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[XLA:GPU] [XLA:GPU] Clang-tidy fixes for xla/service/gpu/gpu_hlo_schedule.cc
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[XLA:GPU] [XLA:GPU] Clang-tidy fixes for xla/service/gpu/gpu_hlo_schedule.cc
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Expands the test coverage of GetReducedIntervals(), since this is now the preferred way of interacting with the Memory Term Reducer.
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Expands the test coverage of GetReducedIntervals(), since this is now the preferred way of interacting with the Memory Term Reducer.
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PR_kwDOArmXAs5vdMNz
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Optimize gradient of `tf.reshape()` when one input dimension is dynamic.
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Optimize gradient of `tf.reshape()` when one input dimension is dynamic.
Previously, if there was any unknown dimension in the shape of the input to `tf.reshape()` the gradient function would add a `tf.shape()` op that would introduce a data dependency on the input tensor, potentially keeping it live until the backward pass. However, in most cases* when only one of the input dimensions is unknown (e.g. a dynamic batch size) we can use the ability to pass a wildcard `-1` dimension to the `tf.reshape()` in the gradient function, and eliminate the data dependency.
(* Except when one or more of the input dimensions is 0, in which case the semantics of the wildcard are surprising, and resolve to 1.)
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PR_kwDOArmXAs5vdDzK
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allow a tpu pjrt client to be created with extra options
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PR_kwDOArmXAs5vdCQY
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Keep outside compilation attribute of DecomposeResourceGather op in TableGen decompose_resource_ops rewrites
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NONE
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Keep outside compilation attribute of DecomposeResourceGather op in TableGen decompose_resource_ops rewrites
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/67751 from Intel-tensorflow:gaurides/update_bf16_lists 59cf5f8eba0a29e55c05122c534c13a3d1731e64
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PR_kwDOArmXAs5vc-ct
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[xla:spmd] Fix a problem in generating code for concat that requires padding.
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[xla:spmd] Fix a problem in generating code for concat that requires padding.
When an internal concat operand requires padding, previously, we don't zero out
the padded elements and the generated code produces incorrect results.
Fix two existing tests.
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[XLA:GPU] Clang-tidy fixes for gpu_norm_runner.h, in_place_dynamic_update_slice_mlir.h, tiling_util.h and indexing_analysis.h
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[XLA:GPU] Clang-tidy fixes for gpu_norm_runner.h, in_place_dynamic_update_slice_mlir.h, tiling_util.h and indexing_analysis.h
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Clean unnecessary include and definition from kernel.h.
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Clean unnecessary include and definition from kernel.h.
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PR_kwDOArmXAs5vc8cH
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Reverts f6a2b933193c44bf382e47e099122deccc4d2f8c
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Reverts f6a2b933193c44bf382e47e099122deccc4d2f8c
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12465 from openxla:fix_flag 4a050dce154b924b98134294f3f15ffce35bdcbb
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PR_kwDOArmXAs5vc7Ze
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Fix build error for third_party/tensorflow/core/tfrt/gpu.
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Fix build error for third_party/tensorflow/core/tfrt/gpu.
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PR_kwDOArmXAs5vcyHb
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Eliminate deprecated Create methods, and use the KernelFactory methods everywhere.
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Eliminate deprecated Create methods, and use the KernelFactory methods everywhere.
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PR_kwDOArmXAs5vcpEz
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disables `detect_odr_violation` for `validator_runner_test`
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disables `detect_odr_violation` for `validator_runner_test`
Runtimes-on-demand is causing AddressSanitizer to be unhappy with `validator_runner_test` due to some ODR violation. Given that we don't use what's causing this error, we're turning it off.
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PR_kwDOArmXAs5vcm2c
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[tfl-converter-optimize]
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NONE
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[tfl-converter-optimize]
Add pattern to fold quant weights into transpose conv.
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PR_kwDOArmXAs5vcSnC
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[XLA] Fix incorrect comment.
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[XLA] Fix incorrect comment.
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PR_kwDOArmXAs5vcRYp
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PR #12465: [GPU] Fix handling of xla_gpu_require_complete_aot_autotune_results flag.
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PR #12465: [GPU] Fix handling of xla_gpu_require_complete_aot_autotune_results flag.
Imported from GitHub PR https://github.com/openxla/xla/pull/12465
Copybara import of the project:
--
4a050dce154b924b98134294f3f15ffce35bdcbb by Ilia Sergachev <[email protected]>:
[GPU] Fix handling of xla_gpu_require_complete_aot_autotune_results flag.
Merging this change closes #12465
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12465 from openxla:fix_flag 4a050dce154b924b98134294f3f15ffce35bdcbb
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PR_kwDOArmXAs5vcPD5
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[XLA:GPU] Clang-tidy fixes for xla/service/gpu/runtime/sequential_thunk.cc
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[XLA:GPU] Clang-tidy fixes for xla/service/gpu/runtime/sequential_thunk.cc
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PR_kwDOArmXAs5vcOPr
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Update to Triton version that contains previously patched fixes.
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Update to Triton version that contains previously patched fixes.
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[XLA:GPU] Clang-tidy fixes for xla/service/gpu/runtime/fft_thunk.cc
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[XLA:GPU] Clang-tidy fixes for xla/service/gpu/runtime/fft_thunk.cc
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Add a command line parameter in benchmark_tool to set the XNNPack cache file.
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Add a command line parameter in benchmark_tool to set the XNNPack cache file.
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Use absl::Status instead of xla::Status now that they're identical.
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Use absl::Status instead of xla::Status now that they're identical.
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PR_kwDOArmXAs5vcAVa
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Reduce messages from sharding_utils.cc
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Reduce messages from sharding_utils.cc
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PR_kwDOArmXAs5vb_LX
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Remove `gpu_any` tag in favor of `requires-gpu-nvidia`
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NONE
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Remove `gpu_any` tag in favor of `requires-gpu-nvidia`
This also enables v100 tests.
We now use the same sets of tags for gpu tests between internal and external, modulo the extra tags for local execution of gpu tests.
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PR_kwDOArmXAs5vbsva
| 67,569 |
Remove unused translate_tf_dialect.
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NONE
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Remove unused translate_tf_dialect.
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PR_kwDOArmXAs5vblqq
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Fix up some unreachable code warnings on mobile platforms
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Fix up some unreachable code warnings on mobile platforms
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PR_kwDOArmXAs5vbdu7
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Always use std::array.
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Always use std::array.
`Eigen::array` is being removed upstream in favor of `std::array`.
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I_kwDOArmXAs6I2Tk6
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Unexpected behavior in tf.data.Dataset.from_generator when there is a naming collision
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[
"Hi @kralka ,\r\n\r\nI have replicated the issue with `TF2.15v` and `tf-nightly` as well. On Colab the code executes indefinitely due to naming collision of generator and finally runtime will crash without any logs. Attaching [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/07facdb64fa42d474b68cbe875040fd7/67566.ipynb) for reference.",
"@SuryanarayanaY thank you for your response. One can get a crash (as opposed to indefinite execution without errors) in an official docker (`docker pull tensorflow/tensorflow:latest-gpu`). I will provide concrete steps if needed.",
"Hi @SuryanarayanaY is there some plan to resolve this issue? Do you know what causes it? In case resolving would require too large changes do you think it is worth putting a note in the documentation since it is rather hard to figure out what causes this behavior?\r\n\r\nPersonally I would be interested in the cause of this since some magic needs to be happening here :-)",
"Sorry my fault. This is not a TensorFlow issue.\r\n\r\n```python\r\nmy_list = [1, 2, 3]\r\n\r\ndef gen():\r\n yield from my_list\r\n\r\nmy_list = [5, 6, 7]\r\n\r\nassert list(gen()) == [5, 6, 7]\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/67566\">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/67566\">No</a>\n"
] | 2024-05-14T16:46:32 | 2024-06-04T13:36:47 | 2024-06-04T13:36:43 |
NONE
| null | null | null |
### Issue type
Documentation Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15.0
### Custom code
Yes
### OS platform and distribution
colab
### Mobile device
_No response_
### Python version
3.10.12
### Bazel version
_No response_
### GCC/compiler version
11.4.0
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When creating a `tf.data.Dataset` object using `from_generator` a naming collision with an iterable used in the `generator` parameter causes a crash.
On Ubuntu I got the following error (not in colab):
date: F external/local_tsl/tsl/platform/default/env.cc:74] Check failed: ret == 0 (11 vs. 0)Thread tf_data_private_threadpool creation via pthread_create() failed.
### Standalone code to reproduce the issue
```shell
import sys
print(f"Python version: {sys.version}")
import tensorflow as tf
print(f"{tf.__version__ = }")
# This runs as expected:
ds_no_name_collision = list(range(100))
def gen():
print("gen called")
for x in ds_no_name_collision:
print(f"{x = }")
yield x
# Notice there is no name collision:
ds_without_name_collision = tf.data.Dataset.from_generator(gen, output_signature=tf.TensorSpec(shape=(), dtype=tf.int32))
print("All good so far")
for x in ds_without_name_collision.take(10):
print(x)
# This fails:
ds_name_collision = list(range(100))
def gen():
print("gen called")
for x in ds_name_collision:
print(f"{x = }")
yield x
# Notice the name collision:
ds_name_collision = tf.data.Dataset.from_generator(gen, output_signature=tf.TensorSpec(shape=(), dtype=tf.int32))
print("All good so far")
for x in ds_name_collision.take(10):
print(x)
```
### Relevant log output
```shell
Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0]
tf.__version__ = '2.15.0'
All good so far
gen called
x = 0
x = 1
tf.Tensor(0, shape=(), dtype=int32)
tf.Tensor(1, shape=(), dtype=int32)
x = 2
x = 3
x = 4
tf.Tensor(2, shape=(), dtype=int32)
x = 5
tf.Tensor(3, shape=(), dtype=int32)
tf.Tensor(4, shape=(), dtype=int32)
tf.Tensor(5, shape=(), dtype=int32)
x = 6
x = 7
x = 8
x = 9
tf.Tensor(6, shape=(), dtype=int32)
tf.Tensor(7, shape=(), dtype=int32)
tf.Tensor(8, shape=(), dtype=int32)
tf.Tensor(9, shape=(), dtype=int32)
All good so far
gen called
gen called
gen called
gen called
gen called
gen called
gen called
[log truncated]
```
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[XLA:GPU] Run autotuner with cublas config only if `--xla_gpu_cublas_fallback=true`. Currently we always compile in cublas by default and only later drop it from possible list of configs if the flag is set to false.
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[XLA:GPU] Run autotuner with cublas config only if `--xla_gpu_cublas_fallback=true`. Currently we always compile in cublas by default and only later drop it from possible list of configs if the flag is set to false.
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PR_kwDOArmXAs5vbBgg
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Support converting strings to unsigned integer types in `tf.strings.to_number`.
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Support converting strings to unsigned integer types in `tf.strings.to_number`.
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PR_kwDOArmXAs5vatAI
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Introduce reference implementation of DRQ (dynamically quantized) TransposeConv with per-channel quantization
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Introduce reference implementation of DRQ (dynamically quantized) TransposeConv with per-channel quantization
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PR_kwDOArmXAs5vamUN
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Run OpenGL-based inference always on the current MP GL context/thread.
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Run OpenGL-based inference always on the current MP GL context/thread.
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Fix a bug in `validate_colocate` when a variable is not created in a strategy scope.
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Fix a bug in `validate_colocate` when a variable is not created in a strategy scope.
In some cases `v._distribute_strategy` can be None, which was causing an AttributeError when trying to check if the `extended` field matched the expected value.
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PR #12433: [GPU] Make cuDNN fusion test run on H100.
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PR #12433: [GPU] Make cuDNN fusion test run on H100.
Imported from GitHub PR https://github.com/openxla/xla/pull/12433
Copybara import of the project:
--
856f1ddb05b0cecf1092aa3547611274b27f0796 by Ilia Sergachev <[email protected]>:
[GPU] Make cuDNN fusion test run on H100.
Merging this change closes #12433
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12433 from openxla:cudnn_test_h100 856f1ddb05b0cecf1092aa3547611274b27f0796
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[XLA:GPU] Add utility functions to iterate over instructions matching opcode.
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[XLA:GPU] Add utility functions to iterate over instructions matching opcode.
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I_kwDOArmXAs6I0lzg
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Tensorflow using Intel GPU instead of NVIDIA GPU
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"@fzd370 In TensorFlow v2.10, the default behavior is to choose the GPU with the lowest ID. This typically leads to selecting the integrated Intel GPU over a dedicated Nvidia GPU.Please ensure you're running your Python scripts or Jupyter notebooks within the activated virtual environment where you installed TensorFlow with GPU support. Kindly double-check your CUDA and cuDNN installations. \r\n\r\nThank you!",
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NONE
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### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.10.1
### Custom code
Yes
### OS platform and distribution
Windows 11
### Mobile device
_No response_
### Python version
3.9.19
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.2.2/8.1.0.77
### GPU model and memory
Nvidia Geforce GTX 1650 - 4GB
### Current behavior?
I've installed the tensorflow in virtual environment in Anaconda using official documentation and it is running fine. Whenever I'm training my model I see that the tensorflow is using Intel GPU instead of Nvidia GPU. The default GPU for tensorflow is Intel GPU. How do I change this so that tensorflow uses Nvidia GPU. I've installed CUDA and cuDNN as mentioned in the documentation.
As it is mentioned in the offical documentation that if you have more than one GPU in your system, the GPU with the lowest ID will be selected by default. How do I change this? Please help.
I
### Standalone code to reproduce the issue
```shell
I followed the steps mentioned in the documentation.
```
### Relevant log output
_No response_
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Remove null pointer from error message
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[
"Hi @majiddadashi Any update on this PR? Please. Thank you!"
] | 2024-05-14T13:35:12 | 2024-06-07T17:23:04 | null |
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Fixes #67556
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I_kwDOArmXAs6I0kwl
| 67,556 |
Remove null pointer from error message in mlir::odml
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[
"Hi @apach301 is this issue still open?\r\nIf so, could you tell me how to replicate the bug so that I may have a go at it?",
"> Hi @apach301 is this issue still open?\n> \n> If so, could you tell me how to replicate the bug so that I may have a go at it?\n\nThis bug was detected by static analysis, therefore I have no input to reproduce it during execution. But I propose a fix in PR #67557",
"Hi @apach301 the PR looks great, if you would like any help with CI, do let me know.\r\n\r\nThanks and regards",
"will wait for review.",
"> will wait for review.\r\n\r\nHi @pkgoogle How to enable CI tests run in PR #67557? Is it wait any approval to run?",
"Hi @apach301, we'll have to wait for review, we run CI/CD internally and review the PR."
] | 2024-05-14T13:34:35 | 2024-05-29T19:00:39 | null |
CONTRIBUTOR
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.16.1
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Possible null pointer dereference in `mlir::odml::MatchInverseScalesOperand()`: undefined variable `inverse_scale_constant_op` is printed in error message.
https://github.com/tensorflow/tensorflow/blob/08a13d2991724a4b0039237ce07340e16b760656/tensorflow/compiler/mlir/lite/stablehlo/transforms/compose_uniform_quantized_type_pass.cc#L172-L178
### Standalone code to reproduce the issue
```shell
Bug was found with Svace static analyzer
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5vZp6x
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Add a flag to override the GEMM autotuner with a specified textproto configuration.
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Add a flag to override the GEMM autotuner with a specified textproto configuration.
This makes it easier to debug issues with Triton, since [persisted autotuning](https://openxla.org/xla/persisted_autotuning) does not offer a workflow that would be efficient enough:
- `--xla_gpu_dump_autotune_results_to=` doesn't work if the autotuner crashes, so we would need to construct the cache manually from the logs; furthermore, the cache only applies to a specific HW, so we can't use it to override the autotuner on a different GPU
- `--xla_gpu_load_autotune_results_from=` reads a file so we need to copy the file into the source tree, and modify the BUILD file to include it into the build; with this new flag, we can just copy the flag from the VLOG without having to fiddle around with any files
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Bazel - Tensorflow build failed
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[
"@AceAbhi147 Could you please confirm that you have followed the steps properly and also referred the tested build configurations? Please let us know!\r\n\r\n\r\n\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/67554\">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/67554\">No</a>\n"
] | 2024-05-14T13:05:25 | 2024-05-30T01:49:39 | 2024-05-30T01:49:36 |
NONE
| null | null | null |
I have a ubuntu 22.0.4 LTS machine running on virtual box. I am trying to build tensorflow by cloning the repo and following the steps defined here: [build from source].(https://www.tensorflow.org/install/source) I have checkout the master branch. And I have installed llvm-14 clang-14
rather than 17 as I was getting some not found error.
This is the command I am using to build tensorflow:
`bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow_cpu`
But after waiting for what seems like hours, I am getting this error:
`[12,514 / 15,964] 3 actions running
Compiling tensorflow/compiler/mlir/tensorflow/ir/tf_ops_n_z.cc; 720s local
Compiling tensorflow/compiler/mlir/tensorflow/ir/tf_ops.cc; 681s local
ERROR: /home/ace-ventura/tensorflow/tensorflow/compiler/mlir/tensorflow/BUILD:424:11: Compiling tensorflow/compiler/mlir/tensorflow/ir/tf_ops.cc failed: (Exit 1): gcc failed: error executing command (from target //tensorflow/compiler/mlir/tensorflow:tensorflow_ops) /usr/bin/gcc -U_FORTIFY_SOURCE -fstack-protector -Wall -Wunused-but-set-parameter -Wno-free-nonheap-object -fno-omit-frame-pointer -g0 -O2 '-D_FORTIFY_SOURCE=1' -DNDEBUG -ffunction-sections ... (remaining 187 arguments skipped)
gcc: fatal error: Killed signal terminated program cc1plus
compilation terminated.
Target //tensorflow/tools/pip_package:wheel failed to build
Use --verbose_failures to see the command lines of failed build steps.
INFO: Elapsed time: 1144.307s, Critical Path: 724.97s
INFO: 176 processes: 4 internal, 172 local.
FAILED: Build did NOT complete successfully
`
If someone can take a look at this, it will great.
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Add missing return in error check in mlir::TFTPU
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[
"Hi @jpienaar Any update on this PR? Please. Thank you!"
] | 2024-05-14T12:28:28 | 2024-06-07T17:22:29 | null |
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Fixes #67551
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Missing return in error check in mlir::TFTPU
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CONTRIBUTOR
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.16.1
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Possible null pointer dereference may occur because of missing return in error check:
https://github.com/tensorflow/tensorflow/blob/63bb7339a2bc98af97c61e86eedbbd60c123529c/tensorflow/compiler/mlir/tensorflow/transforms/extract_tpu_copy_with_dynamic_shape_op.cc#L61-L65
### Standalone code to reproduce the issue
```shell
Bug was found with Svace static analyzer
```
### Relevant log output
_No response_
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Avoid FindIndex function by using the inverse permutation.
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Avoid FindIndex function by using the inverse permutation.
Let p be a permutation. FindIndex(p, x) should return y where x = p[y].
This is equivalent to p^-1[x] = p^-1[p[y]] = y, so y = p^-1[x].
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PR_kwDOArmXAs5vYzIq
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[Triton] Bring back a test to show that TF32 is not supported for 8-bit or less types with F32.
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[Triton] Bring back a test to show that TF32 is not supported for 8-bit or less types with F32.
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Canonicalize affine expression trees in simplifier.
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Canonicalize affine expression trees in simplifier.
I don't know where the current non-determinism comes from, but this should
fix it.
The particular order is not important, but it being canonical is. These
expressions are used for codegen in the new emitters, so the simplification
must be deterministic.
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Move variable declaration into preprocessor branch
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Move variable declaration into preprocessor branch
This avoid warnings about unused variables when the ROCm branch is taken.
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Update ops-related pbtxt files.
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Update ops-related pbtxt files.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12463 from ROCm:ci_fix_build_break_20240514 fd95e342a4e8de6c8348df3576510584a0020150
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PR #12434: [GPU] Fix OSS compilation problems in a previously disabled test.
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PR #12434: [GPU] Fix OSS compilation problems in a previously disabled test.
Imported from GitHub PR https://github.com/openxla/xla/pull/12434
Copybara import of the project:
--
723c9bb29adfcc33c015b74f90ce8024c2f79255 by Ilia Sergachev <[email protected]>:
[GPU] Fix OSS compilation problems in a previously disabled test.
Merging this change closes #12434
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12434 from openxla:fix_triton_test 723c9bb29adfcc33c015b74f90ce8024c2f79255
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[XLA:GPU] Introduce `GpuCompiler::GetToolkit` method for subclasses to provide the CUDA or ROCm versions.
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[XLA:GPU] Introduce `GpuCompiler::GetToolkit` method for subclasses to provide the CUDA or ROCm versions.
Remove all `#if GOOGLE_CUDA` and friends from gpu_compiler.cc
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"@tanpengshi The FlexDelegate functionality is integrated within the TensorFlow Lite interpreter itself when built with Select Ops enabled. Could you please upgrade to the latest TF version and let us know as the older TF versions are not actively supported. Thank you!",
"I have tried invoking the interpreter and I get the error\r\n\r\n```\r\n guard let interpreter = tflite else {\r\n print(\"TFLite Error: Interpreter is nil.\")\r\n return\r\n }\r\n do {\r\n let inputData = Data(buffer: UnsafeBufferPointer(start: dataBuffer, count: dataBuffer.count))\r\n try interpreter.copy(inputData, toInputAt: 0)\r\n try interpreter.invoke()\r\n\r\n let output = getTensorOutput(interpreter: interpreter)\r\n let detectedActionIndex = output.argmax()\r\n print(\"Detected Action Index is: \\(detectedActionIndex)\")\r\n } catch {\r\n print(\"TFLite Error: \\(error.localizedDescription)\")\r\n }\r\n```\r\n\r\nTensorFlow Lite Error: Select TensorFlow op(s), included in the given model, is(are) not supported by this interpreter. Make sure you apply/link the Flex delegate before inference. For the Android, it can be resolved by adding \"org.tensorflow:tensorflow-lite-select-tf-ops\" dependency. See instructions: https://www.tensorflow.org/lite/guide/ops_select\r\nTensorFlow Lite Error: Node number 76 (FlexTensorListReserve) failed to prepare.\r\nTFLite Error: Must call allocateTensors().\r\n\r\nI have already \"import TensorFlowLiteSelectTfOps\" in my Swift file. \r\n\r\n\r\n\r\nI have accidentally deleted the CoreMLDelegate and also the MetalDelegate from the library. Would it cause this error? I have deleted these libraries and I don't know how to recover them. When I do:\r\n\r\n```\r\nrm -rf Pods Podfile.lock\r\npod install\r\n```\r\n\r\nThe files are still missing. I really appreciate your help! I have come very close to the solution for my App!\r\n",
"@tanpengshi Directly recovering deleted libraries from within your project isn't possible. Could you try to use the latest TFlite version as newer versions might include these delegates within the framework itself, eliminating the need for separate libraries. After reinstalling/updating, clean and rebuild your project to ensure the changes take effect.\r\n\r\nThank you!",
"Are you suggesting that I recreate a new project?\r\n\r\nNow i proceed to try a more conventional method using the Podfile:\r\n\r\n```\r\n# Uncomment the next line to define a global platform for your project\r\nplatform :ios, '17.0'\r\n\r\ntarget 'FacialRecognition' do\r\n # Comment the next line if you don't want to use dynamic frameworks\r\n use_frameworks!\r\n #pod 'TensorFlowLiteSwift', :path => '../../local-podspecs/TensorFlowLiteSwift.podspec'\r\n # pod 'TensorFlowLiteSelectTfOps', :path => '../../local-podspecs/TensorFlowLiteSelectTfOps.podspec'\r\n # pod 'TensorFlowLiteSwift' # or 'TensorFlowLiteObjC'\r\n # pod 'TensorFlowLiteSelectTfOps', '~> 0.0.1-nightly'\r\n\r\n\r\n pod 'TensorFlowLiteSwift'\r\n pod 'TensorFlowLiteSelectTfOps', '~> 0.0.1-nightly'\r\n\r\n # Pods for FacialRecognition\r\n\r\n target 'FacialRecognitionTests' do\r\n inherit! :search_paths\r\n # Pods for testing\r\n end\r\n\r\n target 'FacialRecognitionUITests' do\r\n # Pods for testing\r\n end\r\n\r\n # Add these lines to ensure consistent EXCLUDED_ARCHS settings\r\n post_install do |installer|\r\n installer.pods_project.targets.each do |target|\r\n target.build_configurations.each do |config|\r\n config.build_settings['EXCLUDED_ARCHS[sdk=iphonesimulator*]'] = 'arm64'\r\n end\r\n end\r\n end\r\n\r\nend\r\n```\r\n\r\nBut when I build my project, I get:\r\n\r\n\r\n\r\nWhen I check the 'Pods' directory, I don't see a 'resources-to-copy-FacialRecognition' file",
"I am now able to solved the issue above and the TensorFlow Lite model with SelectOps is able to successfully run by following the suggestion here:\r\n\r\n[https://stackoverflow.com/questions/76792138/sandbox-bash72986-deny1-file-write-data-users-xxx-ios-pods-resources-to-co](https://stackoverflow.com/questions/76792138/sandbox-bash72986-deny1-file-write-data-users-xxx-ios-pods-resources-to-co)\r\n\r\nHowever my app size is over 200MB because of the library! Hence, I need to do a Selective Build which effectively return me to the first solution. In my Podfile, I did for the first solution:\r\n\r\n```\r\nplatform :ios, '17.0'\r\n\r\ntarget 'FacialRecognition' do\r\n # Comment the next line if you don't want to use dynamic frameworks\r\n use_frameworks!\r\n pod 'TensorFlowLiteSwift', :path => '../../local-podspecs/TensorFlowLiteSwift.podspec'\r\n\r\n # Pods for FacialRecognition\r\n\r\n target 'FacialRecognitionTests' do\r\n inherit! :search_paths\r\n # Pods for testing\r\n end\r\n\r\n target 'FacialRecognitionUITests' do\r\n # Pods for testing\r\n end\r\n\r\n # Add these lines to ensure consistent EXCLUDED_ARCHS settings\r\n post_install do |installer|\r\n installer.pods_project.targets.each do |target|\r\n target.build_configurations.each do |config|\r\n config.build_settings['EXCLUDED_ARCHS[sdk=iphonesimulator*]'] = 'arm64'\r\n end\r\n end\r\n end\r\n\r\nend\r\n```\r\n\r\nIn my TensorFlowLiteSwift.podspec file, I have:\r\n\r\n```\r\nPod::Spec.new do |s|\r\n s.name = 'TensorFlowLiteSwift'\r\n s.version = '2.7.0'\r\n s.authors = 'Google Inc.'\r\n s.license = { :type => 'Apache' }\r\n s.homepage = 'https://github.com/tensorflow/tensorflow'\r\n s.source = { :git => 'https://github.com/tensorflow/tensorflow.git', :tag => \"v#{s.version}\" }\r\n s.summary = 'TensorFlow Lite for Swift'\r\n s.description = <<-DESC\r\n\r\n TensorFlow Lite is TensorFlow's lightweight solution for Swift developers. It\r\n enables low-latency inference of on-device machine learning models with a\r\n small binary size and fast performance supporting hardware acceleration.\r\n DESC\r\n\r\n s.ios.deployment_target = '17.0'\r\n\r\n s.module_name = 'TensorFlowLite'\r\n s.static_framework = true\r\n\r\n tfl_dir = 'tensorflow/lite/'\r\n swift_dir = tfl_dir + 'swift/'\r\n\r\n s.default_subspec = 'Core'\r\n\r\n s.subspec 'Core' do |core|\r\n # Adjust the path to point to your custom frameworks\r\n core.vendored_frameworks = [\r\n 'frameworks/TensorFlowLiteC.framework',\r\n 'frameworks/TensorFlowLiteSelectTfOps.framework'\r\n ]\r\n \r\n core.source_files = swift_dir + 'Sources/*.swift'\r\n core.exclude_files = swift_dir + 'Sources/{CoreML,Metal}Delegate.swift'\r\n\r\n core.test_spec 'Tests' do |ts|\r\n ts.source_files = swift_dir + 'Tests/*.swift'\r\n ts.exclude_files = swift_dir + 'Tests/MetalDelegateTests.swift'\r\n ts.resources = [\r\n tfl_dir + 'testdata/add.bin',\r\n tfl_dir + 'testdata/add_quantized.bin',\r\n ]\r\n end\r\n end\r\n\r\n s.subspec 'CoreML' do |coreml|\r\n coreml.source_files = swift_dir + 'Sources/CoreMLDelegate.swift'\r\n coreml.dependency 'TensorFlowLiteSwift/Core', \"#{s.version}\"\r\n end\r\n\r\n s.subspec 'Metal' do |metal|\r\n metal.source_files = swift_dir + 'Sources/MetalDelegate.swift'\r\n metal.dependency 'TensorFlowLiteSwift/Core', \"#{s.version}\"\r\n\r\n metal.test_spec 'Tests' do |ts|\r\n ts.source_files = swift_dir + 'Tests/{Interpreter,MetalDelegate}Tests.swift'\r\n ts.resources = [\r\n tfl_dir + 'testdata/add.bin',\r\n tfl_dir + 'testdata/add_quantized.bin',\r\n tfl_dir + 'testdata/multi_add.bin',\r\n ]\r\n end\r\n end\r\nend\r\n```\r\n\r\nBut using this method, with MetalDelegate and CoreMLDelegate libraries deleted, I have FlexDelegate not found error.\r\n",
"@tanpengshi Thank you for the update. Glad the issue has been resolved. \r\nCould you please let us know if we can close the issue?\r\nThank you!",
"Sure, we can close this issue, and I will open a next one regarding the new issue",
"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/67538\">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/67538\">No</a>\n",
"@tanpengshi Thank you!"
] | 2024-05-14T09:50:17 | 2024-05-17T04:05:37 | 2024-05-17T04:04:27 |
NONE
| null | null | null |
IDE: Xcode 15
Platform: iOS17
TensorFlow version: r2.9
I am developing both iOS and Android apps that are running with TensorFlow Lite model. Because my model uses LSTM, I have to make use of TFSelectOps.
In addition, because the TensorFlowLiteSelectTFOps library is large in memory size, I have to do a selective build. After much effort, I have succeeded in making my TensorFlowLite model running smoothly on the Android app based on the selectively built libraries.
On the iOS however, I used a bazel build:
```
bash tensorflow/lite/ios/build_frameworks.sh \
--input_models=model1.tflite,model2.tflite \
--target_archs=x86_64,armv7,arm64
```
to generate the:
1. TensorFlowLiteSelectTfOps.framework
2. TensorFlowLiteC.framework
After that I edited the TensorFlowLiteSwift.podspec and TensorFlowLiteSelectTfOps.podspec to include these 2 frameworks, then I successfully built the library on XCode, using Podfile. I have gone very far and I am able to import TensorFlowLiteSelectTfOps in my codes and run the "Interpreter" variable:
```
class TensorFlowModel {
var interpreter: Interpreter?
init(modelPath: String) {
do {
// Initialize the Flex Delegate
let flexDelegate = MetalDelegate() // Create an instance of FlexDelegate
// Initialize the Interpreter with the Flex Delegate
let options = Interpreter.Options() // You can configure options if needed
let delegates = [flexDelegate] // Create an array of delegates
interpreter = try Interpreter(modelPath: modelPath, options: options, delegates: delegates)
// Allocate tensors
try interpreter?.allocateTensors()
} catch {
print("Failed to create TensorFlow Lite interpreter: \(error)")
}
}
func runModel() {
do {
try interpreter?.invoke()
// Process the output
} catch {
print("Failed to invoke TensorFlow Lite interpreter: \(error)")
}
}
}
```
There are no errors which is good, but the problem is I cannot find and import the FlexDelegate library because my TFlite model runs on SelectOps. There are no FlexDelegate swift files that were being built.
Thanks in advance! And I hope to complete my iOS project.
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Integrate LLVM at llvm/llvm-project@79a6a7e28fff
Updates LLVM usage to match
[79a6a7e28fff](https://github.com/llvm/llvm-project/commit/79a6a7e28fff)
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Fix tile size for matmuls with 8-bit operands in autotuner
The result of `GetExhaustiveTritonConfigs` is cached in the autotuner class instance, so should not depend on the input dot instruction. Therefore, removing the `has_8_bit_operand` parameter from the method signature.
Instead, adjust the `block_k` attribute in the config normalization phase.
Additionally, treat 8-bit floating point types in the same manner as integer types.
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[
"Hi **@LongZE666** ,\r\n- Sorry for the delay, Can you please try with recent TF version? I tried with TF-nightly and I cannot reproduce the error. It might be solved in upcoming version. Please check the [gist](https://colab.sandbox.google.com/gist/Venkat6871/14f9445b68ea666c07e766277ecd3299/67531_2-16-1-nightly-v.ipynb) here.\r\n\r\nThank you!",
"I can reproduce this problem on tensorflow version 2.16.1, and it can be executed normally on tf-nightly.",
"Hi **@LongZE666** ,\r\n- Yes, I also reproduce same error with version 2.16.1. But it is working fine with tf-nightly. So, It might be solve in upcoming version.\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/67531\">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/67531\">No</a>\n"
] | 2024-05-14T08:47:15 | 2024-06-07T01:50:58 | 2024-06-07T01:50:55 |
NONE
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### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
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?
Triggered when input parameters `features` and `labels` are incorrect.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
features = tf.constant([], shape=[3,0], dtype=tf.float64)
labels = tf.constant([], shape=[0], dtype=tf.float64)
tf.raw_ops.SoftmaxCrossEntropyWithLogits(features=features, labels=labels)
```
### Relevant log output
```shell
ASAN Report:
AddressSanitizer:DEADLYSIGNAL
=================================================================
==2083523==ERROR: AddressSanitizer: SEGV on unknown address 0x000000000000 (pc 0x7fe71e96a65b bp 0x7ffd6f3a7700 sp 0x7ffd6f3a7620 T0)
==2083523==The signal is caused by a READ memory access.
==2083523==Hint: address points to the zero page.
#0 0x7fe71e96a65b in std::_Function_handler<void (long, long), tensorflow::functor::XentFunctorBase<Eigen::ThreadPoolDevice, double>::operator()(Eigen::ThreadPoolDevice const&, Eigen::DSizes<long, 2> const&, Eigen::array<long, 2ul> const&, Eigen::array<long, 2ul> const&, Eigen::TensorMap<Eigen::Tensor<double const, 2, 1, long>, 16, Eigen::MakePointer>, Eigen::TensorMap<Eigen::Tensor<double const, 2, 1, long>, 16, Eigen::MakePointer>, Eigen::TensorMap<Eigen::Tensor<double, 2, 1, long>, 16, Eigen::MakePointer>, Eigen::TensorMap<Eigen::Tensor<double, 1, 1, long>, 16, Eigen::MakePointer>, Eigen::TensorMap<Eigen::Tensor<double, 2, 1, long>, 16, Eigen::MakePointer>)::{lambda(long, long)#1}>::_M_invoke(std::_Any_data const&, long&&, long&&) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x38bcb65b)
#1 0x7fe6fb7f638b in Eigen::ThreadPoolDevice::parallelFor(long, Eigen::TensorOpCost const&, std::function<long (long)>, std::function<void (long, long)>) const (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x15a5738b)
#2 0x7fe71e976680 in tensorflow::SoftmaxXentWithLogitsOp<Eigen::ThreadPoolDevice, double>::Compute(tensorflow::OpKernelContext*) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x38bd7680)
#3 0x7fe74fe0714a in tensorflow::ThreadPoolDevice::Compute(tensorflow::OpKernel*, tensorflow::OpKernelContext*) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x1cdc14a)
#4 0x7fe74f98dfe6 in tensorflow::(anonymous namespace)::SingleThreadedExecutorImpl::Run(tensorflow::Executor::Args const&) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x1862fe6)
#5 0x7fe74f86a306 in tensorflow::FunctionLibraryRuntimeImpl::RunSync(tensorflow::FunctionLibraryRuntime::Options, unsigned long, absl::lts_20230802::Span<tensorflow::Tensor const>, std::vector<tensorflow::Tensor, std::allocator<tensorflow::Tensor> >*) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x173f306)
#6 0x7fe74f8c7f74 in tensorflow::ProcessFunctionLibraryRuntime::RunMultiDeviceSync(tensorflow::FunctionLibraryRuntime::Options const&, unsigned long, std::vector<std::variant<tensorflow::Tensor, tensorflow::TensorShape>, std::allocator<std::variant<tensorflow::Tensor, tensorflow::TensorShape> > >*, std::function<absl::lts_20230802::Status (tensorflow::ProcessFunctionLibraryRuntime::ComponentFunctionData const&, tensorflow::ProcessFunctionLibraryRuntime::InternalArgs*)>) const (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x179cf74)
#7 0x7fe74f8d58b1 in tensorflow::ProcessFunctionLibraryRuntime::RunSync(tensorflow::FunctionLibraryRuntime::Options const&, unsigned long, absl::lts_20230802::Span<tensorflow::Tensor const>, std::vector<tensorflow::Tensor, std::allocator<tensorflow::Tensor> >*) const (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x17aa8b1)
#8 0x7fe7179f72eb in tensorflow::KernelAndDeviceFunc::Run(tensorflow::ScopedStepContainer*, tensorflow::EagerKernelArgs const&, std::vector<std::variant<tensorflow::Tensor, tensorflow::TensorShape>, std::allocator<std::variant<tensorflow::Tensor, tensorflow::TensorShape> > >*, tsl::CancellationManager*, std::optional<tensorflow::EagerFunctionParams> const&, std::optional<tensorflow::ManagedStackTrace> const&, tsl::CoordinationServiceAgent*) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31c582eb)
#9 0x7fe71788cc2c in tensorflow::EagerKernelExecute(tensorflow::EagerContext*, absl::lts_20230802::InlinedVector<tensorflow::TensorHandle*, 4ul, std::allocator<tensorflow::TensorHandle*> > const&, std::optional<tensorflow::EagerFunctionParams> const&, tsl::core::RefCountPtr<tensorflow::KernelAndDevice> const&, tensorflow::GraphCollector*, tsl::CancellationManager*, absl::lts_20230802::Span<tensorflow::TensorHandle*>, std::optional<tensorflow::ManagedStackTrace> const&) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31aedc2c)
#10 0x7fe71788f66a in tensorflow::ExecuteNode::Run() (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31af066a)
#11 0x7fe7179c9939 in tensorflow::EagerExecutor::SyncExecute(tensorflow::EagerNode*) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31c2a939)
#12 0x7fe71787cde4 in tensorflow::(anonymous namespace)::EagerLocalExecute(tensorflow::EagerOperation*, tensorflow::TensorHandle**, int*) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31addde4)
#13 0x7fe717880dd4 in tensorflow::DoEagerExecute(tensorflow::EagerOperation*, tensorflow::TensorHandle**, int*) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31ae1dd4)
#14 0x7fe71788ad26 in tensorflow::EagerExecute(tensorflow::EagerOperation*, tensorflow::TensorHandle**, int*) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31aebd26)
#15 0x7fe706873c33 in tensorflow::EagerOperation::Execute(absl::lts_20230802::Span<tensorflow::AbstractTensorHandle*>, int*) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x20ad4c33)
#16 0x7fe7179bee5e in tensorflow::CustomDeviceOpHandler::Execute(tensorflow::ImmediateExecutionOperation*, tensorflow::ImmediateExecutionTensorHandle**, int*) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31c1fe5e)
#17 0x7fe6f63f145b in TFE_Execute (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x1065245b)
#18 0x7fe74dd9f274 in TFE_Py_FastPathExecute_C(_object*) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../_pywrap_tensorflow_internal.so+0x3c2274)
#19 0x7fe6e1d5cccb in pybind11::cpp_function::initialize<pybind11_init__pywrap_tfe(pybind11::module_&)::{lambda(pybind11::args)#61}, pybind11::object, pybind11::args, pybind11::name, pybind11::scope, pybind11::sibling>(pybind11_init__pywrap_tfe(pybind11::module_&)::{lambda(pybind11::args)#61}&&, pybind11::object (*)(pybind11::args), pybind11::name const&, pybind11::scope const&, pybind11::sibling const&)::{lambda(pybind11::detail::function_call&)#3}::_FUN(pybind11::detail::function_call&) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/_pywrap_tfe.so+0xb7ccb)
#20 0x7fe6e1e73899 in pybind11::cpp_function::dispatcher(_object*, _object*, _object*) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/_pywrap_tfe.so+0x1ce899)
#21 0x51ad66 (/usr/bin/python3.11+0x51ad66)
#22 0x4e75db in _PyObject_MakeTpCall (/usr/bin/python3.11+0x4e75db)
#23 0x4fb151 in _PyEval_EvalFrameDefault (/usr/bin/python3.11+0x4fb151)
#24 0x531822 in _PyFunction_Vectorcall (/usr/bin/python3.11+0x531822)
#25 0x541194 in PyObject_Call (/usr/bin/python3.11+0x541194)
#26 0x4fefe0 in _PyEval_EvalFrameDefault (/usr/bin/python3.11+0x4fefe0)
#27 0x62e1b3 (/usr/bin/python3.11+0x62e1b3)
#28 0x4f3a66 in PyEval_EvalCode (/usr/bin/python3.11+0x4f3a66)
#29 0x647c36 (/usr/bin/python3.11+0x647c36)
#30 0x64534f (/usr/bin/python3.11+0x64534f)
#31 0x650d14 (/usr/bin/python3.11+0x650d14)
#32 0x650a63 in _PyRun_SimpleFileObject (/usr/bin/python3.11+0x650a63)
#33 0x650832 in _PyRun_AnyFileObject (/usr/bin/python3.11+0x650832)
#34 0x64f786 in Py_RunMain (/usr/bin/python3.11+0x64f786)
#35 0x61ee0c in Py_BytesMain (/usr/bin/python3.11+0x61ee0c)
#36 0x7fe7fbe3cd8f (/lib/x86_64-linux-gnu/libc.so.6+0x29d8f)
#37 0x7fe7fbe3ce3f in __libc_start_main (/lib/x86_64-linux-gnu/libc.so.6+0x29e3f)
#38 0x61ec94 in _start (/usr/bin/python3.11+0x61ec94)
AddressSanitizer can not provide additional info.
SUMMARY: AddressSanitizer: SEGV (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x38bcb65b) in std::_Function_handler<void (long, long), tensorflow::functor::XentFunctorBase<Eigen::ThreadPoolDevice, double>::operator()(Eigen::ThreadPoolDevice const&, Eigen::DSizes<long, 2> const&, Eigen::array<long, 2ul> const&, Eigen::array<long, 2ul> const&, Eigen::TensorMap<Eigen::Tensor<double const, 2, 1, long>, 16, Eigen::MakePointer>, Eigen::TensorMap<Eigen::Tensor<double const, 2, 1, long>, 16, Eigen::MakePointer>, Eigen::TensorMap<Eigen::Tensor<double, 2, 1, long>, 16, Eigen::MakePointer>, Eigen::TensorMap<Eigen::Tensor<double, 1, 1, long>, 16, Eigen::MakePointer>, Eigen::TensorMap<Eigen::Tensor<double, 2, 1, long>, 16, Eigen::MakePointer>)::{lambda(long, long)#1}>::_M_invoke(std::_Any_data const&, long&&, long&&)
==2083523==ABORTING
```
```
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Segmentation fault (core dumped) in `tf.raw_ops.FusedResizeAndPadConv2D`
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"@SuryanarayanaY I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/ab3b3fc7c53e6036db08266a6734e73e/67529.ipynb). Thank you!"
] | 2024-05-14T08:39:21 | 2024-05-15T06:49:41 | null |
NONE
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### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
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?
Illegal `size` will trigger a segfault.
### Standalone code to reproduce the issue
```shell
# Signal --4;2024-05-13 05:48:16.272992: 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`.2024-05-13 05:48:16.273352: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.2024-05-13 05:48:16.277715: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.2024-05-13 05:48:16.331241: 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.2024-05-13 05:48:17.210828: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT2024-05-13 05:48:17.792175: E tensorflow/core/util/util.cc:131] oneDNN supports DT_HALF only on platforms with AVX-512. Falling back to the default Eigen-based implementation if present.
# FusedResizeConv2DUsingGemmOp
import tensorflow as tf
mode = "REFLECT"
strides = [1, 1, 1, 1]
padding = "VALID"
resize_align_corners = False
input = tf.constant(0, shape=[1,2,3,2], dtype=tf.float16)
size = tf.constant([65534,65534], shape=[2], dtype=tf.int32)
paddings = tf.constant(0, shape=[4,2], dtype=tf.int32)
filter = tf.constant(0, shape=[1,2,2,2], dtype=tf.float16)
tf.raw_ops.FusedResizeAndPadConv2D(input=input, size=size, paddings=paddings, filter=filter, mode=mode, strides=strides, padding=padding, resize_align_corners=resize_align_corners)
```
### Relevant log output
```shell
2024-05-14 08:31:20.675971: E tensorflow/core/util/util.cc:131] oneDNN supports DT_HALF only on platforms with AVX-512. Falling back to the default Eigen-based implementation if present.
Segmentation fault (core dumped)
```
ASAN report:
```
AddressSanitizer:DEADLYSIGNAL
AddressSanitizer:DEADLYSIGNAL
=================================================================
AddressSanitizerAddressSanitizerAddressSanitizer:DEADLYSIGNAL
:DEADLYSIGNAL
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AddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizer:DEADLYSIGNAL
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AddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizer:DEADLYSIGNAL
AddressSanitizerAddressSanitizer:DEADLYSIGNAL
AddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizer:DEADLYSIGNAL
AddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizer:DEADLYSIGNAL
AddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizer:DEADLYSIGNAL
AddressSanitizerAddressSanitizerAddressSanitizer:DEADLYSIGNAL
:DEADLYSIGNAL
AddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizerAddressSanitizer:DEADLYSIGNAL
AddressSanitizer:DEADLYSIGNAL
AddressSanitizer:DEADLYSIGNAL
:DEADLYSIGNAL
:DEADLYSIGNAL
AddressSanitizer:DEADLYSIGNAL
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:DEADLYSIGNAL
AddressSanitizer:DEADLYSIGNAL
:DEADLYSIGNAL
:DEADLYSIGNAL
:DEADLYSIGNAL
:DEADLYSIGNAL
:DEADLYSIGNAL
:DEADLYSIGNAL
:DEADLYSIGNAL
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:DEADLYSIGNAL
:DEADLYSIGNAL
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:DEADLYSIGNAL
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AddressSanitizer:DEADLYSIGNAL
:DEADLYSIGNAL
:DEADLYSIGNAL
:DEADLYSIGNAL
:DEADLYSIGNAL
:DEADLYSIGNAL
:DEADLYSIGNAL
:DEADLYSIGNAL
:DEADLYSIGNAL
AddressSanitizer==2060653==ERROR: AddressSanitizer: SEGV on unknown address 0x7fbb6ce8a800 (pc 0x7fc21cc305a9 bp 0x7fc1b01c05c0 sp 0x7fc1b01c03b0 T93)
:DEADLYSIGNAL
:DEADLYSIGNAL
AddressSanitizer:DEADLYSIGNAL
AddressSanitizer:DEADLYSIGNAL
AddressSanitizer:DEADLYSIGNAL
:DEADLYSIGNAL
:DEADLYSIGNAL
==2060653==The signal is caused by a WRITE memory access.
AddressSanitizer:DEADLYSIGNAL
AddressSanitizer:DEADLYSIGNAL
AddressSanitizerAddressSanitizer:DEADLYSIGNAL
AddressSanitizer:DEADLYSIGNAL
AddressSanitizer:DEADLYSIGNAL
AddressSanitizer:DEADLYSIGNAL
AddressSanitizer:DEADLYSIGNAL
AddressSanitizer:DEADLYSIGNAL
:DEADLYSIGNAL
:DEADLYSIGNAL
#0 0x7fc21cc305a9 in Eigen::TensorEvaluator<Eigen::TensorContractionOp<Eigen::array<Eigen::IndexPair<long>, 1ul> const, Eigen::TensorMap<Eigen::Tensor<Eigen::half const, 2, 1, long>, 16, Eigen::MakePointer> const, Eigen::TensorMap<Eigen::Tensor<Eigen::half const, 2, 1, long>, 16, Eigen::MakePointer> const, Eigen::NoOpOutputKernel const> const, Eigen::ThreadPoolDevice>::EvalParallelContext<Eigen::TensorEvaluator<Eigen::TensorContractionOp<Eigen::array<Eigen::IndexPair<long>, 1ul> const, Eigen::TensorMap<Eigen::Tensor<Eigen::half const, 2, 1, long>, 16, Eigen::MakePointer> const, Eigen::TensorMap<Eigen::Tensor<Eigen::half const, 2, 1, long>, 16, Eigen::MakePointer> const, Eigen::NoOpOutputKernel const> const, Eigen::ThreadPoolDevice>::NoCallback, true, true, false, 0>::pack_rhs(long, long) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x394ae5a9)
#1 0x7fc21cc31692 in Eigen::TensorEvaluator<Eigen::TensorContractionOp<Eigen::array<Eigen::IndexPair<long>, 1ul> const, Eigen::TensorMap<Eigen::Tensor<Eigen::half const, 2, 1, long>, 16, Eigen::MakePointer> const, Eigen::TensorMap<Eigen::Tensor<Eigen::half const, 2, 1, long>, 16, Eigen::MakePointer> const, Eigen::NoOpOutputKernel const> const, Eigen::ThreadPoolDevice>::EvalParallelContext<Eigen::TensorEvaluator<Eigen::TensorContractionOp<Eigen::array<Eigen::IndexPair<long>, 1ul> const, Eigen::TensorMap<Eigen::Tensor<Eigen::half const, 2, 1, long>, 16, Eigen::MakePointer> const, Eigen::TensorMap<Eigen::Tensor<Eigen::half const, 2, 1, long>, 16, Eigen::MakePointer> const, Eigen::NoOpOutputKernel const> const, Eigen::ThreadPoolDevice>::NoCallback, true, true, false, 0>::enqueue_packing_helper(long, long, long, bool) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x394af692)
#2 0x7fc24fd0b121 in Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::WorkerLoop(int) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x41fd121)
#3 0x7fc24fd00326 in void absl::lts_20230802::internal_any_invocable::RemoteInvoker<false, void, tsl::thread::EigenEnvironment::CreateThread(std::function<void ()>)::{lambda()#1}&>(absl::lts_20230802::internal_any_invocable::TypeErasedState*) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x41f2326)
#4 0x7fc24e62625c in tsl::(anonymous namespace)::PThread::ThreadFn(void*) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x2b1825c)
#5 0x7fc2f9885ac2 (/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)
#6 0x7fc2f9916a03 in __clone (/lib/x86_64-linux-gnu/libc.so.6+0x125a03)
AddressSanitizer can not provide additional info.
SUMMARY: AddressSanitizer: SEGV (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x394ae5a9) in Eigen::TensorEvaluator<Eigen::TensorContractionOp<Eigen::array<Eigen::IndexPair<long>, 1ul> const, Eigen::TensorMap<Eigen::Tensor<Eigen::half const, 2, 1, long>, 16, Eigen::MakePointer> const, Eigen::TensorMap<Eigen::Tensor<Eigen::half const, 2, 1, long>, 16, Eigen::MakePointer> const, Eigen::NoOpOutputKernel const> const, Eigen::ThreadPoolDevice>::EvalParallelContext<Eigen::TensorEvaluator<Eigen::TensorContractionOp<Eigen::array<Eigen::IndexPair<long>, 1ul> const, Eigen::TensorMap<Eigen::Tensor<Eigen::half const, 2, 1, long>, 16, Eigen::MakePointer> const, Eigen::TensorMap<Eigen::Tensor<Eigen::half const, 2, 1, long>, 16, Eigen::MakePointer> const, Eigen::NoOpOutputKernel const> const, Eigen::ThreadPoolDevice>::NoCallback, true, true, false, 0>::pack_rhs(long, long)
Thread T93 created by T0 here:
#0 0x7fc2f9bab685 in __interceptor_pthread_create ../../../../src/libsanitizer/asan/asan_interceptors.cpp:216
#1 0x7fc24e633fbf in tsl::(anonymous namespace)::PThread::PThread(tsl::ThreadOptions const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, absl::lts_20230802::AnyInvocable<void ()>) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x2b25fbf)
#2 0x7fc24e63450a in tsl::(anonymous namespace)::PosixEnv::StartThread(tsl::ThreadOptions const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, absl::lts_20230802::AnyInvocable<void ()>) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x2b2650a)
#3 0x7fc24fd093a8 in Eigen::ThreadPoolTempl<tsl::thread::EigenEnvironment>::ThreadPoolTempl(int, bool, tsl::thread::EigenEnvironment) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x41fb3a8)
#4 0x7fc24fd0d498 in tsl::thread::ThreadPool::ThreadPool(tsl::Env*, tsl::ThreadOptions const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, int, bool, Eigen::Allocator*) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x41ff498)
#5 0x7fc24d7ef278 in tensorflow::LocalDevice::EigenThreadPoolInfo::EigenThreadPoolInfo(tensorflow::SessionOptions const&, int, tsl::Allocator*) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x1ce1278)
#6 0x7fc24d7f05c5 in tensorflow::LocalDevice::LocalDevice(tensorflow::SessionOptions const&, tensorflow::DeviceAttributes const&) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x1ce25c5)
#7 0x7fc24d7e3111 in tensorflow::ThreadPoolDevice::ThreadPoolDevice(tensorflow::SessionOptions const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, tsl::gtl::IntType<tensorflow::Bytes_tag_, long>, tensorflow::DeviceLocality const&, tsl::Allocator*) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x1cd5111)
#8 0x7fc24d7dc1dd in tensorflow::ThreadPoolDeviceFactory::CreateDevices(tensorflow::SessionOptions const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::vector<std::unique_ptr<tensorflow::Device, std::default_delete<tensorflow::Device> >, std::allocator<std::unique_ptr<tensorflow::Device, std::default_delete<tensorflow::Device> > > >*) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x1cce1dd)
#9 0x7fc24d9a667c in tensorflow::DeviceFactory::AddCpuDevices(tensorflow::SessionOptions const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::vector<std::unique_ptr<tensorflow::Device, std::default_delete<tensorflow::Device> >, std::allocator<std::unique_ptr<tensorflow::Device, std::default_delete<tensorflow::Device> > > >*) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x1e9867c)
#10 0x7fc24d9a6bbd in tensorflow::DeviceFactory::AddDevices(tensorflow::SessionOptions const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::vector<std::unique_ptr<tensorflow::Device, std::default_delete<tensorflow::Device> >, std::allocator<std::unique_ptr<tensorflow::Device, std::default_delete<tensorflow::Device> > > >*) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x1e98bbd)
#11 0x7fc1f3dd6db8 in TFE_NewContext (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x10654db8)
#12 0x7fc1df839e70 in pybind11::cpp_function::initialize<pybind11_init__pywrap_tfe(pybind11::module_&)::{lambda(TFE_ContextOptions const*)#9}, pybind11::object, TFE_ContextOptions const*, pybind11::name, pybind11::scope, pybind11::sibling, pybind11::return_value_policy>(pybind11_init__pywrap_tfe(pybind11::module_&)::{lambda(TFE_ContextOptions const*)#9}&&, pybind11::object (*)(TFE_ContextOptions const*), pybind11::name const&, pybind11::scope const&, pybind11::sibling const&, pybind11::return_value_policy const&)::{lambda(pybind11::detail::function_call&)#3}::_FUN(pybind11::detail::function_call&) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/_pywrap_tfe.so+0x1b6e70)
#13 0x7fc1df851899 in pybind11::cpp_function::dispatcher(_object*, _object*, _object*) (/mnt//venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/_pywrap_tfe.so+0x1ce899)
#14 0x51ad66 (/usr/bin/python3.11+0x51ad66)
==2060653==ABORTING
```
```
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Aborted (core dumped) in `QuantizedInstanceNorm`
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"Hi @LongZE666 ,\r\n\r\nI have replicated the behaviour with tf-nightly. Attached snapshot below. Thanks!\r\n\r\n<img width=\"1494\" alt=\"Screenshot 2024-05-15 at 9 33 20 AM\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/c6073836-e118-45bc-86ef-fd3cf391c831\">\r\n"
] | 2024-05-14T08:30:42 | 2024-05-15T04:04:41 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
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?
Abort is triggered when the input x does not meet the condition of dimension 4.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
output_range_given = False
given_y_min = 0
given_y_max = 0
variance_epsilon = 1e-05
min_separation = 0.001
x = tf.constant([], shape=[0,0,0,1,1,0,0,0,11,9], dtype=tf.quint8)
x_min = tf.constant(-3.5e+35, shape=[], dtype=tf.float32)
x_max = tf.constant(0, shape=[], dtype=tf.float32)
tf.raw_ops.QuantizedInstanceNorm(x=x, x_min=x_min, x_max=x_max, output_range_given=output_range_given, given_y_min=given_y_min, given_y_max=given_y_max, variance_epsilon=variance_epsilon, min_separation=min_separation)
```
### Relevant log output
```shell
2024-05-14 08:25:37.034690: F tensorflow/core/framework/tensor_shape.cc:45] Check failed: NDIMS == dims() (4 vs. 10)Asking for tensor of 4 dimensions from a tensor of 10 dimensions
Aborted (core dumped)
```
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I_kwDOArmXAs6Ix3fk
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Aborted (core dumped) in `tf.raw_ops.FusedPadConv2D`
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[
"@SuryanarayanaY I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/f7ccbeafd541d3d1bfcc1844e6820161/67527.ipynb).\r\nThank you!"
] | 2024-05-14T08:23:10 | 2024-05-15T06:12:48 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
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?
When `input` dimension is less than 4， it will cause abort.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
mode = "REFLECT"
strides = [1, 1, 1, 1]
padding = "VALID"
input = tf.constant(1.5e+300, shape=[], dtype=tf.float64)
paddings = tf.constant(65534, shape=[4,2], dtype=tf.int32)
filter = tf.constant([], shape=[1,1,0,1,0], dtype=tf.float64)
tf.raw_ops.FusedPadConv2D(input=input, paddings=paddings, filter=filter, mode=mode, strides=strides, padding=padding)
```
### Relevant log output
```shell
2024-05-14 08:19:37.953832: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (1 vs. 0)
Aborted (core dumped)
```
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| 2,294,748,224 |
PR_kwDOArmXAs5vXOXT
| 67,526 |
Revive MLIR interpreter.
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Revive MLIR interpreter.
This is needed for debugging MLIR emitters. So far, the new GPU ops aren't
supported. This just reverts the deletion, with a few changes:
- removal of depreacted dialects (gml_st, thlo, lhlo, deallocation, xla CPU, xla rt)
- adjustments for upstream changes (expand_shape, vector combiners, scf.parallel)
- removal of mhlo ComputeReshapeShape
- replacement of tsl::Status with absl::Status
- move it out of mlir_hlo and adjust to TF style guide (...mostly)
The tooling still needs to be hooked up to the GPU compiler.
Reverts changelist 568496965
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Automated Code Change
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Automated Code Change
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[PJRT:IFRT] Remove overload of PjRtLoadedExecutable::Create() that takes an unique_ptr argument.
Cleanup only, NFC intended.
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Add a typedef to allow users to easily create the correct Factory type to create a TypedKernel.
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Add a typedef to allow users to easily create the correct Factory type to create a TypedKernel.
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PR_kwDOArmXAs5vU3PW
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[XLA:CPU] Remove unnecessary include of absl/log/check.h.
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[XLA:CPU] Remove unnecessary include of absl/log/check.h.
This resolves naming conflicts with tsl and absl.
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PR_kwDOArmXAs5vU2uB
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Improve test coverage of fallback_state.cc
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Improve test coverage of fallback_state.cc
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PR_kwDOArmXAs5vU0XB
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#tf-data Support random access for the unbatch dataset.
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#tf-data Support random access for the unbatch dataset.
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gpu_delegate: Update ADD / MUL broadcast support
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gpu_delegate: Update ADD / MUL broadcast support
4D + 3D works with broadcast when 4D batch is 1.
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Add some internal change.
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PR_kwDOArmXAs5vUm0g
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[XLA:GPU] Clang-tidy fixes for xla/service/gpu/runtime/convolution_thunk.cc
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[XLA:GPU] Clang-tidy fixes for xla/service/gpu/runtime/convolution_thunk.cc
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PR_kwDOArmXAs5vUmX9
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Cleanup change: tf::Status is an alias for absl::Status.
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Cleanup change: tf::Status is an alias for absl::Status.
Add tests
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[Multi-host GPU]Add a utility function to build GpuTopologyProto from GlobalTopologyProto.
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[Multi-host GPU]Add a utility function to build GpuTopologyProto from GlobalTopologyProto.
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PR_kwDOArmXAs5vUf9C
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#tf-buildcop Fix test timeout.
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#tf-buildcop Fix test timeout.
Error:
```
//tensorflow/core/data/service/snapshot:snapshot_stream_writer_checkpoint_test TIMEOUT in 3 out of 3 in 96.0s
Stats over 3 runs: max = 96.0s, min = 66.2s, avg = 79.4s, dev = 12.4s
tensorflow/core/data/service/snapshot/snapshot_stream_writer_checkpoint_test/test.log
tensorflow/core/data/service/snapshot/snapshot_stream_writer_checkpoint_test/test_attempts/attempt_1.log
tensorflow/core/data/service/snapshot/snapshot_stream_writer_checkpoint_test/test_attempts/attempt_2.log
```
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PR_kwDOArmXAs5vUckC
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Remove testonly tag from shlo converter
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Remove testonly tag from shlo converter
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Remove testonly tag from shlo converter
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Remove testonly tag from shlo converter
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Remove EmitErrorReporter class, since it isn't used.
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Remove EmitErrorReporter class, since it isn't used.
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Remove testonly tag from shlo converter
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Remove testonly tag from shlo converter
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Remove testonly tag from shlo converter
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Remove testonly tag from shlo converter
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Remove testonly tag from shlo converter
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Remove testonly tag from shlo converter
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Update visibility in third_party/tensorflow/python/platform/BUILD.
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Remove the test only tag from odml converter target.
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Remove the test only tag from odml converter target.
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Support per-axis embedding lookup reference kernel
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Support per-axis embedding lookup reference kernel
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PR_kwDOArmXAs5vUQl1
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Lift Kernel::Create and TypedKernel::Create methods out into separate factory objects to remove circular dependencies.
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[] | 2024-05-13T21:30:20 | 2024-05-14T16:08:17 | 2024-05-14T16:08:16 |
NONE
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Lift Kernel::Create and TypedKernel::Create methods out into separate factory objects to remove circular dependencies.
Existing Create methods left in place until all uses are eliminated.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12433 from openxla:cudnn_test_h100 856f1ddb05b0cecf1092aa3547611274b27f0796
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PR_kwDOArmXAs5vUFYx
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Migrate coord agent and service to use absl mutex/condvar libraries directly + some clang fixes.
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NONE
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Mark `gpu_sparse_dot_test` as `no_oss` due to missing dep
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NONE
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Mark `gpu_sparse_dot_test` as `no_oss` due to missing dep
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