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Fixes https://github.com/keras-team/tf-keras/issues/777
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The following table shows the default absolute and relative error tolerances for each numeric datatype.
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| ---------------- | ----------- | ---------- | ----------- | --------- |
| double | 1e-4 | 1e-4 | 4.45e-308 | 1.11e-14 |
| float | 1e-4 | 1e-4 | 2.35e-38 | 5.96e-6 |
| float16 | 1e-3 | 1e-3 | 1.22e-4 | 5e-4 |
| bfloat16 | 2e-3 | 2e-2 | 2.35e-38 | 1.6e-2 |
| complex<double> | 1e-4 | 1e-4 | 4.45e-308 | 1.11e-14 |
| complex<float> | 1e-4 | 1e-4 | 2.35e-38 | 5.96e-6 |
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By setting `jax.config.update('jax_cpu_enable_async_dispatch', False)`, one could opt out of the change and recover the old behavior.
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because the hlo->sharding() and operand->sharding() will have conflicting
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} | Simplify JAX lowering rules for cumulative sum
Rely on XLA decomposition.
# JAX GPU microbenchmarks
285us for cumsum over 1e8 elements
449us for cumsum over 1e8 elements.
# JAX CPU microbenchmarks:
1.8s vs. 0.7s for 50 iterations over cumsum over 1e7 elements
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This fixes the following build error:
```
ERROR: /tf/xla/xla/service/gpu/tests/BUILD:939:9: Compiling xla/service/gpu/tests/gpu_fused_mha_test.cc failed: (Exit 1): clang failed: error executing command (from target //xla/service/gpu/tests:gpu_fused_mha_test_gpu) /usr/lib/llvm-17/bin/clang -MD -MF bazel-out/k8-opt/bin/xla/service/gpu/tests/_objs/gpu_fused_mha_test_gpu/gpu_fused_mha_test.d ... (remaining 345 arguments skipped)
xla/service/gpu/tests/gpu_fused_mha_test.cc:121:24: error: moving a temporary object prevents copy elision [-Werror,-Wpessimizing-move]
121 | CountFMHACalls(std::move(reference_module->Clone())));
| ^
xla/service/gpu/tests/gpu_fused_mha_test.cc:129:44: error: moving a temporary object prevents copy elision [-Werror,-Wpessimizing-move]
129 | CountFMHACalls(std::move(test_module->Clone())));
| ^
2 errors generated.
```
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} | Integrate LLVM at llvm/llvm-project@3a8316216807
Updates LLVM usage to match
[3a8316216807](https://github.com/llvm/llvm-project/commit/3a8316216807)
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} | Limit runtime determined PTX version by compile time CUDA version
The previously introduced new PTX version logic determines
the supported PTX version based on the `ptxas` tool version at runtime.
If a user has a CUDA toolkit installed that is newer than what
XLA was compiled with we might set a PTX version that is not
supported by XLA/LLVM.
To avoid that this change determines the PTX version from the
minimum of both the compile time and the runtime CUDA version.
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So that both CPU and GPU code can reuse the same implementation. At the moment each uses its own version that are identical.
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} | [XLA:GPU] Support fusion of dynamic-slice into triton gemm.
This change fuses dynamic-slice into triton gemm kernels provided the slice is taken along the major-most dimension, leaving all other dimensions the same. No further fusion occurs in operands of the dynamic slice, meaning the resulting triton gemm must take in all operands of the dynamic slice as parameters.
Autotuning can handle dynamic-slice just fine, because of the instruction's semantics, which ensures that it never reads out of bounds (the offsets are clamped to a valid region).
The original author of this CL is jvstokes and I did a comprehensive refactor.
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} | PR #11567: Allowing permutations of terms in //xla/service/gpu/fusions:transpose_mlir_test
Imported from GitHub PR https://github.com/openxla/xla/pull/11567
This expands the pattern matcher used by //xla/service/gpu/fusions:transpose_mlir_test, so that it reports a match even if some terms are permuted. (Without the change, I see two subtests of this test fail on ROCm. With the change, the test passes there.)
Copybara import of the project:
--
72cfce5bc6c642475ffb79ce2c3c65ae86f9262a by Eugene Kuznetsov <[email protected]>:
Allowing permutations of terms in //xla/service/gpu/fusions:transpose_mlir_test
Merging this change closes #11567
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11567 from ROCm:pattern_match_permutations 72cfce5bc6c642475ffb79ce2c3c65ae86f9262a
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} | Integrate LLVM at llvm/llvm-project@aefad851672e
Updates LLVM usage to match
[aefad851672e](https://github.com/llvm/llvm-project/commit/aefad851672e)
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11241 from Intel-tensorflow:kanvi/bmm-mul-add_bf16 8fb80082e8c545e007ab7a2d363eb7b7c251fd07
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} | [XLA:GPU] Replace dynamic-slice with slice during autotuning
Dynamic slice requires specific values for the indices that it takes in as operands. However, under computation extraction in the autotuner, all operands are replaced with random parameter values (which then represent incorrect/invalid offsets.) In order to avoid this, we replace dynamic-slice instructions with static slice instructions indexed at 0 along all dimensions.
This CL is needed for a later CL which will add support for fusing dynamic-slice into triton fusions.
The original author of this CL is jvstokes and I rewrote it a bit.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12161 from jaro-sevcik:serialize-as-stream-executor-executable 818765f7b1f5f7282d3f2756b2a03ac556201a6c
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} | PR #12224: [GPU] Fix handling of flags in the cuDNN FMHA test.
Imported from GitHub PR https://github.com/openxla/xla/pull/12224
The test got broken by https://github.com/openxla/xla/commit/8799ff0f4b2aa3437a9963f7c9968ee361a62fd7, this commit fixes it.
Copybara import of the project:
--
80528497321ee6020126b15035050f4c1a0beea9 by Ilia Sergachev <[email protected]>:
[GPU] Fix handling of flags in the cuDNN FMHA test.
Merging this change closes #12224
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12224 from openxla:fix_fmha_test 80528497321ee6020126b15035050f4c1a0beea9
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} | PR #11241: [XLA:CPU] Enable BMM+Mul+Add for bf16
Imported from GitHub PR https://github.com/openxla/xla/pull/11241
This PR enable BatchMatmul + Mul + Add fusion for BF16 and also fixes a bug for the same.
Copybara import of the project:
--
abdbf9b89925c8553296122c846327b6dffa86ce by Kanvi Khanna <[email protected]>:
Enable BMM+Mul+Add for bf16
--
6674ac76b868f0399731d6185287eec76244df3a by Kanvi Khanna <[email protected]>:
fix test
--
8fb80082e8c545e007ab7a2d363eb7b7c251fd07 by Kanvi Khanna <[email protected]>:
address review comment, fix test, format
Merging this change closes #11241
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11241 from Intel-tensorflow:kanvi/bmm-mul-add_bf16 8fb80082e8c545e007ab7a2d363eb7b7c251fd07
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} | PR #11425: [XLA:GPU] Add sycl platform
Imported from GitHub PR https://github.com/openxla/xla/pull/11425
It is a sub PR of https://github.com/openxla/xla/pull/9042 to add sycl platform
Copybara import of the project:
--
7d0c37057c673f784089ce961d24276a496d43bd by Sheng, Yang <[email protected]>:
Add sycl platform
--
8d1196831a20f69ba8880d8549007358928f6be1 by Sheng, Yang <[email protected]>:
Add comments back
Merging this change closes #11425
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11425 from Intel-tensorflow:yang/sycl-platform 8d1196831a20f69ba8880d8549007358928f6be1
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} | Move fuel consumption point to an earlier stage in the cudnn fused convolution rewriter
The call to `ConsumeFuel` happened after `EnsureIfConvBiasActivation` had already rewritten
the convolution instruction. In order to effectively use `ConsumeFuel` for model
bisection I'm moving the check to before any changes are made to the instruction.
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} | Make Blas version optional in HloAlgorithmDenylist
Most cuDNN algorithms nowadays don't call cuBlas kernels anymore,
so the Blas version is often not relevant when adding a new entry
to the denylist.
So this change makes it possible to leave the blas version field empty and it will just match any blas version.
It also does various cleanups:
- Consistent includes and dependencies
- Usage of GMock matchers in the tests
- Reenabling MSAN (works fine)
The original behaviour stays intact, so it shouldn't break any user of the denylist feature.
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} | PR #12243: Add Support for FP8 Data Types in cudnn_fusion_compiler.
Imported from GitHub PR https://github.com/openxla/xla/pull/12243
Copybara import of the project:
--
cee34b537bf34ba3413fa39d01f3267b50eb344a by Elfie Guo <[email protected]>:
Add support for FP8 datatype in cudnn_fusion_compiler.
Merging this change closes #12243
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12243 from elfiegg:fp8_triton cee34b537bf34ba3413fa39d01f3267b50eb344a
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"@PriyajeetGoswami,\r\n\r\nI was able to clone the tensorflow repository without any problem on Ubuntu. I observed that you are using Bazel 6.2, \r\nGCC 9.3 which is incompatible with TF v2.7.0. And also TF v2.7.0 is a pretty older version, please try to install the latest stable version.\r\n\r\nCould you please create a virtual environment and try to install the tensorflow as mentioned in this official document [link](https://www.tensorflow.org/install/source) and have a look at the [compatible](https://www.tensorflow.org/install/source#gpu) tested build configurations as well. Please find the attached screenshot for reference.\r\n\r\n\r\n\r\n\r\nIs there any specific reason to install tensorflow v2.7, because as mentioned above v2.7 is the pretty older version. It's unlikely for TF 2.7 version to receive any bug fixes except when we have security patches. There is a high possibility that this was fixed with later TF versions. Thank you!",
"I was trying TF 2.7 because of some project requirement but moved on from that, used latest version of TF, it worked. Thanks",
"@PriyajeetGoswami,\r\nGlad the issue is resolved. Please feel free to move this issue to closed status. Thank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/67145\">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/67145\">No</a>\n"
] | 2024-05-08T07:04:46 | 2024-05-31T01:49:34 | 2024-05-31T01:49:26 | NONE | null | null | null | ### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.7.0
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.8.10
### Bazel version
6.2.1
### GCC/compiler version
9.3.0
### CUDA/cuDNN version
11.2
### GPU model and memory
_No response_
### Current behavior?
https://www.tensorflow.org/install/source
Followed the instructions here to create a tensorflow docker with gpu support, unable to build the tensorlfow using bazel using the following command bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow --config=cuda
### Standalone code to reproduce the issue
```shell
I want to create a docker using which I build tflite model in C++ which can access the Nvidia gpu.
```
### Relevant log output
```shell
root@b7bdee9743e8:/tensorflow/tensorflow# bazel build //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow --config=cuda
Starting local Bazel server and connecting to it...
WARNING: Option 'java_toolchain' is deprecated
WARNING: Option 'host_java_toolchain' is deprecated
INFO: Options provided by the client:
Inherited 'common' options: --isatty=1 --terminal_columns=191
INFO: Reading rc options for 'build' from /tensorflow/tensorflow/.bazelrc:
Inherited 'common' options: --experimental_repo_remote_exec
INFO: Reading rc options for 'build' from /tensorflow/tensorflow/.bazelrc:
'build' options: --define framework_shared_object=true --java_toolchain=@tf_toolchains//toolchains/java:tf_java_toolchain --host_java_toolchain=@tf_toolchains//toolchains/java:tf_java_toolchain --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/common,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/fallback,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils
INFO: Found applicable config definition build:short_logs in file /tensorflow/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file /tensorflow/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:cuda in file /tensorflow/tensorflow/.bazelrc: --repo_env TF_NEED_CUDA=1 --crosstool_top=@local_config_cuda//crosstool:toolchain --@local_config_cuda//:enable_cuda
INFO: Found applicable config definition build:linux in file /tensorflow/tensorflow/.bazelrc: --copt=-w --host_copt=-w --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++14 --host_cxxopt=-std=c++14 --config=dynamic_kernels --distinct_host_configuration=false --experimental_guard_against_concurrent_changes
INFO: Found applicable config definition build:dynamic_kernels in file /tensorflow/tensorflow/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS
WARNING: Option 'java_toolchain' is deprecated
WARNING: Option 'host_java_toolchain' is deprecated
INFO: Repository local_config_cuda instantiated at:
/tensorflow/tensorflow/WORKSPACE:15:14: in <toplevel>
/tensorflow/tensorflow/tensorflow/workspace2.bzl:1079:19: in workspace
/tensorflow/tensorflow/tensorflow/workspace2.bzl:94:19: in _tf_toolchains
Repository rule cuda_configure defined at:
/tensorflow/tensorflow/third_party/gpus/cuda_configure.bzl:1448:33: in <toplevel>
ERROR: An error occurred during the fetch of repository 'local_config_cuda':
Traceback (most recent call last):
File "/tensorflow/tensorflow/third_party/gpus/cuda_configure.bzl", line 1401, column 38, in _cuda_autoconf_impl
_create_local_cuda_repository(repository_ctx)
File "/tensorflow/tensorflow/third_party/gpus/cuda_configure.bzl", line 978, column 35, in _create_local_cuda_repository
cuda_config = _get_cuda_config(repository_ctx, find_cuda_config_script)
File "/tensorflow/tensorflow/third_party/gpus/cuda_configure.bzl", line 666, column 30, in _get_cuda_config
config = find_cuda_config(repository_ctx, find_cuda_config_script, ["cuda", "cudnn"])
File "/tensorflow/tensorflow/third_party/gpus/cuda_configure.bzl", line 643, column 41, in find_cuda_config
exec_result = _exec_find_cuda_config(repository_ctx, script_path, cuda_libraries)
File "/tensorflow/tensorflow/third_party/gpus/cuda_configure.bzl", line 637, column 19, in _exec_find_cuda_config
return execute(repository_ctx, [python_bin, "-c", decompress_and_execute_cmd])
File "/tensorflow/tensorflow/third_party/remote_config/common.bzl", line 230, column 13, in execute
fail(
Error in fail: Repository command failed
Could not find any cublas_api.h matching version '' in any subdirectory:
''
'include'
'include/cuda'
'include/*-linux-gnu'
'extras/CUPTI/include'
'include/cuda/CUPTI'
of:
'/usr'
'/usr/lib/x86_64-linux-gnu'
'/usr/local/cuda'
'/usr/local/cuda/lib64/stubs'
'/usr/local/cuda/targets/x86_64-linux/lib'
ERROR: /tensorflow/tensorflow/WORKSPACE:15:14: fetching cuda_configure rule //external:local_config_cuda: Traceback (most recent call last):
File "/tensorflow/tensorflow/third_party/gpus/cuda_configure.bzl", line 1401, column 38, in _cuda_autoconf_impl
_create_local_cuda_repository(repository_ctx)
File "/tensorflow/tensorflow/third_party/gpus/cuda_configure.bzl", line 978, column 35, in _create_local_cuda_repository
cuda_config = _get_cuda_config(repository_ctx, find_cuda_config_script)
File "/tensorflow/tensorflow/third_party/gpus/cuda_configure.bzl", line 666, column 30, in _get_cuda_config
config = find_cuda_config(repository_ctx, find_cuda_config_script, ["cuda", "cudnn"])
File "/tensorflow/tensorflow/third_party/gpus/cuda_configure.bzl", line 643, column 41, in find_cuda_config
exec_result = _exec_find_cuda_config(repository_ctx, script_path, cuda_libraries)
File "/tensorflow/tensorflow/third_party/gpus/cuda_configure.bzl", line 637, column 19, in _exec_find_cuda_config
return execute(repository_ctx, [python_bin, "-c", decompress_and_execute_cmd])
File "/tensorflow/tensorflow/third_party/remote_config/common.bzl", line 230, column 13, in execute
fail(
Error in fail: Repository command failed
Could not find any cublas_api.h matching version '' in any subdirectory:
''
'include'
'include/cuda'
'include/*-linux-gnu'
'extras/CUPTI/include'
'include/cuda/CUPTI'
of:
'/usr'
'/usr/lib/x86_64-linux-gnu'
'/usr/local/cuda'
'/usr/local/cuda/lib64/stubs'
'/usr/local/cuda/targets/x86_64-linux/lib'
ERROR: @local_config_cuda//:enable_cuda :: Error loading option @local_config_cuda//:enable_cuda: Repository command failed
Could not find any cublas_api.h matching version '' in any subdirectory:
''
'include'
'include/cuda'
'include/*-linux-gnu'
'extras/CUPTI/include'
'include/cuda/CUPTI'
of:
'/usr'
'/usr/lib/x86_64-linux-gnu'
'/usr/local/cuda'
'/usr/local/cuda/lib64/stubs'
'/usr/local/cuda/targets/x86_64-linux/lib'
```
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} | Disable numerically unstable cuDNN engine 14 for certain convolutions
A customer reported a numerical issue on V100 with cuDNN 9 that I as was able
to track down to a single convolution and cuDNN engine.
Using random data with this convolution gives me reasonable results, so it's not yet clear to me if this is a cuDNN issue or just unfortunate numerics.
For the time being let's disable the algorithm for this one convolution. The customer confirmed that it is fixing their issue.
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"Hi @Liqi1003 ,\r\n\r\nThe difference seems precision related because of XLA fusion. Please find the developer [comment](https://github.com/tensorflow/tensorflow/issues/62287#issuecomment-1809045878) on same for more details.",
"Hi @SuryanarayanaY,\r\n\r\nThanks for the pointer!\r\n\r\nTo test whether it is a problem caused by XLA, I tried to disable XLA by adding `TF_XLA_FLAGS=--tf_xla_auto_jit=-1` before the command, as mentioned [here](https://docs.nvidia.com/deeplearning/frameworks/tensorflow-user-guide/index.html#enabling-xla). However, I still see the log indicates XLA compilation is enabled, and the outputs are the same as the one I posted above. Am I using the wrong way to disable XLA?\r\n\r\nAlso, I was only able to reproduce this problem in tensorflow 2.13. Using google colab with tensorflow 2.16.1, running the same code does not result in such inconsistency. Here is the [colab](https://colab.research.google.com/drive/1vruD1VYxN9JoB_YCzRJ8_EULHXWR3lfe#scrollTo=LEMZWXoBlkpr).\r\n\r\nI wonder if it is a bug that was silently fixed in later versions? Thanks!",
"Hi @Liqi1003 ,\r\n\r\nIt seems 2.16v has better precision than earlier versions. May be there seems some internal amendments which I am not aware.\r\nAs it resolved in latest version can we mark this as closed. Thanks!",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/67137\">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/67137\">No</a>\n"
] | 2024-05-08T02:49:29 | 2024-05-10T00:39:51 | 2024-05-10T00:39:48 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
tf 2.13.0
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 20.04.5
### Mobile device
_No response_
### Python version
3.8.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.8/8.7
### GPU model and memory
_No response_
### Current behavior?
I am reporting an issue encountered during the distributed training of a model with different types of devices using tensorflow. I initially encountered the bug with multiple GPUs involved, but reproduced the bug in a single GPU case. The version of tensorflow used is 2.13.0.
It is very likely an edge case. This inconsistency only occurs with the specific initial weights and inputs we provided. To make the difference more apparent given a limited amount of training data, we deliberately chose a relatively high learning rate (lr=10.0).
Before executing the code, put the [model](https://drive.google.com/file/d/10_nNhQe19cJ6Ki8EIGPU0FjUZP2Oungj/view?usp=drive_link) inside the same directory of the reproduce code, so that the model weights can be loaded. It is important to load the model weights, as random initial weights cannot reproduce this bug.
### Standalone code to reproduce the issue
```shell
import sys
import keras
import random
import numpy as np
import tensorflow as tf
from keras import layers
from keras import optimizers
print("python version:", sys.version)
print("tensorflow version:", tf.__version__)
print("numpy version:", np.__version__)
def train_using_strategy(strategy, train_input, train_label, test_input):
# Load model
with strategy.scope():
model = keras.models.load_model("./model.keras")
optimizer = optimizers.SGD(learning_rate=10.0)
loss = tf.keras.losses.CategoricalCrossentropy(
from_logits=True,
reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE)
model.compile(optimizer=optimizer, loss=loss)
# Train for 1 step
model.fit(
train_input, train_label, verbose=0, shuffle=False, batch_size=2400)
pred = model(test_input)
pred = tf.nn.softmax(pred)
return pred
# Set random seeds
seed = 54078027
random.seed(seed)
tf.random.set_seed(seed)
# Training data, batch_size=2400
train_input = tf.random.uniform(shape=(2400, 32, 32, 3))
train_label = tf.one_hot(tf.random.uniform(
shape=(2400, ), minval=0, maxval=10, dtype=tf.int32), 10)
test_input = tf.random.uniform(shape=(1, 32, 32, 3))
test_label = tf.one_hot(tf.random.uniform(
shape=(1, ), minval=0, maxval=10, dtype=tf.int32), 10)
# Original model
layer_0 = layers.Input(shape=(32, 32, 3,))
layer_1 = layers.Conv2D(
filters=5,
kernel_size=(13, 13),
strides=(1, 1),
padding="valid",
data_format="channels_last",
dilation_rate=(1, 1),
activation="tanh",
use_bias=True,
kernel_initializer="random_uniform",
bias_initializer="random_uniform")(layer_0)
layer_2 = layers.ReLU(max_value=0.08354582293069757)(layer_1)
layer_3 = layers.Flatten()(layer_2)
layer_4 = layers.Dense(
units=10,
activation="linear",
use_bias=False,
kernel_initializer="random_uniform",
bias_initializer="random_uniform")(layer_3)
layer_5 = layers.Reshape((10,))(layer_4)
model = keras.Model(layer_0, layer_5)
model.summary()
model.load_weights("./tensorflow.h5")
# Alternatively, load the model directly
# model = keras.models.load_model("./tensorflow.h5")
keras.models.save_model(model, "./model.keras")
res_cpu = train_using_strategy(
strategy=tf.distribute.MirroredStrategy(devices=["/CPU:0"]),
train_input=train_input,
train_label=train_label,
test_input=test_input)
res_gpu = train_using_strategy(
strategy=tf.distribute.MirroredStrategy(devices=["/GPU:0"]),
train_input=train_input,
train_label=train_label,
test_input=test_input)
print("max diff:", np.max(np.abs(res_cpu - res_gpu)))
print("result on CPU device:", res_cpu)
print("result on GPU device:", res_gpu)
```
### Relevant log output
```shell
2024-05-08 01:19:26.978000: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-05-08 01:19:28.059507: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
python version: 3.8.10 (default, Nov 22 2023, 10:22:35)
[GCC 9.4.0]
tensorflow version: 2.13.0
numpy version: 1.23.5
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:1', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:2', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:3', device_type='GPU')]
2024-05-08 01:19:29.719405: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 10696 MB memory: -> device: 0, name: NVIDIA TITAN V, pci bus id: 0000:af:00.0, compute capability: 7.0
2024-05-08 01:19:29.720298: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 11539 MB memory: -> device: 1, name: NVIDIA TITAN Xp, pci bus id: 0000:3b:00.0, compute capability: 6.1
2024-05-08 01:19:29.721009: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 11539 MB memory: -> device: 2, name: NVIDIA TITAN Xp, pci bus id: 0000:5e:00.0, compute capability: 6.1
2024-05-08 01:19:29.721728: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 11539 MB memory: -> device: 3, name: NVIDIA TITAN Xp, pci bus id: 0000:86:00.0, compute capability: 6.1
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 32, 32, 3)] 0
conv2d (Conv2D) (None, 20, 20, 5) 2540
re_lu (ReLU) (None, 20, 20, 5) 0
flatten (Flatten) (None, 2000) 0
dense (Dense) (None, 10) 20000
reshape (Reshape) (None, 10) 0
=================================================================
Total params: 22540 (88.05 KB)
Trainable params: 22540 (88.05 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
2024-05-08 01:19:30.445908: W tensorflow/core/framework/dataset.cc:956] Input of GeneratorDatasetOp::Dataset will not be optimized because the dataset does not implement the AsGraphDefInternal() method needed to apply optimizations.
2024-05-08 01:19:31.126108: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f57a8015840 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2024-05-08 01:19:31.126233: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2024-05-08 01:19:31.154914: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
2024-05-08 01:19:31.408683: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:432] Loaded cuDNN version 8700
2024-05-08 01:19:32.675226: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f5c85a4c040 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2024-05-08 01:19:32.675279: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA TITAN V, Compute Capability 7.0
2024-05-08 01:19:32.675292: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (1): NVIDIA TITAN Xp, Compute Capability 6.1
2024-05-08 01:19:32.675304: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (2): NVIDIA TITAN Xp, Compute Capability 6.1
2024-05-08 01:19:32.675316: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (3): NVIDIA TITAN Xp, Compute Capability 6.1
max diff: 0.01396697
result on CPU device: tf.Tensor(
[[1.7262544e-03 1.6841354e-02 1.6154546e-02 4.0267220e-01 1.6546119e-03
7.9213362e-14 4.0498021e-01 1.1821460e-01 1.8025419e-02 1.9730769e-02]],
shape=(1, 10), dtype=float32)
result on GPU device: tf.Tensor(
[[1.73812674e-03 1.88294742e-02 1.49312345e-02 4.12326276e-01 1.74528488e-03
7.97685756e-14 4.08124179e-01 1.04247630e-01 1.85339060e-02 1.95238516e-02]],
shape=(1, 10), dtype=float32)
```
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"@neonarc4 If you are facing the error as \"_image_ops.so not found\" , it indicates that TensorFlow is unable to locate a required library file for image processing functionalities. Please try to reinstall TF with GPU support and follow the instructions [here](https://www.tensorflow.org/install/pip). Kindly fill the issue template for easy analysis of the issue.\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/67136\">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/67136\">No</a>\n"
] | 2024-05-08T01:42:56 | 2024-05-29T01:50:57 | 2024-05-29T01:50:52 | NONE | null | null | null | eucid = tfa.image.euclidean_dist_transform(gray)
coming from tfa image ecudiean
```py
eucid = tfa.image.euclidean_dist_transform(gray)
File "C:\Users\haide\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow_addons\image\distance_transform.py", line 67, in euclidean_dist_transform
output = _image_so.ops.addons_euclidean_distance_transform(images, dtype)
File "C:\Users\haide\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow_addons\utils\resource_loader.py", line 68, in ops
self._ops = tf.load_op_library(
File "C:\Users\haide\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow\python\framework\load_library.py", line 54, in load_op_library
lib_handle = py_tf.TF_LoadLibrary(library_filename)
tensorflow.python.framework.errors_impl.NotFoundError: C:\Users\haide\AppData\Local\Programs\Python\Python310\lib\site-packages\tensorflow_addons\custom_ops\image\_image_ops.so not found
``` | {
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} | gpu_delegate: Update GPU MUL compatibility checker
ADD, MUL only works when two inputs has the same number of dimension on GPU.
Make sure if the condition is checked properly.
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} | Add Slice in `IsSupportedConstantExpression()` in `xla::HloConstantSplitter`.
A slice of a constant expression is also a constant expression.
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} | Introduce ReplaceWhileOperandShape api to HloInstruction. This function provides an interface to change the shape an tuple operand in while loops.
The default implementation (ShapeTransformer) propagates shape change through instructions in which the output shape is directly inferred from operands, namely, gte, tuple, and nested while loops.
As the result, if the changed operand of the while loop is only used by the mentioned instructions, the call to ReplaceWhileOperandShape guarantees validity of the hlo graph after shape replacement. Currently, the replace function simply bails and returns false if there are any users other than the mentioned instructions.
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} | PR #12223: [GPU][NFC] Remove some uses of the deprecated runtime executable flag.
Imported from GitHub PR https://github.com/openxla/xla/pull/12223
Copybara import of the project:
--
64be02d67df35156d4ee55ce0d3fbe80eff148ea by Ilia Sergachev <[email protected]>:
[GPU][NFC] Remove some uses of the deprecated runtime executable flag.
Merging this change closes #12223
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12223 from openxla:flag_cleanup 64be02d67df35156d4ee55ce0d3fbe80eff148ea
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} | Add AlgebraicSimplifier pass directly in front of HloConstantFolding.
We have cases with DynamicSlice(Broadcast) which can be simplified away which
would otherwise be constant folded. The HloEvaluator is really slow for
DynamicSlice and Slice, as it will evalute the whole first operand, so if we
can simplify it away, it will help.
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"Hi **@x0w3n** ,\r\n\r\nSorry for the delay, I tried to run your code on colab using TF v2.16, nightly and faced the same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/88012d21fb32b2731770644742419b0e/67098_2-16-1-v_gpu.ipynb) here for reference.\r\n\r\nThank you!",
"> Hi **@x0w3n** ,\r\n> \r\n> Sorry for the delay, I tried to run your code on colab using TF v2.16, nightly and faced the same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/88012d21fb32b2731770644742419b0e/67098_2-16-1-v_gpu.ipynb) here for reference.\r\n> \r\n> Thank you!\r\nOK. Thank you!\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/67098\">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/67098\">No</a>\n"
] | 2024-05-07T12:48:54 | 2024-05-29T14:10:40 | 2024-05-28T12:01:57 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### 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
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When the fft_length parameter of tf.irfftnd/ifftnd/irfftnd is less than or equal to 0, it will trigger a crash, resulting in Check failed.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
input1 = tf.random.normal([10,10,10,10,10], dtype=tf.float32)
input2 = tf.random.normal([10,10,10,10,10], dtype=tf.float32)
input3 = tf.complex(input1,input2)
tf.irfftnd(input3,-3,-3) #crash
# tf.ifftnd(input3,-3,-3) #crash
# tf.irfftnd(input3,-3,-3) #crash
```
### Relevant log output
```shell
2024-05-07 07:46:42.396021: F tensorflow/core/framework/tensor_shape.cc:45] Check failed: NDIMS == dims() (1 vs. 0)Asking for tensor of 1 dimensions from a tensor of 0 dimensions
Aborted (core dumped)
```
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} | Avoid large operands in constant folding.
We already have code that counts the total size of operands. However it still
has a check that the number of users of an operand is 1. This stems from a time
where we tried to evaluate whether constant folding will reduce the total
memory size, so if there was more than 1 user, it should not be counted, as the
operand will remain. But with the current code it makes no sense at all.
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"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/67096\">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/67096\">No</a>\n"
] | 2024-05-07T12:26:57 | 2024-05-07T14:14:38 | 2024-05-07T14:14:34 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
r2.14
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Hi there, I want to know how to use **Correct Command Line** of **tf-opt**
[tf-opt](https://github.com/tensorflow/tensorflow/blob/99d80a9e254c9df7940b2902b14d15914dbbbcd9/tensorflow/compiler/mlir/BUILD#L160) can't use command line of `--tf-init-text-file-to-import --tf-saved-model-dir`? But i saw options in [tf_passes.td](https://github.com/tensorflow/tensorflow/blob/99d80a9e254c9df7940b2902b14d15914dbbbcd9/tensorflow/compiler/mlir/tensorflow/transforms/tf_passes.td#L2258)
How to Modify command like this:
```
$ tf-opt --tf-init-text-file-to-import --tf-saved-model-dir
tf-opt: Unknown command line argument '--tf-saved-model-dir'. Try: './bazel-bin/tensorflow/compiler/mlir/tf-opt --help'
```
### Standalone code to reproduce the issue
```shell
tf-opt --tf-init-text-file-to-import --tf-saved-model-dir
```
### Relevant log output
_No response_ | {
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"Hi @flh0923 ,\r\n\r\nI am sorry to say that TF1.x versions are not supported now. Please upgrade to latest TF versions.",
"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/67095\">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/67095\">No</a>\n"
] | 2024-05-07T12:25:47 | 2024-05-24T01:49:09 | 2024-05-24T01:49:06 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
TF1.13.2
### Custom code
Yes
### OS platform and distribution
ubuntu18.04
### Mobile device
ubuntu18.04
### Python version
2.7
### Bazel version
0.19.2
### GCC/compiler version
7
### CUDA/cuDNN version
cuda10,cudnn7
### GPU model and memory
1050
### Current behavior?
在编译segnetm时候出现错误
### Standalone code to reproduce the issue
```shell
#set (EIGEN3_INCLUDE_DIR "/home/hjx/.cache/bazel/_bazel_hjx/f43d7462b7259647736d249f2d5ed079/external/eigen_archive")
set( LIBS
${OpenCV_LIBS}
${EIGEN3_LIBS}
${Pangolin_LIBRARIES}
${PCL_LIBRARIES}
${OCTOMAP_LIBRARIES}
${PROJECT_SOURCE_DIR}/../../../Thirdparty/DBoW2/lib/libDBoW2.so
${PROJECT_SOURCE_DIR}/../../../Thirdparty/g2o/lib/libg2o.so
${PROJECT_SOURCE_DIR}/../../../build/libmyORB_SLAM2_PointMap_SegNetM.so
/usr/lib/x86_64-linux-gnu/libglog.so
-lboost_system
${PROJECT_SOURCE_DIR}/../../../Examples/myROS/myORB_SLAM2_PointMap_SegNetM/libsegmentation/build/libmysegmentation.so
)
add_definitions( ${PCL_DEFINITIONS} )
link_directories( ${PCL_LIBRARY_DIRS} )
rosbuild_add_executable(myTUM
myros_tum_realtime.cc
)
target_link_libraries(myTUM
${LIBS}
)
```
### Relevant log output
```shell
/home/flh/tensorflow-1.13.2/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/MatMatProductAVX2.h:162:38: error: ‘void Eigen::internal::operator()(Eigen::QInt16*, const DataMapper&, Index, Index, Index, Index)’ must be a nonstatic member function
Index stride, Index offset) {
^
/home/flh/tensorflow-1.13.2/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/MatMatProductAVX2.h:549:24: error: wrong number of template arguments (5, should be at least 3)
Index, ShardingType> {
^
home/flh/tensorflow-1.13.2/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/MatMatProductAVX2.h:49:76: error: wrong number of template arguments (5, should be at least 3)
class TensorContractionBlocking<QInt16, QInt16, QInt16, Index, ShardingType> {
^
In file included from /usr/include/eigen3/unsupported/Eigen/CXX11/Tensor:112:0,
```
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} | Support MOF with variadic reductions.
I thought this wasn't a thing. It is a thing.
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} | Change weight-only PTQ to use symmetric quantization
This is to conform to the constraints of zero points being zero on rhs of quantized stablehlo.dot_general op. (dot_general C15 in https://github.com/openxla/stablehlo/blob/main/docs/spec.md#constraints-29)
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