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"@x0w3n,\r\nI tried to execute the mentioned code on the tf-nightly GPU environment and haven't found out_of_range issues. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/30dc59b453b8cf03daf6defe345bece3/untitled1916.ipynb) and let me know if it is working as expected. Thank you!",
"> @x0w3n, I tried to execute the mentioned code on the tf-nightly GPU environment and haven't found out_of_range issues. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/30dc59b453b8cf03daf6defe345bece3/untitled1916.ipynb) and let me know if it is working as expected. Thank you!\r\n\r\nIt seems that colab is not outputting debug messages, I can see the debug messages output when I run it with the idea tool on a physical machine. As the last line of the debug message in the image shows:\r\n\r\n",
"i have the same problem!!",
"@x0w3n,\r\nI tried to execute the mentioned code on the tensorflow 2.16 GPU environment and haven't faced the issue. Also I observed that the mentioned OUT_OF_RANGE is the warning which might not effect the execution of the code and the output.\r\n\r\n\r\n\r\n\r\nThank you!",
"I think this output may be caused by gpu unavailability. Although it does not affect the execution of the code, the reason for this output is still unknown. Thank you!",
"@x0w3n,\r\nThe above code was executed in the GPU environment, and the output was also expected. In the screenshot it was available. Could you please provide the GPU details which you are trying. I suspect it might the GPU issue for the different output in your machine. Thank you!"
] | 2024-05-24T11:24:11 | 2024-06-11T06:07:46 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
3.12.3
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When calling and iterating over the results of some of tf.data's methods it outputs "Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence".
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
def dataset_test():
dataset = tf.data.Dataset.from_tensor_slices(["apple", "banana", "cherry"])
# dataset = tf.data.TextLineDataset(["file1.txt", "file2.txt"]).range(10) # same output
# dataset = tf.data.TFRecordDataset(["file1.tfrecords", "file2.tfrecords"]).range(10) # same output
# dataset = tf.data.Dataset.from_tensors([1,2,3]).range(10) # same output
print(list(dataset.as_numpy_iterator()))
if __name__ == "__main__":
dataset_test()
```
### Relevant log output
```shell
2024-05-24 06:23:06.268182: 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`.
2024-05-24 06:23:06.309884: 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.
2024-05-24 06:23:06.908161: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-05-24 06:23:07.558225: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22137 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:18:00.0, compute capability: 8.6
2024-05-24 06:23:07.558795: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 22453 MB memory: -> device: 1, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:af:00.0, compute capability: 8.6
2024-05-24 06:23:07.917142: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence
```
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} | PR #12952: [ROCM] hipblas-lt: bias pointer workaround
Imported from GitHub PR https://github.com/openxla/xla/pull/12952
This is a workaround for ROCM-6.2 version where the behaviour of bias pointer was changed. New version shall work fine both for old and new rocm versions
@xla-rotation: could you have a look please ?
Copybara import of the project:
--
1bd40dfa043978873652b38532e6a7c073fa2468 by Pavel Emeliyanenko <pavel.emeliyanenko@amd.com>:
added bias pointer workaround
--
d351a84a3cfc61fa3672a12cda9df3265780a7d4 by Pavel Emeliyanenko <pavel.emeliyanenko@amd.com>:
added comment
Merging this change closes #12952
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12952 from ROCm:ci_hipblaslt-bias-epilogue-fix d351a84a3cfc61fa3672a12cda9df3265780a7d4
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} | Consistently use {} for Interval construction.
Apparently some compiler has a problem with ().
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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/68590/checks?check_run_id=25370964139) 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:GPU] Fix compute time calculation in the performance model.
The compute time should be calculated based on the number of active cores, which is the minimum of the number of blocks and the number of cores on the device.
Originally suggested in https://github.com/openxla/xla/pull/12208.
Co-authored-by: lingzhi98 <lingzhi.zhou@intel.com>
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"Hi @nikechase3 ,\r\n\r\nCan you provide the code for detect.py or can you provide the link for the same , also can you tell me what is setup.sh about ?",
"it is on the tensorflow lite example repository, i tried yesterday, and it worked but i fear that next time the same thing will happen again",
"hi @nikechase3 ,\r\n\r\nCan you please retry one more time in a different python enviornment and let me know .",
"i ve tried again and the results are unreliable between just opening the window on display without any output or just lagging",
"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/68585\">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/68585\">No</a>\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/68585\">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/68585\">No</a>\n"
] | 2024-05-24T07:47:39 | 2024-05-27T15:35:53 | 2024-05-27T15:35:49 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.11
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
Linux raspberrypi 6.1.21-v7
### Python version
3.7.3
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
-
### GPU model and memory
_No response_
### Current behavior?
ive tried using the setup `sh setup.sh` but it will revert my opencv version to 4.5.x and wont detect it which in turn result in an import error. so i use an opencv with version 4.6.x and it shows the code under.
### Standalone code to reproduce the issue
```shell
python3 detect.py --model efficientdet_lite0.tflite
```
### Relevant log output
```shell
python3 detect.py --model efficientdet_lite0.tflite
Traceback (most recent call last):
File "detect.py", line 150, in <module>
main()
File "detect.py", line 146, in main
int(args.numThreads), bool(args.enableEdgeTPU))
File "detect.py", line 63, in run
detector = vision.ObjectDetector.create_from_options(options)
File "/home/haikalasa/tflitr/lib/python3.7/site-packages/tensorflow_lite_support/python/task/vision/object_detector.py", line 88, in create_from_options
options.base_options.to_pb2(), options.detection_options.to_pb2())
RuntimeError: Unable to open file at efficientdet_lite0.tflite
```
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Updates LLVM usage to match
[fddf350f9640](https://github.com/llvm/llvm-project/commit/fddf350f9640)
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} | PR #13020: Support lowering XLA clamp instruction to cuDNN.
Imported from GitHub PR https://github.com/openxla/xla/pull/13020
Support lowering XLA clamp instruction to cuDNN.
cc @sergachev
Copybara import of the project:
--
47dc71f2a0d5887461a0b7d985328442e0e8da2f by Elfie Guo <elfieg@nvidia.com>:
Support lowering clamp instruction to cuDNN.
Merging this change closes #13020
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13020 from elfiegg:fp8_triton 47dc71f2a0d5887461a0b7d985328442e0e8da2f
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} | PR #12297: Fix side output index computation for transpose fusion
Imported from GitHub PR https://github.com/openxla/xla/pull/12297
There exist bug in ComputeThreadIdToOutputIndexing func. Currently, this func can not calculate indexing map correctly for sided output. Fix it and add corresponding test.
Copybara import of the project:
--
807c3cc82b1cb58ded59e8900135c29ee949c605 by Zhou, Lingzhi <lingzhi.zhou@intel.com>:
fix sided ouput index computation for transpose fusion
--
0ca08e78c1da3c43dee32460e1244a8d7e6e2aea by Zhou, Lingzhi <lingzhi.zhou@intel.com>:
restore
--
2f187c842eeddaf941416b321b9fe7c032721a59 by Zhou, Lingzhi <lingzhi.zhou@intel.com>:
format
--
fdb4d065da077e70a9b3544b4df78ce0b555012a by Zhou, Lingzhi <lingzhi.zhou@intel.com>:
remove change to input indexing due to equivalence
--
5484f950e16b6c97b65ca9d56bf6fa3dd65b909f by Zhou, Lingzhi <lingzhi.zhou@intel.com>:
use transpose hero index
--
5f08e19051e235419c8ac7f7dbdfb8c2ba5934f2 by Zhou, Lingzhi <lingzhi.zhou@intel.com>:
typo
--
4804d1cfa6f22a09a29760753cbda3030485a0fa by Zhou, Lingzhi <lingzhi.zhou@intel.com>:
fix build error
--
468cc85886ae15b60dcc8e5edc17530e3d62efa8 by Zhou, Lingzhi <lingzhi.zhou@intel.com>:
fix condition when side ouput inputs shape is not equal to output shape
--
5f33d319c0be6cb8d840d35c8a54210a9a4aeec0 by Zhou, Lingzhi <lingzhi.zhou@intel.com>:
fix transpose test
--
d362fb2dffca7a6172d2386db3d105ac91a96ee0 by Zhou, Lingzhi <lingzhi.zhou@intel.com>:
fix error caused by func signature change
--
c5fb450cf36a4e07b2e2de0e71e4c01cb1d1e866 by Zhou, Lingzhi <lingzhi.zhou@intel.com>:
fix test
--
cf78f46a2961a58ee31636022055e9f04b594fd0 by Zhou, Lingzhi <lingzhi.zhou@intel.com>:
add comment to illustrate what input indexing mean for side ouput
--
54789eb05ecc751881ea34c02346d19a1660bf9d by Zhou, Lingzhi <lingzhi.zhou@intel.com>:
add test to check if root0 and root1 has same input indexing when share input
--
f7db8e3e3f928311f210b3dd4111937a129b839c by Zhou, Lingzhi <lingzhi.zhou@intel.com>:
fix hlo verifier error
--
8567c4e9dd516120e0fe14a1bcc1ee573eb7da4c by Zhou, Lingzhi <lingzhi.zhou@intel.com>:
missing conflict
--
f0c1a66df84b2af1f8673b9ab7b2a4fb7c2144f3 by Zhou, Lingzhi <lingzhi.zhou@intel.com>:
format
--
7763742ff0138f6e297487414858be42ac671697 by Zhou, Lingzhi <lingzhi.zhou@intel.com>:
fix bugs caused by vectorization support of transpose mlir emitter
--
bc1d3854e90f66b2874d54bbcdd48a45d8dccf1c by Zhou, Lingzhi <lingzhi.zhou@intel.com>:
set correuse ranpose hero index
Merging this change closes #12297
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12297 from lingzhi98:lingzhi/fix_transpose_index_computation bc1d3854e90f66b2874d54bbcdd48a45d8dccf1c
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Imported from GitHub PR https://github.com/openxla/xla/pull/13039
Help to identify command buffer perf issues.
Copybara import of the project:
--
05c24d4707017755ee75a1d6058928fccfac1f83 by Shawn Wang <shawnw@nvidia.com>:
Add debug code to dump if GpuExecutable has any memory addressed changed
--
5fabaab97573a8059f0fe993514c03332c7e6ae8 by Shawn Wang <shawnw@nvidia.com>:
add comments
Merging this change closes #13039
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} | Add support for int4 in dequantize op.
This CL adds int4 support inside the reference/optimized dequantize op including per-channel dequantization.
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} | Slightly refactor IsSupportedF8Pattern in gemm_rewriter.cc.
This makes no functional changes.
The overall logic is a bit more clear now. A type InstrPath is added, aliasing the type 'std::vector<std::pair<HloInstruction *, int>>' which was previously passed around. This type is now always ordered from operand to user instead of being reversed within IsSupportedF8Pattern. The comments for several functions were expanded. FindF8SubgraphRecursive now only returns an InstrPath instead of both returning one and taking one in.
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} | Update robin_map dependency to v1.3.0.
This will be needed for a future nanobind v2.0 update.
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} | PR #13034: Fix typo
Imported from GitHub PR https://github.com/openxla/xla/pull/13034
Copybara import of the project:
--
6f47dc87ed665bc6723e6fb62f93fbcc20c858b5 by Dimitris Vardoulakis <dvardoulakis@nvidia.com>:
Fix typo
Merging this change closes #13034
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13034 from dimvar:dv-typo-fix 6f47dc87ed665bc6723e6fb62f93fbcc20c858b5
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} | PR #13021: Fix build issue of gpu_sparse_dot_test
Imported from GitHub PR https://github.com/openxla/xla/pull/13021
replaces EXPECT_OK with TF_EXPECT_OK and added relevant header file and dependency.
Error:
#10 1431.9 xla/service/gpu/tests/gpu_sparse_dot_test.cc:134:3: error: use of undeclared identifier 'EXPECT_OK'
[1792](https://gitlab-master.nvidia.com/dl/openxla/ci/-/jobs/94442414#L1792)#10 1431.9 134 | EXPECT_OK(dense_module);
[1793](https://gitlab-master.nvidia.com/dl/openxla/ci/-/jobs/94442414#L1793)#10 1431.9 | ^
[1794](https://gitlab-master.nvidia.com/dl/openxla/ci/-/jobs/94442414#L1794)#10 1431.9 xla/service/gpu/tests/gpu_sparse_dot_test.cc:137:3: error: use of undeclared identifier 'EXPECT_OK'
[1795](https://gitlab-master.nvidia.com/dl/openxla/ci/-/jobs/94442414#L1795)#10 1431.9 137 | EXPECT_OK(dense_result);
[1796](https://gitlab-master.nvidia.com/dl/openxla/ci/-/jobs/94442414#L1796)#10 1431.9 | ^
[1797](https://gitlab-master.nvidia.com/dl/openxla/ci/-/jobs/94442414#L1797)#10 1431.9 xla/service/gpu/tests/gpu_sparse_dot_test.cc:158:3: error: use of undeclared identifier 'EXPECT_OK'
[1798](https://gitlab-master.nvidia.com/dl/openxla/ci/-/jobs/94442414#L1798)#10 1431.9 158 | EXPECT_OK(sparse_module);
[1799](https://gitlab-master.nvidia.com/dl/openxla/ci/-/jobs/94442414#L1799)#10 1431.9 | ^
[1800](https://gitlab-master.nvidia.com/dl/openxla/ci/-/jobs/94442414#L1800)#10 1431.9 xla/service/gpu/tests/gpu_sparse_dot_test.cc:161:3: error: use of undeclared identifier 'EXPECT_OK'
[1801](https://gitlab-master.nvidia.com/dl/openxla/ci/-/jobs/94442414#L1801)#10 1431.9 161 | EXPECT_OK(sparse_result);
[1802](https://gitlab-master.nvidia.com/dl/openxla/ci/-/jobs/94442414#L1802)#10 1431.9 | ^
Copybara import of the project:
--
a7d07b02230794d9c13175b39473666d5d067c54 by hmonishN <hmonish@nvidia.com>:
fixing build issue for this test
--
e45b3b975c40b318ac4918859b364d8e8c3b304a by hmonishN <hmonish@nvidia.com>:
fix build error
--
077b7a5b8277cb2e55b57aca64102871326afd0d by Harshit Monish <143435143+hmonishN@users.noreply.github.com>:
Incorporated review comment.
Merging this change closes #13021
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13021 from hmonishN:hmonish/fix_gpu_sparse_dot_test_build_issue 077b7a5b8277cb2e55b57aca64102871326afd0d
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} | Add support for local wheel files in hermetic python
This allows users to specify a list of workspaces that contain pre-built local wheels without need to manually add them in requirements.txt files.
The wheels will be automatically processed by bazel rules and injected into the requirements_lock_<py_version>.txt on the fly (assuming `HERMETIC_PYTHON_VERSION=py_version`).
This feature is mainly inspired by pytorch/xla demand, since building pytorch/xla implies first building pytorch repo locally and then pointing to its artifacts (both raw .so files and entire .whl) in pytorch/xla build.
This also helps JAX to facilitate build_jaxlib=false case, as it would eliminate need to manually update requirements_locak.txt files in JAX CI as well.
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[10dc3a8e916d](https://github.com/llvm/llvm-project/commit/10dc3a8e916d)
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This is a part of the ongoing effort https://github.com/openxla/xla/issues/7234
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Before, we only allowed it for reduce epilogues, but after a recent fix this
should also work correctly for other fusions with epilogues.
Reverts changelist 602615649
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This fixes xla/tests:reduce_test_gpu.
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They are expecting the set of generated instructions to include wgmma-related code, and fail otherwise.
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} | Integrate LLVM at llvm/llvm-project@10dc3a8e916d
Updates LLVM usage to match
[10dc3a8e916d](https://github.com/llvm/llvm-project/commit/10dc3a8e916d)
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} | Don't fold constants in affine_apply.fold.
This sometimes (rarely) fails with "folder reused existing op
for one result but constant materialization failed for another
result". Not sure why, but it's just a not very important
runtime optimization
anyway.
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getIntOrFloatBitWidth asserts that the type is an integer, but
we only check the type after calling it.
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#21369
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12991 from openxla:gemm_fusion_autotuner_refactor 44682f67eb6e9c521f381a7c3dd93fee95f59366
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} | Simplify arith.minsi and maxsi.
Reverts 1165601296e2baba5535c8f9ba7510e1373b9d6c
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} | PR #12991: [GPU] Refactor GEMM fusion autotuner.
Imported from GitHub PR https://github.com/openxla/xla/pull/12991
Copybara import of the project:
--
f6e85691b51bdd17fc6167ad8f8cb00d7411fec9 by Ilia Sergachev <isergachev@nvidia.com>:
[GPU] Replace a hash map by a vector of pairs in GEMM fusion autotuner.
This will be needed for multi-host autotuning to split tasks between hosts reliably.
--
44682f67eb6e9c521f381a7c3dd93fee95f59366 by Ilia Sergachev <isergachev@nvidia.com>:
[GPU][NFC] Refactor for loop iterators.
Merging this change closes #12991
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12991 from openxla:gemm_fusion_autotuner_refactor 44682f67eb6e9c521f381a7c3dd93fee95f59366
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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/68518/checks?check_run_id=25323331169) 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] Move stream insertion operator for C API datatypes to global namespace.
In case this operator is called by a user using a macro, it happens in the global namespace. If the stream insertion operator is not found there, ADL never looks in `xla::ffi` namespace (because C API enums are defined in global namespace), so the generic `std` version for int is found instead. It results in cryptic messages like 'Wrong scalar data type: expected 4 but got 5'.
Moving stream insertion operator to global namespace solves this problem.
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But only if this leads to simplification.
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"@olive380 If you don't require any padding for the output, simply omit the output_padding argument altogether. The default behavior in TensorFlow 2.16 for Conv1DTranspose is to not add any padding, which aligns with your case of using output_padding=0. Also please refer to the Keras [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv1DTranspose) for the Conv1DTranspose layer specifically. This might provide additional details and examples regarding the output_padding argument.\r\n\r\nThank you!",
"@sushreebarsa Thank you for the answer, but your proposal does not solve the issue since the behavior of TensorFlow is totally different if output_padding is omitted or set to zero in all versions of the code up to 2.15. If you are not aware of this, please have a look at the deconv_output_length method defined in /tensorflow/python/keras/utils/conv_utils.py available [here](https://github.com/tensorflow/tensorflow/blob/43fcba8aeb627182afea8c5655f55d0557ab53dd/tensorflow/python/keras/utils/conv_utils.py#L4). So, please fix the bug in version 2.16 to allow set output_padding=0 in Conv1DTraspose and Conv2DTranspose.\r\n\r\nThank you !",
"@olive380,\r\nThank you for the issue. By default Tensorflow v2.16.1 contains keras3.0 version which might be the issue, as you mentioned this feature is available in Tensorflow v2.15 which contains tf-keras. If the same feature needs to be implemented in the keras3.0, could you please raise the request in [keras-team/Keras](https://github.com/keras-team/keras/issues) repo for the quick response. Thank you!"
] | 2024-05-23T09:21:38 | 2024-06-05T14:32:54 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf2.16.1
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 20.04 LTS
### Mobile device
_No response_
### Python version
3.9.16
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
With TF2.16, it is no more possible to set output_padding=0 when using Conv1DTranspose.
This feature is still needed and was allowed in previous versions of TF (e..g TF2.15).
### Standalone code to reproduce the issue
```shell
To reproduce the bug, please use the following python script :
[models.zip](https://github.com/tensorflow/tensorflow/files/15414612/models.zip)
```
### Relevant log output
```shell
File "tf2.16/lib/python3.9/site-packages/keras/src/layers/convolutional/conv1d_transpose.py", line 113, in __init__
super().__init__(
File "tf2.16/lib/python3.9/site-packages/keras/src/layers/convolutional/base_conv_transpose.py", line 111, in __init__
self.output_padding = standardize_tuple(
File "tf2.16/lib/python3.9/site-packages/keras/src/utils/argument_validation.py", line 51, in standardize_tuple
raise ValueError(error_msg)
ValueError: The `output_padding` argument must be a tuple of 1 integers. Received output_padding=0, including values {0} that do not satisfy `value > 0`
```
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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/68504/checks?check_run_id=25320431544) 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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} | PR #10301: [XLA:CPU][oneDNN] Convolution XLA HLO Pattern Matcher with oneDNN custom call rewrite
Imported from GitHub PR https://github.com/openxla/xla/pull/10301
This PR enables oneDNN library call for the matched XLA HLO Convolution pattern through custom_call instruction. In particular, this PR:
1. Adds oneDNN convolution rewriter pass that will rewrite HLO Convolution to oneDNN Convolution.
2. Refactors backend config to enhance code reusability.
3. Adds a convolution test file to verify rewrite and execution result
Copybara import of the project:
--
b7d0abb4f683595c91bf144bcf1b208254ca5c74 by Akhil Goel <akhil.goel@intel.com>:
Add onednn convolution support
--
fbf544ea346250129c00023da7e193aecad5375b by Akhil Goel <akhil.goel@intel.com>:
Remove unused symbol from BUILD file
--
76c079109361c75a6c512d32801039ed7a3f30e1 by Akhil Goel <akhil.goel@intel.com>:
Fix buildifier error
--
01f59d2d11481a1ac0b3e7a491261e1ee541aac0 by Akhil Goel <akhil.goel@intel.com>:
Address Review Comments
--
667502387a855ed0974822334283b890f84d1c34 by Akhil Goel <akhil.goel@intel.com>:
Refactor oneDNN rewritability check to a separate function
Merging this change closes #10301
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10301 from Intel-tensorflow:akhil/conv_fwd acddc5a8f306d6d0da036e4ef1b9112fe719d16e
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"Hi @changhuilin Can you please review this PR ? Thank you!"
] | 2024-05-23T07:59:12 | 2024-06-07T17:32:15 | null | CONTRIBUTOR | null | false | {
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} | This is a part of the Multiple Stream Groups feature. With this new feature, users are allowed to create multiple stream groups in one GPU device, and the computation graph can be distributed to different stream groups for paralleled execution to increase GPU utilization and throughput and decrease latency. This is essential for accelerating workloads containing many small-sized GPU kernels. We got 8x speedup easily on recommender online inference systems. Please refer to the 'Performance' part in our [document](https://docs.google.com/document/d/1yL3lWk_iFKqLTyekkuaiKXZ78I0lPmD5kM1fghHRs4Y/edit?usp=sharing) for detailed and more experiment results.
Full multi-stream inference implementation please see https://github.com/tensorflow/tensorflow/compare/master...buptzyb:MultipleStreamGroups.
This PR provides the multi-stream related helper functions in DeviceNameUtils.
When the multi-stream feature is enabled, TF will create multiple StreamDevice objects, each associated with one stream group. The name of a StreamDevice is like `/device:STREAM_GPU_0:2`, which means its real GPU device is `/device:GPU:0` and it manages the second stream group. The helper functions provide interconversion between Device names and StreamDevice names.
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} | Support more operators on Intervals.
- comparison of intervals with intervals (not just intervals
with points)
- addition and multiplication - implement saturating semantics
for these. Also fix our current multiplication bound.
- min and max
And add a bunch of tests. This is in preparation for more arith
simplification.
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"Hi @buptzyb Can you please resolve the conflicts? Thank you!"
] | 2024-05-23T07:27:45 | 2024-06-07T17:31:47 | null | CONTRIBUTOR | null | false | {
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} | This is a part of the _Multiple Stream Groups_ feature. With this new feature, users are allowed to create multiple stream groups in one GPU device, and the computation graph can be distributed to different stream groups for paralleled execution to increase GPU utilization and throughput and decrease latency. This is essential for accelerating workloads containing many small-sized GPU kernels. We got 8x speedup easily on recommender online inference systems. Please refer to the 'Performance' part in our [document](https://docs.google.com/document/d/1yL3lWk_iFKqLTyekkuaiKXZ78I0lPmD5kM1fghHRs4Y/edit?usp=sharing) for detailed and more experiment results.
Full multi-stream inference implementation please see https://github.com/tensorflow/tensorflow/compare/master...buptzyb:MultipleStreamGroups.
This PR provides the stream_id interface in stream_executors and devices, but is not used so far. With the new knobs, one stream_executor will be in charge of one stream group, different from the original assumption of one GPU device.
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} | Fold sequences of apply_indexing ops.
But only if this leads to simplification.
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