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I_kwDOArmXAs6EfgWM
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Feature Request: Integrate different Digital Signal Processing into tf.signal
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[
"Triage Notes:\r\nI was able to reproduce the issue on tensorflow [v2.15](https://colab.research.google.com/gist/tilakrayal/88cef6ca49b51fb7907cb70636886284/julius4tensorflow.ipynb) and [2.16](https://colab.research.google.com/gist/tilakrayal/ea3c49cb16275387f510a6e844b15566/julius4tensorflow.ipynb). Kindly find the gist of it here."
] | 2024-04-03T12:50:37 | 2024-04-04T08:47:40 | null |
NONE
| null | null | null |
### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15
### 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?
Currently, TensorFlow offers a range of signal processing functions under tf.signal, but there's a noticeable gap in advanced audio data augmentation functionalities directly comparable to those available in the PyTorch ecosystem, particularly in the [julius](https://github.com/adefossez/julius/) library.
The julius library provides a suite of efficient DSP operations. These are crucial for building robust audio recognition and processing models, as they allow for the creation of diverse training datasets, enhancing model generalization.
Integrating similar functionalities into the TensorFlow ecosystem would greatly benefit researchers and developers working on audio-related projects by providing them with native tools for complex audio data augmentation. This integration would streamline workflows, allowing for seamless data preprocessing and augmentation within TensorFlow, thereby reducing the need for external libraries or frameworks and ensuring compatibility and performance optimization.
It would be great to have a similar library as julius to be supported in tf.signal. Would that make sense?
I made a small notebook to implement a function similar to https://github.com/adefossez/julius/blob/main/julius/lowpass.py
https://colab.research.google.com/drive/1lxM0wDbmWIFMOGpT_gRNz_5Ug-e1vNbr#scrollTo=QuEOc3zUnSHX
### Relevant log output
_No response_
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PR_kwDOArmXAs5rjlp_
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[xla:ffi] Unit tests for CPU type-safe custom call API
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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/64988/checks?check_run_id=23390210688) 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."
] | 2024-04-03T11:49:26 | 2024-04-04T12:27:14 | 2024-04-04T12:27:13 |
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[xla:ffi] Unit tests for CPU type-safe custom call API
Port existing CPU custom call tests to the new FFI API. Add new test cases, mostly taken from GPU custom call tests.
Implements #10060.
The tests are disabled for now, they will be enabled when #10056 is finished.
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Cannot build TFLite 2.16.1 for RISC-V due to the bug in cpuinfo (missing sys/hwprobe.h)
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[
"Hi @eltimen,\r\n\r\nThe [commit ](https://github.com/pytorch/cpuinfo/commit/313524ab20d2041854af8ad07bf726ddd485d258) you have \r\nprovided for cpuinfo was merged under pytorch repo. But under TensorFlow repo (tensorflow/lite/tools/cmake/modules/cpuinfo.cmake), a [PR](https://github.com/tensorflow/tensorflow/pull/63850/files) for `cpuinfo.cmake` still under open. Once it is reviewed and merged, it will be updated on the next coming release.\r\nThank You",
"Hi @LakshmiKalaKadali,\r\nThank you, I hadn't noticed that a PR to fix this had already been created.",
"Hi @eltimen,\r\n\r\nThank you for the response. We will update you once the PR #63850 is merged. Also please keep track of the same [PR](https://github.com/tensorflow/tensorflow/pull/63850/files) .\r\n\r\nThank You",
"hi All! \r\n\r\ntensorflow 2.16.1 - tflite build does not generate a static libraries in **/clog-build**. I see that the clog-build in the version r2.10. Do you have an any idea how can i fix it?\r\n\r\nerror output:\r\n\r\ngmake[2]: *** No rule to make target '../../tflite_build/_deps/clog-build/libclog.a', needed by 'testapp/testapp'. Stop.",
"Hi NominBa,\r\n\r\nCould you please open a new issue.\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/64987\">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/64987\">No</a>\n"
] | 2024-04-03T11:43:02 | 2024-05-23T01:49:05 | 2024-05-23T01:48:54 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tflite 2.16.1
### Custom code
Yes
### OS platform and distribution
Linux
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
GCC 13.2.0 RISC-V GNU Compiler Toolchain link: https://github.com/riscv-collab/riscv-gnu-toolchain/releases/download/2023.11.17/riscv64-glibc-ubuntu-20.04-gcc-nightly-2023.11.17-nightly.tar.gz
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I'm trying to build TensorFlow Lite 2.16.1 using CMake for RISC-V using GCC toolchain for this platform. The build fails with the following error inside `cpuinfo` dependency:
```
[ 0%] Building C object _deps/cpuinfo-build/CMakeFiles/cpuinfo.dir/src/riscv/linux/riscv-hw.c.o
build/cpuinfo/src/riscv/linux/riscv-hw.c:1:10: fatal error: sys/hwprobe.h: No such file or directory
1 | #include <sys/hwprobe.h>
| ^~~~~~~~~~~~~~~
```
This bug is already fixed in cpuinfo by [this commit](https://github.com/pytorch/cpuinfo/commit/313524ab20d2041854af8ad07bf726ddd485d258).
Can you bump cpuinfo to the latest commit (or to any version including this fix) in the next TF release?
### Standalone code to reproduce the issue
```shell
cmake -S tensorflow/lite -B build -D CMAKE_BUILD_TYPE=Release -D CMAKE_SYSTEM_PROCESSOR=riscv64 -D BUILD_SHARED_LIBS=ON -D TFLITE_ENABLE_GPU=ON -D CMAKE_SYSTEM_NAME=Linux -D CMAKE_C_COMPILER=/opt/riscv_gcc/bin/riscv64-unknown-linux-gnu-gcc -D CMAKE_CXX_COMPILER=/opt/riscv_gcc/bin/riscv64-unknown-linux-gnu-g++ -D CMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER -D CMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY -D CMAKE_FIND_ROOT_PATH_MODE_INCLUDE=ONLY -D CMAKE_FIND_ROOT_PATH_MODE_PACKAGE=ONLY
```
### Relevant log output
_No response_
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Add ODML e2e coverage test for `jax.lax.add`.
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Add ODML e2e coverage test for `jax.lax.add`.
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Integrate Triton up to [e902d3b6](https://github.com/openai/triton/commits/e902d3b617b100f38caac5c4f74e462edab5f18a)
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Integrate Triton up to [e902d3b6](https://github.com/openai/triton/commits/e902d3b617b100f38caac5c4f74e462edab5f18a)
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Support lifting of same shape bias for `stablehlo.convolution`.
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Support lifting of same shape bias for `stablehlo.convolution`.
Although it is common to write models where 1D constant would be broadcasted, it is possible to explicitly give bias with the desired shape.
The examples in `odml_coverage_test` are such.
Handle such cases where the bias has the same shape as target accumulation.
Additionally, add `FindOperandType` for finding an operand of specific type and respective tests.
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No optimized fast float implementation for `Logistic`
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[
"@dieterd-sentea Could you please update your build process to use the new Eigen version and let us know?\r\nThank you!",
"Hi @sushreebarsa,\r\n\r\nI assume that TFLite built with the newest Eigen version, with https://gitlab.com/libeigen/eigen/-/merge_requests/1576 now merged, does not exhibit the issue.\r\n\r\nThe (arguably confusing) workaround that we use at the moment, until the Eigen dependency in this repository gets bumped, is the following patch:\r\n\r\n```diff\r\ndiff --git a/tensorflow/lite/tools/cmake/modules/eigen.cmake b/tensorflow/lite/tools/cmake/modules/eigen.cmake\r\nindex 93a63d280d8..f6b14b562b7 100644\r\n--- a/tensorflow/lite/tools/cmake/modules/eigen.cmake\r\n+++ b/tensorflow/lite/tools/cmake/modules/eigen.cmake\r\n@@ -99,4 +99,5 @@ set(EIGEN_TEST_SYCL OFF CACHE BOOL \"Disable Sycl test\")\r\n set(EIGEN_SYCL_TRISYCL OFF CACHE BOOL \"Disable triSYCL test\")\r\n # Make sure only MPL2.0 or more permissively licensed code is included.\r\n add_compile_definitions(EIGEN_MPL2_ONLY)\r\n+add_compile_definitions(EIGEN_GPU_CC)\r\n add_subdirectory(\"\\${eigen_SOURCE_DIR}\" \"\\${eigen_BINARY_DIR}\")\r\n```\r\n\r\nOf course, this abuses the fact that `EIGEN_GPU_CC` is only used to enable the fast float logistic implementation again in the buggy Eigen versions (and is used nowhere else). In the newest Eigen version, this workaround is not needed anymore, and is in fact undesirable (in case Eigen want to refactor `EIGEN_CPUCC` to `EIGEN_CPU_CC` at some point, to give an example).",
"Correction on previous message: I have not explicitly tested if latest Eigen master (which might contain other unrelated changes) is working for us. I'll find out and let you know.",
"I can confirm that we experience no issues on tf-nightly (a700cea6b22840a3656f951f16603657685e7d89) with the following patch, which updates Eigen to latest master https://gitlab.com/libeigen/eigen/-/commit/b2c9ba2beef4b5fd61513d73911c678e93c8dd9d:\r\n\r\n```diff\r\ndiff --git a/tensorflow/lite/tools/cmake/modules/eigen.cmake b/tensorflow/lite/tools/cmake/modules/eigen.cmake\r\nindex 1bb203388a0..f6cc23e578f 100644\r\n--- a/tensorflow/lite/tools/cmake/modules/eigen.cmake\r\n+++ b/tensorflow/lite/tools/cmake/modules/eigen.cmake\r\n@@ -23,7 +23,7 @@ OverridableFetchContent_Declare(\r\n eigen\r\n GIT_REPOSITORY https://gitlab.com/libeigen/eigen.git\r\n # Sync with tensorflow/third_party/eigen3/workspace.bzl\r\n- GIT_TAG aa6964bf3a34fd607837dd8123bc42465185c4f8\r\n+ GIT_TAG b2c9ba2beef4b5fd61513d73911c678e93c8dd9d\r\n # It's not currently (cmake 3.17) possible to shallow clone with a GIT TAG\r\n # as cmake attempts to git checkout the commit hash after the clone\r\n # which doesn't work as it's a shallow clone hence a different commit hash.\r\ndiff --git a/third_party/eigen3/workspace.bzl b/third_party/eigen3/workspace.bzl\r\nindex 027454e46dd..7065b289e04 100644\r\n--- a/third_party/eigen3/workspace.bzl\r\n+++ b/third_party/eigen3/workspace.bzl\r\n@@ -7,8 +7,8 @@ def repo():\r\n \r\n # Attention: tools parse and update these lines.\r\n # LINT.IfChange\r\n- EIGEN_COMMIT = \"aa6964bf3a34fd607837dd8123bc42465185c4f8\"\r\n- EIGEN_SHA256 = \"35ba771e30c735a4215ed784d7e032086cf89fe6622dce4d793c45dd74373362\"\r\n+ EIGEN_COMMIT = \"b2c9ba2beef4b5fd61513d73911c678e93c8dd9d\"\r\n+ EIGEN_SHA256 = \"9e0dbce1464f8e05f9e4ea66c0a92850c4aade71c330130f8960f3ab3592d5c4\"\r\n # LINT.ThenChange(//tensorflow/lite/tools/cmake/modules/eigen.cmake)\r\n \r\n tf_http_archive(\r\ndiff --git a/third_party/xla/third_party/tsl/third_party/eigen3/workspace.bzl b/third_party/xla/third_party/tsl/third_party/eigen3/workspace.bzl\r\nindex 027454e46dd..7065b289e04 100644\r\n--- a/third_party/xla/third_party/tsl/third_party/eigen3/workspace.bzl\r\n+++ b/third_party/xla/third_party/tsl/third_party/eigen3/workspace.bzl\r\n@@ -7,8 +7,8 @@ def repo():\r\n \r\n # Attention: tools parse and update these lines.\r\n # LINT.IfChange\r\n- EIGEN_COMMIT = \"aa6964bf3a34fd607837dd8123bc42465185c4f8\"\r\n- EIGEN_SHA256 = \"35ba771e30c735a4215ed784d7e032086cf89fe6622dce4d793c45dd74373362\"\r\n+ EIGEN_COMMIT = \"b2c9ba2beef4b5fd61513d73911c678e93c8dd9d\"\r\n+ EIGEN_SHA256 = \"9e0dbce1464f8e05f9e4ea66c0a92850c4aade71c330130f8960f3ab3592d5c4\"\r\n # LINT.ThenChange(//tensorflow/lite/tools/cmake/modules/eigen.cmake)\r\n \r\n tf_http_archive(\r\n```\r\n\r\nFunnily enough, the overflow issue that we observed does not appear on tf-nightly (but does occur on v2.14.0 and v2.15.0), but this is orthogonal to this issue which is about the fast float implementation for `Logistic`.",
"So I've investigated this a bit further and we observe a nice speedup with the above patch (up to 6% for a certain model that contains a sigmoid layer).\r\n\r\nNote btw that we do not use XNNPACK delegate, i.e., we configure the project with `cmake -DTFLITE_ENABLE_XNNPACK=OFF`. This is important to note, as otherwise float32 logistic goes through XNNPACK instead of Eigen, as can be seen here: https://github.com/tensorflow/tensorflow/blob/ade45358646f2c7ed2ce6792d0234d83ad2dca3f/tensorflow/lite/kernels/activations.cc#L1130",
"Hi @dieterd-sentea, can we say that this is resolved now on the master branch at least? Let me know if you disagree or if there is any other action needed from our side. Thanks.",
"@pkgoogle LGTM",
"So indeed 476aaacc4298e4f5a772fa585ce57604e6522f6e resolves this. 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/64981\">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/64981\">No</a>\n"
] | 2024-04-03T09:23:27 | 2024-05-14T07:06:50 | 2024-05-14T07:06:06 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
_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?
Commit https://gitlab.com/libeigen/eigen/-/commit/a30ecb7221a46824b85cad5f9016efe6e5871d69 of the third-party Eigen library disabled the fast float implementation of the `Logistic` (or sigmoid if you like) operator for most (all?) Tensorflow Lite backends.
The fallout of this is more severe than just the speed impact: relying on overflow behavior of `exp(x)` for large `x`, as used in the generic fallback implementation, triggers a bug on our embedded platform, causing glitches where `logistic(x)` in some cases becomes 0 instead of 1 for large `x` (around 88.9).
I would propose to bump the Eigen dependency if this gets fixed upstream (see also https://gitlab.com/libeigen/eigen/-/merge_requests/1576), as this effectively fixes the issue.
### Standalone code to reproduce the issue
```shell
N/A
```
### Relevant log output
_No response_
|
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PR #11172: GpuTimer: use delay kernel to improve accuracy
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PR #11172: GpuTimer: use delay kernel to improve accuracy
Imported from GitHub PR https://github.com/openxla/xla/pull/11172
Originally imported from GitHub PR https://github.com/openxla/xla/pull/9757.
This ensures that all the device operations to be timed are queued to
the relevant stream before any of them are executed, resulting in a more
accurate measurement. This is skipped if CUDA_LAUNCH_BLOCKING=1 or
unified addressing is not available.
Only launch the delay kernel if it has been explicitly flagged that a
warm-up run, which might have triggered lazy kernel loading, has been
executed.
Reproducing information from https://github.com/openxla/xla/pull/9757 below:
Quoting my comment in the code:
```c++
// When a timer is created it launches a delay kernel into the given stream and
// queues a start event immediately afterwards. This delay kernel blocks
// execution on the stream until GetElapsedDuration() is called, at which point
// an end event is queued and the delay kernel exits. This allows the device
// execution time of the tasks queued to the stream while the timer is active
// to be measured more accurately.
```
this should improve the accuracy of the measurements that are used to make
auto-tuning decisions, especially for small kernels.
With the example HLO:
```
parameter_0 = bf16[128,12,128]{2,1,0} parameter(0)
parameter_1 = bf16[12,128,128]{2,1,0} parameter(1)
ROOT dot.26 = bf16[12,128,128]{2,1,0} dot(parameter_0, parameter_1),
lhs_batch_dims={1}, lhs_contracting_dims={0}, rhs_batch_dims={0},
rhs_contracting_dims={2}
```
a cuBLAS kernel was selected with a CUPTI/NSys-measured runtime of
3.9µs and before this change, GpuTimer measured a runtime of 17.5µs.
With this change, GpuTimer measures a runtime of 8.1µs.
With the example HLO:
```
p0 = bf16[128,1536]{0,1} parameter(0)
p1 = s8[1536,12288]{0,1} parameter(1)
c = bf16[1536,12288]{0,1} convert(p1)
ROOT d = bf16[128,12288]{1,0} dot(p0, c), lhs_contracting_dims={1},
rhs_contracting_dims={0}
```
five cuDNN plans were auto-tuned, with CUPTI/Nsys-measured runtimes of
{34.9, 18.5, 30.9, 32.4, 31.5}µs. Before this change, GpuTimer measured
runtimes of {76.6, 40.0, 52.5, 55.7, 51.8}µs. With this change, GpuTimer
measures runtimes of {39.6, 23.0, 35.4, 35.1, 35.1}µs.
In summary, with this change, GpuTimer gives results that are much
closer to the CUPTI/Nsys measurements, with a ~uniform offset of ~4µs. A
constant offset doesn't matter for auto-tuning.
There are a couple of edge cases that have special treatment:
- if `CUDA_LAUNCH_BLOCKING=1` then the delay kernel will not achieve anything,
so we don't launch it
- if any code in the timed region synchronises the device then the host will
wait for the delay kernel to time out before actually launching the kernels
to be measured. This will lead to a poor quality measurement, and an error
message is printed. This condition can be met if one of the kernels being
measured is lazily loaded, as lazy loading can trigger synchronisation. Best
practice is to execute a warmup run (without timing enabled) before the timed
execution.
The `GpuTimer::Create*` signatures that were deprecated in https://github.com/openxla/xla/pull/9841 do not benefit
from these accuracy improvements.
Copybara import of the project:
--
bcbe70801e92ba11ca65caef8bb05c847472d4ba by Olli Lupton <[email protected]>:
GpuTimer: use delay kernel to improve accuracy
Originally imported from GitHub PR #9757.
This ensures that all the device operations to be timed are queued to
the relevant stream before any of them are executed, resulting in a more
accurate measurement. This is skipped if CUDA_LAUNCH_BLOCKING=1 or
unified addressing is not available.
Quoting my comment in the code:
// When a timer is created it launches a delay kernel into the given stream and
// queues a start event immediately afterwards. This delay kernel blocks
// execution on the stream until GetElapsedDuration() is called, at which point
// an end event is queued and the delay kernel exits. This allows the device
// execution time of the tasks queued to the stream while the timer is active
// to be measured more accurately.
this should improve the accuracy of the measurements that are used to make
auto-tuning decisions, especially for small kernels.
With the example HLO:
parameter_0 = bf16[128,12,128]{2,1,0} parameter(0)
parameter_1 = bf16[12,128,128]{2,1,0} parameter(1)
ROOT dot.26 = bf16[12,128,128]{2,1,0} dot(parameter_0, parameter_1),
lhs_batch_dims={1}, lhs_contracting_dims={0}, rhs_batch_dims={0},
rhs_contracting_dims={2}
a cuBLAS kernel was selected with a CUPTI/NSys-measured runtime of
3.9µs and before this change, GpuTimer measured a runtime of 17.5µs.
With this change, GpuTimer measures a runtime of 8.1µs.
With the example HLO:
p0 = bf16[128,1536]{0,1} parameter(0)
p1 = s8[1536,12288]{0,1} parameter(1)
c = bf16[1536,12288]{0,1} convert(p1)
ROOT d = bf16[128,12288]{1,0} dot(p0, c), lhs_contracting_dims={1},
rhs_contracting_dims={0}
five cuDNN plans were auto-tuned, with CUPTI/Nsys-measured runtimes of
{34.9, 18.5, 30.9, 32.4, 31.5}µs. Before this change, GpuTimer measured
runtimes of {76.6, 40.0, 52.5, 55.7, 51.8}µs. With this change, GpuTimer
measures runtimes of {39.6, 23.0, 35.4, 35.1, 35.1}µs.
In summary, with this change, GpuTimer gives results that are much
closer to the CUPTI/Nsys measurements, with a ~uniform offset of ~4µs. A
constant offset doesn't matter for auto-tuning.
There are a couple of edge cases that have special treatment:
- if `CUDA_LAUNCH_BLOCKING=1` then the delay kernel will not achieve anything,
so we don't launch it
- if any code in the timed region synchronises the device then the host will
wait for the delay kernel to time out before actually launching the kernels
to be measured. This will lead to a poor quality measurement, and an error
message is printed. This condition can be met if one of the kernels being
measured is lazily loaded, as lazy loading can trigger synchronisation. Best
practice is to execute a warmup run (without timing enabled) before the timed
execution.
The `GpuTimer::Create*` signatures that were deprecated in #9841 do not benefit
from these accuracy improvements.
--
684275034a8bea55b3cbb9a1b40104feb1addad1 by Olli Lupton <[email protected]>:
GpuTimer: use delay kernel on request only.
Only launch the delay kernel if it has been explicitly flagged that a
warm-up run, which might have triggered lazy kernel loading, has been
executed.
Merging this change closes #11172
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11172 from olupton:re-land-delay-kernel 684275034a8bea55b3cbb9a1b40104feb1addad1
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Add `load()` statements for the builtin Bazel java rules
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Add `load()` statements for the builtin Bazel java rules
Loads are being added in preparation for moving the rules out of Bazel and into `rules_java`.
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Relax subgraph byte constraint to `required_bytes <= bytes` rather than exact match.
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Relax subgraph byte constraint to `required_bytes <= bytes` rather than exact match.
This has been an error raised for some issues [github example](https://github.com/tensorflow/tensorflow/issues/14929).
If the required bytes are covered by the current model, there is no need to raise an error due to exact size mismatch on user's side.
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"@jimwang118 \r\nHi,\r\n This issue is about old Tensorflow: 2.10.0.\r\n Could you confirm the version?\r\n\r\n Have you install LLVM? please refer to: https://www.tensorflow.org/install/source_windows#install_llvm\r\n I suggest to follow all steps in guide: https://www.tensorflow.org/install/source_windows\r\n\r\n If you really work with 2.10.0, I suggest to try same case with latest Tensorflow.\r\n Because old TF (2.10.0) is hard to maintain, so all bugs will be fixed in latest version, instead of based on 2.10.0.\r\n\r\n Thank you!",
"Compile the latest version and use the following command, but the same problem occurs.\r\n`'F:/vcpkg/installed/x64-windows/tools/bazel' --output_user_root='F:/vcpkg/buildtrees/tensorflow/.bzl' --max_idle_secs=1 build --verbose_failures --jobs=9 --python_path='F:/vcpkg/downloads/tools/msys2/70baefe917a26e68/mingw64/bin/python3.exe' --define=no_tensorflow_py_deps=true ///tensorflow:tensorflow.dll ///tensorflow:install_headers`\r\n\r\nIs llvm necessary to compile tensorflow?",
"@NeoZhangJianyu I installed llvm17.0.2 locally, but the following problem still occurred during compilation.\r\n`ERROR: F:/vcpkg/buildtrees/tensorflow/.bzl/3tyt3lpr/external/llvm-project/llvm/BUILD.bazel:622:10: Linking external/llvm-project/llvm/llvm-min-tblgen.exe [for tool] failed: (Exit 1201): link.exe failed: error executing command (from target @llvm-project//llvm:llvm-min-tblgen) `\r\n[build-x64-windows-dbg-err.log](https://github.com/tensorflow/tensorflow/files/14900156/build-x64-windows-dbg-err.log)\r\n",
"2.10 is last version to support native windows.\r\nnewer versions are only supported in WSL2.\r\n\r\nIf you try latest version, please build it in WSL2 in window, instead of native.\r\nIf you want to use 2.10, I suggest to install the binary directly.\r\n\r\nIf you still try with 2.10, please follow the steps in guide: https://www.tensorflow.org/install/source_windows.\r\nIn the log, looks like the build cmd is not same as the guide.",
"Hi **@jimwang118** ,\r\n- Sorry for the delay, Ensure that all necessary dependencies for tensorFlow are installed and properly configured on your system. This includes libraries like CUDA, cuDNN, Bazel, and others.\r\n- Make sure your environment variables are correctly set, especially those related to compiler paths, library paths, and Bazel configuration.\r\n- Could you please go through this [documentation](https://www.tensorflow.org/install/source_windows) once. \r\n- Note: Starting in TF 2.11, CUDA build is not supported for Windows. For using TensorFlow GPU on Windows, you will need to build/install TensorFlow in WSL2 or use tensorflow-cpu with TensorFlow-DirectML-Plugin.\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/64969\">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/64969\">No</a>\n"
] | 2024-04-03T06:29:44 | 2024-04-25T01:48:40 | 2024-04-25T01:48:37 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.10.0
### Custom code
Yes
### OS platform and distribution
windows 10
### Mobile device
_No response_
### Python version
3.11
### Bazel version
bazel 5.2.0
### GCC/compiler version
_No response_
### CUDA/cuDNN version
NO
### GPU model and memory
_No response_
### Current behavior?
When I compile xxx under Windows, the following error appears.
[675 / 976] Compiling snappy.cc; 0s local ... (5 actions, 4 running)
ERROR: F:/vcpkg/buildtrees/tensorflow/.bzl/3tyt3lpr/external/llvm-project/mlir/BUILD.bazel:6592:10: Linking external/llvm-project/mlir/mlir-tblgen.exe failed: (Exit 1201): link.exe failed: error executing command
### Standalone code to reproduce the issue
```shell
Reproduction steps：
git clone https://github.com/microsoft/vcpkg.git
cd vcpkg
open powershell
.\bootstrap-vcpkg.bat
./vcpkg install tensorflow
```
### Relevant log output
[build-x64-windows-dbg-err.log](https://github.com/tensorflow/tensorflow/files/14847808/build-x64-windows-dbg-err.log)
_No response_
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Fix a breakage
Reverts f8226259068cec549df18a83329ca7e7dde4e9ab
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Support shape transpose in `hlo_sharding_util::ReshapeSharding`.
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Support shape transpose in `hlo_sharding_util::ReshapeSharding`.
Before this cl, `hlo_sharding_util::ReshapeSharding` can handle the cases where source and target shapes can be transformed to each other by merging and splitting dimension sizes. It returns `std::nullopt` if transpose is needed between source and target shapes.
This cl extracts the gcd(source_sharding_tile_size, target_shape) when `source_shape % source_sharding_tile_size == 0` in the major dimensions. An example is shown below.
```
input_shape: [6, 4]
output_shape: [2, 2, 3, 2]
input_sharding: {devices=[6,1]<=[6]}
```
output_sharding is `{devices=[2,1,1,1,3]<=[6] last_tile_dim_replicate}`. Before this cl, the output_sharding is `{replicated}`.
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Enable sharing Const kernels between subgraphs.
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Enable sharing Const kernels between subgraphs.
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Ensure that the module we consume has no unused computations. This can causes issues as we clone modules to support try_multiple_mesh_shapes, and cloning an HLO module removes dead computations leading to mismatches.
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Ensure that the module we consume has no unused computations. This can causes issues as we clone modules to support try_multiple_mesh_shapes, and cloning an HLO module removes dead computations leading to mismatches.
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Add unbounded dynamism test for SendOp.
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Add unbounded dynamism test for SendOp.
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Add unbounded dynamism test for RecvOp.
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Add unbounded dynamism test for RecvOp.
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Add unbounded dynamism test for OutfeedOp.
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Add unbounded dynamism test for InfeedOp.
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Please cherry-pick a73ce1c2ac0a951e46dda8aa2d82a8352233031a to 2.16.1
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[
"Hi @xwin, apologies, but generally speaking we do not cherry-pick this long after release unless it is an exceptionally critical fix. Please go ahead and use/build against nightly for now, thanks.",
"OK. Thank you for the response.",
"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/64959\">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/64959\">No</a>\n"
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NONE
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Could you please cherry pick this commit that fixes host flatc build for cross compilation.
https://github.com/tensorflow/tensorflow/commit/a73ce1c2ac0a951e46dda8aa2d82a8352233031a
The commit made a while ago but never made it into 2.16 release for some reason.
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PR_kwDOArmXAs5rf5cs
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Integrate LLVM at llvm/llvm-project@d8db13edd60b
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Integrate LLVM at llvm/llvm-project@d8db13edd60b
Updates LLVM usage to match
[d8db13edd60b](https://github.com/llvm/llvm-project/commit/d8db13edd60b)
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Add SourceLocation information to xla::Internal errors.
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Add SourceLocation information to xla::Internal errors.
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[xla][gpu] Implement pipelined-p2p-rewriter.
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[xla][gpu] Implement pipelined-p2p-rewriter.
This pass rewrite pipelined point-to-point communication by rotating the
SendDone and RecvDone operations in a while-body to the beginning of the next iteration.
The SendDone and RecvDone operations for the last iteration are moved to the
while-op calling computation, after the while-op.
Add the pass to the GPU post-scheduler pipeline.
This is another approach to achieve the code pattern to pipeline two Send-Recv
chains decomposed from a collective-permute with a source-target pair cycle for
performance. The pipelined Send-Recv pattern puts SendDone and RecvDone before
Send and Recv in the while-body, and if we generate such code pattern too early
in the GPU compilation pipeline, copy-insertion may generate copies of Send
causing Send and SendDone with different buffers and thus correctness problem.
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[xla][gpu] Change the point-to-point pipeliner to produce an intermediate form
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[xla][gpu] Change the point-to-point pipeliner to produce an intermediate form
of pipelined code.
The pipeliner now rotates all point-to-point instructions for a communication
chain, including Send, Recv, SendDone and RecvDone, in a while-body. We will
add a post-scheduling pass to further transform such a pipelined loop by
pushing the SendDone and RecvDone to the next loop iteration.
Adjust the p2p-schedule-preparation pass to reflect this change.
|
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Add source locations to FAILED_PRECONDITION errors.
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NONE
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Add source locations to FAILED_PRECONDITION errors.
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PR_kwDOArmXAs5rfix6
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Use absl::Status errors rather than the tsl equivalents.
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Use absl::Status errors rather than the tsl equivalents.
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PR_kwDOArmXAs5rfgNl
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Minimize number of Copybara transforms that operate on `tensorflow/third_party`
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NONE
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Minimize number of Copybara transforms that operate on `tensorflow/third_party`
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PR_kwDOArmXAs5rffAp
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[xla:gpu][NFC] Rename DynamicAddressComputationFusion back to AddressComputationFusion
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[xla:gpu][NFC] Rename DynamicAddressComputationFusion back to AddressComputationFusion
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A couple minor bug fixes:
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A couple minor bug fixes:
1. When adding unknown shardings to input parameter tuples, ignore modules with no input parameters.
2. Skip misaligned sharding checks for outfeed, send and send-done ops.
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Register fake quant gradient ops as not differentiable
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Register fake quant gradient ops as not differentiable
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PR_kwDOArmXAs5rezE3
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[XLA:LatencyHidingScheduler] Schedule while and its tuple back to back.
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[XLA:LatencyHidingScheduler] Schedule while and its tuple back to back.
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PR_kwDOArmXAs5repqQ
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Simplify LogicalBufferStruct constructor.
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Simplify LogicalBufferStruct constructor.
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[xla] Add a method to express that we want to schedule a node as early as
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[xla] Add a method to express that we want to schedule a node as early as
possible.
We have kForceDelay to express that we want to schedule a node as late as
possible. This change adds kForceEarly for similar purpose.
This is used for the GPU scheduler initially, for scheduling pipelined Recv
close to RecvDone, so that copies of RecvDone can be removed.
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PR_kwDOArmXAs5reSn8
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[XLA:GPU] Forbid fusing concatenations in dots when the non-contracting dimension is split.
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[XLA:GPU] Forbid fusing concatenations in dots when the non-contracting dimension is split.
This is not handled correctly by codegen at this point.
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PR_kwDOArmXAs5reRdB
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Used std::move so that copy is not maintained
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Used std::move so that copy is not maintained
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PR_kwDOArmXAs5reNW7
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Simple scaled dot product attention kernel. GenAI ops custom registerer.
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PR_kwDOArmXAs5reK4A
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PR #11122: [ROCm] Fix RCCL hang on rocm5.7
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PR #11122: [ROCm] Fix RCCL hang on rocm5.7
Imported from GitHub PR https://github.com/openxla/xla/pull/11122
Copybara import of the project:
--
94f7dc85f6ab07343ddb565b0aac0ed908fd66b4 by Dragan Mladjenovic <[email protected]>:
[ROCm] Allow ncclCommInitRankConfig on rocm5.7
--
2d196b645ccac046309d44f747d42777f00f1c9b by Dragan Mladjenovic <[email protected]>:
[ROCm] Fix sporadic hangs in CommInitRanks
Pointer to comm_handle must stay valid untill GroupEnd call
on pre 2.18 nccl. Only after that comm_handle contains a valid value.
Enforce this by using std::vector instead of temp local.
Merging this change closes #11122
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11122 from ROCm:5.7-rccl-hang 2d196b645ccac046309d44f747d42777f00f1c9b
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PR_kwDOArmXAs5reHnB
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Only use `config-cuda-only` tag under `if_google` wrapper
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Only use `config-cuda-only` tag under `if_google` wrapper
This is an NFC, but an attempt to make clear that CI on GitHub does not read this tag for any reason, only internal CI needs this. This came up during https://github.com/openxla/xla/pull/11094
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PR_kwDOArmXAs5reHlp
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Ensure that the module's buffer donor config and the input output alias config are preserved. These seem to be getting overwritten when we move computations across modules after auto-sharding.
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Ensure that the module's buffer donor config and the input output alias config are preserved. These seem to be getting overwritten when we move computations across modules after auto-sharding.
Reverts fd5333cc3d6801cb5ce51d351ab20b0fd658f516
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11122 from ROCm:5.7-rccl-hang 2d196b645ccac046309d44f747d42777f00f1c9b
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PR_kwDOArmXAs5reEXD
| 64,939 |
Integrate LLVM at llvm/llvm-project@9df19ce40281
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Integrate LLVM at llvm/llvm-project@9df19ce40281
Updates LLVM usage to match
[9df19ce40281](https://github.com/llvm/llvm-project/commit/9df19ce40281)
Reverts e5c11f3d1e1bef39cb1ea0937ac5addf04942f32
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PR_kwDOArmXAs5reBbu
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PR #11141: Add warmup iterations introduced in #9757
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PR #11141: Add warmup iterations introduced in #9757
Imported from GitHub PR https://github.com/openxla/xla/pull/11141
#9757 has been rolled back and re-landed multiple times.
This commit includes just those parts that should not cause problems, shrinking the remaining diff.
Copybara import of the project:
--
6981be3191a94040c97e42a231166f7db07f8a5f by Olli Lupton <[email protected]>:
Add warmup iterations introduced in #9757
That PR has been rolled back and relanded multiple times. This commit
includes just those parts that should not cause problems with Google
production workflows.
Merging this change closes #11141
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11141 from olupton:add-warmup-iterations 6981be3191a94040c97e42a231166f7db07f8a5f
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PR_kwDOArmXAs5rd8Rv
| 64,937 |
Convert unquantized XlaCallModule to func.call
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Convert unquantized XlaCallModule to func.call
Some composite ops may be unquantized due to not supported or selective quantization. We still want to convert them to func.call. This CL converts any unquantized XlaCallModule ops to func.call. This covers both srq and weight-only cases.
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Roll back LiteralBase::Hash change due to performance regression.
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Roll back LiteralBase::Hash change due to performance regression.
Reverts fd5333cc3d6801cb5ce51d351ab20b0fd658f516
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PR_kwDOArmXAs5rd7N5
| 64,935 |
Re-enable persistent cache on cpu.
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Re-enable persistent cache on cpu.
CPU cache key now includes machine attributes, so there should no longer
be a problem with incompatible CPUs accessing the same cache entry.
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PR_kwDOArmXAs5rd1BT
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Add an attribute for mixed priority batching policy
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Add an attribute for mixed priority batching policy
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PR_kwDOArmXAs5rd0c6
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Add absl::SourceLocation to xla::Cancelled, xla::NotFound, xla::Unavailable, and xla::Unknown.
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Add absl::SourceLocation to xla::Cancelled, xla::NotFound, xla::Unavailable, and xla::Unknown.
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PR_kwDOArmXAs5rdzf1
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Deserialize MLRT Bytecode path in AOT GPU
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Deserialize MLRT Bytecode path in AOT GPU
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PR_kwDOArmXAs5rdysY
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[xla:ffi] Compute correct byte size of a DeviceMemoryBase for FFI buffers
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[xla:ffi] Compute correct byte size of a DeviceMemoryBase for FFI buffers
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PR_kwDOArmXAs5rdvMI
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Optimize LiteralBase::Hash by doing type switch once per hash call instead of once per subshape.
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Optimize LiteralBase::Hash by doing type switch once per hash call instead of once per subshape.
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PR_kwDOArmXAs5rduw-
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Small fix to avoid crashes.
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Small fix to avoid crashes.
Reverts e5c11f3d1e1bef39cb1ea0937ac5addf04942f32
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I_kwDOArmXAs6EYgAQ
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`SystemError` in `tf.raw_ops.ParallelConcat` when `dtype` of `shape` is `tf.qint16` or `tf.quint16`.
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[
"Hi @xl1043237213 ,\r\n\r\nAs per documentation the `shape` of this Op should be :\r\n\r\n> shape:\tA [tf.TensorShape](https://www.tensorflow.org/api_docs/python/tf/TensorShape) or list of ints. the final shape of the result; should be equal to the shapes of any input but with the number of input values in the first dimension.\r\n\r\nIt works for list or tf.TensorShape . You are trying to pass a Tensor instead of TensorShape. Hence the error.",
"Thanks for your response.🙂\r\nIt is an exception input, but when the `dtype` of `shape` is `tf.int32` or other types, the code throws the error `TypeError`. I think `TypeError` is a correct error, but `SystemError` is an abnormal error.",
"Hi @xl1043237213 ,\r\n\r\nI tested the code snippet with tf-nightly and it is raising `InvalidArgumentError` . Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/293743ef0d589e3d8e575937acc968b1/64928_r1.ipynb#scrollTo=6n0nTzA4tJWj) for reference. Could you please test and confirm the behaviour with nightly?",
"Hi @SuryanarayanaY.\r\nI found that the triggering of `SystemError` may be related to the execution environment. In the original environment, I updated the tf-nightly version and found that I could re-trigger `SystemError` in CPU execution environment. However, when I restarted the new colab environment,`SystemError` disappeared. Then, in the new environment, I tried to repeat the code execution 10 times and found that 7 times I could trigger `SystemError` and 3 times I could trigger `InvalidArgumentError`.\r\n\r\nBelow is the relevant colab link:\r\n`SystemError` : https://colab.research.google.com/drive/160dforWR2pxr4xlpDnfl-aZzyNnCYTkF?usp=sharing\r\n`InvalidArgumentError` : https://colab.research.google.com/drive/1dxxe2hLpYWYYDC8zNDpH1Q2uCybX9tMv?usp=sharing",
"Hi @xl1043237213 ,\r\n\r\nThis seems inconsistent behaviour though this Op raising intended exception sometimes and other times exception followed by system error which is confusing.This is not a service denial or vulnerability issue though.\r\n\r\nEscalating to SME for their comments. Thanks!\r\n"
] | 2024-04-02T17:03:12 | 2024-04-17T06:58:37 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
The parameter `shape` of API `tf.raw_ops.ParallelConcat` cannot correctly filter tensor input with `dtype` as `qint16` or `quint16`, which will trigger a `SystemError`.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/160dforWR2pxr4xlpDnfl-aZzyNnCYTkF?usp=sharing
```
### Relevant log output
```shell
SystemError: _PyEval_EvalFrameDefault returned a result with an exception set
```
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PR_kwDOArmXAs5rdqQO
| 64,927 |
Reverts e5c11f3d1e1bef39cb1ea0937ac5addf04942f32
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Reverts e5c11f3d1e1bef39cb1ea0937ac5addf04942f32
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I_kwDOArmXAs6EYbA9
| 64,926 |
TypeError: metaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its bases
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[
"@MDENDAN TF v1.x is not actively supported so we request you to kindly upgrade to the latest TF version. Please follow the migration document [here](https://www.tensorflow.org/guide/migrate). 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/64926\">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/64926\">No</a>\n",
"pip install absl-py==1.1.0"
] | 2024-04-02T16:54:03 | 2024-06-11T05:35:47 | 2024-04-20T01:47:19 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 1.14
### Custom code
Yes
### OS platform and distribution
Windows 11
### Mobile device
_No response_
### Python version
3.6
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
cudatoolkit=10.1
### GPU model and memory
Nvidia rtx 3050 4gb
### Current behavior?
I created a new env using conda with python version 3.6 using command: conda create -n myenv -c conda-forge --strict-channel-priority python=3.6
Next I activate the environment and install tensorflow-gpu=1.14 and cudatoolkit=10.0
On trying to import tensorflow i am getting meta class conflict error
C:\Users\MD IMRAN\miniconda3\envs\aai\lib\site-packages\tensorflow\python\framework\dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
C:\Users\MD IMRAN\miniconda3\envs\aai\lib\site-packages\tensorflow\python\framework\dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
C:\Users\MD IMRAN\miniconda3\envs\aai\lib\site-packages\tensorflow\python\framework\dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
C:\Users\MD IMRAN\miniconda3\envs\aai\lib\site-packages\tensorflow\python\framework\dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
C:\Users\MD IMRAN\miniconda3\envs\aai\lib\site-packages\tensorflow\python\framework\dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
C:\Users\MD IMRAN\miniconda3\envs\aai\lib\site-packages\tensorflow\python\framework\dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "C:\Users\MD IMRAN\miniconda3\envs\aai\lib\site-packages\tensorflow\__init__.py", line 28, in <module>
from tensorflow.python import pywrap_tensorflow # pylint: disable=unused-import
File "C:\Users\MD IMRAN\miniconda3\envs\aai\lib\site-packages\tensorflow\python\__init__.py", line 63, in <module>
from tensorflow.python.framework.framework_lib import * # pylint: disable=redefined-builtin
File "C:\Users\MD IMRAN\miniconda3\envs\aai\lib\site-packages\tensorflow\python\framework\framework_lib.py", line 25, in <module>
from tensorflow.python.framework.ops import Graph
File "C:\Users\MD IMRAN\miniconda3\envs\aai\lib\site-packages\tensorflow\python\framework\ops.py", line 54, in <module>
from tensorflow.python.platform import app
File "C:\Users\MD IMRAN\miniconda3\envs\aai\lib\site-packages\tensorflow\python\platform\app.py", line 23, in <module>
from absl.app import run as _run
File "C:\Users\MD IMRAN\miniconda3\envs\aai\lib\site-packages\absl\app.py", line 37, in <module>
from absl import flags
File "C:\Users\MD IMRAN\miniconda3\envs\aai\lib\site-packages\absl\flags\__init__.py", line 35, in <module>
from absl.flags import _argument_parser
File "C:\Users\MD IMRAN\miniconda3\envs\aai\lib\site-packages\absl\flags\_argument_parser.py", line 82, in <module>
class ArgumentParser(Generic[_T], metaclass=_ArgumentParserCache):
TypeError: metaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its bases
### Standalone code to reproduce the issue
```shell
I have explain everything above
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5rdkaC
| 64,925 |
#shlo_ref Add 4 bit with 8 bit storage support.
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#shlo_ref Add 4 bit with 8 bit storage support.
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PR_kwDOArmXAs5rdOZM
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Add F16 wrapper type
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Add F16 wrapper type
_Float16 is not available on the GCC until version 13, which is very recent. This change transforms the F16 into a wrapper type that will always be used regardless of compiler support. It defines conversion and arithmetic operators. If _Float16 is available, that will be used as the underlying type. If it is not, we'll use a uint16_t bits format and the FP16 library for converting between float and uint16_t.
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I_kwDOArmXAs6EX19R
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`SystemError` in `tf.signal.frame` when `dtype` of `frame_step` is `tf.uint64` and its value is too large.
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"Hi **@xl1043237213** ,\r\nI tried to run your code on colab using TF-nightly and faced the same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/4d103416aefde7f6ff9721b154d494a8/64923_2-17.ipynb) here for reference.\r\n\r\nThank you!"
] | 2024-04-02T15:36:54 | 2024-04-24T06:44:18 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When the `dtype` of `frame_step` in `tf.signal.frame` is `tf.uint64` and its value is close to 2^64,`SystemError` will be triggered.
Taking `frame_step = tf.constant(18446744069414584320, dtype=tf.uint64)` as an example, in eager mode, the API will trigger a `SystemError` when it is called.
But there is something very strange. This error can only be triggered once in the same runtime environment, after which Python can correctly recognize the dtype difference between the arguments in the execution, and the error message changes from `SystemError` to `InvalidArgumentError`. I don't know what happened.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1fx6qyTVXmPW8YWUN_IBNCmrTLn90N0St?usp=sharing
```
### Relevant log output
```shell
SystemError: <class 'tensorflow.python.framework.ops.EagerTensor'> returned a result with an exception set
```
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I_kwDOArmXAs6EXwlH
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tf.linalg.normalize generates wrong output in tflite version running on mobile GPU
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"Hi @MinaBabahaji-ML, I followed your directions exactly with a fresh git clone, on this step:\r\n\r\n```\r\nadb shell \"cd /data/local/tmp && LD_LIBRARY_PATH=. ./model_test --model=model_files/sample.tflite --output_shape=1,26,3\"\r\n```\r\n\r\nI get this error:\r\n```\r\nterminating with uncaught exception of type std::runtime_error: You must provide input and output shapes.\r\nAborted \r\n```\r\n\r\nI am using an emulator but I do not believe this issue is related to that, perhaps you are more familiar with your code so if you know how to fix this please let me know.",
"Thank you [pkgoogle](https://github.com/pkgoogle). Could you please confirm that after cloning the repository, you checked out the right branch? (git checkout normalize_layer).\r\n\r\nIt seems that you are on main branch",
"Hi @MinaBabahaji-ML, Thank you... I have openCL installed on my mac and my AVD has graphics set to Automatic and I run into this issue:\r\n```sh\r\nadb shell \"cd /data/local/tmp && LD_LIBRARY_PATH=. ./model_test --model=model_files/sample.tflite --output_shape=1,26,3\"\r\nINFO: Created TensorFlow Lite delegate for GPU.\r\nINFO: Initialized TensorFlow Lite runtime.\r\nVERBOSE: Replacing 4 out of 4 node(s) with delegate (TfLiteGpuDelegateV2) node, yielding 1 partitions for the whole graph.\r\nERROR: Can not open OpenCL library on this device - undefined symbol: clGetCommandBufferInfoKHR\r\nERROR: Falling back to OpenGL\r\nERROR: TfLiteGpuDelegate Init: [GL_INVALID_VALUE]: A numeric argument is out of range.\r\nINFO: Created 0 GPU delegate kernels.\r\nERROR: TfLiteGpuDelegate Prepare: delegate is not initialized\r\nERROR: Node number 4 (TfLiteGpuDelegateV2) failed to prepare.\r\nERROR: Restored original execution plan after delegate application failure.\r\nSegmentation fault \r\n```\r\n\r\nThis is likely an emulator problem, Hi @miaout17, can you please take a look? Thanks.",
"Hi @MinaBabahaji-ML, we have a new workflow with [AI-Edge-Torch](https://github.com/google-ai-edge/ai-edge-torch), you can find more information here: [googleblog](https://developers.googleblog.com/en/ai-edge-torch-high-performance-inference-of-pytorch-models-on-mobile-devices/). Perhaps this issue does not exhibit itself if you use this workflow.\r\n\r\nI have actually created a simple script for converting your model here:\r\n\r\n```py\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport ai_edge_torch\r\n\r\n\r\nclass DummyModel(nn.Module):\r\n def __init__(self):\r\n super().__init__()\r\n\r\n def forward(self, x):\r\n return F.normalize(x, dim=2)\r\n\r\n\r\nbatch_size = 1\r\ninput_shape = (batch_size, 26, 3)\r\nmodel = DummyModel()\r\nsample_inputs = (torch.randn(*input_shape),)\r\n\r\nedge_model = ai_edge_torch.convert(model.eval(), sample_inputs)\r\nedge_model.export(\"dummy_model.tflite\")\r\n```\r\n\r\nIf you want to, you can actually try visualizing the result in [model-explorer](https://github.com/google-ai-edge/model-explorer) as well.\r\n\r\nPlease try them out and let us know if this resolves your issue. If you still need further help, feel free to open a new issue at the respective repo."
] | 2024-04-02T15:27:57 | 2024-06-11T18:17:07 | null |
NONE
| null | null | null |
### 1. System information
- Android S10 snapdragon 855
- TensorFlow built from source
- TensorFlow 2.15.0
### 2. Issue Summary:
When running a TensorFlow Lite model (`tf.linalg.normalize `layer) on an Android 12 device with TensorFlow version 2.15 built from source, using the OpenCL delegate, I am experiencing wrong outputs (This is not hapenning for alll the inputs, but some inputs). Model is converted with `fp16` quantization. This issue can't be reproduced with the XNNPACK delegate. The model is shared in the github repository, but I could regenerate the issue with the following dummy model:
```
import tensorflow as tf
def build_dummy_model(input_shape, name="dummy_model"):
inputs = tf.keras.Input(shape=input_shape, name="input_layer")
x_norm = tf.linalg.normalize(inputs, axis=2, name=f"{name}_norm_pred_bones")[0]
model = tf.keras.Model(inputs=inputs, outputs=x_norm, name=name)
return model
input_shape = (26, 3)
model = build_dummy_model(input_shape)
```
### 2. Code
I did not encounter any issues in converting the model, but when running the converted model on the phone with the OpenCL delegate, the output has a big distance from XNNPACK delegate and keras model. The code, model and steps to reproduce the problem can be found in the following GitHub repository:
[GitHub Repository Link](https://github.com/MinaBabahaji-ML/GPU_problem/tree/normalize_layer)
### 3. logs:
```
INFO: Created TensorFlow Lite delegate for GPU.
INFO: Initialized TensorFlow Lite runtime.
VERBOSE: Replacing 4 out of 4 node(s) with delegate (TfLiteGpuDelegateV2) node, yielding 1 partitions for the whole graph.
INFO: Initialized OpenCL-based API.
INFO: Created 1 GPU delegate kernels.
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
VERBOSE: Replacing 2 out of 4 node(s) with delegate (TfLiteXNNPackDelegate) node, yielding 3 partitions for the whole graph.
OpenCL output:
-0, 0, 0, -0, 0, 0, 0, 0, -0, -0.0643921, -0.828613, -0.555664, 0.235107, -0.74707, -0.62207, -0.233398, -0.95752, -0.165894, 0.585449, -0.73291, -0.346191, 0.0108871, -0.659668, -0.751465, 0.243408, -0.935059, -0.25708, -0, 0, -0, -0, 0, -0, -0.0350647, 0.999023, -0.00702286, -0, -0, -0, 0, 0, 0, -0.361328, 0.753418, 0.548828, 0.329346, 0.64209, -0.691895, 0.298828, 0.947754, -0.106201, -0.62207, -0.208618, -0.754395, 0.107788, 0.822266, -0.559082, 0.242432, 0.935059, 0.258545, 0.186646, 0.345703, -0.919922, -0.0539856, 0.639648, 0.76709, -0.193604, 0.981445, -0.0037384, -0.0666504, 0.289795, 0.955078, 0.345215, 0.792969, 0.502441, 0.0843506, -0.280762, 0.956055,
xnnpack output:
-0.104339, 0.939571, 0.326067, -0.953553, 0.295249, 0.0597043, 0.886078, 0.461906, -0.0388445, -0.0644245, -0.828944, -0.555608, 0.235118, -0.747084, -0.62176, -0.23349, -0.958077, -0.166043, 0.585523, -0.733017, -0.346193, 0.0108894, -0.659484, -0.751639, 0.243398, -0.935215, -0.257159, -0.113086, 0.989401, -0.0910858, -0.119016, 0.962996, -0.241812, -0.0350888, 0.999359, -0.00702636, -0.999308, -0.0174536, -0.0328572, 0.999252, 0.0147311, 0.0357636, -0.361413, 0.753619, 0.549035, 0.329349, 0.642243, -0.692137, 0.298923, 0.948343, -0.106257, -0.622155, -0.208634, -0.754582, 0.107761, 0.822209, -0.558891, 0.242461, 0.935058, 0.258611, 0.186559, 0.345574, -0.91966, -0.0539725, 0.639653, 0.766767, -0.193588, 0.981076, -0.00373641, -0.0666187, 0.289595, 0.954828, 0.345324, 0.7928, 0.502215, 0.0843756, -0.28086, 0.956033,
```
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PR_kwDOArmXAs5rdDco
| 64,921 |
Pass MLIR bytecode across XLA Extension boundary for JAX when converting StableHLO<->MHLO
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[] | 2024-04-02T15:27:45 | 2024-04-05T23:00:34 | 2024-04-05T23:00:34 |
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Pass MLIR bytecode across XLA Extension boundary for JAX when converting StableHLO<->MHLO
Reverts cd3b38bfb0215e65d765dd766dad8e98068759db
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I_kwDOArmXAs6EXoZl
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Unknown operation used in model that is not explicitly declared
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[
"Hi @TimiOkusi ,\r\n\r\nThe 1.x versions are not supported now. Please provide the context and error log for Tf2.x versions. \r\n\r\nAs I understand the reported error is with TF1.8 version, and if so we can't support it. Please test with Tf2.x versions and preferably with latest versions and report the problems faced with all the required details for debug. 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.",
"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/64920\">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/64920\">No</a>\n"
] | 2024-04-02T15:12:30 | 2024-04-19T01:47:45 | 2024-04-19T01:47:42 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.15 and 1.8
### 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?
I am working on a uni project, a fall detection app built using Android studio and machine learning model.
The model uses time series data gotten from mobile sensors.
I have trained the model using model architecture LSTM -> TimeDistributedLayer -> Dense layer -> Dense layer (Output)
I did this initially on colab which uses tensorflow version 2.15, but when I tried to export it as a tensorflow lite model I was getting some errors during the conversion suggesting me to use SelectOps or flex ops which are because an operation is not supported by tensorflow lite’s standard operations.
Doing that and importing into Android studio, I couldn’t initialize the model without getting error saying “Unsupported data type 14 in tensor” which was probably as a result of Android not having the package to run the added operation.
I have tried importing flex ops into Android studio but didn’t work. Might have done it wrongly though.
Then I backdated the tensorflow version to v1.8 to use freeze graph which exported without any errors.
When i tried doing inferencing, I was getting the error “No OpKernel was registered to support Op ‘ClipByValue’ with these attrs. Register device: CPU, Register Kernel: <No registered kernel >”
I inspected the freeze graph model using Netron and found the clipbyvalue op was being used in the LSTM layer and I’ve tried to declare in the optimizer not to use it though by default no clipping is used but nothing works.
I have also gone through my code to find any non-standard or custom operation used but can’t find any.
So I’m just stuck rn
Below is an image for inferencing
below is the image from netron of the freeze graph model
### Standalone code to reproduce the issue
```shell
df = pd.read_csv("SisFall_All.csv")
df.drop(columns=['acc2_x', 'acc2_y', 'acc2_z'], inplace=True)
df.set_index(df.index, inplace=True)
# Resample the DataFrame index to downsample from 200Hz to 50Hz
df = df.iloc[::4]
# Reset the index to retain the original row indices
df.reset_index(drop=True, inplace=True)
acc1_resolution = 13
acc1_range = 16
gyro_resolution = 16
gyro_range = 2000
# Convert accelerometer data to gravity units
df['acc1_x'] = (2 * acc1_range / 2**acc1_resolution) * df['acc1_x']
df['acc1_y'] = (2 * acc1_range / 2**acc1_resolution) * df['acc1_y']
df['acc1_z'] = (2 * acc1_range / 2**acc1_resolution) * df['acc1_z']
# Convert gyroscope data to degrees per second
df['gyro_x'] = (2 * gyro_range / 2**gyro_resolution) * df['gyro_x']
df['gyro_y'] = (2 * gyro_range / 2**gyro_resolution) * df['gyro_y']
df['gyro_z'] = (2 * gyro_range / 2**gyro_resolution) * df['gyro_z']
# Convert activity labels to "ADL" or "Fall" based on their first letter
df['label'] = df['label'].apply(lambda x: 'ADL' if x.startswith('D') else 'Fall' if x.startswith('F') else x)
df.drop(columns=['Subject ID_Trial'], inplace=True)
time_steps = 200
features = 6
step = 20
segments = []
labels = []
for i in range(0, len(df) - time_steps, step):
axs = df['acc1_x'].values[i: i + time_steps]
ays = df['acc1_y'].values[i: i + time_steps]
azs = df['acc1_z'].values[i: i + time_steps]
gxs = df['gyro_x'].values[i: i + time_steps]
gys = df['gyro_y'].values[i: i + time_steps]
gzs = df['gyro_z'].values[i: i + time_steps]
# amags = df['acc_magnitude'].values[i: i + time_steps]
# label_counts = np.unique(df['label'][i: i + time_steps], return_counts=True)
# label = label_counts[0][np.argmax(label_counts[1])]
# segments.append([axs, ays, azs, gxs, gys, gzs])
# labels.append(label)
label = stats.mode(df['label'][i: i + time_steps])
label = label[0][0]
segments.append([axs, ays, azs, gxs, gys, gzs])
labels.append(label)
np.array(segments).shape
reshaped_segment = np.asarray(segments, dtype = np.float32).reshape(-1, time_steps, features)
reshaped_segment.shape
from sklearn.preprocessing import LabelEncoder
label_encoder = LabelEncoder()
labels = label_encoder.fit_transform(labels)
print(labels)
print(labels.shape)
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(reshaped_segment, labels, test_size = 0.2, random_state = 42)
model = Sequential()
# RNN layer
model.add(LSTM(128, input_shape = (200, 6), return_sequences = True, name = 'lstm_1'))
# Apply Dense operations individually to each time sequence
model.add(TimeDistributed(Dense(64, activation='relu'), name='time_distributed'))
# Flatten layer
model.add(Flatten(name='flatten'))
# Dense layer with ReLu
model.add(Dense(64, activation='relu', name='dense_1'))
# Softmax layer
model.add(Dense(2, activation = 'softmax', name='output'))
# Compile model
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
from keras.callbacks import ModelCheckpoint
callbacks = [ModelCheckpoint('model.h5', save_weights_only=False, save_best_only=True, verbose=1)]
history = model.fit(X_train, y_train, epochs = 10, validation_split = 0.20, batch_size = 256,
verbose = 1, callbacks = callbacks)
from keras.models import load_model
model = load_model('model.h5')
model.summary()
loss, accuracy = model.evaluate(X_test, y_test, batch_size = 128, verbose = 1)
print("Test Accuracy :", accuracy)
print("Test Loss :", loss)
from keras import backend as k
from tensorflow.python.tools import freeze_graph, optimize_for_inference_lib
input_node_name = ['lstm_1_input']
output_node_name = 'output/Softmax'
model_name = 'fall_model'
tf.train.write_graph(k.get_session().graph_def, 'models', model_name + '_graph.pbtxt')
saver = tf.train.Saver()
saver.save(k.get_session(), 'models/' + model_name + '.chkp')
freeze_graph.freeze_graph('models/' +model_name + '_graph.pbtxt', None, False, 'models/' +model_name+'.chkp',output_node_name, 'save/restore_all', 'save/Const:0', 'models/frozen_'+model_name+'.pb', True, "")
```
### Relevant log output
```shell
FATAL EXCEPTION: main
Process: com.example.qdetect, PID: 7760
java.lang.IllegalArgumentException: No OpKernel was registered to support Op 'ClipByValue' with these attrs. Registered devices: [CPU], Registered kernels:
<no registered kernels>
[[Node: lstm_1/while/clip_by_value = ClipByValue[T=DT_FLOAT](lstm_1/while/add_2, lstm_1/while/Const, lstm_1/while/Const_1)]]
at org.tensorflow.Session.run(Native Method)
at org.tensorflow.Session.access$100(Session.java:48)
at org.tensorflow.Session$Runner.runHelper(Session.java:298)
at org.tensorflow.Session$Runner.run(Session.java:248)
at org.tensorflow.contrib.android.TensorFlowInferenceInterface.run(TensorFlowInferenceInterface.java:228)
at org.tensorflow.contrib.android.TensorFlowInferenceInterface.run(TensorFlowInferenceInterface.java:197)
at org.tensorflow.contrib.android.TensorFlowInferenceInterface.run(TensorFlowInferenceInterface.java:187)
at com.example.qdetect.pbClassifier.predict(pbClassifier.java:32)
at com.example.qdetect.MainActivity.predictActivity(MainActivity.java:172)
at com.example.qdetect.MainActivity.onSensorChanged(MainActivity.java:118)
at android.hardware.SystemSensorManager$SensorEventQueue.dispatchSensorEvent(SystemSensorManager.java:1108)
at android.os.MessageQueue.nativePollOnce(Native Method)
at android.os.MessageQueue.next(MessageQueue.java:335)
at android.os.Looper.loopOnce(Looper.java:187)
at android.os.Looper.loop(Looper.java:319)
at android.app.ActivityThread.main(ActivityThread.java:8913)
at java.lang.reflect.Method.invoke(Native Method)
at com.android.internal.os.RuntimeInit$MethodAndArgsCaller.run(RuntimeInit.java:608)
at com.android.internal.os.ZygoteInit.main(ZygoteInit.java:1103)
```
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PR_kwDOArmXAs5rc16Y
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Add `load()` statements for the builtin Bazel java rules
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Add `load()` statements for the builtin Bazel java rules
Loads are being added in preparation for moving the rules out of Bazel and into `rules_java`.
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This CL introduces 'PluginProgram' in IFRT and exposes this in python via `xla_client.compile_ifrt_program()`.
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This CL introduces 'PluginProgram' in IFRT and exposes this in python via `xla_client.compile_ifrt_program()`.
The IFRT `PluginProgram` is simply a wrapper for arbitrary byte-strings: an IFRT backend that recognizes `PluginProgram` can interpret the byte-string in any way it sees fit.
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I_kwDOArmXAs6EWS-y
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`SystemError` in `tf.ensure_shape` and `tf.compat.v1.ensure_shape` when `dtype` of `shape` is `tf.uint64` and its value is too large.
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[
"@xl1043237213 Could you use a smaller integer data type like int32 for the shape tensor if the maximum re-presentable value is sufficient as per the needs?\r\nThank you!",
"Thanks for your response.\r\n`shape = tf.constant([18446743219011059112, 1], dtype=tf.uint64)` is an illegal input. The `shape` input should be 'A TensorShape representing the shape of this tensor, a TensorShapeProto, a list, a tuple, or None'. However, `tf.ensure_shape` and `tf.compat.v1.ensure_shape` do not handle this error cases correctly, which may affect the stability of the code at runtime.",
"@xl1043237213 Before using tf.ensure_shape, could you explicitly validate the shape argument. You can check if it's a tf.TensorShape object, a list/tuple of integers, or None.\r\nThank you!",
"Thanks for your answer, I'd still hope that tensorflow would fix the bug if it could.",
"@SuryanarayanaY I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/094fa8e68d0206f0609ae09a75ffc3e2/system_error_tf_ensure_shape.ipynb#scrollTo=YOK2Rcq7PfBK).\r\nThank you!\r\n"
] | 2024-04-02T12:46:59 | 2024-04-17T07:03:46 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When the `dtype` of `shape` in `tf.ensure_shape` or `tf.v1.ensure_shape` is `tf.uint64` and its value is close to 2^64,`SystemError` and `OverflowError` will be triggered.
Taking `shape = tf.constant([18446743219011059112, 1], dtype=tf.uint64)` as an example, in eager mode, these APIs will trigger a `SystemError` when it is called. I think it is a bug.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1sy4yzSx-n6HqS9oMvPv46WasMtMsCU_R?usp=sharing
```
### Relevant log output
```shell
SystemError: <built-in function isinstance> returned a result with an exception set
```
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PR_kwDOArmXAs5rbk9i
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Rename WeightOnlyPreset to WeightOnlyPtqPreset
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Rename WeightOnlyPreset to WeightOnlyPtqPreset
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PR_kwDOArmXAs5rbj11
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Integrate LLVM at llvm/llvm-project@9df19ce40281
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Integrate LLVM at llvm/llvm-project@9df19ce40281
Updates LLVM usage to match
[9df19ce40281](https://github.com/llvm/llvm-project/commit/9df19ce40281)
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This is follow up to https://github.com/openxla/xla/commit/0ab2be0b5a575da3206d2c2f92b85e6346708405,
which got reverted here https://github.com/openxla/xla/commit/b4a46471fd1b80452f913d927e7db04d4a6e260b
Reverts 5c66d026b089c47b021a65663153a268f6598e03
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Switch `xla_cpu` dialect to MLIR properties.
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Switch `xla_cpu` dialect to MLIR properties.
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Apply correct formatting.
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[] | 2024-04-02T08:43:44 | 2024-04-02T10:11:41 | 2024-04-02T10:11:40 |
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