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} | [XLA:GPU][NFC] Make GpuCompilerTest.CollectivePermuteDecompositionAndPipelining less brittle.
Previously, the test was relying on both
1. exact instruction names, and
2. explicit ordering of many pure and re-orderable HLO operations.
Both of these things can not be relied upon for testing, and this came up
as a blocker when trying to remove passes from the pipeline.
This change removes all the matches on exact instruction names and minimizes
the number of instructions that need to be explicitly ordered---while aiming
to preserve the spirit of the test.
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"@rkdtmdals7710 For such issues the recommended solution is to upgrade your protobuf to version 3.19.0 or later. You can usually do this using your package manager (e.g., `pip install --upgrade protobuf`).\r\nAlso please verify the TensorFlow IO Installation,\r\n\r\n```\r\npip install tensorflow-io\r\nimport tensorflow_io as tfio\r\nprint(tfio.__version__)\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/67086\">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/67086\">No</a>\n"
] | 2024-05-07T10:10:13 | 2024-05-30T01:49:50 | 2024-05-30T01:49:46 | NONE | null | null | null | I created a tflite model from yolov5n [export.py -- weights mymodel.pt -- include tflite] for the learning model I learned from yolov5n. By the way, /home/pi1/Desktop/project3/examples/lite/examples/object_detection/raspberry_pi. It works fine when I run the existing detect.py , but when I use the above model, I get the following error.
(0507) pi1@raspberrypi:~/Desktop/project3/examples/lite/examples/object_detection/raspberry_pi $ python detect.py --model mymodel.tflite
Traceback (most recent call last):
File "detect.py", line 15, in <module>
import tensorflow_io as tfio
File "/home/pi1/miniconda3/envs/0507/lib/python3.7/site-packages/tensorflow_io/__init__.py", line 17, in <module>
from tensorflow_io.python.api import * # pylint: disable=wildcard-import
File "/home/pi1/miniconda3/envs/0507/lib/python3.7/site-packages/tensorflow_io/python/api/__init__.py", line 19, in <module>
from tensorflow_io.python.ops.io_dataset import IODataset
File "/home/pi1/miniconda3/envs/0507/lib/python3.7/site-packages/tensorflow_io/python/ops/__init__.py", line 24, in <module>
import tensorflow as tf
File "/home/pi1/miniconda3/envs/0507/lib/python3.7/site-packages/tensorflow/__init__.py", line 37, in <module>
from tensorflow.python.tools import module_util as _module_util
File "/home/pi1/miniconda3/envs/0507/lib/python3.7/site-packages/tensorflow/python/__init__.py", line 37, in <module>
from tensorflow.python.eager import context
File "/home/pi1/miniconda3/envs/0507/lib/python3.7/site-packages/tensorflow/python/eager/context.py", line 28, in <module>
from tensorflow.core.framework import function_pb2
File "/home/pi1/miniconda3/envs/0507/lib/python3.7/site-packages/tensorflow/core/framework/function_pb2.py", line 16, in <module>
from tensorflow.core.framework import attr_value_pb2 as tensorflow_dot_core_dot_framework_dot_attr__value__pb2
File "/home/pi1/miniconda3/envs/0507/lib/python3.7/site-packages/tensorflow/core/framework/attr_value_pb2.py", line 16, in <module>
from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
File "/home/pi1/miniconda3/envs/0507/lib/python3.7/site-packages/tensorflow/core/framework/tensor_pb2.py", line 16, in <module>
from tensorflow.core.framework import resource_handle_pb2 as tensorflow_dot_core_dot_framework_dot_resource__handle__pb2
File "/home/pi1/miniconda3/envs/0507/lib/python3.7/site-packages/tensorflow/core/framework/resource_handle_pb2.py", line 16, in <module>
from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
File "/home/pi1/miniconda3/envs/0507/lib/python3.7/site-packages/tensorflow/core/framework/tensor_shape_pb2.py", line 42, in <module>
serialized_options=None, file=DESCRIPTOR),
File "/home/pi1/miniconda3/envs/0507/lib/python3.7/site-packages/google/protobuf/descriptor.py", line 561, in __new__
_message.Message._CheckCalledFromGeneratedFile()
TypeError: Descriptors cannot not be created directly.
If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0.
If you cannot immediately regenerate your protos, some other possible workarounds are:
1. Downgrade the protobuf package to 3.20.x or lower.
2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower).
More information: https://developers.google.com/protocol-buffers/docs/news/2022-05-06#python-updates
When I downgrade protobuf, I get the following error. Please solve the problem.
(0507) pi1@raspberrypi:~/Desktop/project3/examples/lite/examples/object_detection/raspberry_pi $ python detect.py --model mymodel.tflite
/home/pi1/miniconda3/envs/0507/lib/python3.7/site-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/home/pi1/miniconda3/envs/0507/lib/python3.7/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']
caused by: ['/home/pi1/miniconda3/envs/0507/lib/python3.7/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so: undefined symbol: _ZN10tensorflow8internal15LogMessageFatalC1EPKci']
warnings.warn(f"unable to load libtensorflow_io_plugins.so: {e}")
/home/pi1/miniconda3/envs/0507/lib/python3.7/site-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/home/pi1/miniconda3/envs/0507/lib/python3.7/site-packages/tensorflow_io/python/ops/libtensorflow_io.so']
caused by: ['/home/pi1/miniconda3/envs/0507/lib/python3.7/site-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZN10tensorflow4data11DatasetBase8FinalizeEPNS_15OpKernelContextESt8functionIFNS_8StatusOrISt10unique_ptrIS1_NS_4core15RefCountDeleterEEEEvEE']
warnings.warn(f"file system plugins are not loaded: {e}")
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
Traceback (most recent call last):
File "detect.py", line 152, in <module>
main()
File "detect.py", line 148, in main
int(args.numThreads), bool(args.enableEdgeTPU))
File "detect.py", line 65, in run
detector = vision.ObjectDetector.create_from_options(options)
File "/home/pi1/miniconda3/envs/0507/lib/python3.7/site-packages/tensorflow_lite_support/python/task/vision/object_detector.py", line 91, in create_from_options
options.base_options.to_pb2(), options.detection_options.to_pb2())
RuntimeError: Input tensor has type kTfLiteFloat32: it requires specifying NormalizationOptions metadata to preprocess input images.
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"Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/67085/checks?check_run_id=24673327503) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request."
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} | [XLA:FFI] Add a `BufferShape` alias.
This change introduces a `BufferShape` alias for custom call users' convenience. This new alias eliminates the need for verbose code such as `using Shape = decltype(Buffer<PrimitiveType::F32>::dimensions)`.
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} | PR #12185: [ROCM] fixing smale build brakes in RedzoneAllocator, rocm_dnn and hip_blas_lt
Imported from GitHub PR https://github.com/openxla/xla/pull/12185
Here are fixes after the following PRs:
- rocm_dnn AllocateOwnedArray: https://github.com/openxla/xla/pull/11654
- hipblas_lt: https://github.com/openxla/xla/issues/11514
- and also RedzoneAllocator: https://github.com/openxla/xla/commit/d8f0c1acdb79c18cdce0a050b1d7c6baa8b9f14b
@xla-rotation: could you please have a look ?
Copybara import of the project:
--
4ad8abec5e0d0135f0844ae9e6acafc10ed84092 by Pavel Emeliyanenko <[email protected]>:
fixing build brakes
Merging this change closes #12185
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12185 from ROCm:ci_build_brake_fix_240506 4ad8abec5e0d0135f0844ae9e6acafc10ed84092
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} | PR #12195: [GPU][NFC] Simplify generation of cuDNN uid-pointer maps.
Imported from GitHub PR https://github.com/openxla/xla/pull/12195
Copybara import of the project:
--
484b7b1e0c74caad30a29e4d7b0dee801830c10e by Ilia Sergachev <[email protected]>:
[GPU][NFC] Simplify generation of cuDNN uid-pointer maps.
Merging this change closes #12195
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12195 from openxla:cudnn_cleanup 484b7b1e0c74caad30a29e4d7b0dee801830c10e
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} | Add support for the --xla_dump_module_metadata flag to GPU backend.
So far, this flag was only used for the TPU backend, but it can be useful for
the GPU backend, too. The module metadata dump contains timing information for
every HLO pass together with the pass name, the pipeline name and whether the
module was changed by the pass.
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Some ops have quotes in their metadata and if not escaped these lead to errors when rendering a dot graph.
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} | [XLA:GPU][NFC] Make GpuCompilerTest.CollectivePermuteDecompositionAndPipelining less brittle.
Previously, the test was relying on both
1. exact instruction names, and
2. explicit ordering of many pure and re-orderable HLO operations.
Both of these things can not be relied upon for testing, and this came up
as a blocker when trying to remove passes from the pipeline.
This change removes all the matches on exact instruction names and minimizes
the number of instructions that need to be explicitly ordered---while aiming
to preserve the spirit of the test.
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Along with other readability fixes:
* Remove obsolete TODOs
* Raname obscure names
* Include missing header files.
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"Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/67074/checks?check_run_id=24667655557) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request."
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} | [xla:ffi] Port PJRT CPU test to use FFI API
Port cpu_client_test to use FFI API (new, type-safe custom call API).
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} | [xla:ffi] Port LLVM IR CPU test to use FFI API
Port alias_analysis_test to use FFI API (new, type-safe custom call API).
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} | PR #12199: [XLA:GPU] only enable cudnn fmha dbias on hopper + cudnn 8.9.6
Imported from GitHub PR https://github.com/openxla/xla/pull/12199
Dbias requires hopper + cudnn 8.9.6, hence this PR.
Copybara import of the project:
--
ba4d8f507b1bae75d249fae1bf3d84c4e57199fa by cjkkkk <[email protected]>:
init
--
68978ddc378c6a5a891187a043fc2a696c45f478 by cjkkkk <[email protected]>:
fix rewriter test
Merging this change closes #12199
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12199 from Cjkkkk:enable_dbias_only_hopper_cudnn8.9.6 68978ddc378c6a5a891187a043fc2a696c45f478
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} | PR #12193: [GPU] Move workspace to be the last operand of cuDNN FMHA.
Imported from GitHub PR https://github.com/openxla/xla/pull/12193
This will enable a large simplification in a next PR.
Copybara import of the project:
--
ad747544396d4920e60946d7a0f00300f47e07d2 by Ilia Sergachev <[email protected]>:
[GPU] Move workspace to be the last operand of cuDNN FMHA.
Merging this change closes #12193
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12193 from openxla:move_fmha_workspace ad747544396d4920e60946d7a0f00300f47e07d2
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"Hi **@yogashreeNaik** ,\r\n- Could you check the version of tensorflow you are using is compatible with your system and python environment.\r\n- Ensure that all necessary system dependencies, such as CUDA and cuDNN for GPU support, are correctly installed and compatible with your TensorFlow version.\r\n- Try reinstalling TensorFlow in your virtual environment, By using pip:\r\n```\r\npip uninstall tensorflow\r\npip install tensorflow\r\n```\r\nThank you!",
"> Hi **@yogashreeNaik** ,\r\n> \r\n> * Could you check the version of tensorflow you are using is compatible with your system and python environment.\r\n> * Ensure that all necessary system dependencies, such as CUDA and cuDNN for GPU support, are correctly installed and compatible with your TensorFlow version.\r\n> * Try reinstalling TensorFlow in your virtual environment, By using pip:\r\n> \r\n> ```\r\n> pip uninstall tensorflow\r\n> pip install tensorflow\r\n> ```\r\n> \r\n> Thank you!\r\n\r\nI'll it . Thank you..\r\n",
"Hi @yogashreeNaik ,\r\n- Could you please confirm if this issue is resolved for you ? Please feel free to close the issue.\r\n\r\nThank you!\r\n",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"> Hi @yogashreeNaik ,\r\n> \r\n> * Could you please confirm if this issue is resolved for you ? Please feel free to close the issue.\r\n> \r\n> Thank you!\r\n\r\nYeah, it's resolved now, thank you!!",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/67067\">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/67067\">No</a>\n"
] | 2024-05-07T05:15:17 | 2024-06-12T10:27:42 | 2024-06-12T10:27:39 | NONE | null | null | null | Traceback (most recent call last):
File "C:\Users\AVITA\anaconda3\envs\myEnv\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 62, in <module>
from tensorflow.python._pywrap_tensorflow_internal import *
ImportError: DLL load failed while importing _pywrap_tensorflow_internal: A dynamic link library (DLL) initialization routine failed.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\AVITA\Desktop\Yogashree\pyprojects\test.py", line 1, in <module>
import tensorflow as tf
File "C:\Users\AVITA\anaconda3\envs\myEnv\lib\site-packages\tensorflow\__init__.py", line 37, in <module>
from tensorflow.python.tools import module_util as _module_util
File "C:\Users\AVITA\anaconda3\envs\myEnv\lib\site-packages\tensorflow\python\__init__.py", line 36, in <module>
from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
File "C:\Users\AVITA\anaconda3\envs\myEnv\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 77, in <module>
raise ImportError(
ImportError: Traceback (most recent call last):
File "C:\Users\AVITA\anaconda3\envs\myEnv\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 62, in <module>
from tensorflow.python._pywrap_tensorflow_internal import *
ImportError: DLL load failed while importing _pywrap_tensorflow_internal: A dynamic link library (DLL) initialization routine failed.
Failed to load the native TensorFlow runtime.
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"These are my default python paths: \r\nINFO: Reading rc options for 'build' from /mnt/c/tensorflow-2.5.0/.tf_configure.bazelrc:\r\n 'build' options: --action_env PYTHON_BIN_PATH=/usr/bin/python3 --action_env PYTHON_LIB_PATH=/usr/lib/python3/dist-packages --python_path=/usr/bin/python3",
"@Thunderzen,\r\nThere are a few more simultaneous issues... If you feel you can make progress on a root cause PR please feel free to submit one that we can review.\r\n\r\nAs a Temporary fix, could you please try adding python definitions in the {project}/WORKSPACE file, then it might work. and Project can be built successfully. \r\n\r\n\r\n```python\r\n# bazel_skylib and rules_python are from @org_tensorflow/WORKSPACE.\r\nhttp_archive(\r\n name = \"bazel_skylib\",\r\n sha256 = \"74d544d96f4a5bb630d465ca8bbcfe231e3594e5aae57e1edbf17a6eb3ca2506\",\r\n urls = [\r\n \"https://storage.googleapis.com/mirror.tensorflow.org/github.com/bazelbuild/bazel-skylib/releases/download/1.3.0/bazel-skylib-1.3.0.tar.gz\",\r\n \"https://github.com/bazelbuild/bazel-skylib/releases/download/1.3.0/bazel-skylib-1.3.0.tar.gz\",\r\n ],\r\n)\r\n\r\nhttp_archive(\r\n name = \"rules_python\",\r\n sha256 = \"9d04041ac92a0985e344235f5d946f71ac543f1b1565f2cdbc9a2aaee8adf55b\",\r\n strip_prefix = \"rules_python-0.26.0\",\r\n url = \"https://github.com/bazelbuild/rules_python/releases/download/0.26.0/rules_python-0.26.0.tar.gz\",\r\n)\r\n\r\n# Copied from `@org_tensorflow//:WORKSPACE`.\r\nload(\r\n \"@rules_python//python:repositories.bzl\",\r\n \"py_repositories\",\r\n \"python_register_toolchains\",\r\n)\r\npy_repositories()\r\n\r\nload(\r\n \"@org_tensorflow//tensorflow/tools/toolchains/python:python_repo.bzl\",\r\n \"python_repository\",\r\n)\r\npython_repository(name = \"python_version_repo\")\r\nload(\"@python_version_repo//:py_version.bzl\", \"HERMETIC_PYTHON_VERSION\")\r\n\r\n# Toolchain setup here is to please the TF workspace scripts,\r\n# and we do not use this Python version to build pip packages.\r\npython_register_toolchains(\r\n name = \"python\",\r\n ignore_root_user_error = True,\r\n python_version = HERMETIC_PYTHON_VERSION,\r\n)\r\n```\r\n\r\nAlso tensorflow v2.5 is pretty old, please try to upgrade to the latest tensorflow v2.15 or v2.16. 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/67062\">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/67062\">No</a>\n"
] | 2024-05-07T03:18:25 | 2024-05-24T01:49:12 | 2024-05-24T01:49:08 | NONE | null | null | null | ### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.5
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
3.10.12
### Bazel version
3.7.2
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
While building tensorflow lite using bazel, I encountered the following errors in the log output. Here is my bazel command:
/c/tensorflow-2.5.0/tflite_build$ bazel build --config=elinux_armhf -c opt --crosstool_top=//toolchain:armv7-toolchain //tensorflow/lite:libtensorflowlite.so
### Standalone code to reproduce the issue
```shell
NIL
```
### Relevant log output
```shell
ERROR: An error occurred during the fetch of repository 'local_execution_config_python':
Traceback (most recent call last):
File "/mnt/c/tensorflow-2.5.0/third_party/py/python_configure.bzl", line 212, column 41, in _create_local_python_repository
python_include = _get_python_include(repository_ctx, python_bin)
File "/mnt/c/tensorflow-2.5.0/third_party/py/python_configure.bzl", line 152, column 21, in _get_python_include
result = execute(
File "/mnt/c/tensorflow-2.5.0/third_party/remote_config/common.bzl", line 219, column 13, in execute
fail(
Error in fail: Problem getting python include path.
<string>:1: DeprecationWarning: The distutils package is deprecated and slated for removal in Python 3.12. Use setuptools or check PEP 632 for potential alternatives
<string>:1: DeprecationWarning: The distutils.sysconfig module is deprecated, use sysconfig instead
Is the Python binary path set up right? (See ./configure or PYTHON_BIN_PATH.) Is distutils installed?
```
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} | [mhlo] Remove called computation and execution thread from async_update and async_done
For consistency with HLO counterpart get called computation and execution thread by following the async chain.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12154 from philipphack:u_fp8_scale_xla c7944fc3b2b7fdc45a06d058dea856fff2bd7e15
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Error message:
```
[ RUN ] QuantizedInstanceNormTest.TestClamp
tensorflow/core/kernels/quantized_instance_norm_test.cc:120: Failure
Expected: (max_diff()) <= (0.1), actual: 0.165527 vs 0.1
2024-05-06 18:40:43.391840: I tensorflow/core/kernels/quantized_instance_norm_test.cc:121] max diff 0.165527
[ FAILED ] QuantizedInstanceNormTest.TestClamp (3 ms)
```
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Copy shape inference logic from Shape inference of Gather in XLA [1]. This makes grappler to properly infer shapes. The Grappler only relies on TensorFlow shape inference, and it invalidates all previously inferred shapes saved in `_output_shapes` attribute.
[1] https://github.com/openxla/xla/blob/3cce1630d278c56d1c0edf0b898cee3be2e01cbc/xla/service/shape_inference.cc#L3862
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} | [xla:ffi] Add xla::ffi::ExecutionContext to pass user data to FFI handlers at run time
There are two kinds of data that can be added to execution context:
1. Opaque pointers with a deleter for users that register context types that are can be defined in shared libraries and can't depend on XLA internals
2. Types ExecutionContext::UserData for FFI handlers in the same process
Next step is to plumb execution context all the way up to xla::ExecuteOptions and PjRt
Reverts 15f8f10f345aa41475cd3b6cf2f162a2fb1aa152
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12154 from philipphack:u_fp8_scale_xla c7944fc3b2b7fdc45a06d058dea856fff2bd7e15
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We can't use swisstable here because this code requires an exception-safe hashtable so switch to std::unordered_map.
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Reason: Broke JAX Arm CI
```
E AssertionError: [b"'-prefer-256-bit' is not a recognized feature for this target (ignoring feature)"
```
Reverts 58d6f04ae41c442626f705eb03385c3de50266c0
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The op is only needed by KernelGen, and must be removed from CHLO as per the Dynamism 101 RFC: https://github.com/openxla/stablehlo/blob/main/rfcs/20230704-dynamism-101.md#p1
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} | [XLA:GPU] Fix in-place dynamic update slice emitter for producer-consumer fusion.
For a case when we have a potential dus-producer and bitcast-consumer fusion, the current code wouldn't see that we can use DUS emitter and will use cost model as for loop emitter.
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} | #tensorflow Skip failing test.
Error message:
```
[ RUN ] QuantizedInstanceNormTest.TestClamp
tensorflow/core/kernels/quantized_instance_norm_test.cc:120: Failure
Expected: (max_diff()) <= (0.1), actual: 0.165527 vs 0.1
2024-05-06 18:40:43.391840: I tensorflow/core/kernels/quantized_instance_norm_test.cc:121] max diff 0.165527
[ FAILED ] QuantizedInstanceNormTest.TestClamp (3 ms)
```
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} | Bumps [werkzeug](https://github.com/pallets/werkzeug) from 3.0.1 to 3.0.3.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/pallets/werkzeug/releases">werkzeug's releases</a>.</em></p>
<blockquote>
<h2>3.0.3</h2>
<p>This is the Werkzeug 3.0.3 security release, which fixes security issues and bugs but does not otherwise change behavior and should not result in breaking changes.</p>
<p>PyPI: <a href="https://pypi.org/project/Werkzeug/3.0.3/">https://pypi.org/project/Werkzeug/3.0.3/</a>
Changes: <a href="https://werkzeug.palletsprojects.com/en/3.0.x/changes/#version-3-0-3">https://werkzeug.palletsprojects.com/en/3.0.x/changes/#version-3-0-3</a>
Milestone: <a href="https://github.com/pallets/werkzeug/milestone/35?closed=1">https://github.com/pallets/werkzeug/milestone/35?closed=1</a></p>
<ul>
<li>Only allow <code>localhost</code>, <code>.localhost</code>, <code>127.0.0.1</code>, or the specified hostname when running the dev server, to make debugger requests. Additional hosts can be added by using the debugger middleware directly. The debugger UI makes requests using the full URL rather than only the path. GHSA-2g68-c3qc-8985</li>
<li>Make reloader more robust when <code>""</code> is in <code>sys.path</code>. <a href="https://redirect.github.com/pallets/werkzeug/issues/2823">#2823</a></li>
<li>Better TLS cert format with <code>adhoc</code> dev certs. <a href="https://redirect.github.com/pallets/werkzeug/issues/2891">#2891</a></li>
<li>Inform Python < 3.12 how to handle <code>itms-services</code> URIs correctly, rather than using an overly-broad workaround in Werkzeug that caused some redirect URIs to be passed on without encoding. <a href="https://redirect.github.com/pallets/werkzeug/issues/2828">#2828</a></li>
<li>Type annotation for <code>Rule.endpoint</code> and other uses of <code>endpoint</code> is <code>Any</code>. <a href="https://redirect.github.com/pallets/werkzeug/issues/2836">#2836</a></li>
</ul>
<h2>3.0.2</h2>
<p>This is a fix release for the 3.0.x feature branch.</p>
<ul>
<li>Changes: <a href="https://werkzeug.palletsprojects.com/en/3.0.x/changes/#version-3-0-2">https://werkzeug.palletsprojects.com/en/3.0.x/changes/#version-3-0-2</a></li>
</ul>
</blockquote>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a href="https://github.com/pallets/werkzeug/blob/main/CHANGES.rst">werkzeug's changelog</a>.</em></p>
<blockquote>
<h2>Version 3.0.3</h2>
<p>Released 2024-05-05</p>
<ul>
<li>
<p>Only allow <code>localhost</code>, <code>.localhost</code>, <code>127.0.0.1</code>, or the specified
hostname when running the dev server, to make debugger requests. Additional
hosts can be added by using the debugger middleware directly. The debugger
UI makes requests using the full URL rather than only the path.
:ghsa:<code>2g68-c3qc-8985</code></p>
</li>
<li>
<p>Make reloader more robust when <code>""</code> is in <code>sys.path</code>. :pr:<code>2823</code></p>
</li>
<li>
<p>Better TLS cert format with <code>adhoc</code> dev certs. :pr:<code>2891</code></p>
</li>
<li>
<p>Inform Python < 3.12 how to handle <code>itms-services</code> URIs correctly, rather
than using an overly-broad workaround in Werkzeug that caused some redirect
URIs to be passed on without encoding. :issue:<code>2828</code></p>
</li>
<li>
<p>Type annotation for <code>Rule.endpoint</code> and other uses of <code>endpoint</code> is
<code>Any</code>. :issue:<code>2836</code></p>
</li>
<li>
<p>Make reloader more robust when <code>""</code> is in <code>sys.path</code>. :pr:<code>2823</code></p>
</li>
</ul>
<h2>Version 3.0.2</h2>
<p>Released 2024-04-01</p>
<ul>
<li>Ensure setting <code>merge_slashes</code> to <code>False</code> results in <code>NotFound</code> for
repeated-slash requests against single slash routes. :issue:<code>2834</code></li>
<li>Fix handling of <code>TypeError</code> in <code>TypeConversionDict.get()</code> to match
<code>ValueError</code>. :issue:<code>2843</code></li>
<li>Fix <code>response_wrapper</code> type check in test client. :issue:<code>2831</code></li>
<li>Make the return type of <code>MultiPartParser.parse</code> more precise.
:issue:<code>2840</code></li>
<li>Raise an error if converter arguments cannot be parsed. :issue:<code>2822</code></li>
</ul>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/pallets/werkzeug/commit/f9995e967979eb694d6b31536cc65314fd7e9c8c"><code>f9995e9</code></a> release version 3.0.3</li>
<li><a href="https://github.com/pallets/werkzeug/commit/3386395b24c7371db11a5b8eaac0c91da5362692"><code>3386395</code></a> Merge pull request from GHSA-2g68-c3qc-8985</li>
<li><a href="https://github.com/pallets/werkzeug/commit/890b6b62634fa61224222aee31081c61b054ff01"><code>890b6b6</code></a> only require trusted host for evalex</li>
<li><a href="https://github.com/pallets/werkzeug/commit/71b69dfb7df3d912e66bab87fbb1f21f83504967"><code>71b69df</code></a> restrict debugger trusted hosts</li>
<li><a href="https://github.com/pallets/werkzeug/commit/d2d3869525a4ffb2c41dfb2c0e39d94dab2d870c"><code>d2d3869</code></a> endpoint type is Any (<a href="https://redirect.github.com/pallets/werkzeug/issues/2895">#2895</a>)</li>
<li><a href="https://github.com/pallets/werkzeug/commit/7080b55acd48b68afdda65ee6c7f99e9afafb0ba"><code>7080b55</code></a> endpoint type is Any</li>
<li><a href="https://github.com/pallets/werkzeug/commit/7555eff296fbdf12f2e576b6bbb0b506df8417ed"><code>7555eff</code></a> remove iri_to_uri redirect workaround (<a href="https://redirect.github.com/pallets/werkzeug/issues/2894">#2894</a>)</li>
<li><a href="https://github.com/pallets/werkzeug/commit/97fb2f722297ae4e12e36dab024e0acf8477b3c8"><code>97fb2f7</code></a> remove _invalid_iri_to_uri workaround</li>
<li><a href="https://github.com/pallets/werkzeug/commit/249527ff981e7aa22cd714825c5637cc92df7761"><code>249527f</code></a> make cn field a valid single hostname, and use wildcard in SANs field. (<a href="https://redirect.github.com/pallets/werkzeug/issues/2892">#2892</a>)</li>
<li><a href="https://github.com/pallets/werkzeug/commit/793be472c9d145eb9be7d4200672d1806289d84a"><code>793be47</code></a> update adhoc tls dev cert format</li>
<li>Additional commits viewable in <a href="https://github.com/pallets/werkzeug/compare/3.0.1...3.0.3">compare view</a></li>
</ul>
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} | PR #12161: Test for deserialization of GPU executables
Imported from GitHub PR https://github.com/openxla/xla/pull/12161
This patch tests that serialization and deserialization of GPU executables
(e.g., for JAX compilation cache) succeeds.
The test covers the fix in [PR 12184](https://github.com/openxla/xla/pull/12184).
For completeness, PR 12184 fixes a problem where executable would be serialized
as ExecutableAndOptionsProto by PjRtStreamExecutorClient::SerializeExecutable, but
StreamExecutorGpuClient::DeserializeExecutable would first try to deserialize
as StreamExecutorExecutableProto and sometimes succeed, reading mangled data.
The test in this patch exercises one such example.
Copybara import of the project:
--
818765f7b1f5f7282d3f2756b2a03ac556201a6c by Jaroslav Sevcik <[email protected]>:
Avoid deserializing GPU executables as StreamExecutorExecutableProto
Merging this change closes #12161
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12161 from jaro-sevcik:serialize-as-stream-executor-executable 818765f7b1f5f7282d3f2756b2a03ac556201a6c
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This feature is no longer supported as Pepperjack M1 gets deprecated.
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JIT compilation of single-core TPU graphs via Phase 1 Bridge is no longer supported as Pepperjack M1 gets deprecated. This ensures such graphs are no longer passed into the Bridge.
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"Hi @garryyan2 ,\r\n\r\nThe warning will arise when we call a function multiple times (for eg in a loop) that is decorated with tf.function decorator. Since retracing is computationally expensive this will affect performance but the model will not have any issue wrt its metrics or predictions except time complexity increases.\r\n\r\nYou can check the info in warning and take necessary steps. Also you can pass `reduce_retracing=True` to `tf.function` as argument.\r\n\r\nHowever provided code snippet I can't see any tf.function decorator being used. I can't reproduce the issue either due to missing dependencies as per [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/5c4b4fcf5b9631511ebefb476a5817ae/67033.ipynb).",
"Hi @SuryanarayanaY \r\n\r\nThank you very much for looking into the problem. I am sorry that the code will not run. Now, I have tried out the updated code in colab as in [here.](https://colab.research.google.com/drive/1jDMSBwGDOtwhZh8E7gnzismTlv4Ebwo2#scrollTo=XTt7RdXPcY_r&line=128&uniqifier=1) It should run.\r\n\r\nThe code runs without any issues. The reason is that colab is still in version 2.15.1. The problem is not the warning in Tensorflow version 2.16.1. The problem is the code runs about 100 times slower in the new version. I want to used the new version because that is the version that I am able to have both CUDA/cuDNN and tensorflow installed in order to run the code in GPU.",
"Hi @garryyan2 ,\r\n\r\nI can't access the attached colab of your's. Please submit the same.\r\n\r\nPlease confirm you have installed `tensorflow[and-cuda]` package that can install compatible nvidia libraries and ensure GPUs are detectable by testing with `tf.config.list_physical_devices('GPU')` .",
"Hi @SuryanarayanaY \r\n\r\nHere is the code:\r\n\r\nimport os\r\nos.environ['KMP_DUPLICATE_LIB_OK']='True'\r\nimport numpy as np\r\nimport tensorflow as tf\r\nimport tensorboard\r\nimport time\r\nfrom tensorflow import keras\r\nfrom datetime import datetime\r\nfrom tensorflow.keras.utils import plot_model\r\nfrom tensorflow.keras.layers import Dense\r\nimport random\r\n\r\n\r\n\r\nclass trn_sizes:\r\n\r\n\r\n fm_length = 256\r\n fm_step = 128\r\n LSTMCell = 20\r\n #features = 14\r\n layerOne = 20\r\n layerTwo = 16\r\n layerThree = 8\r\n cepsTone = 12\r\n timeStep = 1 # is 1 only for now\r\n batches = 250\r\n batch_samples = batches*timeStep*fm_step\r\n noise_insert_time = 2 # second\r\n noise_chi2_df = 10\r\n sampling_rate = 16000\r\n time_per_batch = batches*timeStep*fm_step/sampling_rate # 2s for 250 batch size\r\n noise_chi2_scale = noise_insert_time*sampling_rate/noise_chi2_df\r\n\r\n\r\ntrn_sizes = trn_sizes()\r\n\r\nclass inputBatch(object, ):\r\n\r\n\r\n def __init__(self, trn_sizes):\r\n self.state = 0 #\r\n self.iirPinkSt = [0.0, 0.0, 0.0]\r\n self.pinkCount = 0\r\n self.pinkLeft = 0\r\n self.pinkBlkCnt = 0\r\n self.noisePower = 0.02\r\n self.iirPinkB1 = [0.010000000000000, -0.018940429700000, 0.009585641562817, -0.000621320035262]\r\n self.iirPinkA1 = [1.000000000000000, -2.479309080000000 , 1.985012853639686, -0.505600430025288]\r\n self.remainingSamples = 0\r\n self.labelIdx = 0 # index based on the batch\r\n self.samplesToBeFill = trn_sizes.batch_samples\r\n self.vadSampleIdx = 0 # index based on the audio file\r\n self.vadLabel = np.zeros(trn_sizes.batches)\r\n self.samplesOut = np.zeros(trn_sizes.batch_samples, dtype=float)\r\n self.indexa = 0\r\n\r\n\r\n def getBatch4(self, trn_sizes):\r\n\r\n samples = np.random.normal(0, 0.1, (trn_sizes.batches-1)*trn_sizes.cepsTone)\r\n mfccs = tf.convert_to_tensor(samples, dtype=tf.float32)\r\n mfccs3 = tf.reshape(mfccs, [trn_sizes.batches-1, 1, trn_sizes.cepsTone], )\r\n vadLabel3 = np.random.randint(2, size =(249, 1), dtype=np.int32)\r\n vadLabelTf = tf.convert_to_tensor(vadLabel3, dtype=tf.float32)\r\n return {mfccs3.ref(), vadLabelTf.ref()}\r\n\r\n\r\ndef create_model(trn_sizes):\r\n\r\n inputs = keras.Input(shape=(1,trn_sizes.cepsTone), name=\"digits\")\r\n x0 = keras.layers.Dense(trn_sizes.layerOne, activation=\"relu\")(inputs)\r\n y1 = keras.layers.GRU(trn_sizes.LSTMCell)(x0)\r\n x1 = keras.layers.Dense(trn_sizes.layerTwo, activation=\"relu\")(y1)\r\n x2 = keras.layers.Dense(trn_sizes.layerThree, activation=\"relu\")(x1)\r\n outputs = keras.layers.Dense(1, activation='sigmoid', name=\"predictions\")(x2)\r\n model = keras.Model(inputs=inputs, outputs=outputs)\r\n\r\n return model\r\n\r\n\r\nFLAGS = None\r\n\r\ndef main(FLAGS):\r\n\r\n print('tensorflow version: ' + tf.__version__)\r\n print(tf.version.GIT_VERSION, tf.version.VERSION)\r\n\r\n random.seed(11)\r\n\r\n vad = inputBatch(trn_sizes)\r\n vadModel = create_model(trn_sizes)\r\n vadModel.compile(loss='binary_crossentropy',\r\n optimizer=keras.optimizers.Adam(learning_rate=1e-4),\r\n metrics=['accuracy'])\r\n\r\n vadModel.build(input_shape = [None, 10, trn_sizes.cepsTone])\r\n\r\n\r\n print('model length = ', len(vadModel.weights))\r\n\r\n print(vadModel.summary())\r\n\r\n batch_size=trn_sizes.batches;\r\n\r\n start_time1 = time.perf_counter()\r\n\r\n\r\n for i in range(10):\r\n mfccs3, vadLabel = vad.getBatch4(trn_sizes)\r\n if i % 10 == 0:\r\n print('mfccs3 value = ', tf.keras.backend.eval(mfccs3.deref()[1,:]) )\r\n print('vadLabel value = ', tf.keras.backend.eval(vadLabel.deref()[0:3]) )\r\n\r\n logs = vadModel.train_on_batch(mfccs3.deref(), vadLabel.deref())\r\n\r\n\r\n if i %10 == 0:\r\n print('string logs = ', str(logs))\r\n end_time1 = time.perf_counter()\r\n total_duration1 = end_time1 - start_time1\r\n print( f\"total_training time is { total_duration1: .2f}s \")\r\n\r\n print(\"training is done!\")\r\n\r\n\r\nmain(FLAGS)\r\n\r\nYes, tensorflow is installed with [and-cuda]. The GPU is detected.\r\n\r\nThank you for your help.\r\n"
] | 2024-05-06T17:46:32 | 2024-05-08T05:52:21 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2,16,1
### Custom code
No
### OS platform and distribution
windows 10
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
The problem happens in both windows and WSL (Ubuntu )operating system. Tensorflow 2.15.1 and earlier versions don't have the problem. The warning messages will be given after the train_on_batch() is called 5 and 6 times. The function is about 100 times slower in Tensorflow 2.16.1 in comparison to earlier versions.
### Standalone code to reproduce the issue
```shell
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
import numpy as np
import tensorflow as tf
import tensorboard
import time
from tensorflow import keras
from datetime import datetime
from tensorflow.keras.utils import plot_model
from tensorflow.keras.layers import Dense
import random
class trn_sizes:
fm_length = 256
fm_step = 128
LSTMCell = 20
#features = 14
layerOne = 20
layerTwo = 16
layerThree = 8
cepsTone = 12
timeStep = 1 # is 1 only for now
batches = 250
batch_samples = batches*timeStep*fm_step
noise_insert_time = 2 # second
noise_chi2_df = 10
sampling_rate = 16000
time_per_batch = batches*timeStep*fm_step/sampling_rate # 2s for 250 batch size
noise_chi2_scale = noise_insert_time*sampling_rate/noise_chi2_df
trn_sizes = trn_sizes()
class inputBatch(object, ):
def __init__(self, trn_sizes):
self.state = 0 #
self.iirPinkSt = [0.0, 0.0, 0.0]
self.pinkCount = 0
self.pinkLeft = 0
self.pinkBlkCnt = 0
self.noisePower = 0.02
self.iirPinkB1 = [0.010000000000000, -0.018940429700000, 0.009585641562817, -0.000621320035262]
self.iirPinkA1 = [1.000000000000000, -2.479309080000000 , 1.985012853639686, -0.505600430025288]
self.remainingSamples = 0
self.labelIdx = 0 # index based on the batch
self.samplesToBeFill = trn_sizes.batch_samples
self.vadSampleIdx = 0 # index based on the audio file
self.vadLabel = np.zeros(trn_sizes.batches)
self.samplesOut = np.zeros(trn_sizes.batch_samples, dtype=float)
self.indexa = 0
def getBatch4(self, trn_sizes):
samples = np.random.normal(0, 0.1, (trn_sizes.batches-1)*trn_sizes.cepsTone)
mfccs = tf.convert_to_tensor(samples, dtype=tf.float32)
mfccs3 = tf.reshape(mfccs, [trn_sizes.batches-1, 1, trn_sizes.cepsTone], )
vadLabel3 = np.random.randint(2, size =(249, 1), dtype=np.int32)
vadLabelTf = tf.convert_to_tensor(vadLabel3, dtype=tf.float32)
return {mfccs3.ref(), vadLabelTf.ref()}
def create_model(trn_sizes):
inputs = keras.Input(shape=(1,trn_sizes.cepsTone), name="digits")
x0 = keras.layers.Dense(trn_sizes.layerOne, activation="relu")(inputs)
y1 = keras.layers.GRU(trn_sizes.LSTMCell)(x0)
x1 = keras.layers.Dense(trn_sizes.layerTwo, activation="relu")(y1)
x2 = keras.layers.Dense(trn_sizes.layerThree, activation="relu")(x1)
outputs = keras.layers.Dense(1, activation='sigmoid', name="predictions")(x2)
model = keras.Model(inputs=inputs, outputs=outputs)
return model
FLAGS = None
def main(FLAGS):
print('tensorflow version: ' + tf.__version__)
print(tf.version.GIT_VERSION, tf.version.VERSION)
dirName = os.path.dirname(os.path.abspath(__file__)) +'/MSLB0'
random.seed(11)
vad = inputBatch(trn_sizes)
vadModel = create_model(trn_sizes)
vadModel.compile(loss='binary_crossentropy',
optimizer=keras.optimizers.Adam(learning_rate=1e-4),
metrics=['accuracy'])
vadModel.build(input_shape = [None, 10, trn_sizes.cepsTone])
print('model length = ', len(vadModel.weights))
print(vadModel.summary())
batch_size=trn_sizes.batches;
tensorboard = keras.callbacks.TensorBoard(
#log_dir='/tmp/my_tf_logs',
log_dir= "logs\\scalars\\" + datetime.now().strftime("%Y%m%d-%H%M%S"),
histogram_freq=0,
write_graph=True,
update_freq="batch"
)
tensorboard.set_model(vadModel)
start_time1 = time.perf_counter()
for i in range(10):
mfccs3, vadLabel = vad.getBatch4(trn_sizes)
if i % 10 == 0:
print('mfccs3 value = ', tf.keras.backend.eval(mfccs3.deref()[1,:]) )
print('vadLabel value = ', tf.keras.backend.eval(vadLabel.deref()[0:3]) )
logs = vadModel.train_on_batch(mfccs3.deref(), vadLabel.deref())
if i %10 == 0:
print('string logs = ', str(logs))
end_time1 = time.perf_counter()
total_duration1 = end_time1 - start_time1
print( f"total_training time is { total_duration1: .2f}s ")
print("training is done!")
main(FLAGS)
```
### Relevant log output
```shell
WARNING:tensorflow:5 out of the last 5 calls to <function TensorFlowTrainer.make_train_function.<locals>.one_step_on_iterator at 0x0000023C2F73E020> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.
```
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https://api.github.com/repos/tensorflow/tensorflow/issues/67032 | https://api.github.com/repos/tensorflow/tensorflow | https://api.github.com/repos/tensorflow/tensorflow/issues/67032/labels{/name} | https://api.github.com/repos/tensorflow/tensorflow/issues/67032/comments | https://api.github.com/repos/tensorflow/tensorflow/issues/67032/events | https://github.com/tensorflow/tensorflow/pull/67032 | 2,281,401,260 | PR_kwDOArmXAs5uqiPu | 67,032 | [XLA:GPU] Disable cuDNN FMHA by default. | {
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"url": "https://api.github.com/repos/tensorflow/tensorflow/pulls/67032",
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"diff_url": "https://github.com/tensorflow/tensorflow/pull/67032.diff",
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"merged_at": "2024-05-07T12:29:04"
} | [XLA:GPU] Disable cuDNN FMHA by default.
cuDNN FMHA dispatches pattern-matched regions to a FlashAttention kernel by
default. FlashAttention does not preserve numerics, and thus an illegal
optimization to have on by default.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12193 from openxla:move_fmha_workspace ad747544396d4920e60946d7a0f00300f47e07d2
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Inspired by https://github.com/openxla/xla/pull/11753
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} | Modify the GatherV2 xla kernels to default to returning a constant of 0 if any of the dimensions is 0.
Modify the TensorListSetItem to check dimensions and return the unmodified list in the event the update dimension is larger since DynamicUpdateSlice would fail.
These changes are to match the behaviour of the MLIR bridge.
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} | PR #12050: [XLA:GPU] Force nccl command buffer to update even memory pointers do not change
Imported from GitHub PR https://github.com/openxla/xla/pull/12050
This PR force command buffer to do cuda graph update if it contains nccl collective operators, because NCCL requires all devices to make the collective call even in the cuda-graph capturing mode.
Copybara import of the project:
--
1834ac6f72efa5882e58914f3001ac5cb7489725 by Shawn Wang <[email protected]>:
force nccl command buffer to update even if memory pointers do not change
Merging this change closes #12050
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12050 from shawnwang18:shawnw/force_nccl_cmd_buffer_update 1834ac6f72efa5882e58914f3001ac5cb7489725
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} | PR #12171: [XLA: GPU] add more checks for barrierCmd unittest
Imported from GitHub PR https://github.com/openxla/xla/pull/12171
Copybara import of the project:
--
30f9abd60c34f01c5fc77f7745275141130afce6 by Shawn Wang <[email protected]>:
add more checks for barrierCmd
Merging this change closes #12171
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12171 from shawnwang18:shawnw/more_barrier_cmd_test 30f9abd60c34f01c5fc77f7745275141130afce6
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"A colab environment with the bug and the fix: https://colab.research.google.com/drive/196CeYNTFTXdSj2-1sBlqQvrWIER3Q36L?usp=sharing",
"@SuryanarayanaY I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/14b475bfad895b3a6c2e1770acc0eddc/67023.ipynb).\r\nThank you!"
] | 2024-05-06T14:57:57 | 2024-05-15T06:22:55 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15.0
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
3.10.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
`tf.keras.layers.GlobalAveragePooling1D` cannot be called on an empty tensor with an empty mask. This can cause issue when using a model that uses this layer under a distributed strategy, e.g. `MirroredStrategy`, which will distribute the data over multiple GPUs. For instance, for a dataset with 3 samples, a batch_size of 2 and 3 GPUs, one of the GPUs will have empty batches which will cause the error.
This is due to the casting `math_ops.cast(mask, inputs[0].dtype)` in `GlobalAveragePooling1D::call()` which implies a non-empty inputs tensor, while `math_ops.cast(mask, inputs.dtype`) should do the trick without causing the error.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
x = np.random.rand(0, 3, 4)
mask = np.random.rand(0, 3)
print(x, mask)
y = tf.keras.layers.GlobalAveragePooling1D()(x, mask=mask)
print(y)
```
### Relevant log output
```shell
[] []
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-20-0f7c323aaaab> in <cell line: 7>()
5 print(x, mask)
6
----> 7 y = tf.keras.layers.GlobalAveragePooling1D()(x, mask=mask)
8 print(y)
1 frames
/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py in error_handler(*args, **kwargs)
68 # To get the full stack trace, call:
69 # `tf.debugging.disable_traceback_filtering()`
---> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb
/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py in raise_from_not_ok_status(e, name)
5881 def raise_from_not_ok_status(e, name) -> NoReturn:
5882 e.message += (" name: " + str(name if name is not None else ""))
-> 5883 raise core._status_to_exception(e) from None # pylint: disable=protected-access
5884
5885
InvalidArgumentError: Exception encountered when calling layer 'global_average_pooling1d_8' (type GlobalAveragePooling1D).
{{function_node __wrapped__StridedSlice_device_/job:localhost/replica:0/task:0/device:CPU:0}} slice index 0 of dimension 0 out of bounds. [Op:StridedSlice] name: global_average_pooling1d_8/strided_slice/
Call arguments received by layer 'global_average_pooling1d_8' (type GlobalAveragePooling1D):
• inputs=tf.Tensor(shape=(0, 3, 4), dtype=float32)
• mask=array([], shape=(0, 3), dtype=float64)
```
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} | PR #12012: Update cuDNN frontend version.
Imported from GitHub PR https://github.com/openxla/xla/pull/12012
Update cuDNN frontend version from 1.2.1 to 1.3.
Copybara import of the project:
--
ea12e3765a225fb360add9d424248ffa0954ce95 by Elfie Guo <[email protected]>:
Update cuDNN frontend version.
Merging this change closes #12012
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12012 from elfiegg:cudnn_fix ea12e3765a225fb360add9d424248ffa0954ce95
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} | Integrate LLVM at llvm/llvm-project@6217abce86b5
Updates LLVM usage to match
[6217abce86b5](https://github.com/llvm/llvm-project/commit/6217abce86b5)
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} | [XLA] Refactor `BackendConfigWrapper` and add tests.
This change:
- Folds `HloInstruction::GetBackendConfigInternal` into `BackendConfigWrapper` where is really belongs.
- Removes `mutable` from `HloInstruction::backend_config_`.
- Changes `BackendConfigWrapper` to use constructors wherever possible, instead of methods, so that the need for synchronization is reduced.
- Improves the documentation to correctly reflect that this class is symmetric with respect to which of its defining members was initialized first and which is the cache.
- Adds tests to ensure the new implementation is thread-safe.
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} | Fix reduction group computation for MLIR emitter.
Currently, there are cases where we duplicate reductions into more than
one group. This essentially means the grouping is buggy, since the
goal is to share reads. I will only fix the grouping for the new
emitters, since I can't rule out that the current grouping is actually
better in some cases (and we need to validate the performance for the
new emitters anyway). See OneGroup in reduction_base_test for an
example.
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} | PR #12031: add gemm autotune log dump
Imported from GitHub PR https://github.com/openxla/xla/pull/12031
This PR adds option to dump all gemm fusion autotune results as AutotuningLogs:
- the dump contains all autotune results for all backends including cublas,cudnn, and triton.
- also add fusion name and count (time of occurrence in a given hlo)
Copybara import of the project:
--
e65340a856101401e9ee56b4ac258cef9c10c517 by Amir Samani <[email protected]>:
add gemm autotune log dump
Merging this change closes #12031
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12031 from Amir-19:gemm_fusion_log e65340a856101401e9ee56b4ac258cef9c10c517
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} | Integrate LLVM at llvm/llvm-project@18e7dcb7c576
Updates LLVM usage to match
[18e7dcb7c576](https://github.com/llvm/llvm-project/commit/18e7dcb7c576)
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} | Don't MOF computations that are already fused.
Like the previous fix for priority fusion, MOF sometimes generates nested
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makes codegen more complex, since it needs to be able to handle such
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The difference in runtimes in the original benchmarks that
motivated this change seem rather large. We can surely
do better than that. Atomics contribute a lot of complexity,
so if possible, we should try to avoid them.
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} | Use the correct dependency for the included gpu_init.h header.
We don't need to depend on the _impl target, it is added automatically to all
xla_cc_test targets. Depend on the non-implementation target instead.
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"Hi @B-JackMao ,\r\n\r\nI haven't seen any tflite official documentation mentioning the concurrent model execution on GPU. If i find something i will let you know.",
"Hi @pkgoogle ,\r\n\r\nI saw examples of running two models concurrently on dedicated desktop gpu's using tensorflow but i am not sure if its possible in tflite on android. ",
"Hi @B-JackMao, the GPU Delegate will automatically execute tasks in parallel. Please review the speed section of the description here: https://www.tensorflow.org/lite/performance/gpu . Of course depending on the nature of the tasks and the various dependencies it may or may not be easily parallelizable but the algorithm will try its best to. Let us know if that answers your question, thanks!",
"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."
] | 2024-05-06T07:22:57 | 2024-06-01T01:51:38 | 2024-06-01T01:51:38 | NONE | null | null | null | I want to know if TFLITE currently supports running two or more CNN tasks in parallel on a mobile phone's GPU. Can TFLITE run multiple CNN models concurrently on the GPU to accelerate inference? | {
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Currently, priority fusion generates nested fusion instructions
inside reducers, scatter combiners, etc. This doesn't make sense,
since these instructions are already run inside a fusion, but
easily leads to codegen bugs.
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FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11981 from ROCm:ci_pjrt_c_api_gpu_test_fix d114eb0deb691b5cf6907880f26d241a2813f26d
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} | PR #11967: [XLA:GPU] Add cuDNN flash attention dbias computation and broadcast bias
Imported from GitHub PR https://github.com/openxla/xla/pull/11967
* add cuDNN flash attention dbias computation. make sure dbias only pattern match cases where reduction is done on batch dim. Only bias with [1, N, S, T] is supported.
* add cuDNN broadcast bias support. cuDNN supports broadcast bias, [1, N, S, T], [1, 1, S, T] and [B, 1, S, T] are supported.
Copybara import of the project:
--
0b3c19fba294402b735c1dc091cc57a955f5f334 by cjkkkk <[email protected]>:
add dbias
--
2d9256c7f4c441824a831f2238ff7fdf2447f7f9 by cjkkkk <[email protected]>:
remove extra ()
--
31750864e1bbf677cd82a8d4300fdb0a90d86e41 by cjkkkk <[email protected]>:
directly use rewriter result
Merging this change closes #11967
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11967 from Cjkkkk:flash_attention_dbias 31750864e1bbf677cd82a8d4300fdb0a90d86e41
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