments, an exception is thrown.
I believe that passing keyword arguments should throw an exception, since there is no warning that the autograd will be returned and not the custom grad.
### Standalone code to reproduce the issue
```shell
@tf.custom_gradient
def bar(x, y):
def grad(upstream):
return 1000 * 2 * (x - y), -2 * (x - y)
return tf.reduce_mean(tf.math.square(x - y), axis=0), grad
x = tf.constant([[4], [5], [6]], dtype=tf.float32)
y = tf.constant([[5], [7], [9]], dtype=tf.float32)
# first call with positional arguments, this works as expected
with tf.GradientTape(persistent=True) as tape:
tape.watch(x)
tape.watch(y)
z = bar(x, y)
print("without keywords...")
print(z)
print(tape.gradient(z, x))
print(tape.gradient(z, y))
print()
# next, call with all keyword arguments, this returns the autograd and not the custom grad
with tf.GradientTape(persistent=True) as tape:
tape.watch(x)
tape.watch(y)
z = bar(x=x, y=y)
print("with keywords...")
print(z)
print(tape.gradient(z, x))
print(tape.gradient(z, y))
# last, call with a mix of positional and keyword args, exception is thrown
with tf.GradientTape(persistent=True) as tape:
tape.watch(x)
tape.watch(y)
z = bar(x, y=y)
print("with keywords...")
print(z)
print(tape.gradient(z, x))
print(tape.gradient(z, y))
```
### Relevant log output
_No response_
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Automated Code Change
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[
"Hi @Zoeeeeey ,\r\n\r\nThanks for reporting. As mentioned I have tested the code snippet in tf-nightly without tf.function decorator and it executes fine. With tf.function decorator it seems going through infinite loop as I waited for more than 5 mins and still it continuous to execute but couldn't get `segmentation fault` error. \r\n\r\n<img width=\"1511\" alt=\"Screenshot 2024-04-17 at 11 58 57 AM\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/6b4a76b6-8738-481a-a1dd-94c4da598695\">\r\n",
"Hi @SuryanarayanaY ,\r\n\r\n1.When I tried to replicate this reported, I initially encountered a situation where there was 'no response' and 'no termination' during the first run. \r\n\r\nWhen I used `ctrl+c` to terminate the program and ran it again, I encountered a Segmentation fault (core dumped).\r\n\r\nMy envs which occurs segfault：\r\n```\r\nPython 3.9\r\ntensorflow 2.16.1\r\ngcc version 7.5.0 (Ubuntu 7.5.0-3ubuntu1~18.04)\r\nnvcc: NVIDIA (R) Cuda compiler driver\r\nCopyright (c) 2005-2020 NVIDIA Corporation\r\nBuilt on Mon_Nov_30_19:08:53_PST_2020\r\nCuda compilation tools, release 11.2, V11.2.67\r\nBuild cuda_11.2.r11.2/compiler.29373293_0\r\n```\r\n\r\n2.Nevertheless, there are situations where the behavior is inconsistent, whether adding `@tf.function` or not. \r\n\r\nFor example, the part that contains pass can be replaced with a print statement. \r\n\r\nWhen `@tf.function` is added, the program fails to terminate normally. \r\n\r\nWhy is this the case?\r\n\r\n3.Additionally, I have a question about the `tf.config.experimental_connect_to_cluster` function. \r\n\r\nI'm unclear about the exact content that should be passed into the first parameter `cluster_spec_or_resolver`.\r\n\r\nFor example, in this case, a cluster is created with `tf.train.ClusterSpec`, but the specified workers (such as `\"worker0.example.com:2222\"`) do not actually exist. \r\n\r\nWhat should be the correct content to set for this parameter?\r\n\r\n4.Additionally, to facilitate your replication, I have attempted to create a gist in Colab. Please consult it.(https://colab.research.google.com/gist/Zoeeeeey/1782cc57b0a63d8c6a022eeae0977235/62522_tf-nightly-v.ipynb)\r\n\r\nThank you for any response. ;->"
] | 2024-04-16T10:53:07 | 2024-04-22T03:15:29 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
tf 2.16.1
### 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
_No response_
### CUDA/cuDNN version
12.0
### GPU model and memory
_No response_
### Current behavior?
When configuring `tf.config.experimental_connect_to_cluster` and using the `@tf.function` decorator on a function,
even though the function's content is `pass`, a `Segmentation fault (core dumped)` still occurs.
When the `@tf.function` decorator is commented out, the program executes with a return code of 0 and no errors occur.
### Standalone code to reproduce the issue
```shell
This simple code repeats the problem:
import tensorflow as tf
cluster = tf.train.ClusterSpec({"worker": ["worker0.example.com:2222",
"worker1.example.com:2222",
"worker2.example.com:2222"],
"ps": ["ps0.example.com:2222",
"ps1.example.com:2222"]})
tf.config.experimental_connect_to_cluster(
cluster,
job_name='worker',
task_index=0
)
@tf.function
def test():
pass
test()
```
### Relevant log output
```shell
The error message I got:
2024-04-15 13:29:11.263846: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-04-15 13:29:11.264202: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.
2024-04-15 13:29:11.267063: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.
2024-04-15 13:29:11.305386: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-04-15 13:29:11.992670: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-04-15 13:29:12.465636: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
E0415 13:29:12.509477560 2432185 server_chttp2.cc:40] {"created":"@1713187752.509411414","description":"No address added out of total 1 resolved","file":"external/com_github_grpc_grpc/src/core/ext/transport/chttp2/server/chttp2_server.cc","file_line":395,"referenced_errors":[{"created":"@1713187752.509406740","description":"Failed to add any wildcard listeners","file":"external/com_github_grpc_grpc/src/core/lib/iomgr/tcp_server_posix.cc","file_line":342,"referenced_errors":[{"created":"@1713187752.509393568","description":"Unable to configure socket","fd":15,"file":"external/com_github_grpc_grpc/src/core/lib/iomgr/tcp_server_utils_posix_common.cc","file_line":216,"referenced_errors":[{"created":"@1713187752.509389969","description":"Address already in use","errno":98,"file":"external/com_github_grpc_grpc/src/core/lib/iomgr/tcp_server_utils_posix_common.cc","file_line":189,"os_error":"Address already in use","syscall":"bind"}]},{"created":"@1713187752.509406103","description":"Unable to configure socket","fd":15,"file":"external/com_github_grpc_grpc/src/core/lib/iomgr/tcp_server_utils_posix_common.cc","file_line":216,"referenced_errors":[{"created":"@1713187752.509403589","description":"Address already in use","errno":98,"file":"external/com_github_grpc_grpc/src/core/lib/iomgr/tcp_server_utils_posix_common.cc","file_line":189,"os_error":"Address already in use","syscall":"bind"}]}]}]}
2024-04-15 13:29:12.509541: E tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:608] UNKNOWN: Could not start gRPC server
2024-04-15 13:29:12.509869: E tensorflow/core/common_runtime/eager/context_distributed_manager.cc:450] Could not start gRPC server
Segmentation fault (core dumped)
```
```
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does pluggable custom device support distributed runtime?
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"@Hoonly Pluggable custom devices in TensorFlow generally do not directly support distributed runtime on their own. Distributed runtime in TensorFlow refers to running computations across multiple machines or devices for tasks that are too large for a single machine. Could you please refer to [this](https://www.tensorflow.org/api_docs/python/tf/distribute/Strategy) guide to explore TF distributed strategies?\r\nPlease upgrade to the latest TF version as the older ones are not actively supported.\r\n\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/65831\">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/65831\">No</a>\n"
] | 2024-04-16T09:49:57 | 2024-05-09T01:48:53 | 2024-05-09T01:48:44 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.6.5
### Custom code
Yes
### OS platform and distribution
Ubuntu1804
### Mobile device
_No response_
### Python version
3.8
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
how to support distributed training with pluggable device
### Standalone code to reproduce the issue
```shell
None
```
### Relevant log output
_No response_
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Import msvcp140.dll and msvcp140_1.dll from a custom location
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"Hi **@admshumar** ,\r\nCould you please verify your python/os version. And you may also refer to [#35618](https://github.com/tensorflow/tensorflow/issues/35618).if you still face any issues please let us.\r\n\r\nThank you!",
"Hi @Venkat6871,\r\nIt seems to be sufficient to use Python's os module to include custom DLL locations prior to importing TensorFlow:\r\n`from os import add_dll_directory`\r\n`add_dll_directory(\"custom_dll_dir\")`\r\n`import tensorflow as tf`"
] | 2024-04-16T09:16:52 | 2024-04-25T10:53:33 | 2024-04-25T10:53:32 |
NONE
| null | null | null |
### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.10
### Custom code
No
### OS platform and distribution
Microsoft Windows 10
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
On Microsoft Windows, TensorFlow requires either of the Microsoft Visual C++ runtime libraries msvcp140.dll or msvcp140_1.dll when initialising. When both DLLs are missing, TensorFlow exits as follows:
_ImportError: Could not find the DLL(s) 'msvcp140.dll or msvcp140_1.dll'. TensorFlow requires that these DLLs be installed in a directory that is named in your %PATH% environment variable. You may install these DLLs by downloading "Microsoft C++ Redistributable for Visual Studio 2015, 2017 and 2019" for your platform from this URL: https://support.microsoft.com/help/2977003/the-latest-supported-visual-c-downloads_
Placing 'msvcp140.dll or msvcp140_1.dll' in a custom directory and appending the custom directory to PATH prior to importing TensorFlow is insufficient to expose the DLLs to TensorFlow.
There are two options to deal with this:
(1) Download and install "Microsoft C++ Redistributable for Visual Studio 2015, 2017 and 2019", as recommended. The problem with this is that if there is no control over the target environment, then there is no guarantee that the desired DLL files will exist at runtime.
(2) Make the call to ctypes in tensorflow.platform.python.self_check.preload_check configurable with a custom path for the DLL files.
I would prefer option (2), as it allows tighter control over the DLL files.
### Standalone code to reproduce the issue
```shell
# Import TensorFlow without msvcp140.dll and msvcp140_1.dll in a system directory or Python
# directory
import tensorflow
```
### Relevant log output
```shell
C:\Python\lib\site-packages\tensorflow\__init__.py:37: in <module>
from tensorflow.python.tools import module_util as _module_util
C:\Python\lib\site-packages\tensorflow\python\__init__.py:36: in <module>
from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
C:\Python\lib\site-packages\tensorflow\python\pywrap_tensorflow.py:26: in <module>
self_check.preload_check()
C:\Python\lib\site-packages\tensorflow\python\platform\self_check.py:50: in preload_check
raise ImportError(
E ImportError: Could not find the DLL(s) 'msvcp140.dll or msvcp140_1.dll'. TensorFlow requires that these DLLs be installed in a directory that is named in your %PATH% environment variable. You may install these DLLs by downloading "Microsoft C++ Redistributable for Visual Studio 2015, 2017 and 2019" for your platform from this URL: https://support.microsoft.com/help/2977003/the-latest-supported-visual-c-downloads
```
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[xla:cpu] Restore the verbose version of custom call tests
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[xla:cpu] Restore the verbose version of custom call tests
Reverts 71f2fdb. Applies necessary changes to the new tests.
Rollback reason: The more verbose version provides more context and is self-documenting; thus, it is easier to understand.
Reverts 8a2d31612fbafc7019bb15ad0a5752178cce745c
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11524 from ROCm:ci_gemm_limit_solutions b8d2a95773ca80d8cb4a2fdac1d2624e4ae2d5c8
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[xla:gpu] Remove `GatherSimplifier` and `ScatterSimplifier` from `runSPMDPasses`, to avoid breaking the parallel dimensions pattern for propagation and partitioning in GSPMD.
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[xla:gpu] Remove `GatherSimplifier` and `ScatterSimplifier` from `runSPMDPasses`, to avoid breaking the parallel dimensions pattern for propagation and partitioning in GSPMD.
Those passes are applied in `RunOptimizationPasses` which is after partitioning.
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function object mismatch in TFOpLambda
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"@elad-c,\r\nLooks like this is an issue with the keras and also I can see that there is another issue raised for the similar error in the keras repo. Could you please feel free to move this issue to closed status and let me know if anything is missed here. Thank you!",
"Ok, thanks.",
"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/65813\">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/65813\">No</a>\n"
] | 2024-04-16T08:18:17 | 2024-04-21T06:48:34 | 2024-04-21T06:48:31 |
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### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 20.04
### 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?
The function object stored in the `function` attribute of a `TFOpLambda` in a functional model doesn't match the original tensorflow function I used to build the model. This happens for `tf.add`, `tf.multiply` & `tf.matmul`. Doesn't happen for `tf.subtract` and `tf.divide`.
This behaviour started in version 2.15.
The output of TF 2.14 is:
1) layer match: True
2) add match: True
3) subtract match: True
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
from keras.src.layers.core import TFOpLambda
_in = tf.keras.layers.Input((8,))
out = tf.add(_in, 4)
out = tf.subtract(out, 4)
model = tf.keras.Model(inputs=_in, outputs=out)
print(f'1) layer match: {isinstance(model.layers[1], TFOpLambda)}')
print(f'2) add match: {model.layers[1].function is tf.add}')
print(f'3) subtract match: {model.layers[2].function is tf.subtract}')
```
### Relevant log output
```shell
1) layer match: True
2) add match: False
3) subtract match: True
```
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PR #11524: [ROCM] added flag limiting the number of gemm solutions to be checked & get real IDs for hipblas-lt
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PR #11524: [ROCM] added flag limiting the number of gemm solutions to be checked & get real IDs for hipblas-lt
Imported from GitHub PR https://github.com/openxla/xla/pull/11524
In this PR I add a new XLA flag **xla_gpu_autotune_max_solutions** which limits the number of GEMM solutions to be using for GEMM autotuning. This is a temporary measure while rocBLAS returns way too many solutions which can significantly slow-down autotuning for real applications.
Besides, I also added a "workaround" to obtain the real solution IDs for hipblas-lt which might be later used for deterministic autotuning.
@xla-rotation: would you have a look please ?
Copybara import of the project:
--
b8d2a95773ca80d8cb4a2fdac1d2624e4ae2d5c8 by Pavel Emeliyanenko <[email protected]>:
added flag limiting the number of gemm solutions to be checked & get real solution IDs for hipblas-lt
Merging this change closes #11524
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11524 from ROCm:ci_gemm_limit_solutions b8d2a95773ca80d8cb4a2fdac1d2624e4ae2d5c8
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PR #11557: Fix unittest build error
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PR #11557: Fix unittest build error
Imported from GitHub PR https://github.com/openxla/xla/pull/11557
This PR fix the build error for tests:
./bazel-6.5.0rc1-linux-x86_64 \
test \
--flaky_test_attempts=1 \
--config=cuda \
--compilation_mode=dbg \
--action_env TF_CUDA_COMPUTE_CAPABILITIES=compute_90 \
--test_summary=detailed \
--runs_per_test=1 \
--nocache_test_results \
--test_output=streamed \
//xla/service/gpu/tests:gemm_rewrite_test_gpu
Copybara import of the project:
--
3b4f5cf65eab164b4fa2d79721ee618da772d600 by Shawn Wang <[email protected]>:
fix unittest build error
Merging this change closes #11557
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Avoid cuDNN initialization in Stream::DeallocateStream
`DeallocateStream` used to call `AsDnn()` which initializes
cuDNN if it wasn't initialized before.
This is not intended in `DeallocateStream` which runs as part
of the destructor.
Instead I access the `dnn_` member variable directly.
Only if cuDNN was initialized previously we do the cleanup.
This was easy to do thanks to Kyle's cleanup from cl/623542918.
This avoid some stack overflow issues seen in
`//third_party/tensorflow/python/distribute:cross_device_ops_test_gpu`
due to excessive cuDNN initialization (even though cuDNN is not needed at all in this test).
Here is a run of this test that demonstrates the fix: http://sponge2/5c72bcc3-380f-4be3-a1f7-6c63d94848af
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`Segmentation fault` in `tf.raw_ops.TensorArrayV3` when the value of `size` is too large.
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[
"Hi @xl1043237213 ,\r\n\r\nI am facing OOM error while checking on Colab(12.67 GB RAM). To confirm the problem could you please confirm the RAM size of your environment. Thanks!",
"Thanks for your response. \r\n`Segmentation fault` can be triggered in memory-constrained conditions. I triggered the error using a lightweight server (2G of memory). On a server with 128G of RAM, the code can be executed, but it fills up all memory. I have updated the colab code and simulated the memory constrained situation using `resource`.",
"Hi @xl1043237213 ,\r\n\r\nThanks for more details. With the modified code I can observe the session crash on colab.Attached screenshot below for reference.\r\n\r\n<img width=\"1512\" alt=\"Screenshot 2024-04-18 at 11 31 23 AM\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/db383f2e-fdb8-485d-adaf-cc4125aa75c3\">\r\n"
] | 2024-04-16T05:25:05 | 2024-04-23T07:15:29 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`Segmentation fault` in `tf.raw_ops.TensorArrayV3` when the value of `size` is too large.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1QR3h-YYHGAXpkqt_xxsZFuEyuP70llBT?usp=sharing
```
### Relevant log output
```shell
segmentation fault (core dumped)
```
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PR_kwDOArmXAs5swPKg
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Clean up XLA Runtime/XLA-CPU-Next related dead logic in PjRt CPU.
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Clean up XLA Runtime/XLA-CPU-Next related dead logic in PjRt CPU.
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