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https://api.github.com/repos/tensorflow/tensorflow/issues/63191
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PR_kwDOArmXAs5o9pDt
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Revert: PR #10037: [NVIDIA XLA] Copy backend config for derived instruction that has same opcode
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Revert: PR #10037: [NVIDIA XLA] Copy backend config for derived instruction that has same opcode
Imported from GitHub PR https://github.com/openxla/xla/pull/10037
SetupDerivedInstruction copies many properties like frontend_attributes, metadata and etc, but it doesnt transfer over backend configs. If the derived instruction has the same opcode as the current one, the backend config is most likely applicable, this change copies over the backend config in that situation.
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Fix data race in HloInstruction::BackendConfigRep
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Fix data race in HloInstruction::BackendConfigRep
`xla::HloInstruction::BackendConfigRep::GetRawString` gets called from multiple threads at the same time in Triton Autotuning.
The implementation of this function caches its results in a mutable member variables in the object.
But this mutable member is not protected by a mutex which leads to concurrent write accesses from
multiple threads and therefore crashes or memory leaks.
So this change is adding a mutex to `mutable std::string raw_string_`.
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[XLA:Python] Restore support for string inputs to mhlo_to_stablehlo.
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[XLA:Python] Restore support for string inputs to mhlo_to_stablehlo.
The previous change made this function accept only bytes (bytecode). Allow textual MLIR as well.
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[XLA:GPU] Clean up ir_emitter_triton_test.cc.
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[XLA:GPU] Clean up ir_emitter_triton_test.cc.
This diff:
0. Replaces ASSERT with EXPECT in line with Google C++ style guide.
1. Expands single letter SSAs introduced by previous PR to make semantic meaning more verbose but also explicit.
2. Gets rid of non-pretty mixed checks of OK + booleans.
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[XLA:GPU][MLIR-based emitters] Implement scatter with atomics.
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[XLA:GPU][MLIR-based emitters] Implement scatter with atomics.
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PR #9757: GpuTimer: improve kernel execution time measurement accuracy
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PR #9757: GpuTimer: improve kernel execution time measurement accuracy
Imported from GitHub PR https://github.com/openxla/xla/pull/9757
This PR changes how `GpuTimer` measures execution time, which should improve the measurement accuracy.
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.
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.
Two parts of the autotuning code are updated to follow this best practice.
The `GpuTimer::Create*` signatures that were deprecated in https://github.com/openxla/xla/pull/9841 do not benefit from these accuracy improvements.
cc: @sergachev @nouiz
Copybara import of the project:
--
2b834f7154158ddc7294d45bb5481b8c38efdc61 by Olli Lupton <[email protected]>:
GpuTimer: use delay kernel to improve accuracy
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.
In addition, make sure that auto-tuning code paths have warm-up
executions of the kernels being measured. The warm-up executions should
not be inside GpuTimer regions.
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.
--
8f5ce0e7e309a46bf69c369bc4b1da8c26fd1579 by Olli Lupton <[email protected]>:
Address CR
Merging this change closes #9757
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/9757 from olupton:gpu-timer-improvement 8f5ce0e7e309a46bf69c369bc4b1da8c26fd1579
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PR #10286: [XLA:GPU] Enable command buffers for cuDNN GEMM fusions.
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PR #10286: [XLA:GPU] Enable command buffers for cuDNN GEMM fusions.
Imported from GitHub PR https://github.com/openxla/xla/pull/10286
The flag currently only affects the new cuDNN fusion integration which itself is not enabled by default yet.
Copybara import of the project:
--
a3bdd159b548032b65d9563d5cd3a6ba07644507 by Ilia Sergachev <[email protected]>:
[XLA:GPU] Enable command buffers for cuDNN GEMM fusions.
The flag currently only affects the new cuDNN fusion integration which itself is not enabled by default yet.
Merging this change closes #10286
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10286 from openxla:cudnn_enable_command_buffers a3bdd159b548032b65d9563d5cd3a6ba07644507
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PR_kwDOArmXAs5o89JA
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PR #9045: Add size for CudaCubinInMemory
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PR #9045: Add size for CudaCubinInMemory
Imported from GitHub PR https://github.com/openxla/xla/pull/9045
This is a sub PR to add SPIR target in openxla.
`CudaCubinInMemory` is an object represents compiled binary for different backends. `char* bytes` is enough for cuda backend, but spir requires additional `int size` to load the binary.
Copybara import of the project:
--
59270e8c02438425e508067aeb92da828630bdcc by Sheng, Yang <[email protected]>:
Add size for CudaCubinInMemory
Merging this change closes #9045
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/9045 from Intel-tensorflow:yang/cubin 59270e8c02438425e508067aeb92da828630bdcc
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[XLA:GPU][MLIR-based emitters] Use rewriter to erase YieldOp after merging blocks.
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[XLA:GPU][MLIR-based emitters] Use rewriter to erase YieldOp after merging blocks.
yieldOp was erased using `yield_op->erase` instead of `rewriter.eraseOp(yield_op)`.
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[Linaro:ARM_CI] Update to new wheel target
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"@nitins17 Is there any action needed from me before this will be merged please?"
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CONTRIBUTOR
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Update the scripts to use the new target for building a wheel
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PR #10297: [NVIDIA GPU] Unroll all nested loops for double buffering transformer
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PR #10297: [NVIDIA GPU] Unroll all nested loops for double buffering transformer
Imported from GitHub PR https://github.com/openxla/xla/pull/10297
The loop_double_buffer_transformer pass currently only unrolls the loops in the main computation.
There are some cases where the memcpies(which this pass is designed to eliminate) would exist in nested loops.
This pr changes the pass to look at all loops in the module include the nested ones and unroll them.
Copybara import of the project:
--
af8c7b1944d14f5268809c5eb0e71166411ddb19 by TJ <[email protected]>:
Unroll all nested loops for double buffering transformer
Merging this change closes #10297
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10297 from Tixxx:tixxx/enable_double_buffer_nested af8c7b1944d14f5268809c5eb0e71166411ddb19
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PR_kwDOArmXAs5o8o0g
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Add sparsity parameters to MakeDotHlo helper
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Add sparsity parameters to MakeDotHlo helper
Update the call sites to skip the creation of dot ops that would drop the sparsity data and result in invalid HLO.
Algebraic simplifier support for sparse dots will follow in the upcoming changelists.
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Add sparse dot op support in algebraic simplifier (slice dot operands)
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Add sparse dot op support in algebraic simplifier (slice dot operands)
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Add sparse dot op support in algebraic simplifier (remove transposes)
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Add sparse dot op support in algebraic simplifier (remove transposes)
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Add sparse dot op support in algebraic simplifier (remove degenerate dimensions)
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Add sparse dot op support in algebraic simplifier (remove degenerate dimensions)
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PR #9319: Avoid rewriting tiny matrices with cublas or triton
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PR #9319: Avoid rewriting tiny matrices with cublas or triton
Imported from GitHub PR https://github.com/openxla/xla/pull/9319
This disables triton/cublas rewriting for matrix multiplication instructions that are too small to benefit from cublas/triton.
In particular, this will avoid triton/cublas if the sum of the sizes of the matrices is <100. This threshold is a conservative estimate and has been measured to be always beneficial (up to generally several times faster) on V100 and H100 GPUs.
Copybara import of the project:
--
b12ff5ade1de6b0243e06998168c712f78c1db5c by Jaroslav Sevcik <[email protected]>:
Avoid rewriting tiny matrices with cublas or triton
--
024d7f9eef4fec9239a62f4bddad7bd5d0c69d29 by Jaroslav Sevcik <[email protected]>:
Fix copy-pasta bug
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f54f9043a77da60e239614c866aca9a21b5aa65f by Jaroslav Sevcik <[email protected]>:
Add tests with testing flag
--
fb689cc164155fe36013a8f5739035b5f77f914c by Jaroslav Sevcik <[email protected]>:
Address reviewer comments
--
a777fab3cf1ce811222e4c4f95f3ee1f717fee4e by Jaroslav Sevcik <[email protected]>:
Test rename
--
bd64e289892b3115e01dffde9bcefee118421bb0 by Jaroslav Sevcik <[email protected]>:
Only allow float dots to reach the backend
--
07bd1ad038ab517fd75673da01b24dbc8773ed3e by Jaroslav Sevcik <[email protected]>:
Add background info into a comment
--
0d18f84f1c93dbc2de5614407b9b1092c3b36986 by Jaroslav Sevcik <[email protected]>:
clang format
--
23ccff6698efafe1fc384b99d706158ee68b075f by Jaroslav Sevcik <[email protected]>:
Also update gemm_broadcast_folding_rewrite test
--
690bb0e1d8b5894f64134d68a031d387399c638f by Jaroslav Sevcik <[email protected]>:
Rename and test
Merging this change closes #9319
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/9319 from jaro-sevcik:no-cublas-or-triton-for-small-matmuls 690bb0e1d8b5894f64134d68a031d387399c638f
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Use hermetic Python in TSL and XLA
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Use hermetic Python in TSL and XLA
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[
"The bug says I reproduced on nightly, this is incorrect, just 2.17.0.",
"@tenchirocom The latest stable TF version is 2.16 could you please try on the same?\r\nPlease ensure there are no conflicting flags that might be affecting the compilation. kindly try cleaning the build directory and rebuilding from scratch?\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/63174\">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/63174\">No</a>\n"
] | 2024-03-07T09:50:06 | 2024-03-29T01:47:04 | 2024-03-29T01:46:57 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.17.0
### Custom code
No
### OS platform and distribution
Linux ubuntu 22.04
### Mobile device
_No response_
### Python version
3.10.12
### Bazel version
baselisk bazel version 6.5.0
### GCC/compiler version
11.4.0
### CUDA/cuDNN version
_No response_
### GPU model and memory
ATA, 16GB
### Current behavior?
successful build
### Standalone code to reproduce the issue
```shell
bazelisk build //tensorflow/tools/pip_package:build_pip_package
```
### Relevant log output
```shell
Build failed after substantial progress (approx 3 hours) with following output:
Starting local Bazel server and connecting to it...
INFO: Reading 'startup' options from /home/tower/tensorflow/.bazelrc: --windows_enab
le_symlinks
INFO: Options provided by the client:
Inherited 'common' options: --isatty=1 --terminal_columns=84
INFO: Reading rc options for 'build' from /home/tower/tensorflow/.bazelrc:
Inherited 'common' options: --experimental_repo_remote_exec
INFO: Reading rc options for 'build' from /home/tower/tensorflow/.bazelrc:
'build' options: --define framework_shared_object=true --define tsl_protobuf_heade
r_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --
spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincomp
atible_remove_legacy_whole_archive --features=-force_no_whole_archive --enable_platf
orm_specific_config --define=with_xla_support=true --config=short_logs --config=v2 -
-define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_l
ibrary --experimental_link_static_libraries_once=false --incompatible_enforce_config
_setting_visibility
INFO: Found applicable config definition build:short_logs in file /home/tower/tensor
flow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file /home/tower/tensorflow/.ba
zelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:linux in file /home/tower/tensorflow/
.bazelrc: --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --
copt=-Wno-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-array-b
ounds --copt=-Wunused-result --copt=-Werror=unused-result --copt=-Wswitch --copt=-We
rror=switch --copt=-Wno-error=unused-but-set-variable --define=PREFIX=/usr --define=
LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE
_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynami
c_kernels --experimental_guard_against_concurrent_changes
INFO: Found applicable config definition build:dynamic_kernels in file /home/tower/t
ensorflow/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_K
ERNELS
INFO: Analyzed target //tensorflow/tools/pip_package:build_pip_package (690 packages
loaded, 43903 targets configured).
INFO: Found 1 target...
ERROR: /home/tower/tensorflow/tensorflow/compiler/mlir/quantization/stablehlo/BUILD:
46:11: Compiling tensorflow/compiler/mlir/quantization/stablehlo/passes/nchw_convolu
tion_to_nhwc.cc failed: (Exit 1): gcc failed: error executing command (from target /
/tensorflow/compiler/mlir/quantization/stablehlo:passes) /usr/lib/ccache/gcc -U_FORT
IFY_SOURCE -fstack-protector -Wall -Wunused-but-set-parameter -Wno-free-nonheap-obje
ct -fno-omit-frame-pointer -g0 -O2 '-D_FORTIFY_SOURCE=1' -DNDEBUG -ffunction-section
s ... (remaining 514 arguments skipped)
tensorflow/compiler/mlir/quantization/stablehlo/passes/nchw_convolution_to_nhwc.cc:
In member function 'llvm::SmallVector<long int> mlir::quant::stablehlo::{anonymous}:
:RewriteNchwConvolutionToNhwc::PermuteShape(llvm::ArrayRef<long int>, llvm::ArrayRef
<long int>) const':
tensorflow/compiler/mlir/quantization/stablehlo/passes/nchw_convolution_to_nhwc.cc:1
99: error: could not convert 'after_shape' from 'SmallVector<[...],4>' to 'SmallVect
or<[...],6>'
199 | return after_shape;
|
Target //tensorflow/tools/pip_package:build_pip_package failed to build
Use --verbose_failures to see the command lines of failed build steps.
INFO: Elapsed time: 65.905s, Critical Path: 38.66s
INFO: 17 processes: 9 internal, 8 local.
FAILED: Build did NOT complete successfully
```
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PR_kwDOArmXAs5o8LYA
| 63,173 |
PR #10315: Add move assignment operator to Shape and ProgramShape
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PR #10315: Add move assignment operator to Shape and ProgramShape
Imported from GitHub PR https://github.com/openxla/xla/pull/10315
The attempt to use `Shape` move assignment operator is found in [ShapeUtil::MakeProgramShape](https://github.com/openxla/xla/blob/main/xla/shape_util.cc#L278)
```
Shape result) {
...
ProgramShape program_shape;
...
*program_shape.mutable_result() = std::move(result);
...
```
Because `Shape` move assignment operator is currently not declared, C++ uses copy assignment operator which copies all Shape internal fields from src Shape (`result`) to dest Shape (`program_shape.result_`).
Copybara import of the project:
--
e5ac9c58ee6f9008e19ec345ae1d05a43fe166fc by Alexander Pivovarov <[email protected]>:
Add move assn op to Shape and ProgramShape
Merging this change closes #10315
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10315 from apivovarov:shape_move_ctor e5ac9c58ee6f9008e19ec345ae1d05a43fe166fc
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[XLA] Do not fail at check when entry computation has a single tuple parameter and allow_spmd_sharding_propagation_to_parameters contains as many elements as tuple size.
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[XLA] Do not fail at check when entry computation has a single tuple parameter and allow_spmd_sharding_propagation_to_parameters contains as many elements as tuple size.
This CL also fixes the bug where sharding propagation used to propagate to **all** elements of a tuple parameter when only some were allowed to. We detect this case with:
- Module's entry parameter has a single parameter and that parameter has a tuple shape.
- The size of allow_spmd_sharding_propagation_to_parameters_vector_ is equal to the number of elements in the tuple.
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PR_kwDOArmXAs5o8H1g
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PR #9354: Remove unused legalize_sparse_ops param from MlirToXlaComputation
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PR #9354: Remove unused legalize_sparse_ops param from MlirToXlaComputation
Imported from GitHub PR https://github.com/openxla/xla/pull/9354
`xla::ParseMlirModuleStringAndConvertToXlaComputation` uses the same params as `xla::MlirToXlaComputation`.
New param `legalize_sparse_ops` was added to `xla::MlirToXlaComputation` on Sep 14, 2023. But this param is unused now.
1. It was decided to remove `legalize_sparse_ops` param from `xla::MlirToXlaComputation`.
2. `options` param has default value `{}` in `MlirToXlaComputation` decl and it is passed by value. In case func caller uses empty options we can simply omit passing `options` param to remove unnecessary obj copy.
Copybara import of the project:
--
9619fa34d03cf2229d3e4a14bc202f4adbe2d03b by Alexander Pivovarov <[email protected]>:
Remove unused legalize_sparse_ops param from MlirToXlaComputation
Merging this change closes #9354
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/9354 from apivovarov:legalize_sparse_ops 9619fa34d03cf2229d3e4a14bc202f4adbe2d03b
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PR_kwDOArmXAs5o7_JL
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Integrate LLVM at llvm/llvm-project@e946b5a87b2d
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Integrate LLVM at llvm/llvm-project@e946b5a87b2d
Updates LLVM usage to match
[e946b5a87b2d](https://github.com/llvm/llvm-project/commit/e946b5a87b2d)
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PR_kwDOArmXAs5o78wW
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Clean up CUDA executable finding logic
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Clean up CUDA executable finding logic
This is cleaning up the logic around `FindCudaExecutable` with the eventual
goal to add version checking (not in this change).
1. I move static caching from `GetToolVersionString` into `GetToolVersion` because
`GetToolVersion` is the only caller of `GetToolVersionString`. It also means we cache the much smaller version number instead an arbitrary long string.
2. I move the check whether a binary exists from `FindCudaExecutable` into `GetToolVersionString`.
I also added an explanation why we are doing this in the first place.
3. I remove caching from `FindCudaExecutable` entirely. The only expensive operation in `FindCudaExecutable`
is the call to `GetToolVersion` which is already cached. What remains is a loop over 10-ish binary paths
that is executed once per compilation. If it turns out that we really need some caching we should move it into NVPTXCompiler and determine the paths only once per lifetime of NVPTXCompiler.
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Add version checks to FindCudaExecutable
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Add version checks to FindCudaExecutable
Currently we look for ptxas and nvlink in a few different places on the host machine, then we choose the first found binary without taking its version into account. If the chosen binary doesn't fulfill our version requirements we will later fail even if there was a suitable ptxas or nvlink in the search path in the first place.
This change makes it take the version of each binary into account when going through the search path. Unsuitable binaries will be discarded right away and the search continues until we are out of locations to check.
This should help with host environments that have multiple CUDA toolkits installed and should make ptxas and nvlink selection more robust.
The concreate changes:
1. `FindCudaExecutable` now also takes a minimum version and a list of forbidden (think buggy) versions that are supposed to be excluded.
2. `WarnIfBadPtxAsVersion` has been removed. It was checking for ptxas < 11.1 which is way older than our minimum supported version of 11.8.
3. There was another version check for `ptxas` in `NVPTXCompiler::ChooseLinkingMethod` which was checking for `version(ptxas)` < 11.8. This has also been removed/replace by the version check described in 4.
4. Version checking for `ptxas` and `nvlink` has been consolidated into 2 methods `FindPtxAsExectuable` and `FindNvLinkExecutable`. It's still not great but at least less spaghetti-like.
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JIT compilation failed.
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[
"This is a fairly old version of TF by now, did you try with a newer one?",
"> This is a fairly old version of TF by now, did you try with a newer one?\r\n\r\nI am on windows native and TF 2.10 is the latest version on TensorFlow that support CUDA on windows. So, I am with it.",
"Hi **@Aditya-Singh-3112** ,\r\n\r\nCould you make sure the frames, mean, and std variables are of compatible types before the subtraction and division. You are already casting frames to tf.float32 during normalization, but ensure that mean and std are also cast to tf.float32 to avoid any type mismatch issues.\r\n\r\n```\r\nmean = tf.math.reduce_mean(frames, axis=[0, 1, 2], keepdims=True)\r\nstd = tf.math.reduce_std(tf.cast(frames, tf.float32), axis=[0, 1, 2], keepdims=True)\r\nframes = tf.cast(frames, tf.float32)\r\nnormalized_frames = (frames - mean) / std\r\n```\r\n\r\nMake sure to adjust the axis parameter in reduce_mean and reduce_std if necessary, depending on the shape of your frames tensor.\r\n\r\nThank you!",
"Hello @Venkat6871 ,\r\n\r\nI casted the mean, frames and std to tf.float32 before normalizing, as you advised and it worked perfectly.\r\n\r\nThank you very much. ",
"I will close this issue now.",
"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/63167\">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/63167\">No</a>\n"
] | 2024-03-07T07:37:25 | 2024-03-12T07:01:34 | 2024-03-12T07:01:30 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.10.10
### Custom code
Yes
### OS platform and distribution
Windows 11
### Mobile device
No
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.8
### GPU model and memory
NVIDIA RTX 3060 12 GB
### Current behavior?
The code should have loaded the val data.
### Standalone code to reproduce the issue
```shell
import os
import cv2
import tensorflow as tf
import numpy as np
import typing as List
import matplotlib.pyplot as plt
import imageio
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)
import gdown
url = 'https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL'
output = 'data.zip'
gdown.download(url, output, quiet=False)
gdown.extractall('data.zip')
vocab = [x for x in "abcdefghijklmnopqrstuvwxyz'?!123456789 "]
char_to_num = tf.keras.layers.StringLookup(vocabulary=vocab, oov_token="")
num_to_char = tf.keras.layers.StringLookup(
vocabulary=char_to_num.get_vocabulary(), oov_token="", invert=True
)
def load_video(path:str):
cap = cv2.VideoCapture(path)
frames = []
for _ in range(int(cap.get(cv2.CAP_PROP_FRAME_COUNT))):
ret, frame = cap.read()
frame = tf.image.rgb_to_grayscale(frame)
frames.append(frame[190:236,80:220,:])
cap.release()
mean = tf.math.reduce_mean(frames)
std = tf.math.reduce_std(tf.cast(frames, tf.float32))
return tf.cast((frames - mean), tf.float32) / std
def load_alignments(path:str):
with open(path, 'r') as f:
lines = f.readlines()
tokens = []
for line in lines:
line = line.split()
if line[2] != 'sil':
tokens = [*tokens,' ',line[2]]
return char_to_num(tf.reshape(tf.strings.unicode_split(tokens, input_encoding='UTF-8'), (-1)))[1:]
def load_data(path: str):
path = bytes.decode(path.numpy())
file_name = path.split('\\')[-1].split('.')[0]
video_path = os.path.join('data','s1',f'{file_name}.mpg')
alignment_path = os.path.join('data','alignments','s1',f'{file_name}.align')
frames = load_video(video_path)
alignments = load_alignments(alignment_path)
return frames, alignments
def mappable_function(path:str):
result = tf.py_function(load_data, [path], (tf.float32, tf.int64))
return result
data = tf.data.Dataset.list_files('./data/s1/*.mpg')
data = data.shuffle(500, reshuffle_each_iteration=False)
data = data.map(mappable_function)
data = data.padded_batch(2, padded_shapes=([75,None,None,None],[40]))
data = data.prefetch(tf.data.AUTOTUNE)
# Added for split
train = data.take(450)
test = data.skip(450)
sample = data.as_numpy_iterator()
val = sample.next(); val[0]
```
### Relevant log output
```shell
---------------------------------------------------------------------------
UnknownError Traceback (most recent call last)
Cell In[16], line 1
----> 1 val = sample.next(); val[0]
File ~\miniconda3\envs\tfgpu\lib\site-packages\tensorflow\python\data\ops\dataset_ops.py:4638, in _NumpyIterator.next(self)
4637 def next(self):
-> 4638 return self.__next__()
File ~\miniconda3\envs\tfgpu\lib\site-packages\tensorflow\python\data\ops\dataset_ops.py:4635, in _NumpyIterator.__next__(self)
4632 numpy.setflags(write=False)
4633 return numpy
-> 4635 return nest.map_structure(to_numpy, next(self._iterator))
File ~\miniconda3\envs\tfgpu\lib\site-packages\tensorflow\python\data\ops\iterator_ops.py:766, in OwnedIterator.__next__(self)
764 def __next__(self):
765 try:
--> 766 return self._next_internal()
767 except errors.OutOfRangeError:
768 raise StopIteration
File ~\miniconda3\envs\tfgpu\lib\site-packages\tensorflow\python\data\ops\iterator_ops.py:749, in OwnedIterator._next_internal(self)
746 # TODO(b/77291417): This runs in sync mode as iterators use an error status
747 # to communicate that there is no more data to iterate over.
748 with context.execution_mode(context.SYNC):
--> 749 ret = gen_dataset_ops.iterator_get_next(
750 self._iterator_resource,
751 output_types=self._flat_output_types,
752 output_shapes=self._flat_output_shapes)
754 try:
755 # Fast path for the case `self._structure` is not a nested structure.
756 return self._element_spec._from_compatible_tensor_list(ret) # pylint: disable=protected-access
File ~\miniconda3\envs\tfgpu\lib\site-packages\tensorflow\python\ops\gen_dataset_ops.py:3016, in iterator_get_next(iterator, output_types, output_shapes, name)
3014 return _result
3015 except _core._NotOkStatusException as e:
-> 3016 _ops.raise_from_not_ok_status(e, name)
3017 except _core._FallbackException:
3018 pass
File ~\miniconda3\envs\tfgpu\lib\site-packages\tensorflow\python\framework\ops.py:7209, in raise_from_not_ok_status(e, name)
7207 def raise_from_not_ok_status(e, name):
7208 e.message += (" name: " + name if name is not None else "")
-> 7209 raise core._status_to_exception(e) from None
UnknownError: {{function_node __wrapped__IteratorGetNext_output_types_2_device_/job:localhost/replica:0/task:0/device:CPU:0}} UnknownError: {{function_node __wrapped__Sub_device_/job:localhost/replica:0/task:0/device:GPU:0}} JIT compilation failed. [Op:Sub]
Traceback (most recent call last):
File "C:\Users\adity\miniconda3\envs\tfgpu\lib\site-packages\tensorflow\python\ops\script_ops.py", line 269, in __call__
return func(device, token, args)
File "C:\Users\adity\miniconda3\envs\tfgpu\lib\site-packages\tensorflow\python\ops\script_ops.py", line 147, in __call__
outputs = self._call(device, args)
File "C:\Users\adity\miniconda3\envs\tfgpu\lib\site-packages\tensorflow\python\ops\script_ops.py", line 154, in _call
ret = self._func(*args)
File "C:\Users\adity\miniconda3\envs\tfgpu\lib\site-packages\tensorflow\python\autograph\impl\api.py", line 642, in wrapper
return func(*args, **kwargs)
File "C:\Users\adity\AppData\Local\Temp\ipykernel_9688\3954971381.py", line 6, in load_data
frames = load_video(video_path)
File "C:\Users\adity\AppData\Local\Temp\ipykernel_9688\1209296760.py", line 13, in load_video
return tf.cast((frames - mean), tf.float32) / std
File "C:\Users\adity\miniconda3\envs\tfgpu\lib\site-packages\tensorflow\python\util\traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\adity\miniconda3\envs\tfgpu\lib\site-packages\tensorflow\python\framework\ops.py", line 7209, in raise_from_not_ok_status
raise core._status_to_exception(e) from None # pylint: disable=protected-access
tensorflow.python.framework.errors_impl.UnknownError: {{function_node __wrapped__Sub_device_/job:localhost/replica:0/task:0/device:GPU:0}} JIT compilation failed. [Op:Sub]
[[{{node EagerPyFunc}}]] [Op:IteratorGetNext]
```
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PR_kwDOArmXAs5o7cVb
| 63,166 |
Support SAME padding legalization when `stride > 1`.
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Support SAME padding legalization when `stride > 1`.
Calculates `SAME` padding based on reference in https://www.tensorflow.org/api_docs/python/tf/nn/convolution like the following:
`output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides[i])`
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Provide an experimental flag to try converting composite ops directly to tflite ops.
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Provide an experimental flag to try converting composite ops directly to tflite ops.
The implementation of the feature to follow.
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Replace `constexpr absl::string_view` with `constexpr StringRef`s.
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Replace `constexpr absl::string_view` with `constexpr StringRef`s.
Within the mlir framework `StringRef` is a more natural choice rather than `absl::string_view`.
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Attach `Method` to matched `XlaCallModuleOp`.
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Attach `Method` to matched `XlaCallModuleOp`.
With this change, matched `XlaCallModuleOp` will carry the corresponding `Method` as an attribute, set within `LiftQuantizableSpotsAsFunctionsPass`.
This will help locally reason about the quantization method to apply to each `XlaCallModuleOp` in the downstream passes (e.g. `QuantizePass`).
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Replace calls to `FindFuncOp`.
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Replace calls to `FindFuncOp`.
When looking for "@main", it is replaced by `FindMainFuncOp`. For other cases, it is replace by `ModuleOp::lookupSymbol`.
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Plugs in the checkpoint adapter API to restore code.
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Plugs in the checkpoint adapter API to restore code.
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PR_kwDOArmXAs5o6as_
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Integrate LLVM at llvm/llvm-project@e946b5a87b2d
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Integrate LLVM at llvm/llvm-project@e946b5a87b2d
Updates LLVM usage to match
[e946b5a87b2d](https://github.com/llvm/llvm-project/commit/e946b5a87b2d)
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Automated rollback of changelist 612998062.
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Automated rollback of changelist 612998062.
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Integrate StableHLO at openxla/stablehlo@d978408a
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Integrate StableHLO at openxla/stablehlo@d978408a
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PR_kwDOArmXAs5o6P8m
| 63,157 |
Support `SAME` padding legalization when `stride > 1`.
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Support `SAME` padding legalization when `stride > 1`.
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PR_kwDOArmXAs5o6Ntk
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Rename "TF Op" labels to "Framework Op" in most cases for generality
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Rename "TF Op" labels to "Framework Op" in most cases for generality
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PR_kwDOArmXAs5o6Mo2
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Check the status of all ops that ran in the pool.
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Check the status of all ops that ran in the pool.
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PR_kwDOArmXAs5o6KH3
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Integrate CHLO->StableHLO lowerings in XLA
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Integrate CHLO->StableHLO lowerings in XLA
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PR_kwDOArmXAs5o6HII
| 63,153 |
Factor out the functionality that collects quantization candidate ops from `StableHloQuantizationPattern`.
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NONE
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Factor out the functionality that collects quantization candidate ops from `StableHloQuantizationPattern`.
The mechanism for collecting candidate ops for quantization is different when `RootOpT` is a `DequantizeOpT` or `QuantizeOpT`.
This change consolidates the representation by having an overload functions `CollectCanidateOps` for each of `DequantizeOpT` and `QuantizeOpT`.
It also imposes extra constraint to the `DequantizeOpT` and `QuantizeOpT` from the template definition level so that checks for the number of users / operands aren't necessary in the rewrite body.
Also includes minor readability changes like:
* Using const refs for `ops_blocklist` and `nodes_blocklist` to avoid extra copies.
* Renaming `quantizing_ops` to `candidate_ops` for clarity.
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PR_kwDOArmXAs5o6AwR
| 63,152 |
Remove duplicate declaration of `IsOpQuantizableStableHlo`.
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NONE
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Remove duplicate declaration of `IsOpQuantizableStableHlo`.
Duplicate declaration is at `stablehlo_op_quant_spec.h` with corresponding definition at `stablehlo_op_quant_spec.cc`.
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PR_kwDOArmXAs5o581A
| 63,151 |
get param memory kinds
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get param memory kinds
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PR_kwDOArmXAs5o58cr
| 63,150 |
Update function aliases in an inline manner
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Update function aliases in an inline manner
outdated function aliases is not used so inline-updating them to avoid creating new map as well as make it possible to clean up the caller code in quantize_model.cc.
As a consequence, read the function_alias in C++ as it is marked as const if passing from python.
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PR_kwDOArmXAs5o54iH
| 63,149 |
`ASSERT_TRUE` the parsed `ModuleOp` outside the `ParseModuleOpString` call.
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`ASSERT_TRUE` the parsed `ModuleOp` outside the `ParseModuleOpString` call.
Current implementation contains an `EXPECT_TRUE` inside `ParseModuleOpString` to check whether the created `OwningOpRef<ModuleOp>` is valid. However, using `ASSERT_TRUE` is more suitable because downstream tests would crash otherwise, if the returned module op is not valid.
Since `ASSERT_*` cannot be placed inside a function, this change takes out the check outside of the `ParseModuleOpString` call.
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PR_kwDOArmXAs5o54TL
| 63,148 |
Duplicate stablehlo.quantization.CalibrationOptions from tensorflow.quantization.CalibrationOptions. And migrate all usage for TF and StableHLO quantizer.
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Duplicate stablehlo.quantization.CalibrationOptions from tensorflow.quantization.CalibrationOptions. And migrate all usage for TF and StableHLO quantizer.
Populate default CalibrationOption during Run.
Add CalibrationOptions to QuantiazationConfig.
Add Calibration option integration tests for StableHLO Quantizer.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/63088 from tensorflow:SuryanarayanaY-patch-13 6dec10d4cbce856abb2f9ebdd61a726ce0655f1d
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PR_kwDOArmXAs5o52I_
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Fix ARM64 XLA presubmit build by switching the bazel build config to the RBE cross-compilation one.
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Fix ARM64 XLA presubmit build by switching the bazel build config to the RBE cross-compilation one.
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Update the visibility for internal tools
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Update the visibility for internal tools
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PR_kwDOArmXAs5o5ybx
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Add tests to evaluate XlaCallModule op.
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Add tests to evaluate XlaCallModule op.
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PR_kwDOArmXAs5o5yM0
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Fixes some typos by renaming dense_shape & dense_shape_ptr local vars in Parse Example's "sparse mode" path to sparse_shape & sparse_shape_ptr.
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Fixes some typos by renaming dense_shape & dense_shape_ptr local vars in Parse Example's "sparse mode" path to sparse_shape & sparse_shape_ptr.
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PR_kwDOArmXAs5o5x5R
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Support `SAME` padding legalization when `stride > 1`.
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Support `SAME` padding legalization when `stride > 1`.
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I_kwDOArmXAs6BgFr2
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TfLite: dynamic sized tensors not supported by XNNPACK
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[
"Hi @suyash-narain,\r\n\r\nPlease verify the issue with latest release TF 2.16.1 with the comitt[ 525cc3b](https://github.com/tensorflow/tensorflow/commit/525cc3b31afb9c656e73810a8210e30cf347b51a).\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/63142\">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/63142\">No</a>\n",
"> Hi @suyash-narain,\r\n> \r\n> Please verify the issue with latest release TF 2.16.1 with the comitt[ 525cc3b](https://github.com/tensorflow/tensorflow/commit/525cc3b31afb9c656e73810a8210e30cf347b51a).\r\n> \r\n> Thank You\r\n\r\nThat helps, but it isn't enough to just have the commit - it needs to be enabled by defining XNNPACK_DELEGATE_ENABLE_SUBGRAPH_RESHAPING=1 for the compiler."
] | 2024-03-07T00:20:10 | 2024-04-04T19:33:51 | 2024-03-27T01:47:16 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf-nightly
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04
### Mobile device
_No response_
### Python version
python 3.10.12
### Bazel version
6.5.0
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I am using the master branch of tensorflow. I built tensorflow-lite using bazel 6.5.0 on linux ubuntu 22.04 x86 system.
The nightly version has this commit already: https://github.com/tensorflow/tensorflow/commit/525cc3b31afb9c656e73810a8210e30cf347b51a
But when I build the tflite benchmark_model as well as tflite benchmark_model_plus_flex, the xnnpack delegate fails to apply.
I am using the attached tflite model: whisper-tiny-en.tflite
[whisper-tiny-en.zip](https://github.com/tensorflow/tensorflow/files/14517043/whisper-tiny-en.zip)
I get the below error message:
> 'Attempting to use a delegate that only supports static-sized tensors with a graph that has dynamic-sized tensors (tensor #220 is a dynamic-sized tensor)'
> ERROR: Failed to apply XNNPACK delegate
> ERROR: Benchmarking failed
I don't face this error when --use_xnnpack=0
are dynamic-sized tensors still not supported for tflite on xnnpack?
thanks
### Standalone code to reproduce the issue
```shell
./benchmark_model --graph=whisper-tiny-en.tflite --use_xnnpack=1
INFO: STARTING!
INFO: Log parameter values verbosely: [0]
INFO: Num threads: [4]
INFO: Graph: [whisper-tiny-en.tflite]
INFO: #threads used for CPU inference: [4]
INFO: Use xnnpack: [1]
INFO: Loaded model whisper-tiny-en.tflite
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
INFO: XNNPACK delegate created.
WARNING: Attempting to use a delegate that only supports static-sized tensors with a graph that has dynamic-sized tensors (tensor#1046 is a dynamic-sized tensor).
ERROR: Failed to apply XNNPACK delegate.
ERROR: Benchmarking failed.
```
### Relevant log output
_No response_
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Rename poorly-named XLA-internal host memory offloading custom-call annotations from PipelineForward/PipelineBackward to MoveToHost/MoveToDevice, respectively.
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Rename poorly-named XLA-internal host memory offloading custom-call annotations from PipelineForward/PipelineBackward to MoveToHost/MoveToDevice, respectively.
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Move `tsl/mkl` to `xla/tsl/mkl`
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Move HasTPUPartitionedCallOpInModule to utils file
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Move HasTPUPartitionedCallOpInModule to utils file
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Test internal config
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Pull shared TF dataflow functionality into tf_dataflow.h.
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Pull shared TF dataflow functionality into tf_dataflow.h.
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i am getting a weired issue while using the script tag of tensorflow
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"Hi @viniith21 ,\r\nIs this related to Tensorflow JS? If so please report it at tfjs repo [here](https://github.com/tensorflow/tfjs/issues).",
"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/63135\">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/63135\">No</a>\n",
"Hi @SuryanarayanaY i fixed this issue please close this issue",
"Closing the issue as per user confirmation."
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indexnew.js:726 Error in main function: TypeError: Failed to fetch
at e.fetch (tfjs-core:17:94486)
at e.<anonymous> (tfjs-core:17:243405)
at tfjs-core:17:2217
at Object.next (tfjs-core:17:2322)
at tfjs-core:17:1259
at new Promise (<anonymous>)
at a (tfjs-core:17:1004)
at e.loadModelJSON (tfjs-core:17:243294)
at e.<anonymous> (tfjs-core:17:244682)
at tfjs-core:17:2217
Please see above issue what is causing it ?
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Could not find a version that satisfies the requirement tensorflow-macos==2.15.0
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[
"@gmyrianthous,\r\nI tried to install the tensorflow v2.15 on MacOS M1 pro with the same command **python -m pip install tensorflow-macos==2.15.0** and it was installed without any error/issues. Kindly find the screenshot for the reference.\r\n\r\nAlso please try to create the new virtual environment and install the tensorflow as per the official document instructions.\r\n\r\n<img width=\"1284\" alt=\"image (24)\" src=\"https://github.com/tensorflow/tensorflow/assets/81610181/7c930c23-d44c-4318-bb0e-d0ca97220db6\">\r\n\r\n<details><summary>Installation Log Details</summary> \r\n\r\n### (tensorflow-mac) rajasekharp-macbookpro:~ rajasekharp$ python -m pip install tensorflow-macos==2.15.0\r\n\r\nCollecting tensorflow-macos==2.15.0\r\n\r\n Downloading tensorflow_macos-2.15.0-cp311-cp311-macosx_12_0_arm64.whl.metadata (4.2 kB)\r\n\r\nCollecting absl-py>=1.0.0 (from tensorflow-macos==2.15.0)\r\n\r\n Using cached absl_py-2.1.0-py3-none-any.whl.metadata (2.3 kB)\r\n\r\nCollecting astunparse>=1.6.0 (from tensorflow-macos==2.15.0)\r\n\r\n Downloading astunparse-1.6.3-py2.py3-none-any.whl.metadata (4.4 kB)\r\n\r\nCollecting flatbuffers>=23.5.26 (from tensorflow-macos==2.15.0)\r\n\r\n Using cached flatbuffers-23.5.26-py2.py3-none-any.whl.metadata (850 bytes)\r\n\r\nCollecting gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 (from tensorflow-macos==2.15.0)\r\n\r\n Downloading gast-0.5.4-py3-none-any.whl.metadata (1.3 kB)\r\n\r\nCollecting google-pasta>=0.1.1 (from tensorflow-macos==2.15.0)\r\n\r\n Downloading google_pasta-0.2.0-py3-none-any.whl.metadata (814 bytes)\r\n\r\nCollecting h5py>=2.9.0 (from tensorflow-macos==2.15.0)\r\n\r\n Downloading h5py-3.10.0-cp311-cp311-macosx_11_0_arm64.whl.metadata (2.5 kB)\r\n\r\nCollecting libclang>=13.0.0 (from tensorflow-macos==2.15.0)\r\n\r\n Downloading libclang-16.0.6-py2.py3-none-macosx_11_0_arm64.whl.metadata (5.2 kB)\r\n\r\nCollecting ml-dtypes~=0.2.0 (from tensorflow-macos==2.15.0)\r\n\r\n Using cached ml_dtypes-0.2.0-cp311-cp311-macosx_10_9_universal2.whl.metadata (20 kB)\r\n\r\nCollecting numpy<2.0.0,>=1.23.5 (from tensorflow-macos==2.15.0)\r\n\r\n Using cached numpy-1.26.4-cp311-cp311-macosx_11_0_arm64.whl.metadata (114 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requests, pyasn1-modules, opt-einsum, ml-dtypes, h5py, google-pasta, astunparse, requests-oauthlib, google-auth, google-auth-oauthlib, tensorboard, tensorflow-macos\r\n\r\nSuccessfully installed MarkupSafe-2.1.5 absl-py-2.1.0 astunparse-1.6.3 cachetools-5.3.3 certifi-2024.2.2 charset-normalizer-3.3.2 flatbuffers-23.5.26 gast-0.5.4 google-auth-2.28.1 google-auth-oauthlib-1.2.0 google-pasta-0.2.0 grpcio-1.62.0 h5py-3.10.0 idna-3.6 keras-2.15.0 libclang-16.0.6 markdown-3.5.2 ml-dtypes-0.2.0 numpy-1.26.4 oauthlib-3.2.2 opt-einsum-3.3.0 packaging-23.2 protobuf-4.25.3 pyasn1-0.5.1 pyasn1-modules-0.3.0 requests-2.31.0 requests-oauthlib-1.3.1 rsa-4.9 six-1.16.0 tensorboard-2.15.2 tensorboard-data-server-0.7.2 tensorflow-estimator-2.15.0 tensorflow-io-gcs-filesystem-0.36.0 tensorflow-macos-2.15.0 termcolor-2.4.0 typing-extensions-4.10.0 urllib3-2.2.1 werkzeug-3.0.1 wheel-0.42.0 wrapt-1.14.1\r\n\r\n(tensorflow-mac) rajasekharp-macbookpro:~ rajasekharp$ python\r\n\r\nPython 3.11.6 (v3.11.6:8b6ee5ba3b, Oct 2 2023, 11:18:21) [Clang 13.0.0 (clang-1300.0.29.30)] on darwin\r\n\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n\r\n### >>> import tensorflow as tf\r\n\r\n###>>> tf.__version__\r\n\r\n###'2.15.0'\r\n\r\n\r\n",
"I have created a new virtual environment. The error persists.\r\n\r\n```\r\n$ python -m pip install tensorflow-macos==2.15.0\r\n\r\nERROR: Could not find a version that satisfies the requirement tensorflow-macos==2.15.0 (from versions: 2.12.0)\r\nERROR: No matching distribution found for tensorflow-macos==2.15.0\r\n```\r\n\r\nAlso, when you say `official document instructions` can you please clarify? It's just a pip install command 🤣 ",
"Also, FYI, I see the same error on a different machine (but still, Apple M1 Pro). So there's something else going wrong here. \r\n\r\nI have even tried with Python 11, but still getting the same error:\r\n\r\n```\r\n(py11-venv) ➜ ~ python --version\r\nPython 3.11.6\r\n(py11-venv) ➜ ~ python -m pip install tensorflow-macos==2.15.0\r\nERROR: Could not find a version that satisfies the requirement tensorflow-macos==2.15.0 (from versions: 2.12.0)\r\nERROR: No matching distribution found for tensorflow-macos==2.15.0\r\n\r\n[notice] A new release of pip is available: 23.2.1 -> 24.0\r\n[notice] To update, run: pip install --upgrade pip\r\n```\r\n\r\n@tilakrayal Can you please try with Python 3.10 and let me know? ",
"@gmyrianthous,\r\nI tried python 3.11 which is compatible with 2.15. Could you please allow me to test with python 3.10 as well, so that I can provide more details/info on the same. Thank you!",
"@gmyrianthous,\r\nI tried installing tensorflow v2.15 with the python version3.10 on Apple M1 pro(OS--macOS Sonoma) and it was installed without any issue/error. Kindly find the screenshot and the log details for the reference.\r\n<img width=\"1512\" alt=\"Screenshot 2024-03-12 at 12 23 49 PM\" src=\"https://github.com/tensorflow/tensorflow/assets/81610181/f19fc455-d091-46d1-839e-5ffcb249af6d\">\r\n\r\n<details><summary>Log Information!</summary>\r\nnallave-macbookpro:~ nallave$ pip3 --version\r\npip 24.0 from /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/pip (python 3.10)\r\nnallave-macbookpro:~ nallave$ python -m pip install tensorflow-macos==2.15.0\r\n-bash: python: command not found\r\nnallave-macbookpro:~ nallave$ python3 -m pip install tensorflow-macos==2.15.0\r\nCollecting tensorflow-macos==2.15.0\r\n Downloading tensorflow_macos-2.15.0-cp310-cp310-macosx_12_0_arm64.whl.metadata (4.2 kB)\r\nCollecting absl-py>=1.0.0 (from tensorflow-macos==2.15.0)\r\n Downloading absl_py-2.1.0-py3-none-any.whl.metadata (2.3 kB)\r\nCollecting astunparse>=1.6.0 (from tensorflow-macos==2.15.0)\r\n Downloading astunparse-1.6.3-py2.py3-none-any.whl.metadata (4.4 kB)\r\nCollecting flatbuffers>=23.5.26 (from tensorflow-macos==2.15.0)\r\n Downloading flatbuffers-24.3.7-py2.py3-none-any.whl.metadata (849 bytes)\r\nCollecting gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 (from tensorflow-macos==2.15.0)\r\n Downloading gast-0.5.4-py3-none-any.whl.metadata (1.3 kB)\r\nCollecting google-pasta>=0.1.1 (from tensorflow-macos==2.15.0)\r\n Downloading google_pasta-0.2.0-py3-none-any.whl.metadata (814 bytes)\r\nCollecting h5py>=2.9.0 (from tensorflow-macos==2.15.0)\r\n Downloading h5py-3.10.0-cp310-cp310-macosx_11_0_arm64.whl.metadata (2.5 kB)\r\nCollecting libclang>=13.0.0 (from tensorflow-macos==2.15.0)\r\n Downloading libclang-16.0.6-py2.py3-none-macosx_11_0_arm64.whl.metadata (5.2 kB)\r\nCollecting ml-dtypes~=0.2.0 (from tensorflow-macos==2.15.0)\r\n Downloading ml_dtypes-0.2.0-cp310-cp310-macosx_10_9_universal2.whl.metadata (20 kB)\r\nCollecting numpy<2.0.0,>=1.23.5 (from tensorflow-macos==2.15.0)\r\n 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0:00:00\r\nDownloading pyasn1-0.5.1-py2.py3-none-any.whl (84 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 84.9/84.9 kB 10.7 MB/s eta 0:00:00\r\nInstalling collected packages: libclang, flatbuffers, wrapt, urllib3, typing-extensions, termcolor, tensorflow-io-gcs-filesystem, tensorflow-estimator, tensorboard-data-server, six, pyasn1, protobuf, packaging, oauthlib, numpy, MarkupSafe, markdown, keras, idna, grpcio, gast, charset-normalizer, certifi, cachetools, absl-py, werkzeug, rsa, requests, pyasn1-modules, opt-einsum, ml-dtypes, h5py, google-pasta, astunparse, requests-oauthlib, google-auth, google-auth-oauthlib, tensorboard, tensorflow-macos\r\nSuccessfully installed MarkupSafe-2.1.5 absl-py-2.1.0 astunparse-1.6.3 cachetools-5.3.3 certifi-2024.2.2 charset-normalizer-3.3.2 flatbuffers-24.3.7 gast-0.5.4 google-auth-2.28.2 google-auth-oauthlib-1.2.0 google-pasta-0.2.0 grpcio-1.62.1 h5py-3.10.0 idna-3.6 keras-2.15.0 libclang-16.0.6 markdown-3.5.2 ml-dtypes-0.2.0 numpy-1.26.4 oauthlib-3.2.2 opt-einsum-3.3.0 packaging-24.0 protobuf-4.25.3 pyasn1-0.5.1 pyasn1-modules-0.3.0 requests-2.31.0 requests-oauthlib-1.4.0 rsa-4.9 six-1.16.0 tensorboard-2.15.2 tensorboard-data-server-0.7.2 tensorflow-estimator-2.15.0 tensorflow-io-gcs-filesystem-0.36.0 tensorflow-macos-2.15.0 termcolor-2.4.0 typing-extensions-4.10.0 urllib3-2.2.1 werkzeug-3.0.1 wrapt-1.14.1\r\nnallave-macbookpro:~ nallave$ python3\r\nPython 3.10.0 (v3.10.0:b494f5935c, Oct 4 2021, 14:59:19) [Clang 12.0.5 (clang-1205.0.22.11)] on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import tensorflow as tf\r\n>>> tf.__version__\r\n'2.15.0'\r\n>>> ",
"@tilakrayal Thanks for your reply. Do you have any clue why this is happening then? As I said, I am pretty sure I am using the latest `pip` version so I don't understand why these versions are not available. ",
"@gmyrianthous,\r\nFrom another open issue https://github.com/tensorflow/tensorflow/issues/63495, I can sense that you are facing an issue with the tensorflow v2.16.1 where the similar [issue](https://github.com/tensorflow/tensorflow/issues/62928) was raised and still it is in open state. Kindly follow the same issue for the updates. Thank you!",
"@tilakrayal I think all these issues are unrelated to each other to be honest.",
"Any thoughts on this dependency hell? ",
"cc: @kulinseth , @learning-to-play"
] | 2024-03-06T16:16:50 | 2024-04-02T14:49:48 | 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
macOS Sonoma 14.1.1
### Mobile device
_No response_
### Python version
3.10.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
Apple M1 Pro
### Current behavior?
I am trying to install `tensorflow` on my macbook pro (Apple M1 pro) and in order to do so, I am trying to install `tensorflow-macos`.
For some reason, any version greater than `2.12.0` cannot be inferred.
```
python -m pip install tensorflow-macos==2.15.0
ERROR: Could not find a version that satisfies the requirement tensorflow-macos==2.15.0 (from versions: 2.9.0, 2.9.1, 2.9.2, 2.10.0, 2.11.0, 2.12.0)
ERROR: No matching distribution found for tensorflow-macos==2.15.0
```
Python Version:
```
python --version
Python 3.10.13
```
I also made sure to upgrade `pip` which is currently set to the latest version:
```
python -m pip --version
pip 24.0 from /path/to/my/venv/lib/python3.10/site-packages/pip (python 3.10)
```
### Standalone code to reproduce the issue
```shell
python -m pip install tensorflow-macos==2.15.0
```
```
### Relevant log output
```shell
ERROR: Could not find a version that satisfies the requirement tensorflow-macos==2.15.0 (from versions: 2.9.0, 2.9.1, 2.9.2, 2.10.0, 2.11.0, 2.12.0)
ERROR: No matching distribution found for tensorflow-macos==2.15.0
```
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I_kwDOArmXAs6BbPqw
| 63,133 |
image similarity model，The input sequence is inconsistent, resulting in inconsistent results
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[
"Hi **@yspyhp** ,\r\n\r\nSorry for the delay, \r\nImage Preprocessing:\r\nEnsure that the images you are using for prediction are preprocessed in the same way as during training. This includes resizing to (224, 224) and applying the same preprocessing steps using preprocess input function.\r\n\r\nModel Loading: \r\nMake sure that the model is loaded properly and the weights are correctly loaded. From the log output, it seems the model is loaded successfully.\r\n\r\nNormalization: \r\nEnsure that the normalization applied during preprocessing is consistent between training and prediction. This includes mean subtraction and scaling.\r\n\r\nData Ordering: \r\nCheck if the data ordering (channels-last or channels-first) is consistent between your training and prediction datasets. ResNet50 usually expects 'channels-last' ordering.\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/63133\">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/63133\">No</a>\n"
] | 2024-03-06T12:34:05 | 2024-03-29T01:47:06 | 2024-03-29T01:46:59 |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.11.0
### 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?
3 files
test.py ==》model definition
image_same_train.py ==> model train
image_same.py ==> model predict
[归档.zip](https://github.com/tensorflow/tensorflow/files/14509907/default.zip)
### Standalone code to reproduce the issue
```shell
import numpy as np
from tensorflow.keras.applications.resnet50 import preprocess_input
from tensorflow.keras.preprocessing import image
from tensorflow.keras import backend as K
from test import similarity_model
def preprocess_image(image_path):
img = image.load_img(image_path, target_size=(224, 224))
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)
img_array = preprocess_input(img_array)
return img_array
def extract_features(model, img_array):
features = model.predict(img_array)
return features.flatten()
# 读取两个图像
# image_path1 = '/home/hr/projects/baidu/bpit-ateam/aib-service-paddlenlp/jietu-1709715631130.jpg'
# image_path1 = '/home/hr/projects/baidu/bpit-ateam/aib-service-paddlenlp/jietu-1709715631130.jpg'
image_path1 = '/home/hr/projects/baidu/bpit-ateam/aib-service-paddlenlp/jietu-1709643042777.jpg'
# image_path2 = '/home/hr/projects/baidu/bpit-ateam/aib-service-paddlenlp/1574821172023.jpg'
image_path2 = '/home/hr/projects/baidu/bpit-ateam/aib-service-paddlenlp/1574820198658.jpg'
K.set_learning_phase(0)
# 加载预训练的VGG16模型（包括权重）
similarity_model.load_weights('/home/hr/projects/baidu/bpit-ateam/aib-service-paddlenlp/weights.h5')
similarity_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
print(similarity_model.summary())
# 预处理图像并提取特征
img_array1 = preprocess_image(image_path1)
img_array2 = preprocess_image(image_path2)
features1 = extract_features(similarity_model, [img_array1, img_array2])
features2 = extract_features(similarity_model, [img_array2, img_array1])
print(features1)
print(features2)
features1，features2 is not equal, please help！！
```
### Relevant log output
```shell
2024-03-06 20:27:00.970308: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-03-06 20:27:01.134346: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-03-06 20:27:01.890378: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/hr/python3/lib:/usr/local/cuda-11.2/lib64:/home/hr/python3/lib:/usr/local/cuda-11.2/lib64:
2024-03-06 20:27:01.890456: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/hr/python3/lib:/usr/local/cuda-11.2/lib64:/home/hr/python3/lib:/usr/local/cuda-11.2/lib64:
2024-03-06 20:27:01.890466: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
2024-03-06 20:27:03.184454: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-03-06 20:27:03.958843: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 18022 MB memory: -> device: 0, name: Tesla V100-SXM2-32GB, pci bus id: 0000:41:00.0, compute capability: 7.0
/home/hr/python3/lib/python3.7/site-packages/keras/backend.py:452: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.
"`tf.keras.backend.set_learning_phase` is deprecated and "
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_2 (InputLayer) [(None, 224, 224, 3 0 []
)]
input_3 (InputLayer) [(None, 224, 224, 3 0 []
)]
resnet50 (Functional) (None, 7, 7, 2048) 23587712 ['input_2[0][0]',
'input_3[0][0]']
flatten (Flatten) (None, 100352) 0 ['resnet50[0][0]']
flatten_1 (Flatten) (None, 100352) 0 ['resnet50[1][0]']
dense (Dense) (None, 256) 25690368 ['flatten[0][0]']
dense_1 (Dense) (None, 256) 25690368 ['flatten_1[0][0]']
dropout (Dropout) (None, 256) 0 ['dense[0][0]']
dropout_1 (Dropout) (None, 256) 0 ['dense_1[0][0]']
lambda (Lambda) (None, 1) 0 ['dropout[0][0]',
'dropout_1[0][0]']
==================================================================================================
Total params: 74,968,448
Trainable params: 51,380,736
Non-trainable params: 23,587,712
__________________________________________________________________________________________________
None
2024-03-06 20:27:10.269453: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:428] Loaded cuDNN version 8101
1/1 [==============================] - 4s 4s/step
1/1 [==============================] - 0s 36ms/step
[0.00075252]
[1.]
```
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I_kwDOArmXAs6BbLCr
| 63,132 |
image similarity model，The input sequence is inconsistent, resulting in inconsistent results
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"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63132\">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/63132\">No</a>\n",
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NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.11.0
### 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?
### model definition
import tensorflow as tf
from keras import backend as K
from keras.layers import Lambda
from tensorflow.keras import layers, models
from tensorflow.keras.applications import ResNet50
tf.random.set_seed(2)
def euclidean_distance(vects):
x, y = vects
euclidean_dist = K.sqrt(K.sum(K.square(x - y), axis=1, keepdims=True))
return 1 / (1 + euclidean_dist)
def build_similarity_model(base_model):
base_model.trainable = False
input1 = layers.Input(shape=(224, 224, 3))
input2 = layers.Input(shape=(224, 224, 3))
x1 = base_model(input1)
f1 = layers.Flatten()(x1)
v1 = layers.Dense(256, activation='relu')(f1)
d1 = layers.Dropout(0.5)(v1)
x2 = base_model(input2)
f2 = layers.Flatten()(x2)
v2 = layers.Dense(256, activation='relu')(f2)
d2 = layers.Dropout(0.5)(v2)
distance = Lambda(euclidean_distance, output_shape=(1,))([d1, d2])
model = models.Model(inputs=[input1, input2], outputs=distance)
return model
base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
similarity_model = build_similarity_model(base_model)
### model train
from tensorflow.keras import optimizers
from tensorflow.keras.applications.resnet50 import preprocess_input
from tensorflow.keras.preprocessing import image
from test import similarity_model
# 读取图像并进行预处理
def preprocess_image(image_path):
img = image.load_img(image_path, target_size=(224, 224))
img_array = image.img_to_array(img)
img_array = preprocess_input(img_array)
return img_array
# 编译模型
similarity_model.compile(optimizer=optimizers.Adam(lr=0.0001), loss='binary_crossentropy', metrics=['accuracy'])
print(similarity_model.summary())
# 准备数据集，这里假设有一个包含正样本和负样本的数据集
# 数据集结构：两个文件夹，分别存放相似和不相似的图像对
# 构造训练数据
X = []
y = []
image_path1 = '/home/hr/projects/baidu/bpit-ateam/aib-service-paddlenlp/1574820198658.jpg'
image_path2 = '/home/hr/projects/baidu/bpit-ateam/aib-service-paddlenlp/jietu-1709643042777.jpg'
image_path3 = '/home/hr/projects/baidu/bpit-ateam/aib-service-paddlenlp/1574821172023.jpg'
img1 = preprocess_image(image_path1)
img2 = preprocess_image(image_path2)
img3 = preprocess_image(image_path3)
X.append([img1, img2])
y.append(1)
X.append([img1, img3])
y.append(0)
X.append([img2, img3])
y.append(0)
X = np.array(X)
y = np.array(y)
# 训练模型
similarity_model.fit([X[:, 0], X[:, 1]], y, validation_data=([X[:, 0], X[:, 1]], y), epochs=100, batch_size=3)
similarity_model.save_weights('/home/hr/projects/baidu/bpit-ateam/aib-service-paddlenlp/weights.h5')
### Standalone code to reproduce the issue
```shell
import numpy as np
from tensorflow.keras.applications.resnet50 import preprocess_input
from tensorflow.keras.preprocessing import image
from tensorflow.keras import backend as K
from test import similarity_model
def preprocess_image(image_path):
img = image.load_img(image_path, target_size=(224, 224))
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)
img_array = preprocess_input(img_array)
return img_array
def extract_features(model, img_array):
features = model.predict(img_array)
return features.flatten()
# 读取两个图像
# image_path1 = '/home/hr/projects/baidu/bpit-ateam/aib-service-paddlenlp/jietu-1709715631130.jpg'
# image_path1 = '/home/hr/projects/baidu/bpit-ateam/aib-service-paddlenlp/jietu-1709715631130.jpg'
image_path1 = '/home/hr/projects/baidu/bpit-ateam/aib-service-paddlenlp/jietu-1709643042777.jpg'
# image_path2 = '/home/hr/projects/baidu/bpit-ateam/aib-service-paddlenlp/1574821172023.jpg'
image_path2 = '/home/hr/projects/baidu/bpit-ateam/aib-service-paddlenlp/1574820198658.jpg'
K.set_learning_phase(0)
# 加载预训练的VGG16模型（包括权重）
similarity_model.load_weights('/home/hr/projects/baidu/bpit-ateam/aib-service-paddlenlp/weights.h5')
similarity_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
print(similarity_model.summary())
# 预处理图像并提取特征
img_array1 = preprocess_image(image_path1)
img_array2 = preprocess_image(image_path2)
features1 = extract_features(similarity_model, [img_array1, img_array2])
features2 = extract_features(similarity_model, [img_array2, img_array1])
print(features1)
print(features2)
```
### Relevant log output
```shell
2024-03-06 20:13:55.955842: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-03-06 20:13:56.121561: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-03-06 20:13:56.864236: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/hr/python3/lib:/usr/local/cuda-11.2/lib64:/home/hr/python3/lib:/usr/local/cuda-11.2/lib64:
2024-03-06 20:13:56.864303: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/hr/python3/lib:/usr/local/cuda-11.2/lib64:/home/hr/python3/lib:/usr/local/cuda-11.2/lib64:
2024-03-06 20:13:56.864313: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
2024-03-06 20:13:58.368233: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-03-06 20:13:59.159186: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 18022 MB memory: -> device: 0, name: Tesla V100-SXM2-32GB, pci bus id: 0000:41:00.0, compute capability: 7.0
/home/hr/python3/lib/python3.7/site-packages/keras/backend.py:452: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.
"`tf.keras.backend.set_learning_phase` is deprecated and "
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_2 (InputLayer) [(None, 224, 224, 3 0 []
)]
input_3 (InputLayer) [(None, 224, 224, 3 0 []
)]
resnet50 (Functional) (None, 7, 7, 2048) 23587712 ['input_2[0][0]',
'input_3[0][0]']
flatten (Flatten) (None, 100352) 0 ['resnet50[0][0]']
flatten_1 (Flatten) (None, 100352) 0 ['resnet50[1][0]']
concatenate (Concatenate) (None, 200704) 0 ['flatten[0][0]',
'flatten_1[0][0]']
dense (Dense) (None, 256) 51380480 ['concatenate[0][0]']
dense_1 (Dense) (None, 1) 257 ['dense[0][0]']
==================================================================================================
Total params: 74,968,449
Trainable params: 51,380,737
Non-trainable params: 23,587,712
__________________________________________________________________________________________________
None
2024-03-06 20:14:05.296236: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:428] Loaded cuDNN version 8101
1/1 [==============================] - 4s 4s/step
1/1 [==============================] - 0s 36ms/step
[0.]
[1.]
```
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PR_kwDOArmXAs5ozVkh
| 63,131 |
Update 'inputCount' as 'outputCount' in the output tensor for loop in experimental Unity Plugin HelloTFLite.cs Sample Code
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In the experimental Unity Plugin HelloTFLite.cs Sample Code, at the second for loop, the bound should be outputCount instead of input count.
Closes the issue [#63093](https://github.com/tensorflow/tensorflow/issues/63093)
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[BUG] Tensorflow lite select ops gives runtime error
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[
"Hi @Jovian-Dsouza, thanks for the information, currently this is a duplicate of https://github.com/tensorflow/tensorflow/issues/60831, please follow there or comment why you think this is not a duplicate, 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/63130\">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/63130\">No</a>\n"
] | 2024-03-06T03:32:48 | 2024-04-02T01:48:19 | 2024-04-02T01:48:17 |
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
Ubuntu 20.04
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
While trying to use tensorflow-lite-select-tf-ops.aar in android studio results in the following runtime error
```
java.lang.UnsatisfiedLinkError: dlopen failed: cannot locate symbol "_ZN6google8protobuf8internal26fixed_address_empty_stringE" referenced by "/data/app/~~PgTBlp4bIwZ8yl02UBjqqg==/com.inseye.core.test-yoruTyRORPvs5Ap3TE6dGw==/base.apk!/lib/x86_64/libtensorflowlite_flex_jni.so".
```
Some fixes suggest `--config=monolithic` to be added to .bazelrc file, however with this build error is noticed for Tensorflow 2.15
```
ERROR: /tensorflow_src/tensorflow/BUILD:1652:19: Action tensorflow/_api/v2/v2.py [for tool] failed: (Aborted): bash failed: error executing command (from target //tensorflow:tf_python_api_gen_v2)
(cd /root/.cache/bazel/_bazel_root/43801f1e35f242fb634ebbc6079cf6c5/execroot/org_tensorflow && \
exec env - \
DOCKER_HOST_CACHEBUSTER=1682977560680045781 \
LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64 \
PATH=/root/.cache/bazelisk/downloads/bazelbuild/bazel-6.1.0-linux-x86_64/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/android/sdk/cmdline-tools/latest/bin:/android/sdk/platform-tools:/android/ndk \
/bin/bash -c 'bazel-out/k8-opt-exec-50AE0418/bin/tensorflow/create_tensorflow.python_api_tf_python_api_gen_v2 --root_init_template=tensorflow/api_template.__init__.py --apidir=bazel-out/k8-opt-exec-50AE0418/bin/tensorflow/_api/v2/ --apiname=tensorflow --apiversion=2 --compat_apiversion=1 --compat_apiversion=2 --compat_init_template=tensorflow/compat_template_v1.__init__.py --compat_init_template=tensorflow/compat_template.__init__.py --packages=tensorflow.python,tensorflow.dtensor.python.accelerator_util,tensorflow.dtensor.python.api,tensorflow.dtensor.python.config,tensorflow.dtensor.python.d_checkpoint,tensorflow.dtensor.python.d_variable,tensorflow.dtensor.python.input_util,tensorflow.dtensor.python.layout,tensorflow.dtensor.python.mesh_util,tensorflow.dtensor.python.tpu_util,tensorflow.dtensor.python.save_restore,tensorflow.lite.python.analyzer,tensorflow.lite.python.lite,tensorflow.lite.python.authoring.authoring,tensorflow.python.modules_with_exports --output_package=tensorflow._api.v2 --use_relative_imports=True --loading=default bazel-out/k8-opt-exec-50AE0418/bin/tensorflow/tf_python_api_gen_v2.params')
# Configuration: 9f320c2ab265ab521267a555257ce138659ef24e739cde7fde73c7665f4cf5e4
# Execution platform: @local_execution_config_platform//:platform
2023-06-09 21:58:46.512000: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2023-06-09 21:58:46.546669: F ./tensorflow/core/framework/variant_op_registry.h:114] Check failed: existing == nullptr (0x2df3898 vs. nullptr)UnaryVariantDeviceCopy for direction: 1 and type_index: tensorflow::Tensor already registered
Target //tmp:tensorflow-lite-select-tf-ops failed to build
ERROR: /tensorflow_src/tensorflow/python/tools/BUILD:303:17 Middleman _middlemen/tensorflow_Spython_Stools_Sprint_Uselective_Uregistration_Uheader-runfiles failed: (Aborted): bash failed: error executing command (from target //tensorflow:tf_python_api_gen_v2)
(cd /root/.cache/bazel/_bazel_root/43801f1e35f242fb634ebbc6079cf6c5/execroot/org_tensorflow && \
exec env - \
DOCKER_HOST_CACHEBUSTER=1682977560680045781 \
LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64 \
PATH=/root/.cache/bazelisk/downloads/bazelbuild/bazel-6.1.0-linux-x86_64/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/android/sdk/cmdline-tools/latest/bin:/android/sdk/platform-tools:/android/ndk \
/bin/bash -c 'bazel-out/k8-opt-exec-50AE0418/bin/tensorflow/create_tensorflow.python_api_tf_python_api_gen_v2 --root_init_template=tensorflow/api_template.__init__.py --apidir=bazel-out/k8-opt-exec-50AE0418/bin/tensorflow/_api/v2/ --apiname=tensorflow --apiversion=2 --compat_apiversion=1 --compat_apiversion=2 --compat_init_template=tensorflow/compat_template_v1.__init__.py --compat_init_template=tensorflow/compat_template.__init__.py --packages=tensorflow.python,tensorflow.dtensor.python.accelerator_util,tensorflow.dtensor.python.api,tensorflow.dtensor.python.config,tensorflow.dtensor.python.d_checkpoint,tensorflow.dtensor.python.d_variable,tensorflow.dtensor.python.input_util,tensorflow.dtensor.python.layout,tensorflow.dtensor.python.mesh_util,tensorflow.dtensor.python.tpu_util,tensorflow.dtensor.python.save_restore,tensorflow.lite.python.analyzer,tensorflow.lite.python.lite,tensorflow.lite.python.authoring.authoring,tensorflow.python.modules_with_exports --output_package=tensorflow._api.v2 --use_relative_imports=True --loading=default bazel-out/k8-opt-exec-50AE0418/bin/tensorflow/tf_python_api_gen_v2.params')
# Configuration: 9f320c2ab265ab521267a555257ce138659ef24e739cde7fde73c7665f4cf5e4
# Execution platform: @local_execution_config_platform//:platform
```
### Standalone code to reproduce the issue
```shell
Following the tutorial: https://www.tensorflow.org/lite/android/lite_build
wget https://github.com/tensorflow/tensorflow/archive/refs/tags/v2.15.0.zip
unzip v2.15.0.zip
cd tensorflow-v2.15.0.zip/tensorflow/lite/tools
docker build . -t tflite-builder -f tflite-android.Dockerfile
cd ../../../
docker run -it -v $PWD:/host_dir tflite-builder bash
sdkmanager \
"build-tools;${ANDROID_BUILD_TOOLS_VERSION}" \
"platform-tools" \
"platforms;android-${ANDROID_API_LEVEL}"
configs=(
'/usr/bin/python3'
'/usr/lib/python3/dist-packages'
'N'
'N'
'Y'
'/usr/lib/llvm-16/bin/clang'
'-Wno-sign-compare -Wno-c++20-designator -Wno-gnu-inline-cpp-without-extern'
'y'
'/android/sdk'
)
printf '%s\n' "${configs[@]}" | ./configure
bash tensorflow/lite/tools/build_aar.sh \
--input_models=mymodel.tflite \
--target_archs=x86,x86_64,arm64-v8a,armeabi-v7a
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5owhhP
| 63,129 |
shlo_ref: Add -llog linkopt on Android builds
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CONTRIBUTOR
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The shlo_ref test binaries were failing to build for Android targets due to an undefined symbol for __android_log_write. This PR adds -llog to the linkopts for all test targets when building for Android.
BUG: b/328138247
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i'm not added to TensorFlow Certificate Network
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[
"@taoufiktalibi,\r\nThank you for attempting the Tensorflow certificate test. This repo is for submitting the bugs,features and PR's which are related to Tensorflow.\r\n\r\n For adding to the TensorFlow Certificate Network and other test related queries, Could you please try to contact **[email protected]** if you passed the exam.\r\n\r\nThank you!",
"thank you\r\ni've been added",
"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/63128\">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/63128\">No</a>\n"
] | 2024-03-05T16:36:08 | 2024-03-07T10:01:08 | 2024-03-07T10:01:05 |
NONE
| null | null | null |
Hi
Despite i earned the certificate and filled the required form received by email , i 've not been added to TensorFlow Certificate Network
Can you help me to solve this please
|
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Update RELEASE.md
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Needs support for the data types : QUI8, QI8, TFL_Quint8, QI16 in ELU's definition at tfl_ops.td
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[
"Thank You @mihaimaruseac . Updated header. ",
"Hi @mihaimaruseac Can you please review this PR ? Thank you!",
"I might be mistaken here but, I think the function might also need the \"QuantizableResult\" trait. [Check my comment in the original issue](https://github.com/tensorflow/tensorflow/issues/63065#issuecomment-1976815555). The TF build I've used had it and the full int8 model I compiled works correctly so far with it.",
"Since this is MLIR, I'm not the right reviewer here"
] | 2024-03-05T12:22:14 | 2024-06-07T06:37:14 | null |
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The ELU's definition in tfl_ops.td , needs support for the following data types: QUI8, QI8, TFL_Quint8, QI16.
Reference: https://github.com/tensorflow/tensorflow/issues/63065
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Update serialization.cc - std::ofstream needs a binary flag for win32
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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/63125/checks?check_run_id=22289927032) 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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In a function 'TfLiteStatus SerializationEntry::SetData' for Win32 the call std::ofstream out_file(temp_filepath.c_str()); requires the binary flag. Otherwise more data than required can be writen.
std::ofstream out_file(temp_filepath.c_str(), std::ios_base::binary);
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[ARM_CI:Linaro] Add Python 3.12 to build list in Actions
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shlo_ref: Add status matchers
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In the OSS build, EXPECT_OK/ASSERT_OK are not available. This PR adds basic versions of those using a matcher.
BUG=b/327297682
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Update version numbers for TensorFlow 2.16.1
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Before merging this PR, please double check that it has correctly updated
`core/public/version.h`, `tools/pip_package/setup.py`, and
`tensorflow/tensorflow.bzl`. Also review the execution notes below:
```
Major: 2 -> 2
Minor: 16 -> 16
Patch: 0 -> 1
No lingering old version strings "2.16.0-rc0" found in source directory
"tensorflow/". Good.
WARNING: Below are potentially instances of lingering old version string
"2.16.0rc0" in source directory "tensorflow/" that are not updated by this
script. Please check them manually!
tensorflow/tools/pip_package/v2/setup.py:325:2.16.0rc0
tensorflow/tools/pip_package/setup.py:338:2.16.0rc0
```
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PR_kwDOArmXAs5ooTGC
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Fix formatting.
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PR_kwDOArmXAs5ooLC-
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Update RELEASE.md with a note about Mac x86 Pip package deprecation
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"> Please also make the change on main if not yet done\r\n\r\nSG, I will replicate the changes on main. "
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Update version numbers for TensorFlow 2.15.1
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Before merging this PR, please double check that it has correctly updated
`core/public/version.h`, `tools/pip_package/setup.py`, and
`tensorflow/tensorflow.bzl`. Also review the execution notes below:
```
Major: 2 -> 2
Minor: 15 -> 15
Patch: 0 -> 1
WARNING: Below are potentially instances of lingering old version string
"2.15.0" in source directory "tensorflow/" that are not updated by this script.
Please check them manually!
tensorflow/lite/core/c/c_api.h:150:2.15.0
tensorflow/lite/tools/versioning/runtime_version.cc:76:2.15.0
tensorflow/lite/tools/versioning/runtime_version.cc:111:2.15.0
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tensorflow/tools/pip_package/setup.py:120:2.15.0
tensorflow/tools/pip_package/setup.py:121:2.15.0
WARNING: Below are potentially instances of lingering old version string
"2.15.0" in source directory "tensorflow/" that are not updated by this script.
Please check them manually!
tensorflow/lite/core/c/c_api.h:150:2.15.0
tensorflow/lite/tools/versioning/runtime_version.cc:76:2.15.0
tensorflow/lite/tools/versioning/runtime_version.cc:111:2.15.0
tensorflow/lite/tools/versioning/runtime_version.cc:134:2.15.0
tensorflow/lite/tools/versioning/runtime_version.cc:141:2.15.0
tensorflow/lite/tools/versioning/runtime_version.cc:270:2.15.0
tensorflow/lite/tools/versioning/runtime_version.cc:376:2.15.0
tensorflow/lite/tools/versioning/runtime_version.cc:380:2.15.0
tensorflow/lite/tools/versioning/runtime_version.cc:433:2.15.0
tensorflow/lite/tools/versioning/runtime_version.cc:434:2.15.0
tensorflow/lite/tools/versioning/runtime_version.cc:435:2.15.0
tensorflow/lite/tools/versioning/runtime_version.cc:436:2.15.0
tensorflow/tools/pip_package/setup.py:50:2.15.0
tensorflow/tools/pip_package/setup.py:120:2.15.0
tensorflow/tools/pip_package/setup.py:121:2.15.0
```
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Update release notes for TensorFlow 2.16.1
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Error occurred when initializing ObjectDetector: Mobile SSD models are expected to have exactly 4 outputs, found 3
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[
"Hi，@nerdyAlezF @srjoglekar246 @lintian06, could you please take a look at this issue?",
"Hi @libofei2004, your link isn't working ... is there any reason you are using an older version? Can you please try with nightly and let us know the results. Thanks.",
"@pkgoogle Thank you, I have read issue#47595 and try to use the code from the link ,it can support me to customize inputs and outputs, the right link is https://github.com/tensorflow/examples/blob/cbe96424b6b930fd74dbd4ef2b1e826a031a3554/lite/examples/object_detection/android/lib_interpreter/src/main/java/org/tensorflow/lite/examples/detection/tflite/TFLiteObjectDetectionAPIModel.java#L56\r\nThe lastest version I also tried, but met the same error.\r\nhttps://github.com/tensorflow/examples/tree/master/lite/examples/object_detection/android\r\nI think my exported tflite model may be not suit for the tensorflow example.",
"The model readme.md is as follows:\r\n\r\nAs mentioned by readme, the outputs num is indeed 3, but tensorflow example needs 4, how to solve it?@pkgoogle",
"Hi @libofei2004, there are multiple things that need updating with this project so I will fix this, then we can address your issue. Thanks.",
"Thank you @pkgoogle ,I will wait.",
"Hi @libofei2004, I was able to compile a newer version of the project by updating the dependencies:\r\n\r\nobject_detection/android/build.gradle.kts:\r\n```kts\r\ndependencies {\r\n classpath 'com.android.tools.build:gradle:8.2.0'\r\n classpath \"org.jetbrains.kotlin:kotlin-gradle-plugin:$kotlin_version\"\r\n classpath 'androidx.navigation:navigation-safe-args-gradle-plugin:2.5.0'\r\n classpath 'de.undercouch:gradle-download-task:4.1.2'\r\n\r\n\r\n // NOTE: Do not place your application dependencies here; they belong\r\n // in the individual module build.gradle files\r\n }\r\n```\r\n\r\nadditionally a namespace needs to be specified, in object_detection/android/app/build.gradle.kts:\r\n```kts\r\n...\r\nandroid {\r\n ...\r\n namespace \"org.tensorflow.lite.examples.objectdetection\"\r\n ...\r\n}\r\n...\r\n```\r\n\r\nBefore I commit this change, I will need to see if there are any other changes needed to support your workflow. It seems the issue is specific to your model ... can you share/upload your model or explain how I can recreate it?\r\n\r\nThanks.",
"@pkgoogle , can you visit my model https://github.com/libofei2004/custom-vision-model, it includes tflite model, tflite float16 model, saved model and tflite float16 with metadata.",
"Hi @libofei2004, this example uses quantized models which is described in the README: https://github.com/tensorflow/examples/tree/master/lite/examples/object_detection/android . There's assumptions built it which make it not work with non-quantized models... are any of your models quantized? if not, can you try with a quantized model?",
"Hi @pkgoogle , I have run the example yet before,I can run it successfully by using the models mentioned in the README,but I can't run my model successfully.I also tied to quantize my model yet. I think the problem is my model's output number is not fit for the example .",
"@libofei2004, can you please share the exact model you \"can't run successfully\"? I tried with model.tflite and when I run it effectively states the model isn't quantized, do you have a quantized model that you are attempting to run it on?",
"@pkgoogle ,My models are all in https://github.com/tensorflow/examples/tree/master/lite/examples/object_detection/android, they can run successfully in the https://github.com/Azure-Samples/cognitive-services-android-customvision-sample but not in tensorflow sample. I have tried to quantized the model by the script https://github.com/libofei2004/custom-vision-model/blob/main/quantization.py but failed,I don't exactly konw how to make a right quantization, maybe the model format exported from the custom vision platform is not standard.",
"Hi @libofei2004, I reread your original description.. so the current problem is that your models have `kTfLiteFloat32` input tensors i.e. non-quantized, we can get around this with writing the metadata which is what you described here:\r\n\r\n> I use metadata_writers py script to add metadata to tflite model and load model in the object_detection example\r\n\r\ncan you please elaborate on how you wrote the meta data with the script? i.e. did you import the tfllite-support library and ran a custom script or did you run a script we already provided? If so, what arguments did you pass? Thanks for your help.",
"@pkgoogle ，My metadata script is https://github.com/libofei2004/custom-vision-model/blob/main/meta.py. Thank you.",
"Hi @libofei2004, please review this [gist](https://colab.sandbox.google.com/gist/pkgoogle/f1fc8c1eed6bcfec49fd722c5e35112c/63115.ipynb). I uploaded all the models that you provided and an original mobilenetv1 model. As you can see your models have 3 outputs instead of the expected 4. This is prior to the metadata issue so the problem is in order to \"drop\" in this model you have to conform to the expected API: https://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/task/vision/detector/ObjectDetector\r\n\r\nPlease review the API documentation. Of course you can use a custom ObjectDetector but you will have to code a bit to make it work with our example. I don't know the details of your model but it might be worth it to see if you can fit the outputs (by modifying the original model) to the expected API (perhaps if an output is unnecessary for your use case you can modify your model to output a dummy value).\r\n\r\nAs I currently see it, it is working as expected. Let me know if you have any additional questions. ",
"@pkgoogle ,I can't visit the gist. I think either modify the tensorflow sample source code to fit the model or modify the model structure to fit the source code. Both are not easy. From the error message, it seems that the error lies in the JNI of the code, so changing the code might not be straightforward.",
"My apologies, updated the wrong link, here's the right one (also edited on the original): [gist](https://colab.sandbox.google.com/gist/pkgoogle/f1fc8c1eed6bcfec49fd722c5e35112c/63115.ipynb). @libofei2004 Your understanding is correct.... the example code is designed to get you started, unfortunately we can not maintain example code for all potential use cases out there. Since your own model doesn't seem to fit well with the API, perhaps you can try the Interpreter API? which is more meant for a lower level of abstraction (and thus can be more customized to your use case):\r\n\r\nhttps://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/InterpreterApi",
"@pkgoogle，thank you, but the Interpreter API is hard to use, is there any examples with the API ?",
"Hi @libofei2004, Does this help you? https://www.tensorflow.org/lite/inference_with_metadata/lite_support, the support library will give you higher level tools for input/output processing.",
"@pkgoogle , with the error message: at org.tensorflow.lite.task.vision.detector.ObjectDetector.initJniWithByteBuffer(Native Method)..., I think the error occurred at the low level （C++），Can I use Java API to modify the number of output parameters?",
"Hi @libofei2004, you would have to modify the API which is more or less located here: https://github.com/tensorflow/tflite-support/blob/master/tensorflow_lite_support/java/src/java/org/tensorflow/lite/task/vision/detector/ObjectDetector.java This would be non-trivial and unless you make a feature request in that repo that improves the optionality of the outputs for the API you would have to maintain that change itself. So... there probably is a way, if you dig down far enough.",
"@pkgoogle ， I don't think I can get the sample running through modifying the ObjectDetector.java, the error must be in \"modelBuffer\" and the real code parses it is in C++.",
"Hi @libofei2004, apologies, I am less familiar with that repo overall, you are correct, I guess this was covered under \"if you dig down far enough\" but yeah I did a little more and it seems like here is probably where you would have to change: https://github.com/tensorflow/tflite-support/blob/master/tensorflow_lite_support/java/src/native/task/vision/detector/object_detector_jni.cc#L115",
"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/63115\">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/63115\">No</a>\n"
] | 2024-03-04T15:48:34 | 2024-04-17T01:48:03 | 2024-04-17T01:47:52 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf2.8
### Custom code
Yes
### OS platform and distribution
android (MIUI 13)
### Mobile device
MI 10 pro
### 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 used custom vision(https://www.customvision.ai/) to train my object detction model, and exported an tflite model , but my exported tensorflow tflite model cannot loaded rightly in tensorflow example（https://github.com/tensorflow/examples/tree/cbe96424b6b930fd74dbd4ef2b1e826a031a3554/lite/examples/object_detection）. It seems lack of metadata. If I use metadata_writers py script to add metadata to tflite model and load model in the object_detection example, an error like "Mobile SSD models are expected to have exactly 4 outputs, found 3" will occured.
I have read https://github.com/tensorflow/tensorflow/issues/47595, but still not find how to solve my problem, it seems customize inputs and outputs for [TFLiteObjectDetectionAPIModel](https://github.com/tensorflow/examples/blob/cbe96424b6b930fd74dbd4ef2b1e826a031a3554/lite/examples/object_detection/android/lib_interpreter/src/main/java/org/tensorflow/lite/examples/detection/tflite/TFLiteObjectDetectionAPIModel.java#L56) cannot solve the problem.
### Standalone code to reproduce the issue
```shell
I just run this code:
https://github.com/tensorflow/examples/tree/cbe96424b6b930fd74dbd4ef2b1e826a031a3554/lite/examples/object_detection
```
### Relevant log output
```shell
FATAL EXCEPTION: main
Process: org.tensorflow.lite.examples.objectdetection, PID: 4946
java.lang.IllegalStateException: Error getting native address of native library: task_vision_jni
at org.tensorflow.lite.task.core.TaskJniUtils.createHandleFromLibrary(TaskJniUtils.java:95)
at org.tensorflow.lite.task.vision.detector.ObjectDetector.createFromBufferAndOptions(ObjectDetector.java:219)
at org.tensorflow.lite.examples.detection.tflite.TFLiteObjectDetectionAPIModel.<init>(TFLiteObjectDetectionAPIModel.java:87)
at org.tensorflow.lite.examples.detection.tflite.TFLiteObjectDetectionAPIModel.create(TFLiteObjectDetectionAPIModel.java:81)
at org.tensorflow.lite.examples.detection.DetectorActivity.onPreviewSizeChosen(DetectorActivity.java:103)
at org.tensorflow.lite.examples.detection.CameraActivity$7.onPreviewSizeChosen(CameraActivity.java:448)
at org.tensorflow.lite.examples.detection.CameraConnectionFragment.setUpCameraOutputs(CameraConnectionFragment.java:360)
at org.tensorflow.lite.examples.detection.CameraConnectionFragment.openCamera(CameraConnectionFragment.java:365)
at org.tensorflow.lite.examples.detection.CameraConnectionFragment.-$$Nest$mopenCamera(Unknown Source:0)
at org.tensorflow.lite.examples.detection.CameraConnectionFragment$3.onSurfaceTextureAvailable(CameraConnectionFragment.java:174)
at android.view.TextureView.getTextureLayer(TextureView.java:410)
at android.view.TextureView.draw(TextureView.java:353)
at android.view.View.updateDisplayListIfDirty(View.java:21885)
at android.view.View.draw(View.java:22743)
at android.view.ViewGroup.drawChild(ViewGroup.java:4542)
at android.view.ViewGroup.dispatchDraw(ViewGroup.java:4300)
at android.view.View.updateDisplayListIfDirty(View.java:21876)
at android.view.View.draw(View.java:22743)
at android.view.ViewGroup.drawChild(ViewGroup.java:4542)
at android.view.ViewGroup.dispatchDraw(ViewGroup.java:4300)
at android.view.View.updateDisplayListIfDirty(View.java:21876)
at android.view.View.draw(View.java:22743)
at android.view.ViewGroup.drawChild(ViewGroup.java:4542)
at android.view.ViewGroup.dispatchDraw(ViewGroup.java:4300)
at android.view.View.draw(View.java:23021)
at android.view.View.updateDisplayListIfDirty(View.java:21885)
at android.view.View.draw(View.java:22743)
at android.view.ViewGroup.drawChild(ViewGroup.java:4542)
at androidx.coordinatorlayout.widget.CoordinatorLayout.drawChild(CoordinatorLayout.java:1246)
at android.view.ViewGroup.dispatchDraw(ViewGroup.java:4300)
at android.view.View.draw(View.java:23021)
at android.view.View.updateDisplayListIfDirty(View.java:21885)
at android.view.View.draw(View.java:22743)
at android.view.ViewGroup.drawChild(ViewGroup.java:4542)
at android.view.ViewGroup.dispatchDraw(ViewGroup.java:4300)
at android.view.View.updateDisplayListIfDirty(View.java:21876)
at android.view.View.draw(View.java:22743)
at android.view.ViewGroup.drawChild(ViewGroup.java:4542)
at android.view.ViewGroup.dispatchDraw(ViewGroup.java:4300)
at android.view.View.updateDisplayListIfDirty(View.java:21876)
at android.view.View.draw(View.java:22743)
at android.view.ViewGroup.drawChild(ViewGroup.java:4542)
at android.view.ViewGroup.dispatchDraw(ViewGroup.java:4300)
at android.view.View.updateDisplayListIfDirty(View.java:21876)
at android.view.View.draw(View.java:22743)
at android.view.ViewGroup.drawChild(ViewGroup.java:4542)
at android.view.ViewGroup.dispatchDraw(ViewGroup.java:4300)
at android.view.View.updateDisplayListIfDirty(View.java:21876)
at android.view.View.draw(View.java:22743)
at android.view.ViewGroup.drawChild(ViewGroup.java:4542)
at android.view.ViewGroup.dispatchDraw(ViewGroup.java:4300)
at android.view.View.draw(View.java:23021)
at com.android.internal.policy.DecorView.draw(DecorView.java:891)
at android.view.View.updateDisplayListIfDirty(View.java:21885)
at android.view.ThreadedRenderer.updateViewTreeDisplayList(ThreadedRenderer.java:534)
at android.view.ThreadedRenderer.updateRootDisplayList(ThreadedRenderer.java:542)
2024-03-04 23:22:36.073 4946-4946 AndroidRuntime org...lite.examples.objectdetection E at android.view.ThreadedRenderer.draw(ThreadedRenderer.java:625)
at android.view.ViewRootImpl.draw(ViewRootImpl.java:4657)
at android.view.ViewRootImpl.performDraw(ViewRootImpl.java:4375)
at android.view.ViewRootImpl.performTraversals(ViewRootImpl.java:3486)
at android.view.ViewRootImpl.doTraversal(ViewRootImpl.java:2277)
at android.view.ViewRootImpl$TraversalRunnable.run(ViewRootImpl.java:9037)
at android.view.Choreographer$CallbackRecord.run(Choreographer.java:1142)
at android.view.Choreographer.doCallbacks(Choreographer.java:946)
at android.view.Choreographer.doFrame(Choreographer.java:875)
at android.view.Choreographer$FrameDisplayEventReceiver.run(Choreographer.java:1127)
at android.os.Handler.handleCallback(Handler.java:938)
at android.os.Handler.dispatchMessage(Handler.java:99)
at android.os.Looper.loopOnce(Looper.java:210)
at android.os.Looper.loop(Looper.java:299)
at android.app.ActivityThread.main(ActivityThread.java:8293)
at java.lang.reflect.Method.invoke(Native Method)
at com.android.internal.os.RuntimeInit$MethodAndArgsCaller.run(RuntimeInit.java:556)
at com.android.internal.os.ZygoteInit.main(ZygoteInit.java:1045)
Caused by: java.lang.IllegalArgumentException: Error occurred when initializing ObjectDetector: Mobile SSD models are expected to have exactly 4 outputs, found 3
at org.tensorflow.lite.task.vision.detector.ObjectDetector.initJniWithByteBuffer(Native Method)
at org.tensorflow.lite.task.vision.detector.ObjectDetector.access$100(ObjectDetector.java:88)
at org.tensorflow.lite.task.vision.detector.ObjectDetector$3.createHandle(ObjectDetector.java:223)
at org.tensorflow.lite.task.core.TaskJniUtils.createHandleFromLibrary(TaskJniUtils.java:91)
... 73 more
```
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PR_kwDOArmXAs5omjF5
| 63,114 |
Fix checkfail in raw_ops.DenseBincount
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"This seems reasonable to me, but I'll let @cantonios to give final approval. Thanks for doing these fixes!"
] | 2024-03-04T15:06:12 | 2024-03-22T06:35:47 | 2024-03-22T06:35:46 |
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The API `raw_ops.DenseBincount` lacks validation of input to be vector. It does have checking for rank<=2 but not check for rank>0. Passing a scalar value causes checkfail with debug build.
Reported at #63068
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Fix heap overflow error in raw_ops.ArgMax
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The API raw_ops.ArgMax implementation copies the value of arg dimension into int32_t without checking exact type which may read out of bounds.
Reported in #63070 .
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Tensorflow memory leak during inference
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[
"Hi @omrir ,\r\n\r\nCould you please confirm the behaviour with latest TF releases i.e TF2.16v and provide the logs along with a reproducible code. Thanks!",
"I upgraded to python 3.10 and TF2.15. It seems that the memory leak does not happen.\r\nNevertheless, with python3.11 and TF2.15, it seems that the memory leak does happen.",
"Hi @omrir ,\r\n\r\nPython 3.11 is supported version for TF>=2.12V. Could you submit a repro code for it? Thanks!",
"The following code seams to leak memory. very small portion (~250kb) in each iteration. TF2.15. python 3.11.\r\n\r\n```\r\nimport os\r\nimport tensorflow as tf\r\nimport numpy as np\r\n\r\nmain_script_pid = os.getpid()\r\nprint(\"PID of the main script's process:\", main_script_pid)\r\n\r\ndummy_frame = np.random.randint(0, 255, size=(128,128, 3), dtype=np.uint8)\r\n\r\nwhile True:\r\n\r\n input_tensor = tf.constant(dummy_frame, dtype=tf.float32)\r\n```",
"Hi @SuryanarayanaY,\r\nAny update?\r\nCan you confirm the memory leak bug?"
] | 2024-03-04T09:22:05 | 2024-03-17T14:17:25 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
v2.13.0-17-gf841394b1b7 2.13.1
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04.6 LTS
### Mobile device
_No response_
### Python version
3.8
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I'm running the following code and noticing a never-ending increase in RAM usage. Eventually, the script terminates with an out-of-memory error. I can't understand what the issue is. I also tried using tf.keras.backend.clear_session() once every 10,000 iterations, but it didn't help. I monitor the specific RAM usage of the PID script. Tensorflow ver is 2.13.1. I would appreciate any insights.
### Standalone code to reproduce the issue
```shell
import os
import tensorflow as tf
import numpy as np
import cv2
import time
main_script_pid = os.getpid()
print("PID of the main script's process:", main_script_pid)
model_path = '.../Models/model_Ch_0_trt'
dummy_frame = np.random.randint(0, 255, size=(128,128, 3), dtype=np.uint8)
img = cv2.cvtColor(dummy_frame, cv2.COLOR_BGR2GRAY)
img = np.expand_dims(img, axis=0)
img = np.expand_dims(img, axis=-1)
img = img / 255.0 # Normalize pixel values to [0, 1]
trt_saved_model = tf.saved_model.load(model_path)
inference_function = trt_saved_model.signatures["serving_default"]
input_tensor_name = list(inference_function.structured_input_signature[1].keys())[0]
output_tensor_name = list(inference_function.structured_outputs.keys())[0]
while True:
predictions = inference_function(**{input_tensor_name: tf.constant(img, dtype=tf.float32)})[output_tensor_name].numpy()
```
### Relevant log output
_No response_
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| 63,111 |
Tflite: android benchmark test get differen result on GPU delegate
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[
"Testing model:[mobilenetv3_large.tflite.zip](https://github.com/tensorflow/tensorflow/files/14479007/mobilenetv3_large.tflite.zip)\r\n",
"I feel an emulator will not be a reasonable test case for this, @impjdi, can you please take a look? Thanks.",
"Uh, I don't own the benchmarks, so it's hard for me to tell what the differences are. @pkgoogle do you happen to know who owns the benchmarks?",
"Hi @JunyoungLim, can you please take a look? Thanks.",
"I encountered the same problem when using GPU inference, but I don’t know how to solve it. I hope to get a reply."
] | 2024-03-04T08:58:55 | 2024-03-25T12:48:53 | null |
NONE
| null | null | null |
Hi,
When I use the [benchmark script](https://www.tensorflow.org/lite/performance/measurement#native_benchmark_binary) and [benchmark apk](https://www.tensorflow.org/lite/performance/measurement#android_benchmark_app) to test my model performance, I get the same performance on CPU of XNNPACK delegate, but different performance on GPU of OpenCL delegate.
And the result
| | benchmark script| | benchmark apk ||
| ----------------- | ---------------- | ------------- |---------------- | ------------- |
| Time performance(ms) | cpu | gpu | cpu | gpu |
| mobilenetv2 | 5.7 | **7.4** |5.6 | **4.3** |
| mobilenetv3_small | 1.8 | **4.9** | 1.7 | **2.1** |
| mobilenetv3_large | 5.3 | **6** | 5 | **3.8** |
The benchmark apk is almost twice as fast as the benchmark script on GPU.
### Q:What's the different between the benchmark script and the apk?
### Running on benchmark script
Log of benchmark script:
```
╰─$ adb install -r -d -g android_aarch64_benchmark_model.apk
╰─$ adb shell /data/local/tmp/android_aarch64_benchmark_model --graph=/data/local/tmp/mobilenetv3_large.tflite \
--num_threads=4 --num_runs=50 --use_gpu=true
INFO: STARTING!
INFO: Log parameter values verbosely: [0]
INFO: Min num runs: [50]
INFO: Num threads: [4]
INFO: Graph: [/data/local/tmp/mobilenetv3_large.tflite]
INFO: #threads used for CPU inference: [4]
INFO: Use gpu: [1]
INFO: Loaded model /data/local/tmp/mobilenetv3_large.tflite
INFO: Initialized TensorFlow Lite runtime.
INFO: Created TensorFlow Lite delegate for GPU.
INFO: GPU delegate created.
VERBOSE: Replacing 126 out of 126 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: Explicitly applied GPU delegate, and the model graph will be completely executed by the delegate.
INFO: The input model file size (MB): 21.9417
INFO: Initialized session in 1342.81ms.
INFO: Running benchmark for at least 1 iterations and at least 0.5 seconds but terminate if exceeding 150 seconds.
INFO: count=72 first=11653 curr=6233 min=5959 max=11653 avg=6835.62 std=903
INFO: Running benchmark for at least 50 iterations and at least 1 seconds but terminate if exceeding 150 seconds.
INFO: count=148 first=6394 curr=6988 min=6096 max=8656 avg=6650.52 std=446
INFO: Inference timings in us: Init: 1342809, First inference: 11653, Warmup (avg): 6835.62, Inference (avg): 6650.52
INFO: Note: as the benchmark tool itself affects memory footprint, the following is only APPROXIMATE to the actual memory footprint of the model at runtime. Take the information at your discretion.
INFO: Memory footprint delta from the start of the tool (MB): init=108.359 overall=108.359
```
### Running on benchmark apk
```
╰─$ adb shell am start -S \
-n org.tensorflow.lite.benchmark/.BenchmarkModelActivity \
--es args '"--graph=/data/local/tmp/mobilenetv3_large.tflite \
--num_threads=4 --num_runs=50 --use_gpu=true"'
```
Log on logcat:
```
03-04 16:23:46.821 6210 6210 I tflite : Initialized OpenCL-based API.
03-04 16:23:46.859 6210 6210 I tflite : Created 1 GPU delegate kernels.
03-04 16:23:46.859 6210 6210 I tflite : Explicitly applied GPU delegate, and the model graph will be completely executed by the delegate.
03-04 16:23:46.859 6210 6210 I tflite : The input model file size (MB): 21.9417
03-04 16:23:46.859 6210 6210 I tflite : Initialized session in 856.303ms.
03-04 16:23:46.860 6210 6210 I tflite : Running benchmark for at least 1 iterations and at least 0.5 seconds but terminate if exceeding 150 seconds.
03-04 16:23:47.164 8268 9284 I DisplayFrameSetting: homeToAppEnd pkg=org.tensorflow.lite.benchmark
03-04 16:23:47.363 6210 6210 I tflite : count=124 first=4601 curr=3901 min=3882 max=4601 avg=4018.02 std=126
03-04 16:23:47.363 6210 6210 I tflite : Running benchmark for at least 50 iterations and at least 1 seconds but terminate if exceeding 150 seconds.
03-04 16:23:48.153 1895 3405 I MiuiNetworkPolicy: bandwidth: 0 KB/s, Max bandwidth: 200 KB/s
03-04 16:23:48.365 6210 6210 I tflite : count=234 first=3909 curr=4552 min=3856 max=5840 avg=4207.86 std=334
03-04 16:23:48.366 6210 6210 I tflite : Inference timings in us: Init: 856303, First inference: 4601, Warmup (avg): 4018.02, Inference (avg): 4207.86
03-04 16:23:48.366 6210 6210 I tflite : Note: as the benchmark tool itself affects memory footprint, the following is only APPROXIMATE to the actual memory footprint of the model at runtime. Take the information at your discretion.
03-04 16:23:48.366 6210 6210 I tflite : Memory footprint delta from the start of the tool (MB): init=103.145 overall=103.145
```
**System information**
- Android Device information: xiaomi-12
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PR_kwDOArmXAs5oi4hR
| 63,110 |
Update Python example of matrix_diag from Tf1.x to TF2.x
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The Python example of API `tf.linalg.diag` uses Tf1.x API call. Updating to TF2.x API.
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WSL2 - TensorFlow Install Issue Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
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[
"Hi **@vatsalraicha** ,\r\n\r\nEnsure Correct Installation of CUDA, cuDNN, and TensorRT:\r\nCUDA and cuDNN: Make sure that CUDA and cuDNN are correctly installed and that TensorFlow can detect them. You have mentioned using CUDA 12.3 and cuDNN v8.6, which should be compatible with TensorFlow 2.15.0. Ensure that the CUDA and cuDNN paths are correctly added to your `PATH` and `LD_LIBRARY_PATH` environment variables.\r\n```\r\nexport PATH=/usr/local/cuda-12.3/bin:$PATH\r\nexport LD_LIBRARY_PATH=/usr/local/cuda-12.3/lib64:$LD_LIBRARY_PATH\r\n```\r\nReinstall TensorFlow:\r\nConsider creating a fresh virtual environment and reinstalling TensorFlow within it. This can help resolve any conflicts or issues with previous installations:\r\n```\r\npython -m venv tf-venv\r\nsource tf-venv/bin/activate\r\npip install --upgrade pip\r\npip install tensorflow==2.15.0\r\n```\r\nTest Your Setup:\r\nAfter ensuring all configurations and installations are correct, test your TensorFlow setup to see if it can access the GPU:\r\n```\r\nimport tensorflow as tf\r\n\r\nprint(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))\r\n```\r\nThank you!\r\n",
"Same error. \r\n\r\nWSL version: 2.0.14.0\r\nKernel version: 5.15.133.1-1\r\nWSLg version: 1.0.59\r\nMSRDC version: 1.2.4677\r\nDirect3D version: 1.611.1-81528511\r\nDXCore version: 10.0.25131.1002-220531-1700.rs-onecore-base2-hyp\r\nWindows version: 10.0.22631.3235\r\n\r\nNVIDIA GeForce RTX 4050\r\nDriver Version: 551.61 CUDA Version: 12.4\r\nCUDA sample functions run without errors\r\n\r\npython 3.10.12\r\ncuda_12.3.2_545.23.08\r\ntensorrt 8.6.1\r\n\r\nPython 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import tensorrt\r\n>>> print(tensorrt.__version__)\r\n8.6.1\r\n>>> import tensorflow as tf\r\n2024-03-05 16:13:59.938649: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n2024-03-05 16:13:59.960210: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2024-03-05 16:13:59.960255: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2024-03-05 16:13:59.960774: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n2024-03-05 16:13:59.964109: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2024-03-05 16:14:00.340405: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n>>> print(tf.config.list_physical_devices('GPU'))\r\n2024-03-05 16:14:19.162569: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2024-03-05 16:14:19.166217: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n2024-03-05 16:14:19.166261: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\r\nYour kernel may have been built without NUMA support.\r\n[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\n>>> print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))\r\nNum GPUs Available: 1\r\n>>>\r\n\r\n",
"@Venkat6871 On WSL2, I dont have CUDA 12.3 director in /usr/local.\r\nSo this path is invalid for me - /usr/local/cuda-12.3/\r\nNow as per NVIDIA documentation, I don't need to install any drivers on WSL2 and it should automatically pick from native Windows.\r\nWhat could be the next step for me here?",
"Same problem",
"Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered google colab error ,how to resolve it\r\n",
"same \r\n2024-04-26 16:37:38.371348: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2024-04-26 16:37:38.371404: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2024-04-26 16:37:38.371421: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n",
"I have the same problem. I am using WSL2.\r\n2024-04-25 16:44:46.357075: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2024-04-25 16:44:46.357110: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2024-04-25 16:44:46.357608: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n",
"After several days struggle, I am able to get rid of this errors. \r\nIt's a version compatibility issue. You need to install the package versions according to the [webpage](https://www.tensorflow.org/install/source#tested_build_configurations). \r\nI was struggled on installing specific CUDA toolkit versions through deb packages, because the version was always updated to the latest version 12.4. In the end, I used the runfiles to install the toolkit.\r\nThe other problem I had was that I started with tensorflow 2.15.1, which I could not get it to work. \r\nThe versions that work for me are:\r\nCUDA toolkit: 12.2. \r\ncudnn: 8.9.7.29\r\nTensorflow: 2.16.1\r\n",
"@garryyan2 Did you achieve this on WSL2 or on Native Linux?\r\nWhat was your CUDA version?\r\nWould you be able to share the Version info that you have setup that works?\r\nTensorRT Version: \r\nNvidia Driver Version: \r\nNVIDIA-SMI Driver Version: 546.17\r\nCUDA Version: \r\nCUDNN Version: \r\nPython Version (if applicable):\r\nWSL version: ",
"@vatsalraicha I am using WSL2. \r\n\r\n$nvcc --version \r\nhas output:\r\nnvcc: NVIDIA (R) Cuda compiler driver\r\nCopyright (c) 2005-2023 NVIDIA Corporation\r\nBuilt on Tue_Jun_13_19:16:58_PDT_2023\r\nCuda compilation tools, release 12.2, V12.2.91\r\nBuild cuda_12.2.r12.2/compiler.32965470_0\r\n\r\n$nvidia-smi\r\nhas output\r\n+-----------------------------------------------------------------------------------------+\r\n| NVIDIA-SMI 550.76.01 Driver Version: 552.22 CUDA Version: 12.4 |\r\n|-----------------------------------------+------------------------+----------------------+\r\nnotice that the CUDA version is different from using command $nvcc --version.\r\n\r\ncudnn version: 8.9.7.29\r\nPython version: Python 3.10.12\r\nWSL version: 5.15.146.1-microsoft-standard-WSL2\r\n\r\nI am not sure about the TensorRT version.\r\n\r\n\r\n\r\n\r\n\r\n",
"> same 2024-04-26 16:37:38.371348: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2024-04-26 16:37:38.371404: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2024-04-26 16:37:38.371421: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n\r\nhow did you solve it? I have the same problem in Colab",
"@gurzelai, for me, I just tried difference version combinations of the tools/packages. I am not not familiar with Colab. You probably can't have the software versions of your choice. "
] | 2024-03-04T01:26:49 | 2024-05-16T15:38:45 | null |
NONE
| null | null | null |
Facing this error message when trying to use tensorflow on WSL2 Ubuntu - Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered.
TF-TRT Warning: Could not find TensorRT
could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
Environment
TensorRT Version: 8.6.1.6
GPU Type: 4080 Laptop GPU
Nvidia Driver Version: NVIDIA-SMI 546.17 Driver Version: 546.17
CUDA Version: 12.3, CUDA Toolkit Version - 11.8
CUDNN Version: v8.6
Operating System + Version: Windows 11
Python Version (if applicable): Python 3.10.13
TensorFlow Version (if applicable): 2.15.0
PyTorch Version (if applicable): N/A
Baremetal or Container (if container which image + tag): N/A
pyenv Version - 2.3.35-11-g9908daf8
WSL version: 2.0.14.0
Kernel version: 5.15.133.1-1
[tensorFlowErrors.txt](https://github.com/tensorflow/tensorflow/files/14475502/tensorFlowErrors.txt)
Steps to reproduce -
Followed the exact steps as mentioned in https://www.tensorflow.org/install/pip?hl=pt
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Adding a GPU model to the compatibility database in tflite
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[
"Hi @rubber815, there is probably a way ... @impjdi, can you please take a look? Thanks.",
"Hi @impjdi , \r\nI would like to know how to submit GPU model information to the compatibility database in tflite? Thanks."
] | 2024-03-04T00:55:09 | 2024-05-07T01:17:17 | null |
NONE
| null | null | null |
### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf2.15
### 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?
I have a question in the "acceleration/compatibility" part in tflite.
As far as I know, Samsung mobile model "samsung_xclipse_920" (S22) information was recently added to the compatibility database to check and support the GPU delegation.
https://github.com/tensorflow/tensorflow/commit/1f3d148aba6a364fb1a271410de4e2f74e32e136
I would like to add "samsung_xclipse_940", a S24 model that was recently released officially on the market,
Are there any ways to do it? Is there anything already in progress? Or, can I upload some PRs (gpu_compatibility.bin ...) related to this manually?
### Standalone code to reproduce the issue
```shell
flatc -t --raw-binary --strict-json database.fbs -- gpu_compatibility.bin
flatc -b database.fbs gpu_compatibility.json
```
### Relevant log output
_No response_
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Adding a GPU model to the compatibility database in tflite
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Fix checkfail with tf.raw_ops.Conv2DBackpropInput with MKL enabled.
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"Hi @SuryanarayanaY Can you please take a look on the above comments from @mihaimaruseac, @cantonios. Thank you!",
"cc: @TensorFlow-MKL ",
"Done the changes as reviewed and suggested by MKL team.",
"Hi @SuryanarayanaY Can you please check @cantonios's [comments](https://github.com/tensorflow/tensorflow/pull/63106#pullrequestreview-2025566458). Thank you!",
"This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This PR was closed because it has been inactive for 14 days since being marked as stale. Please reopen if you'd like to work on this further."
] | 2024-03-03T17:03:16 | 2024-05-28T11:54:40 | 2024-05-28T01:49:23 |
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The API tf.raw_ops.Conv2DBackpropInput with oneDNN enabled causing checkfail if strides<4. This is due to trying to access channel dimension 'C' with NHWC format without validating the strides dimension.
Fixes #62950 .
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The API `tf.raw_ops.DrawBoundingBoxesV2` trying to access the `dim_size(3)` of image tensor before checking its rank. This is causing checkfail if image is not of rank 4.
Fixes #62981
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"@tilakrayal @pkgoogle @LakshmiKalaKadali Hello guys, any suggestion? Maybe there is a more stable version where the delegate is guaranteed to work? Or what could be the problem? I will be glad of any help!",
"Hi @pkgoogle,\r\nPlease look into the issue\r\nThank you",
"Hi @drus-13, can you tell me the exact commands you used to both build the required files, pushed to the phone, and then run the execution on the phone? From reading the above it does not sound like you run any cmake or make commands on the phone. Feel free to exclude any PII, include any relevant information like which directory you are in when running the commands -- it seems you have a separate project with your code -- I understand that may not always be shareable but usually the more details you provide the less guesswork I will have to do. Thanks for your help.\r\n\r\nOne possible alternative is trying to build libtensorflowlite_flex.so with bazel: https://www.tensorflow.org/lite/guide/ops_select and then linking this file with your executable.",
"Hi @pkgoogle, thanks for your help. So, as I wrote above, I built libtensorflowlite_flex.so using bazel and linked the library to my code.\r\n\r\nThe command I used is like\r\n`bazel build -c opt --config=android_arm64 --config=monolithic tensorflow/lite/delegates/flex:tensorflowlite_flex`\r\n\r\nAlternatively, you can try using my sources https://github.com/drus-13/mnist_tf\r\n\r\nEssentially, every piece of the application (lib and app folders) is built using the build_android.sh script",
"H @pkgoogle will there be an opportunity to take a look at what I have attached above. Or do you need any additional information from me?",
"Hi @drus-13, I'm looking into this... I am running into issues building the .so files ... if you can help me understand how you did it. i.e. what NDK and SDK did you use? How did you answer the ./configure script in the tensorflow source root folder?\r\n\r\nWhat command did you use to build libtensorflowlite.so? Something like this?\r\n```\r\nbazel build -c opt --config=android_arm64 --config=monolithic tensorflow/lite:tensorflowlite\r\n```\r\n\r\nThanks for your help",
"Hi @pkgoogle, so for `libtensorflowlite_flex.so` i use configuration like this:\r\n\r\n```\r\nandrey@Andreys-MacBook-Pro tensorflow-r2.14 % ./configure\r\nYou have bazel 6.1.0 installed.\r\nPlease specify the location of python. [Default is /opt/homebrew/opt/[email protected]/bin/python3.11]: /usr/bin/python3\r\n\r\n\r\nFound possible Python library paths:\r\n /Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/site-packages\r\n /Library/Python/3.9/site-packages\r\nPlease input the desired Python library path to use. Default is [/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/site-packages]\r\n\r\nDo you wish to build TensorFlow with ROCm support? [y/N]: N\r\nNo ROCm support will be enabled for TensorFlow.\r\n\r\nDo you wish to build TensorFlow with CUDA support? [y/N]: N\r\nNo CUDA support will be enabled for TensorFlow.\r\n\r\nPlease specify optimization flags to use during compilation when bazel option \"--config=opt\" is specified [Default is -Wno-sign-compare]: \r\n\r\n\r\nWould you like to interactively configure ./WORKSPACE for Android builds? [y/N]: y\r\nSearching for NDK and SDK installations.\r\n\r\nPlease specify the home path of the Android NDK to use. [Default is /Users/andrey/library/Android/Sdk/ndk-bundle]: /Users/andrey/Library/Android/sdk/ndk/21\r\n\r\n\r\nPlease specify the (min) Android NDK API level to use. [Available levels: ['16', '17', '18', '19', '21', '22', '23', '24', '26', '27', '28', '29', '30']] [Default is 26]: \r\n\r\n\r\nPlease specify the home path of the Android SDK to use. [Default is /Users/andrey/library/Android/Sdk]: /Users/andrey/Library/Android/sdk/ \r\n\r\n\r\nEither /Users/andrey/Library/Android/sdk/ does not exist, or it does not contain the subdirectories \"platforms\" and \"build-tools\".\r\nPlease specify the home path of the Android SDK to use. [Default is /Users/andrey/library/Android/Sdk]: /Users/andrey/Library/Android/sdk/ \r\n\r\n\r\nPlease specify the Android SDK API level to use. [Available levels: ['30', '33', '34']] [Default is 34]: \r\n\r\n\r\nPlease specify an Android build tools version to use. [Available versions: ['30.0.2', '33.0.1', '34.0.0']] [Default is 34.0.0]: \r\n\r\n\r\nDo you wish to build TensorFlow with iOS support? [y/N]: N\r\nNo iOS support will be enabled for TensorFlow.\r\n\r\nPreconfigured Bazel build configs. You can use any of the below by adding \"--config=<>\" to your build command. See .bazelrc for more details.\r\n\t--config=mkl \t# Build with MKL support.\r\n\t--config=mkl_aarch64 \t# Build with oneDNN and Compute Library for the Arm Architecture (ACL).\r\n\t--config=monolithic \t# Config for mostly static monolithic build.\r\n\t--config=numa \t# Build with NUMA support.\r\n\t--config=dynamic_kernels\t# (Experimental) Build kernels into separate shared objects.\r\n\t--config=v1 \t# Build with TensorFlow 1 API instead of TF 2 API.\r\nPreconfigured Bazel build configs to DISABLE default on features:\r\n\t--config=nogcp \t# Disable GCP support.\r\n\t--config=nonccl \t# Disable NVIDIA NCCL support.\r\nConfiguration finished\r\nandrey@Andreys-MacBook-Pro tensorflow-r2.14 % bazel build -c opt --config=android_arm64 --config=monolithic tensorflow/lite/delegates/flex:tensorflowlite_flex\r\n\r\n```\r\n\r\nfor `libtensorflowlite.so` same thing:\r\n\r\n```\r\nandrey@Andreys-MacBook-Pro tensorflow-r2.14 % ./configure\r\nYou have bazel 6.1.0 installed.\r\nPlease specify the location of python. [Default is /opt/homebrew/opt/[email protected]/bin/python3.11]: /usr/bin/python3\r\n\r\n\r\nFound possible Python library paths:\r\n /Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/site-packages\r\n /Library/Python/3.9/site-packages\r\nPlease input the desired Python library path to use. Default is [/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/site-packages]\r\n\r\nDo you wish to build TensorFlow with ROCm support? [y/N]: N\r\nNo ROCm support will be enabled for TensorFlow.\r\n\r\nDo you wish to build TensorFlow with CUDA support? [y/N]: N\r\nNo CUDA support will be enabled for TensorFlow.\r\n\r\nPlease specify optimization flags to use during compilation when bazel option \"--config=opt\" is specified [Default is -Wno-sign-compare]: \r\n\r\n\r\nWould you like to interactively configure ./WORKSPACE for Android builds? [y/N]: y\r\nSearching for NDK and SDK installations.\r\n\r\nPlease specify the home path of the Android NDK to use. [Default is /Users/andrey/library/Android/Sdk/ndk-bundle]: /Users/andrey/Library/Android/sdk/ndk/21\r\n\r\n\r\nPlease specify the (min) Android NDK API level to use. [Available levels: ['16', '17', '18', '19', '21', '22', '23', '24', '26', '27', '28', '29', '30']] [Default is 26]: \r\n\r\n\r\nPlease specify the home path of the Android SDK to use. [Default is /Users/andrey/library/Android/Sdk]: /Users/andrey/Library/Android/sdk/ \r\n\r\n\r\nPlease specify the Android SDK API level to use. [Available levels: ['30', '33', '34']] [Default is 34]: \r\n\r\n\r\nPlease specify an Android build tools version to use. [Available versions: ['30.0.2', '33.0.1', '34.0.0']] [Default is 34.0.0]: \r\n\r\n\r\nDo you wish to build TensorFlow with iOS support? [y/N]: N\r\nNo iOS support will be enabled for TensorFlow.\r\n\r\nPreconfigured Bazel build configs. You can use any of the below by adding \"--config=<>\" to your build command. See .bazelrc for more details.\r\n\t--config=mkl \t# Build with MKL support.\r\n\t--config=mkl_aarch64 \t# Build with oneDNN and Compute Library for the Arm Architecture (ACL).\r\n\t--config=monolithic \t# Config for mostly static monolithic build.\r\n\t--config=numa \t# Build with NUMA support.\r\n\t--config=dynamic_kernels\t# (Experimental) Build kernels into separate shared objects.\r\n\t--config=v1 \t# Build with TensorFlow 1 API instead of TF 2 API.\r\nPreconfigured Bazel build configs to DISABLE default on features:\r\n\t--config=nogcp \t# Disable GCP support.\r\n\t--config=nonccl \t# Disable NVIDIA NCCL support.\r\nConfiguration finished\r\nandrey@Andreys-MacBook-Pro tensorflow-r2.14 % bazel build -c opt --config=android_arm64 //tensorflow/lite:libtensorflowlite.so\r\n```\r\n\r\nI hope this helps you, Thanks for your help\r\n\r\n",
"Hi @drus-13, I'm further along .. I modified app/CMakeLists.txt to use relative paths:\r\n\r\n```\r\ncmake_minimum_required(VERSION 3.13)\r\nproject(UseModelTrainerApp)\r\n\r\n# Add the executable\r\nadd_executable(UseModelTrainerApp main.cpp)\r\n\r\n# find_package(boinc CONFIG REQUIRED)\r\n\r\n# target_link_libraries(UseModelTrainerApp\r\n# unofficial::boinc::boinc\r\n# unofficial::boinc::boincapi\r\n# )\r\n\r\n# Create an imported target for libtensorflowlite.so\r\nadd_library(libtensorflowlite SHARED IMPORTED)\r\nset_target_properties(libtensorflowlite PROPERTIES\r\n IMPORTED_LOCATION \"../lib/arm64-v8a/libtensorflowlite.so\"\r\n INTERFACE_INCLUDE_DIRECTORIES \"../lib/include\"\r\n)\r\n\r\n# Create an imported target for libtensorflowlite_flex.so\r\nadd_library(libtensorflowlite_flex SHARED IMPORTED)\r\nset_target_properties(libtensorflowlite_flex PROPERTIES\r\n IMPORTED_LOCATION \"../lib/arm64-v8a/libtensorflowlite_flex.so\"\r\n)\r\n\r\n# Do the same for libModelTrainer.so, and specify its dependency on libtensorflowlite\r\nadd_library(libModelTrainer SHARED IMPORTED)\r\nset_target_properties(libModelTrainer PROPERTIES\r\n IMPORTED_LOCATION \"../lib/build_android_arm64/libModelTrainer.so\"\r\n INTERFACE_INCLUDE_DIRECTORIES \"../lib/include\"\r\n)\r\ntarget_link_libraries(libModelTrainer \r\n INTERFACE\r\n libtensorflowlite \r\n -Wl,--no-as-needed # Need --no-as-needed to link tensorflowlite_flex \r\n libtensorflowlite_flex\r\n)\r\n\r\n# Link UseModelTrainerApp with libModelTrainer only, libtensorflowlite will be included automatically\r\ntarget_link_libraries(UseModelTrainerApp\r\n -Wl,--no-as-needed\r\n libModelTrainer\r\n)\r\n\r\n# If the library uses TensorFlow Lite or other Android NDK libraries, link them as well\r\nfind_library(log-lib log)\r\ntarget_link_libraries(UseModelTrainerApp ${log-lib})\r\n```\r\n\r\nWhen I run build_android.sh:\r\n```sh\r\n./build_android.sh\r\n+++ dirname ./build_android.sh\r\n++ cd .\r\n++ pwd -P\r\n+ PRJ_ROOT=/Users/xxxxx/git/mnist_tf/app\r\n+ BUILD_ROOT=/Users/xxxxxx/git/mnist_tf/app/build_android_arm64\r\n+ '[' -z /Users/xxxxxxx/Library/Android/sdk/ndk/25.2.9519653 ']'\r\n+ rm -rf /Users/xxxxxxx/git/mnist_tf/app/build_android_arm64\r\n+ mkdir -p /Users/xxxxxx/git/mnist_tf/app/build_android_arm64\r\n+ cd /Users/xxxxxxx/git/mnist_tf/app/build_android_arm64\r\n+ cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=install -DCMAKE_TOOLCHAIN_FILE=/Users/xxxxxx/Library/Android/sdk/ndk/25.2.9519653/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_NATIVE_API_LEVEL=26\r\n-- The C compiler identification is Clang 14.0.7\r\n-- The CXX compiler identification is Clang 14.0.7\r\n-- Detecting C compiler ABI info\r\n-- Detecting C compiler ABI info - done\r\n-- Check for working C compiler: /Users/xxxxxxx/Library/Android/sdk/ndk/25.2.9519653/toolchains/llvm/prebuilt/darwin-x86_64/bin/clang - skipped\r\n-- Detecting C compile features\r\n-- Detecting C compile features - done\r\n-- Detecting CXX compiler ABI info\r\n-- Detecting CXX compiler ABI info - done\r\n-- Check for working CXX compiler: /Users/xxxxxxx/Library/Android/sdk/ndk/25.2.9519653/toolchains/llvm/prebuilt/darwin-x86_64/bin/clang++ - skipped\r\n-- Detecting CXX compile features\r\n-- Detecting CXX compile features - done\r\n-- Configuring done (0.7s)\r\nCMake Error in CMakeLists.txt:\r\n Imported target \"libModelTrainer\" includes non-existent path\r\n\r\n \"../lib/include\"\r\n\r\n in its INTERFACE_INCLUDE_DIRECTORIES. Possible reasons include:\r\n\r\n * The path was deleted, renamed, or moved to another location.\r\n\r\n * An install or uninstall procedure did not complete successfully.\r\n\r\n * The installation package was faulty and references files it does not\r\n provide.\r\n\r\n\r\n\r\n-- Generating done (0.0s)\r\nCMake Generate step failed. Build files cannot be regenerated correctly.\r\n```\r\n\r\nI don't have lib/build_android_arm64/libModelTrainer.so\r\n\r\nHow did you build this?\r\n\r\nThanks.",
"Hi @pkgoogle, could you please run similar script build_android.sh from `mnist_tf/lib` folder? this should work\r\n\r\nThanks.",
"Hi @drus-13, I tried that and ran into various include issues, is your environment set up in such a way to pull in additional include files from somewhere else (not in the repo you shared)? That's what it looks like to me.\r\n\r\nI tried to also copy all headers from the tensorflow source (r2.14 branch) as well as absl (HEAD) library into the include folder, my last attempt looked like this:\r\n```sh\r\n./build_android.sh \r\n+++ dirname ./build_android.sh\r\n++ cd .\r\n++ pwd -P\r\n+ PRJ_ROOT=/Users/xxxxxxxxxx/git/mnist_tf/lib\r\n+ BUILD_ROOT=/Users/xxxxxxxxxx/git/mnist_tf/lib/build_android_arm64\r\n+ '[' -z /Users/xxxxxxxxxx/Library/Android/sdk/ndk/25.2.9519653 ']'\r\n+ TFLITE_ANDROID=/Users/xxxxxxxxxx/git/mnist_tf/lib/arm64-v8a/\r\n+ '[' '!' -d /Users/xxxxxxxxxx/git/mnist_tf/lib/arm64-v8a/ ']'\r\n+ echo 'Using TFLite Android library at: /Users/xxxxxxxxxx/git/mnist_tf/lib/arm64-v8a/'\r\nUsing TFLite Android library at: /Users/xxxxxxxxxx/git/mnist_tf/lib/arm64-v8a/\r\n+ rm -rf /Users/xxxxxxxxxx/git/mnist_tf/lib/build_android_arm64\r\n+ mkdir -p /Users/xxxxxxxxxx/git/mnist_tf/lib/build_android_arm64\r\n+ cd /Users/xxxxxxxxxx/git/mnist_tf/lib/build_android_arm64\r\n+ cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=install -DCMAKE_TOOLCHAIN_FILE=/Users/xxxxxxxxxx/Library/Android/sdk/ndk/25.2.9519653/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_NATIVE_API_LEVEL=26\r\n-- The C compiler identification is Clang 14.0.7\r\n-- The CXX compiler identification is Clang 14.0.7\r\n-- Detecting C compiler ABI info\r\n-- Detecting C compiler ABI info - done\r\n-- Check for working C compiler: /Users/xxxxxxxxxx/Library/Android/sdk/ndk/25.2.9519653/toolchains/llvm/prebuilt/darwin-x86_64/bin/clang - skipped\r\n-- Detecting C compile features\r\n-- Detecting C compile features - done\r\n-- Detecting CXX compiler ABI info\r\n-- Detecting CXX compiler ABI info - done\r\n-- Check for working CXX compiler: /Users/xxxxxxxxxx/Library/Android/sdk/ndk/25.2.9519653/toolchains/llvm/prebuilt/darwin-x86_64/bin/clang++ - skipped\r\n-- Detecting CXX compile features\r\n-- Detecting CXX compile features - done\r\n-- Configuring done (3.6s)\r\n-- Generating done (0.0s)\r\n-- Build files have been written to: /Users/xxxxxxxxxx/git/mnist_tf/lib/build_android_arm64\r\n+ make\r\n[ 50%] Building CXX object CMakeFiles/ModelTrainer.dir/ModelTrainer.cpp.o\r\nIn file included from /Users/xxxxxxxxxx/git/mnist_tf/lib/ModelTrainer.cpp:3:\r\nIn file included from /Users/xxxxxxxxxx/git/mnist_tf/lib/ModelTrainer.h:8:\r\nIn file included from /Users/xxxxxxxxxx/git/mnist_tf/lib/include/tensorflow/lite/interpreter.h:21:\r\nIn file included from /Users/xxxxxxxxxx/git/mnist_tf/lib/include/tensorflow/lite/core/interpreter.h:45:\r\nIn file included from /Users/xxxxxxxxxx/git/mnist_tf/lib/include/tensorflow/lite/core/async/async_signature_runner.h:24:\r\nIn file included from /Users/xxxxxxxxxx/git/mnist_tf/lib/include/tensorflow/lite/core/async/async_subgraph.h:27:\r\nIn file included from /Users/xxxxxxxxxx/git/mnist_tf/lib/include/tensorflow/lite/core/subgraph.h:36:\r\n/Users/xxxxxxxxxx/git/mnist_tf/lib/include/tensorflow/lite/core/macros.h:70:1: warning: inline variables are a C++17 extension [-Wc++17-extensions]\r\ninline constexpr unsigned int flatbuffer_size_max = 2147483648;\r\n^\r\n/Users/xxxxxxxxxx/git/mnist_tf/lib/include/tensorflow/lite/core/macros.h:72:1: warning: inline variables are a C++17 extension [-Wc++17-extensions]\r\ninline constexpr char tflite_metadata_buffer_location[] = \"buffer_location\";\r\n^\r\n/Users/xxxxxxxxxx/git/mnist_tf/lib/include/tensorflow/lite/core/macros.h:74:1: warning: inline variables are a C++17 extension [-Wc++17-extensions]\r\ninline constexpr char tflite_metadata_min_runtime_version[] =\r\n^\r\nIn file included from /Users/xxxxxxxxxx/git/mnist_tf/lib/ModelTrainer.cpp:3:\r\nIn file included from /Users/xxxxxxxxxx/git/mnist_tf/lib/ModelTrainer.h:9:\r\nIn file included from /Users/xxxxxxxxxx/git/mnist_tf/lib/include/tensorflow/lite/delegates/flex/delegate.h:19:\r\nIn file included from /Users/xxxxxxxxxx/git/mnist_tf/lib/include/tensorflow/lite/delegates/flex/delegate_data.h:21:\r\nIn file included from /Users/xxxxxxxxxx/git/mnist_tf/lib/include/tensorflow/core/common_runtime/eager/context.h:35:\r\nIn file included from /Users/xxxxxxxxxx/git/mnist_tf/lib/include/tensorflow/c/eager/immediate_execution_context.h:25:\r\nIn file included from /Users/xxxxxxxxxx/git/mnist_tf/lib/include/tensorflow/c/eager/abstract_context.h:20:\r\n/Users/xxxxxxxxxx/git/mnist_tf/lib/include/tensorflow/c/eager/abstract_function.h:18:10: fatal error: 'tensorflow/core/framework/function.pb.h' file not found\r\n#include \"tensorflow/core/framework/function.pb.h\"\r\n ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\n3 warnings and 1 error generated.\r\nmake[2]: *** [CMakeFiles/ModelTrainer.dir/build.make:76: CMakeFiles/ModelTrainer.dir/ModelTrainer.cpp.o] Error 1\r\nmake[1]: *** [CMakeFiles/Makefile2:83: CMakeFiles/ModelTrainer.dir/all] Error 2\r\nmake: *** [Makefile:91: all] Error 2\r\n```\r\n\r\nWe have a couple of options... \r\n1. Try to mirror your environment more exactly.\r\n2. Modifying mnist_tf/lib/CMakeLists.txt to look more like this: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/examples/minimal/CMakeLists.txt\r\n3. Adjust your project to use Bazel\r\n\r\n\"tensorflow/core/framework/function.pb.h\" is a generated file, so this is likely a problem with project setup.",
"Hi @pkgoogle, sorry, there was an unused include in ModelTrainer.h. I updated repo with `libModelTrainer.so` and script build_android.sh from mnist_tf/lib also should work locally. \r\nI hope this will help you, Thanks!\r\n",
"Hi @drus-13, let's take a step back, why are you trying to run a C++ executable on your device? We can certainly make it happen .. but if you're trying to build an app the preferred way is with the Android NDK and JNI if you want to code in C++: https://developer.android.com/ndk/samples/sample_hellojni\r\n\r\nReason I ask is, if we deviate from well defined paths, these type of issues tend to pop up a lot.",
"Hi, @pkgoogle good question, I plan to integrate this solution with another third-party library, and everything is very much tied to cmake and pure C++. As a result, I plan to get a computing application that will be launched(from background) on Android from under another application. In fact, I already tested this solution with tensorflow-lite, everything was successfully assembled and worked in the end, the problem is that I need the flex delegate functionality (saving and loading weights, for example), so there is a need for tensorflow-lite-flex",
"Hi @drus-13, thank you for the additional context, it is certainly always helpful, can you provide the exact adb commands you used to push to the device? Did you just put it all in one directory or is there any structure at the destination/device? Also the command used to actually execute the program.",
"Hi @pkgoogle well, I push all files model.tflite, UseModelTrainerApp, libModelTrainer.so, libtensorflowlite_flex.so, libtensorflowlite.so, test_set.txt, train_set.txt (from pushToDevice folder for example)\r\n-adb push \"file name\" /data/local/tmp\r\n-adb shell\r\n-cd /data/local/tmp\r\n-export LD_LIBRARY_PATH=/data/local/tmp:${LD_LIBRARY_PATH}\r\n-./UseModelTrainerApp",
"Hi @drus-13, I was able to replicate your issue (Though I have not verified if everything we are doing is correct)... It seems linking to a .so file within Android may not be so trivial/well supported, I suspect us using it like normal Linux is causing these things to not link properly, can you try a simple integration of this code/app within your larger Android project and then add \"org.tensorflow:tensorflow-lite-select-tf-ops\" to your dependencies? Can you see if you can make a simple Native C++ project in Android studio and see if your code more or less works that way? Thanks for your help.",
"@pkgoogle Hi! Ok I'll try to make a simple project in android studio and let you know. Maybe there is already source code in examples somewhere?",
"Hi @drus-13, sure thing... here's example code for hello-jni: https://github.com/android/ndk-samples/tree/android-mk/hello-jni, this documentation is probably useful to guide you through it: https://developer.android.com/ndk/samples/sample_hellojni. You can also try asking gemini: https://gemini.google.com/, example prompt: \"Hi Gemini, can you make me a tutorial for creating a simple JNI/NDK hello world project in Android Studio?\"\r\n\r\nI would say get that working on an emulator or your own device then try to integrate your code (it should be mostly copy paste and some mangling of the interfaces from there ... but let me know if you get stuck).",
"Hi @pkgoogle Okay Thanks, I'll try to create a project, but I meant, maybe there is an example project using tensorflow-lite-flex as you suggested?",
"Hi @drus-13, I can't find an existing one but if you follow these directions: https://www.tensorflow.org/lite/guide/ops_select#android_aar, it should integrate flex ops to your project, please try it out and let me know if it doesn't work.",
"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.",
"Can i extend the life of the thread so that the bot doesn’t close it?)\r\nI'll try the suggested version soon and post the results",
"@drus-13 yeah you can just do what you just did, just comment with \"bump\" or something, we still have to be fair to all issues so we will keep the usual policy, but also it does help remind users :).",
"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.",
"bump",
"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/63104\">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/63104\">No</a>\n"
] | 2024-03-02T12:57:37 | 2024-05-16T01:48:57 | 2024-05-16T01:48:54 |
NONE
| null | null | null |
**System information**
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): MacOS
- TensorFlow installed from (source or binary): https://github.com/tensorflow/tensorflow
- TensorFlow version (or github SHA if from source): r2.14
Hi everyone! I have a problem with Tensorflow flex delegate on Android.
I’m trying to run simple model with some functions that require Flex Delegate to be imported.
I built `libModelTrainer.so` and `libtensorflowlite_flex.so` from tensorflow-r2.14 for Android.
I’m using CMake to build my own .so lib with included tf’s lib. So the first CMakeLists.txt looks like this
```
cmake_minimum_required(VERSION 3.10)
project(ModelTrainerLib VERSION 1.0)
# Specify where to find the header files for TF Lite C++
set(INCLUDE_DIRS
${CMAKE_CURRENT_LIST_DIR}/include
${CMAKE_CURRENT_LIST_DIR}/include/flatbuffers/include)
include_directories(${INCLUDE_DIRS})
add_library(tflite SHARED IMPORTED)
add_library(tflite-flex SHARED IMPORTED)
set_target_properties(
tflite PROPERTIES IMPORTED_LOCATION
${CMAKE_CURRENT_LIST_DIR}/arm64-v8a/libtensorflowlite.so
)
set_target_properties(
tflite-flex PROPERTIES IMPORTED_LOCATION
${CMAKE_CURRENT_LIST_DIR}/arm64-v8a/libtensorflowlite_flex.so
)
# Source files
set(SOURCES
ModelTrainer.cpp
)
# Build the main target `ModelTrainer` that will use TF Lite
# Create shared library from sources
add_library(ModelTrainer SHARED ${SOURCES})
# Link the main target with required libs `libtensorflowlite.so`
target_link_libraries(ModelTrainer
tflite
-Wl,--no-as-needed # Need --no-as-needed to link tensorflowlite_flex
tflite-flex
)
```
Than I link my own .so lib with main.cpp like this:
```
cmake_minimum_required(VERSION 3.13)
project(UseModelTrainerApp)
# Add the executable
add_executable(UseModelTrainerApp main.cpp)
# Create an imported target for libtensorflowlite.so
add_library(libtensorflowlite SHARED IMPORTED)
set_target_properties(libtensorflowlite PROPERTIES
IMPORTED_LOCATION "/Users/andrey/Desktop/mnist_tf/lib/arm64-v8a/libtensorflowlite.so"
INTERFACE_INCLUDE_DIRECTORIES "/Users/andrey/Desktop/mnist_tf/lib/include"
)
# Create an imported target for libtensorflowlite_flex.so
add_library(libtensorflowlite_flex SHARED IMPORTED)
set_target_properties(libtensorflowlite_flex PROPERTIES
IMPORTED_LOCATION "/Users/andrey/Desktop/mnist_tf/lib/arm64-v8a/libtensorflowlite_flex.so"
)
# Do the same for libModelTrainer.so, and specify its dependency on libtensorflowlite
add_library(libModelTrainer SHARED IMPORTED)
set_target_properties(libModelTrainer PROPERTIES
IMPORTED_LOCATION "/Users/andrey/Desktop/mnist_tf/lib/build_android_arm64/libModelTrainer.so"
INTERFACE_INCLUDE_DIRECTORIES "/Users/andrey/Desktop/mnist_tf/lib"
)
target_link_libraries(libModelTrainer
INTERFACE
libtensorflowlite
-Wl,--no-as-needed # Need --no-as-needed to link tensorflowlite_flex
libtensorflowlite_flex
)
# Link UseModelTrainerApp with libModelTrainer only, libtensorflowlite will be included automatically
target_link_libraries(UseModelTrainerApp
-Wl,--no-as-needed
libModelTrainer
)
# If the library uses TensorFlow Lite or other Android NDK libraries, link them as well
find_library(log-lib log)
target_link_libraries(UseModelTrainerApp ${log-lib})
```
Than I get executable file successfully. I push these files (my own lib, tf’s libs, executable file and model) to my device with adb. Run program and still get error:
> ERROR: Select TensorFlow op(s), included in the given model, is(are) not supported by this interpreter. Make sure you apply/link the Flex delegate before inference. For the Android, it can be resolved by adding “org.tensorflow:tensorflow-lite-select-tf-ops” dependency. See instructions: Selecione operadores do TensorFlow | TensorFlow Lite
>
> ERROR: Node number 5 (FlexSave) failed to prepare.
I have no idea how to fix it, so I will be very grateful for any help.
|
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I_kwDOArmXAs6A8Nbk
| 63,103 |
Issues in Tensorflow model training
|
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[
"Hi **@cupidp9** ,\r\n\r\n\r\nWhen your system memory peaks, it can lead to the training process being killed to prevent the system from crashing. This is what the \"^C\" in your log indicates—it's the interrupt signal, typically generated by pressing Ctrl+C, but in this context, it might also indicate that the Colab environment killed your process due to excessive resource usage.\r\n\r\nReduce Memory Usage:\r\n\r\nReduce Batch Size: You have already tried reducing the batch size to 2, which is quite small, but if you are still hitting memory limits, consider if there is any other part of your pipeline that can be optimized for memory usage.\r\nSimplify Your Model: If possible, use a simpler or smaller model architecture that requires less memory during training.\r\n\r\nOptimize TensorFlow and GPU Usage:\r\n\r\nSince the logs mention TensorFlow trying to find TensorRT and CUDA devices and failing, it indicates that the training is attempting to run on a GPU but cannot find one. Google Colab offers free access to GPUs and TPUs, but you need to make sure you have requested access to these resources in your notebook \r\n```(Runtime > Change runtime type > Hardware accelerator).```\r\n\r\nThank you!",
"> Hi **@cupidp9** ,\r\n> \r\n> When your system memory peaks, it can lead to the training process being killed to prevent the system from crashing. This is what the \"^C\" in your log indicates—it's the interrupt signal, typically generated by pressing Ctrl+C, but in this context, it might also indicate that the Colab environment killed your process due to excessive resource usage.\r\n> \r\n> Reduce Memory Usage:\r\n> \r\n> Reduce Batch Size: You have already tried reducing the batch size to 2, which is quite small, but if you are still hitting memory limits, consider if there is any other part of your pipeline that can be optimized for memory usage. Simplify Your Model: If possible, use a simpler or smaller model architecture that requires less memory during training.\r\n> \r\n> Optimize TensorFlow and GPU Usage:\r\n> \r\n> Since the logs mention TensorFlow trying to find TensorRT and CUDA devices and failing, it indicates that the training is attempting to run on a GPU but cannot find one. Google Colab offers free access to GPUs and TPUs, but you need to make sure you have requested access to these resources in your notebook `(Runtime > Change runtime type > Hardware accelerator).`\r\n> \r\n> Thank you!\r\n\r\nThanks for your reply! Well, the size of my input images was the problem. They were too large and due to that reason the system memory was going at it's peak and stopping. ",
"Can anyone guide me how to contribute to this open source project i am novice please provide guidance.",
"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/63103\">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/63103\">No</a>\n"
] | 2024-03-01T12:09:24 | 2024-03-06T10:53:28 | 2024-03-06T10:53:25 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.13
### Custom code
No
### OS platform and distribution
Linux Ubuntu 18.04
### 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?
0
I am trying to run a pretrained tensorflow object detection model with my own datasets in google collab. But getting this unknown errors while running the training. I tried changing the batch size=2, and steps=20000, but still the same errors. Its just stopping the training with ^C. I noticed my system memory went its peak just before stopping. is it something due to this?
### Standalone code to reproduce the issue
```shell
!python /content/models/research/object_detection/model_main_tf2.py \
--pipeline_config_path={pipeline_file} \
--model_dir={model_dir} \
--alsologtostderr \
--num_train_steps={num_steps} \
--sample_1_of_n_eval_examples=1
```
### Relevant log output
```shell
2024-03-01 11:15:07.851290: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-03-01 11:15:14.012057: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:268] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0',)
I0301 11:15:14.048205 139949003751424 mirrored_strategy.py:419] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0',)
INFO:tensorflow:Maybe overwriting train_steps: 20000
I0301 11:15:14.210126 139949003751424 config_util.py:552] Maybe overwriting train_steps: 20000
INFO:tensorflow:Maybe overwriting use_bfloat16: False
I0301 11:15:14.210465 139949003751424 config_util.py:552] Maybe overwriting use_bfloat16: False
WARNING:tensorflow:From /content/models/research/object_detection/model_lib_v2.py:563: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.
Instructions for updating:
rename to distribute_datasets_from_function
W0301 11:15:14.290481 139949003751424 deprecation.py:364] From /content/models/research/object_detection/model_lib_v2.py:563: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.
Instructions for updating:
rename to distribute_datasets_from_function
INFO:tensorflow:Reading unweighted datasets: ['/content/train.tfrecord']
I0301 11:15:14.314484 139949003751424 dataset_builder.py:162] Reading unweighted datasets: ['/content/train.tfrecord']
INFO:tensorflow:Reading record datasets for input file: ['/content/train.tfrecord']
I0301 11:15:14.314929 139949003751424 dataset_builder.py:79] Reading record datasets for input file: ['/content/train.tfrecord']
INFO:tensorflow:Number of filenames to read: 1
I0301 11:15:14.315054 139949003751424 dataset_builder.py:80] Number of filenames to read: 1
WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.
W0301 11:15:14.315170 139949003751424 dataset_builder.py:86] num_readers has been reduced to 1 to match input file shards.
WARNING:tensorflow:From /content/models/research/object_detection/builders/dataset_builder.py:100: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.
W0301 11:15:14.353366 139949003751424 deprecation.py:364] From /content/models/research/object_detection/builders/dataset_builder.py:100: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.
WARNING:tensorflow:From /content/models/research/object_detection/builders/dataset_builder.py:235: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.map()
W0301 11:15:14.419352 139949003751424 deprecation.py:364] From /content/models/research/object_detection/builders/dataset_builder.py:235: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.map()
WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow/python/util/dispatch.py:1176: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.
W0301 11:15:24.248110 139949003751424 deprecation.py:364] From /usr/local/lib/python3.10/dist-packages/tensorflow/python/util/dispatch.py:1176: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.
WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow/python/util/dispatch.py:1176: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.
Instructions for updating:
`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.
W0301 11:15:28.897560 139949003751424 deprecation.py:364] From /usr/local/lib/python3.10/dist-packages/tensorflow/python/util/dispatch.py:1176: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.
Instructions for updating:
`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.
WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/tensorflow/python/util/dispatch.py:1176: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
W0301 11:15:32.042234 139949003751424 deprecation.py:364] From /usr/local/lib/python3.10/dist-packages/tensorflow/python/util/dispatch.py:1176: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
2024-03-01 11:15:35.407217: W tensorflow/core/framework/dataset.cc:956] Input of GeneratorDatasetOp::Dataset will not be optimized because the dataset does not implement the AsGraphDefInternal() method needed to apply optimizations.
^C
```
|
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I_kwDOArmXAs6A7doI
| 63,102 |
Support smaller indices in Sparse Matrix
|
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[
"@racinmat,\r\nCould you please elaborate about your Feature. Also, please specify the Use Cases for this feature. Thank you!",
"Hi, how exactly should I elaborate?\r\nThe usecase is simple:\r\nevery usecase where the dimensions are <=int32 will have 2x smaller memory footprint if using int32 instead of int64 for indices.\r\nWe have a use-case where we represent n-gram by sparse matrix, and these sparse matrices take up to 100GB memory, beucase we have lots of them.\r\nImplementing this would lower the memory consumption to 60GB, because we would go from 20 bytes per single value to 12 bytes."
] | 2024-03-01T10:16:40 | 2024-03-06T07:43:36 | null |
NONE
| null | null | null |
### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.13
### Custom code
Yes
### OS platform and distribution
-
### Mobile device
-
### Python version
-
### Bazel version
-
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Sparse tensors have only int64 index https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor
We have some sparse matrices which do not have that big indices, but we have rather lots of numbers, but still it's sparse enough s using dense matrix would be wasteful.
Having support for int32 and int16 for index would lead to lots of memory savings, effectively having 2-4x smaller memory footprint of the sparse matrix.
### Standalone code to reproduce the issue
```shell
>>> import tensorflow as tf
>>> tf.sparse.SparseTensor(indices=[[0, 0], [1, 2]], values=[1, 2], dense_shape=[3, 4])
<tensorflow.python.framework.sparse_tensor.SparseTensor object at 0x000002064DEBFFA0>
>>> tf.sparse.SparseTensor(indices=tf.constant([[0, 0], [1, 2]], dtype=tf.int64), values=[1, 2], dense_shape=[3, 4]).indices
<tf.Tensor: shape=(2, 2), dtype=int64, numpy=
array([[0, 0],
[1, 2]], dtype=int64)>
>>> tf.sparse.SparseTensor(indices=tf.constant([[0, 0], [1, 2]], dtype=tf.int32), values=[1, 2], dense_shape=[3, 4]).indices
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\tools\miniconda3\envs\wanna-hmic-cc\lib\site-packages\tensorflow\python\framework\sparse_tensor.py", line 125, in __init__
indices = ops.convert_to_tensor(
File "C:\tools\miniconda3\envs\wanna-hmic-cc\lib\site-packages\tensorflow\python\profiler\trace.py", line 183, in wrapped
return func(*args, **kwargs)
File "C:\tools\miniconda3\envs\wanna-hmic-cc\lib\site-packages\tensorflow\python\framework\ops.py", line 1662, in convert_to_tensor
raise ValueError(
ValueError: Tensor conversion requested dtype int64 for Tensor with dtype int32: <tf.Tensor: shape=(2, 2), dtype=int32, numpy=
array([[0, 0],
[1, 2]])>
>>> tf.sparse.SparseTensor(indices=tf.constant([[0, 0], [1, 2]], dtype=tf.int16), values=[1, 2], dense_shape=[3, 4]).indices
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\tools\miniconda3\envs\wanna-hmic-cc\lib\site-packages\tensorflow\python\framework\sparse_tensor.py", line 125, in __init__
indices = ops.convert_to_tensor(
File "C:\tools\miniconda3\envs\wanna-hmic-cc\lib\site-packages\tensorflow\python\profiler\trace.py", line 183, in wrapped
return func(*args, **kwargs)
File "C:\tools\miniconda3\envs\wanna-hmic-cc\lib\site-packages\tensorflow\python\framework\ops.py", line 1662, in convert_to_tensor
raise ValueError(
ValueError: Tensor conversion requested dtype int64 for Tensor with dtype int16: <tf.Tensor: shape=(2, 2), dtype=int16, numpy=
array([[0, 0],
[1, 2]], dtype=int16)>
```
### Relevant log output
_No response_
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| 63,101 |
Fix checkfail in Conv2DBackpropFilter
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The API `tf.raw_ops.Conv2DBackpropFilter` not have a check for strides size which may be causing checkfail as reported in #63076.
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[
"HI @kjack45,\r\n\r\nThis issue is exactly duplicate of [63081](https://github.com/tensorflow/tensorflow/issues/63081), please track there and you can close this issue.\r\n\r\nThank You",
"This is just the template, nothing filled."
] | 2024-03-01T07:10:07 | 2024-03-05T14:43:26 | 2024-03-05T14:43:13 |
NONE
|
spam
| null | null |
### 1. System information
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- TensorFlow installation (pip package or built from source):
- TensorFlow library (version, if pip package or github SHA, if built from source):
### 2. Code
Provide code to help us reproduce your issues using one of the following options:
#### Option A: Reference colab notebooks
1) Reference [TensorFlow Model Colab](https://colab.research.google.com/gist/ymodak/e96a4270b953201d5362c61c1e8b78aa/tensorflow-datasets.ipynb?authuser=1): Demonstrate how to build your TF model.
2) Reference [TensorFlow Lite Model Colab](https://colab.research.google.com/gist/ymodak/0dfeb28255e189c5c48d9093f296e9a8/tensorflow-lite-debugger-colab.ipynb): Demonstrate how to convert your TF model to a TF Lite model (with quantization, if used) and run TFLite Inference (if possible).
```
(You can paste links or attach files by dragging & dropping them below)
- Provide links to your updated versions of the above two colab notebooks.
- Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model.
```
#### Option B: Paste your code here or provide a link to a custom end-to-end colab
```
(You can paste links or attach files by dragging & dropping them below)
- Include code to invoke the TFLite Converter Python API and the errors.
- Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model.
```
### 3. Failure after conversion
If the conversion is successful, but the generated model is wrong, then state what is wrong:
- Model produces wrong results and/or has lesser accuracy.
- Model produces correct results, but it is slower than expected.
### 4. (optional) RNN conversion support
If converting TF RNN to TFLite fused RNN ops, please prefix [RNN] in the title.
### 5. (optional) Any other info / logs
Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.
|
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[
"@jaslip,\r\nTensorflow v2.12 is not compatible with compiler gcc 7.5 for the build. As per the official documentation Tensorflow **v2.12** is compatible with **GCC 9.3.1, Bazel 5.3.0, cuDNN - 8.6 and CUDA - 11.8** which are tested build configurations. \r\nhttps://www.tensorflow.org/install/source#gpu\r\n\r\nCould you try **bazel clean --expunge** followed by bazel sync. Also kindly try to install the tensorflow v2.12 with GCC 9.3.1 compiler which is compatible. 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/63099\">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/63099\">No</a>\n"
] | 2024-03-01T06:41:11 | 2024-03-17T01:47:50 | 2024-03-17T01:47:48 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.12
### Custom code
No
### OS platform and distribution
Ubuntu 18.04
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
bazel 5.3.0
### GCC/compiler version
7.50
### CUDA/cuDNN version
11.8/8.8
### GPU model and memory
_No response_
### Current behavior?
I need to build Tensorflow 2.12 with -D_GLIBCXX_USE_CXX11_ABI=0, just building the C++ library.
### Standalone code to reproduce the issue
```shell
Use the build command:
> bazel-5.3.0 build --jobs=2 --local_ram_resources=8000 --local_cpu_resources=2 --verbose_failures --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" //tensorflow/tools/lib_package:libtensorflow
> root@LT457893:~/tensorflow-2.12.0# gcc --version
gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Copyright (C) 2017 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
root@LT457893:~/tensorflow-2.12.0# gcc --verbose
Using built-in specs.
COLLECT_GCC=gcc
COLLECT_LTO_WRAPPER=/usr/lib/gcc/x86_64-linux-gnu/7/lto-wrapper
OFFLOAD_TARGET_NAMES=nvptx-none
OFFLOAD_TARGET_DEFAULT=1
Target: x86_64-linux-gnu
Configured with: ../src/configure -v --with-pkgversion='Ubuntu 7.5.0-3ubuntu1~18.04' --with-bugurl=file:///usr/share/doc/gcc-7/README.Bugs --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++ --prefix=/usr --with-gcc-major-version-only --program-suffix=-7 --program-prefix=x86_64-linux-gnu- --enable-shared --enable-linker-build-id --libexecdir=/usr/lib --without-included-gettext --enable-threads=posix --libdir=/usr/lib --enable-nls --enable-bootstrap --enable-clocale=gnu --enable-libstdcxx-debug --enable-libstdcxx-time=yes --with-default-libstdcxx-abi=new --enable-gnu-unique-object --disable-vtable-verify --enable-libmpx --enable-plugin --enable-default-pie --with-system-zlib --with-target-system-zlib --enable-objc-gc=auto --enable-multiarch --disable-werror --with-arch-32=i686 --with-abi=m64 --with-multilib-list=m32,m64,mx32 --enable-multilib --with-tune=generic --enable-offload-targets=nvptx-none --without-cuda-driver --enable-checking=release --build=x86_64-linux-gnu --host=x86_64-linux-gnu --target=x86_64-linux-gnu
Thread model: posix
gcc version 7.5.0 (Ubuntu 7.5.0-3ubuntu1~18.04)
```
### Relevant log output
```shell
ERROR: /root/tensorflow-2.12.0/tensorflow/core/kernels/BUILD:3542:18: Compiling tensorflow/core/kernels/fft_ops.cc failed: (Exit 1): crosstool_wrapper_driver_is_not_gcc failed: error executing command
(cd /root/.cache/bazel/_bazel_root/98ef3c61d3ed422192afbec266d69b08/execroot/org_tensorflow && \
exec env - \
CUDA_TOOLKIT_PATH=/usr/local/cuda-11.8 \
GCC_HOST_COMPILER_PATH=/usr/bin/x86_64-linux-gnu-gcc-7 \
PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin \
PWD=/proc/self/cwd \
PYTHON_BIN_PATH=/usr/bin/python \
PYTHON_LIB_PATH=/usr/lib/python3/dist-packages \
TF2_BEHAVIOR=1 \
TF_CUDA_COMPUTE_CAPABILITIES=3.5,7.0 \
external/local_config_cuda/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc -MD -MF bazel-out/k8-opt/bin/tensorflow/core/kernels/_objs/fft_ops/fft_ops.pic.d '-frandom-seed=bazel-out/k8-opt/bin/tensorflow/core/kernels/_objs/fft_ops/fft_ops.pic.o' -DEIGEN_MPL2_ONLY '-DEIGEN_MAX_ALIGN_BYTES=64' -DHAVE_SYS_UIO_H -DTF_USE_SNAPPY '-DLLVM_ON_UNIX=1' '-DHAVE_BACKTRACE=1' '-DBACKTRACE_HEADER=<execinfo.h>' '-DLTDL_SHLIB_EXT=".so"' '-DLLVM_PLUGIN_EXT=".so"' '-DLLVM_ENABLE_THREADS=1' '-DHAVE_DEREGISTER_FRAME=1' '-DHAVE_LIBPTHREAD=1' '-DHAVE_PTHREAD_GETNAME_NP=1' '-DHAVE_PTHREAD_H=1' '-DHAVE_PTHREAD_SETNAME_NP=1' '-DHAVE_REGISTER_FRAME=1' '-DHAVE_SETENV_R=1' '-DHAVE_STRERROR_R=1' '-DHAVE_SYSEXITS_H=1' '-DHAVE_UNISTD_H=1' -D_GNU_SOURCE '-DHAVE_LINK_H=1' '-DHAVE_LSEEK64=1' '-DHAVE_MALLINFO=1' '-DHAVE_SBRK=1' '-DHAVE_STRUCT_STAT_ST_MTIM_TV_NSEC=1' '-DLLVM_NATIVE_ARCH="X86"' '-DLLVM_NATIVE_ASMPARSER=LLVMInitializeX86AsmParser' '-DLLVM_NATIVE_ASMPRINTER=LLVMInitializeX86AsmPrinter' 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external/local_config_cuda/cuda -isystem bazel-out/k8-opt/bin/external/local_config_cuda/cuda -isystem external/local_config_cuda/cuda/cuda/include -isystem bazel-out/k8-opt/bin/external/local_config_cuda/cuda/cuda/include -isystem external/local_config_rocm/rocm -isystem bazel-out/k8-opt/bin/external/local_config_rocm/rocm -isystem external/local_config_rocm/rocm/rocm/include -isystem bazel-out/k8-opt/bin/external/local_config_rocm/rocm/rocm/include -isystem external/local_config_rocm/rocm/rocm/include/rocrand -isystem bazel-out/k8-opt/bin/external/local_config_rocm/rocm/rocm/include/rocrand -isystem external/local_config_rocm/rocm/rocm/include/roctracer -isystem bazel-out/k8-opt/bin/external/local_config_rocm/rocm/rocm/include/roctracer -isystem external/llvm-project/llvm/include -isystem bazel-out/k8-opt/bin/external/llvm-project/llvm/include -isystem external/llvm-project/mlir/include -isystem bazel-out/k8-opt/bin/external/llvm-project/mlir/include -isystem external/curl/include -isystem bazel-out/k8-opt/bin/external/curl/include -isystem external/boringssl/src/include -isystem bazel-out/k8-opt/bin/external/boringssl/src/include -isystem external/jsoncpp_git/include -isystem bazel-out/k8-opt/bin/external/jsoncpp_git/include -Wno-builtin-macro-redefined '-D__DATE__="redacted"' '-D__TIMESTAMP__="redacted"' '-D__TIME__="redacted"' -fPIC -U_FORTIFY_SOURCE '-D_FORTIFY_SOURCE=1' -fstack-protector -Wall -fno-omit-frame-pointer -no-canonical-prefixes -fno-canonical-system-headers -DNDEBUG -g0 -O2 -ffunction-sections -fdata-sections -Wno-all -Wno-extra -Wno-deprecated -Wno-deprecated-declarations -Wno-ignored-attributes -Wno-array-bounds -Wunused-result '-Werror=unused-result' -Wswitch '-Werror=switch' '-Wno-error=unused-but-set-variable' -DAUTOLOAD_DYNAMIC_KERNELS '-std=c++17' '-D_GLIBCXX_USE_CXX11_ABI=0' -DEIGEN_AVOID_STL_ARRAY -Iexternal/gemmlowp -Wno-sign-compare '-ftemplate-depth=900' -fno-exceptions '-DGOOGLE_CUDA=1' '-DTENSORFLOW_USE_NVCC=1' '-DTENSORFLOW_USE_XLA=1' -DINTEL_MKL -DAMD_ZENDNN -msse3 -pthread -DNV_CUDNN_DISABLE_EXCEPTION '-DGOOGLE_CUDA=1' -DNV_CUDNN_DISABLE_EXCEPTION '-DTENSORFLOW_USE_XLA=1' '-DINTEL_MKL=1' -c tensorflow/core/kernels/fft_ops.cc -o bazel-out/k8-opt/bin/tensorflow/core/kernels/_objs/fft_ops/fft_ops.pic.o)
# Configuration: 0fe2089a43a86025ffa3034e74530784721ad7532386ba89f77747599e5016b1
# Execution platform: @local_execution_config_platform//:platform
In file included from bazel-out/k8-opt/bin/tensorflow/compiler/xla/autotune_results.pb.h:33:0,
from bazel-out/k8-opt/bin/tensorflow/compiler/xla/stream_executor/device_description.pb.h:33,
from ./tensorflow/compiler/xla/stream_executor/device_description.h:30,
from ./tensorflow/compiler/xla/stream_executor/platform.h:25,
from ./tensorflow/compiler/xla/stream_executor/cuda/cuda_platform_id.h:19,
from ./tensorflow/core/platform/stream_executor.h:19,
from tensorflow/core/kernels/fft_ops.cc:35:
bazel-out/k8-opt/bin/tensorflow/tsl/protobuf/autotuning.pb.h:245:13: warning: In the GNU C Library, "major" is defined
by <sys/sysmacros.h>. For historical compatibility, it is
currently defined by <sys/types.h> as well, but we plan to
remove this soon. To use "major", include <sys/sysmacros.h>
directly. If you did not intend to use a system-defined macro
"major", you should undefine it after including <sys/types.h>.
int32_t major() const;
^~~~~~~~~~~~
In file included from bazel-out/k8-opt/bin/tensorflow/compiler/xla/autotune_results.pb.h:33:0,
from bazel-out/k8-opt/bin/tensorflow/compiler/xla/stream_executor/device_description.pb.h:33,
from ./tensorflow/compiler/xla/stream_executor/device_description.h:30,
from ./tensorflow/compiler/xla/stream_executor/platform.h:25,
from ./tensorflow/compiler/xla/stream_executor/cuda/cuda_platform_id.h:19,
from ./tensorflow/core/platform/stream_executor.h:19,
from tensorflow/core/kernels/fft_ops.cc:35:
bazel-out/k8-opt/bin/tensorflow/tsl/protobuf/autotuning.pb.h:254:13: warning: In the GNU C Library, "minor" is defined
by <sys/sysmacros.h>. For historical compatibility, it is
currently defined by <sys/types.h> as well, but we plan to
remove this soon. To use "minor", include <sys/sysmacros.h>
directly. If you did not intend to use a system-defined macro
"minor", you should undefine it after including <sys/types.h>.
int32_t minor() const;
^~~~~~~~~~~~
bazel-out/k8-opt/bin/tensorflow/tsl/protobuf/autotuning.pb.h:414:13: warning: In the GNU C Library, "major" is defined
by <sys/sysmacros.h>. For historical compatibility, it is
currently defined by <sys/types.h> as well, but we plan to
remove this soon. To use "major", include <sys/sysmacros.h>
directly. If you did not intend to use a system-defined macro
"major", you should undefine it after including <sys/types.h>.
int32_t major() const;
^~~~~~~~~~~~
bazel-out/k8-opt/bin/tensorflow/tsl/protobuf/autotuning.pb.h:423:13: warning: In the GNU C Library, "minor" is defined
by <sys/sysmacros.h>. For historical compatibility, it is
currently defined by <sys/types.h> as well, but we plan to
remove this soon. To use "minor", include <sys/sysmacros.h>
directly. If you did not intend to use a system-defined macro
"minor", you should undefine it after including <sys/types.h>.
int32_t minor() const;
^~~~~~~~~~~~
bazel-out/k8-opt/bin/tensorflow/tsl/protobuf/autotuning.pb.h:1780:13: warning: In the GNU C Library, "major" is defined
by <sys/sysmacros.h>. For historical compatibility, it is
currently defined by <sys/types.h> as well, but we plan to
remove this soon. To use "major", include <sys/sysmacros.h>
directly. If you did not intend to use a system-defined macro
"major", you should undefine it after including <sys/types.h>.
inline int32_t CudnnVersion::major() const {
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
bazel-out/k8-opt/bin/tensorflow/tsl/protobuf/autotuning.pb.h:1800:13: warning: In the GNU C Library, "minor" is defined
by <sys/sysmacros.h>. For historical compatibility, it is
currently defined by <sys/types.h> as well, but we plan to
remove this soon. To use "minor", include <sys/sysmacros.h>
directly. If you did not intend to use a system-defined macro
"minor", you should undefine it after including <sys/types.h>.
inline int32_t CudnnVersion::minor() const {
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
bazel-out/k8-opt/bin/tensorflow/tsl/protobuf/autotuning.pb.h:1844:13: warning: In the GNU C Library, "major" is defined
by <sys/sysmacros.h>. For historical compatibility, it is
currently defined by <sys/types.h> as well, but we plan to
remove this soon. To use "major", include <sys/sysmacros.h>
directly. If you did not intend to use a system-defined macro
"major", you should undefine it after including <sys/types.h>.
inline int32_t ComputeCapability::major() const {
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
bazel-out/k8-opt/bin/tensorflow/tsl/protobuf/autotuning.pb.h:1864:13: warning: In the GNU C Library, "minor" is defined
by <sys/sysmacros.h>. For historical compatibility, it is
currently defined by <sys/types.h> as well, but we plan to
remove this soon. To use "minor", include <sys/sysmacros.h>
directly. If you did not intend to use a system-defined macro
"minor", you should undefine it after including <sys/types.h>.
inline int32_t ComputeCapability::minor() const {
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In file included from ./tensorflow/compiler/xla/stream_executor/device_description.h:30:0,
from ./tensorflow/compiler/xla/stream_executor/platform.h:25,
from ./tensorflow/compiler/xla/stream_executor/cuda/cuda_platform_id.h:19,
from ./tensorflow/core/platform/stream_executor.h:19,
from tensorflow/core/kernels/fft_ops.cc:35:
bazel-out/k8-opt/bin/tensorflow/compiler/xla/stream_executor/device_description.pb.h:202:13: warning: In the GNU C Library, "major" is defined
by <sys/sysmacros.h>. For historical compatibility, it is
currently defined by <sys/types.h> as well, but we plan to
remove this soon. To use "major", include <sys/sysmacros.h>
directly. If you did not intend to use a system-defined macro
"major", you should undefine it after including <sys/types.h>.
int32_t major() const;
^~~~~~~~~~~~
In file included from ./tensorflow/compiler/xla/stream_executor/device_description.h:30:0,
from ./tensorflow/compiler/xla/stream_executor/platform.h:25,
from ./tensorflow/compiler/xla/stream_executor/cuda/cuda_platform_id.h:19,
from ./tensorflow/core/platform/stream_executor.h:19,
from tensorflow/core/kernels/fft_ops.cc:35:
bazel-out/k8-opt/bin/tensorflow/compiler/xla/stream_executor/device_description.pb.h:211:13: warning: In the GNU C Library, "minor" is defined
by <sys/sysmacros.h>. For historical compatibility, it is
currently defined by <sys/types.h> as well, but we plan to
remove this soon. To use "minor", include <sys/sysmacros.h>
directly. If you did not intend to use a system-defined macro
"minor", you should undefine it after including <sys/types.h>.
int32_t minor() const;
^~~~~~~~~~~~
bazel-out/k8-opt/bin/tensorflow/compiler/xla/stream_executor/device_description.pb.h:806:13: warning: In the GNU C Library, "major" is defined
by <sys/sysmacros.h>. For historical compatibility, it is
currently defined by <sys/types.h> as well, but we plan to
remove this soon. To use "major", include <sys/sysmacros.h>
directly. If you did not intend to use a system-defined macro
"major", you should undefine it after including <sys/types.h>.
int32_t major() const;
^~~~~~~~~~~~
bazel-out/k8-opt/bin/tensorflow/compiler/xla/stream_executor/device_description.pb.h:815:13: warning: In the GNU C Library, "minor" is defined
by <sys/sysmacros.h>. For historical compatibility, it is
currently defined by <sys/types.h> as well, but we plan to
remove this soon. To use "minor", include <sys/sysmacros.h>
directly. If you did not intend to use a system-defined macro
"minor", you should undefine it after including <sys/types.h>.
int32_t minor() const;
^~~~~~~~~~~~
bazel-out/k8-opt/bin/tensorflow/compiler/xla/stream_executor/device_description.pb.h:1153:13: warning: In the GNU C Library, "major" is defined
by <sys/sysmacros.h>. For historical compatibility, it is
currently defined by <sys/types.h> as well, but we plan to
remove this soon. To use "major", include <sys/sysmacros.h>
directly. If you did not intend to use a system-defined macro
"major", you should undefine it after including <sys/types.h>.
inline int32_t CudaComputeCapabilityProto::major() const {
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
bazel-out/k8-opt/bin/tensorflow/compiler/xla/stream_executor/device_description.pb.h:1173:13: warning: In the GNU C Library, "minor" is defined
by <sys/sysmacros.h>. For historical compatibility, it is
currently defined by <sys/types.h> as well, but we plan to
remove this soon. To use "minor", include <sys/sysmacros.h>
directly. If you did not intend to use a system-defined macro
"minor", you should undefine it after including <sys/types.h>.
inline int32_t CudaComputeCapabilityProto::minor() const {
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
bazel-out/k8-opt/bin/tensorflow/compiler/xla/stream_executor/device_description.pb.h:1535:13: warning: In the GNU C Library, "major" is defined
by <sys/sysmacros.h>. For historical compatibility, it is
currently defined by <sys/types.h> as well, but we plan to
remove this soon. To use "major", include <sys/sysmacros.h>
directly. If you did not intend to use a system-defined macro
"major", you should undefine it after including <sys/types.h>.
inline int32_t DnnVersionInfoProto::major() const {
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
bazel-out/k8-opt/bin/tensorflow/compiler/xla/stream_executor/device_description.pb.h:1555:13: warning: In the GNU C Library, "minor" is defined
by <sys/sysmacros.h>. For historical compatibility, it is
currently defined by <sys/types.h> as well, but we plan to
remove this soon. To use "minor", include <sys/sysmacros.h>
directly. If you did not intend to use a system-defined macro
"minor", you should undefine it after including <sys/types.h>.
inline int32_t DnnVersionInfoProto::minor() const {
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In file included from ./tensorflow/compiler/xla/stream_executor/platform.h:25:0,
from ./tensorflow/compiler/xla/stream_executor/cuda/cuda_platform_id.h:19,
from ./tensorflow/core/platform/stream_executor.h:19,
from tensorflow/core/kernels/fft_ops.cc:35:
./tensorflow/compiler/xla/stream_executor/device_description.h:58:13: warning: In the GNU C Library, "major" is defined
by <sys/sysmacros.h>. For historical compatibility, it is
currently defined by <sys/types.h> as well, but we plan to
remove this soon. To use "major", include <sys/sysmacros.h>
directly. If you did not intend to use a system-defined macro
"major", you should undefine it after including <sys/types.h>.
this->major = proto.major();
^~~~~~~~~~~~~~~~~~~~
In file included from ./tensorflow/compiler/xla/stream_executor/platform.h:25:0,
from ./tensorflow/compiler/xla/stream_executor/cuda/cuda_platform_id.h:19,
from ./tensorflow/core/platform/stream_executor.h:19,
from tensorflow/core/kernels/fft_ops.cc:35:
./tensorflow/compiler/xla/stream_executor/device_description.h:59:13: warning: In the GNU C Library, "minor" is defined
by <sys/sysmacros.h>. For historical compatibility, it is
currently defined by <sys/types.h> as well, but we plan to
remove this soon. To use "minor", include <sys/sysmacros.h>
directly. If you did not intend to use a system-defined macro
"minor", you should undefine it after including <sys/types.h>.
this->minor = proto.minor();
^~~~~~~~~~~~~~~~~~~~
In file included from ./tensorflow/core/platform/stream_executor.h:21:0,
from tensorflow/core/kernels/fft_ops.cc:35:
./tensorflow/compiler/xla/stream_executor/dnn.h:1101:13: warning: In the GNU C Library, "major" is defined
by <sys/sysmacros.h>. For historical compatibility, it is
currently defined by <sys/types.h> as well, but we plan to
remove this soon. To use "major", include <sys/sysmacros.h>
directly. If you did not intend to use a system-defined macro
"major", you should undefine it after including <sys/types.h>.
: major_(proto.major()), minor_(proto.minor()), patch_(proto.patch()) {}
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In file included from ./tensorflow/core/platform/stream_executor.h:21:0,
from tensorflow/core/kernels/fft_ops.cc:35:
./tensorflow/compiler/xla/stream_executor/dnn.h:1101:13: warning: In the GNU C Library, "minor" is defined
by <sys/sysmacros.h>. For historical compatibility, it is
currently defined by <sys/types.h> as well, but we plan to
remove this soon. To use "minor", include <sys/sysmacros.h>
directly. If you did not intend to use a system-defined macro
"minor", you should undefine it after including <sys/types.h>.
: major_(proto.major()), minor_(proto.minor()), patch_(proto.patch()) {}
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
tensorflow/core/kernels/fft_ops.cc: In member function ‘std::optional<std::unique_ptr<stream_executor::fft::Plan> > tensorflow::{anonymous}::FftPlanCache::Extract(const Key&)’:
**tensorflow/core/kernels/fft_ops.cc:458:12: error: could not convert ‘value’ from ‘tensorflow::{anonymous}::FftPlanCache::Value {aka std::unique_ptr<stream_executor::fft::Plan>}’ to ‘std::optional<std::unique_ptr<stream_executor::fft::Plan> >’
return value;
^~~~~**
Target //tensorflow/tools/lib_package:libtensorflow failed to build
INFO: Elapsed time: 8214.791s, Critical Path: 232.20s
INFO: 3323 processes: 34 internal, 3289 local.
FAILED: Build did NOT complete successfully
root@LT457893:~/tensorflow-2.12.0# gcc --version
```
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I_kwDOArmXAs6A4xIN
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Error: ImportError: cannot import name 'cast' from partially initialized module 'keras.src.backend' (most likely due to a circular import)
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[
"Hi **@NOORLEICESTER** ,\r\nThe import path you're using seems unusual for Keras. Generally, backend functions are accessed through tensorflow.keras.backend or keras.backend if you're using standalone Keras. Ensure you're importing cast correctly, typically:\r\n```\r\nfrom tensorflow.keras.backend import cast\r\n# or, if you're using standalone Keras\r\n# from keras.backend import cast\r\n```\r\nIf you're trying to access a custom function or a function from a different location, verify the path and module initialization.\r\nMake sure that your environment is correctly set up and that you have the right versions of TensorFlow and Keras installed. Issues might arise if there are mismatches or if the installation is corrupted.\r\n\r\nTo check your TensorFlow and Keras version:\r\n```\r\npip show tensorflow keras\r\n```\r\nTo update TensorFlow and Keras:\r\n```\r\npip install --upgrade tensorflow keras\r\n```\r\n\r\nAnd we see that the issue [template]( https://github.com/tensorflow/tensorflow/issues/new/choose) has not been filled, could you please do so as it helps us analyze the issue [tf version, steps followed before you ran into this error or stand alone code/colab gist to reproduce the issue faced. \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.",
"Thank you @Venkat6871 for your comment. Still error!",
"Solved @Venkat6871 thanks",
"in my case tensorflow 2.13 was working fine with keras 2.14.0, but for randomly downgrading for some \r\nrequirement, it showed \"cast\" error, I have tried all method to reinstall keras 2.14 but error was still\r\nthere. then I have deleted\r\n`/anaconda3/envs/tf1/lib/python3.10/site-packages/keras`\r\nfolder (for base env, it looks like \"/usr/local/lib/python3.10/dist-packages/keras\") and reinstalled with\r\n\r\n`pip install keras==2.14.0`\r\n\r\nand restarted the kernel\r\nthen it worked. Basically it was a conflict for randomly upgrading and downgrading\r\nthank you!"
] | 2024-03-01T01:05:00 | 2024-04-05T01:56:37 | 2024-03-25T15:53:57 |
NONE
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ImportError: cannot import name 'cast' from partially initialized module 'keras.src.backend' (most likely due to a circular import)
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Document TF Lite API changes in 2.16 release notes.
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I_kwDOArmXAs6A38-T
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Running gemma model in Mac m2
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[
"@ahmednassar,\r\nLooks like this issue is not related to Gemma Model. Instead of using **pip install tensorflow-macos==2.16.0**, could you please try using **pip install tensorflow** as mentioned in the document. \r\nhttps://github.com/tensorflow/tensorflow/releases\r\n\r\nThank you!",
"Hi @tilakrayal ,\r\n\r\nThe venv has `tensorflow 2.16.0rc0` as shown in the below pip command. I think the problem in `tensorflow-text`, why it ignored `tensorflow` and ask for `tensorflow-macos`. Thanks in advance\r\n\r\n```\r\npip show tensorflow\r\nName: tensorflow\r\nVersion: 2.16.0rc0\r\nSummary: TensorFlow is an open source machine learning framework for everyone.\r\nHome-page: https://www.tensorflow.org/\r\nAuthor: Google Inc.\r\nAuthor-email: [email protected]\r\nLicense: Apache 2.0\r\nLocation: /Users/ahmednassar/Documents/learning/gemma/gemma_env/lib/python3.11/site-packages\r\nRequires: absl-py, astunparse, flatbuffers, gast, google-pasta, grpcio, h5py, keras, libclang, ml-dtypes, numpy, opt-einsum, packaging, protobuf, requests, setuptools, six, tensorboard, tensorflow-io-gcs-filesystem, termcolor, typing-extensions, wrapt\r\n```",
"From another perspective, why tensorflow-macos has no upgrade support keras 3.0.5? @tilakrayal ",
"I have just tested `v2.16.0-rc0` on my Macbook (Apple M1 Pro) and apparently the statement that `tensorflow-macos` is no longer required, is not true. I observe the following error:\r\n\r\n```\r\nFatal Python error: Illegal instruction\r\n```\r\n\r\nwhich normally would get fixed after installing `tensorflow-macos` explicitly. \r\n\r\nI'm wondering if these releases are tested after all. \r\n\r\nPS: Tested with Python 3.11 on macOS Sonoma 14.3.1",
"My Apple is M2, I'm not able to install `tensorflow-text` using pip nor local build as the following\r\n\r\n```\r\npip install -U tensorflow-text==2.16.0rc0\r\nERROR: Could not find a version that satisfies the requirement tensorflow-text==2.16.0rc0 (from versions: none)\r\nERROR: No matching distribution found for tensorflow-text==2.16.0rc0\r\n```\r\n\r\n=========================\r\n```\r\npip install ./tensorflow_text-2.16.0rc0-cp311-cp311-macosx_11_0_arm64.whl\r\nProcessing ./tensorflow_text-2.16.0rc0-cp311-cp311-macosx_11_0_arm64.whl\r\nINFO: pip is looking at multiple versions of tensorflow-text to determine which version is compatible with other requirements. This could take a while.\r\nERROR: Could not find a version that satisfies the requirement tensorflow-macos<2.17,>=2.16.0rc0; platform_machine == \"arm64\" and platform_system == \"Darwin\" (from tensorflow-text) (from versions: 2.12.0, 2.13.0rc0, 2.13.0rc1, 2.13.0rc2, 2.13.0, 2.13.1, 2.14.0rc0, 2.14.0rc1, 2.14.0, 2.14.1, 2.15.0rc0, 2.15.0rc1, 2.15.0)\r\nERROR: No matching distribution found for tensorflow-macos<2.17,>=2.16.0rc0; platform_machine == \"arm64\" and platform_system == \"Darwin\"\r\n```",
"> My Apple is M2, I'm not able to install `tensorflow-text` using pip nor local build as the following\r\n> \r\n> ```\r\n> pip install -U tensorflow-text==2.16.0rc0\r\n> ERROR: Could not find a version that satisfies the requirement tensorflow-text==2.16.0rc0 (from versions: none)\r\n> ERROR: No matching distribution found for tensorflow-text==2.16.0rc0\r\n> ```\r\n> \r\n> =========================\r\n> \r\n> ```\r\n> pip install ./tensorflow_text-2.16.0rc0-cp311-cp311-macosx_11_0_arm64.whl\r\n> Processing ./tensorflow_text-2.16.0rc0-cp311-cp311-macosx_11_0_arm64.whl\r\n> INFO: pip is looking at multiple versions of tensorflow-text to determine which version is compatible with other requirements. This could take a while.\r\n> ERROR: Could not find a version that satisfies the requirement tensorflow-macos<2.17,>=2.16.0rc0; platform_machine == \"arm64\" and platform_system == \"Darwin\" (from tensorflow-text) (from versions: 2.12.0, 2.13.0rc0, 2.13.0rc1, 2.13.0rc2, 2.13.0, 2.13.1, 2.14.0rc0, 2.14.0rc1, 2.14.0, 2.14.1, 2.15.0rc0, 2.15.0rc1, 2.15.0)\r\n> ERROR: No matching distribution found for tensorflow-macos<2.17,>=2.16.0rc0; platform_machine == \"arm64\" and platform_system == \"Darwin\"\r\n> ```\r\n\r\nSame issue here with Mac M2 chip. It says\r\n```\r\nException encountered: GemmaTokenizer requires the `tensorflow-text` package. Please install with `pip install tensorflow-text`\r\n```\r\n\r\nHowever, when I tried to install `tensorflow-text`, it says\r\n\r\n```\r\nERROR: Could not find a version that satisfies the requirement tensorflow-text (from versions: none)\r\nERROR: No matching distribution found for tensorflow-text\r\n```\r\n\r\nAlso, I am curious whether Gemma requires `tensorflow`. I have changed the backend to `torch` using `os.environ[\"KERAS_BACKEND\"] = \"torch\"`. Why does it still require `tensorflow-text`?",
"While we wait for TensorFlow-text to work with TF 2.16, you could try a workaround to use Gemma with TF 2.15.\r\n```\r\n# Install TensorFlow mac-os 2.15\r\n# Install Tensorflow Text 2.15\r\npip install -U keras-nlp\r\npip install -U keras # This needs to be the last one and will update keras to 3.0.5\r\n```\r\nCan you try these steps and see if that resolves the issue of using Gemma model from Keras-NLP?",
"`tensorflow-text` is still having dependency on `tensorflow-macos `, this dependency will be changed to tensorflow and the new release of `tensorflow-text 2.16.2` will be made available with this patch.\r\nTill then, continue using the workaround as suggested in the above comment.",
"That doesn’t work, tensorflow 2.15 downgrade Keras (uninstall and reinstall Keres 2.15). Then I can’t install Keres 3.0.5",
"`tensorflow-text` 2.16.2 will most likely be released today and that should fix this issue.",
"Actually, `tensorflow-text` is not available for Mac M1/M2. You could build your own using this blog - https://forums.developer.apple.com/forums/thread/700906\r\n\r\nBut at this time, we don't have it. Since its a dependency for KerasNLP, we will not be able to use Gemma in mac M1/M2 unless you can build from source for `tensorflow-text`.",
"> `tensorflow-text` 2.16.2 will most likely be released today and that should fix this issue.\r\n\r\nThanks @sampathweb that fixed the issue, one small notice, the fix committed to master not 2.16.2. Thanks again\r\n\r\nPlease advice if we should close the close the issue?",
"@ahmednassar , Please close the issue if you're issue is resolved.",
"The problem have been fixed (here)[https://github.com/tensorflow/tensorflow/issues/63096#issuecomment-1994918684] and I'm able to run the following code in my M2 chip Apple\r\n\r\n```\r\nimport keras\r\nimport keras_nlp\r\nimport numpy as np\r\n\r\ngemma_lm = keras_nlp.models.GemmaCausalLM.from_preset(\"gemma_2b_en\")\r\nout = gemma_lm.generate(\"Keras is a\", max_length=30)\r\n```",
"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63096\">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/63096\">No</a>\n"
] | 2024-02-29T21:43:37 | 2024-03-27T17:49:42 | 2024-03-27T17:49:38 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.15.0
### Custom code
Yes
### OS platform and distribution
Macos 14.3.1
### 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?
In order to run the following gemma code I need keras 3.0.5 and `tensorflow-text` which is not compatible with Keras 3.
`tensorflow-text` needs `tensorflow-macos<2.17,>=2.16` but there is no `tensorflow-macos>=2.16` as the following.
```
import keras
import keras_nlp
import numpy as np
gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset("gemma_2b_en")
out = gemma_lm.generate("Keras is a", max_length=30)
```
```
pip install text/tensorflow_text-2.16.0rc0-cp311-cp311-macosx_11_0_arm64.whl
Processing ./text/tensorflow_text-2.16.0rc0-cp311-cp311-macosx_11_0_arm64.whl
INFO: pip is looking at multiple versions of tensorflow-text to determine which version is compatible with other requirements. This could take a while.
ERROR: Could not find a version that satisfies the requirement tensorflow-macos<2.17,>=2.16.0rc0; platform_machine == "arm64" and platform_system == "Darwin" (from tensorflow-text) (from versions: 2.12.0, 2.13.0rc0, 2.13.0rc1, 2.13.0rc2, 2.13.0, 2.13.1, 2.14.0rc0, 2.14.0rc1, 2.14.0, 2.14.1, 2.15.0rc0, 2.15.0rc1, 2.15.0)
ERROR: No matching distribution found for tensorflow-macos<2.17,>=2.16.0rc0; platform_machine == "arm64" and platform_system == "Darwin"
```
```
pip install tensorflow-macos==2.16
ERROR: Could not find a version that satisfies the requirement tensorflow-macos==2.16 (from versions: 2.12.0, 2.13.0rc0, 2.13.0rc1, 2.13.0rc2, 2.13.0, 2.13.1, 2.14.0rc0, 2.14.0rc1, 2.14.0, 2.14.1, 2.15.0rc0, 2.15.0rc1, 2.15.0)
ERROR: No matching distribution found for tensorflow-macos==2.16
```
### Standalone code to reproduce the issue
```shell
pip install tensorflow-macos==2.16
ERROR: Could not find a version that satisfies the requirement tensorflow-macos==2.16 (from versions: 2.12.0, 2.13.0rc0, 2.13.0rc1, 2.13.0rc2, 2.13.0, 2.13.1, 2.14.0rc0, 2.14.0rc1, 2.14.0, 2.14.1, 2.15.0rc0, 2.15.0rc1, 2.15.0)
ERROR: No matching distribution found for tensorflow-macos==2.16
```
### Relevant log output
_No response_
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I_kwDOArmXAs6A3zy0
| 63,095 |
About VGG 16 install issue
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[
"Hi @KelseyJing ,\r\n\r\nI have tested the import with Keras 2.15v and Keras3 as well and its success.Attached [gist](https://colab.research.google.com/gist/SuryanarayanaY/ca979897e88bbf260b8df5b8613e07e3/63095.ipynb) for reference.Could you please try with fresh installation and let us know the outcome.",
"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/63095\">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/63095\">No</a>\n"
] | 2024-02-29T21:13:46 | 2024-03-16T01:46:19 | 2024-03-16T01:46:16 |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.15.0
### Custom code
Yes
### OS platform and distribution
windows 10
### Mobile device
windows 10
### 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?
It should install VGG 16 successfully!
### Standalone code to reproduce the issue
```shell
from keras.applications import VGG16
model = VGG16(input_shape=(224, 224, 3))
i use this code but when almost finished the installation, then it failed.
c:\Users\KelseyJing\anaconda3\Lib\site-packages\keras\src\applications\vgg16.py:235, in VGG16(include_top, weights, input_tensor, input_shape, pooling, classes, classifier_activation)
233 if weights == "imagenet":
234 if include_top:
--> 235 weights_path = data_utils.get_file(
236 "vgg16_weights_tf_dim_ordering_tf_kernels.h5",
237 WEIGHTS_PATH,
238 cache_subdir="models",
239 file_hash="64373286793e3c8b2b4e3219cbf3544b",
240 )
241 else:
242 weights_path = data_utils.get_file(
243 "vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5",
244 WEIGHTS_PATH_NO_TOP,
245 cache_subdir="models",
246 file_hash="6d6bbae143d832006294945121d1f1fc",
247 )
...
367 )
369 if untar:
370 if not os.path.exists(untar_fpath):
ValueError: Incomplete or corrupted file detected. The auto file hash does not match the provided value of 64373286793e3c8b2b4e3219cbf3544b.
Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
i dont know where is the question, i will be appreciate if you solve my question!
```
### Relevant log output
_No response_
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PR_kwDOArmXAs5oVRKf
| 63,094 |
r2.16 cherry-pick: 20a8fc4339d "Inherit env variables for tpu grpc worker console script"
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[] | 2024-02-29T19:35:22 | 2024-02-29T19:41:23 | 2024-02-29T19:41:22 |
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Refer to the original commit: https://github.com/tensorflow/tensorflow/commit/20a8fc4339db1ef98c537ee368c705bbd9ddaa45
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I_kwDOArmXAs6A2OGn
| 63,093 |
Logic Error in experimental Unity Plugin HelloTFLite.cs Sample Code
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[
"Hi @Tom-James-Marsden,\r\n\r\nI have gone through the file and raised a PR. Please track the PR [#63131](https://github.com/tensorflow/tensorflow/pull/63131).\r\n\r\nThank You\r\n",
"Even though this is such a tiny bug in some experimental code from a project that isn't going to be gone further with I'm dead excited I got to point out and help fix a part of this repository. Whoop whoop",
"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/63093\">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/63093\">No</a>\n"
] | 2024-02-29T16:52:49 | 2024-03-08T14:31:15 | 2024-03-08T14:31:13 |
NONE
| null | null | null |
### Issue type
Documentation Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
tf 2.15
### Custom code
No
### OS platform and distribution
Windows 11
### 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 in HelloTFLite.cs in the experimental Unity Plugin example project there is a logic error at line 59
Here is the function i am looking at
```
void Start () {
Debug.LogFormat("TensorFlow Lite Verion: {0}", Interpreter.GetVersion());
var options = new Interpreter.Options() {
threads = 2,
};
interpreter = new Interpreter(model.bytes, options);
int inputCount = interpreter.GetInputTensorCount();
int outputCount = interpreter.GetOutputTensorCount();
for (int i = 0; i < inputCount; i++) {
Debug.LogFormat("Input {0}: {1}", i, interpreter.GetInputTensorInfo(i));
}
for (int i = 0; i < inputCount; i++) {
Debug.LogFormat("Output {0}: {1}", i, interpreter.GetOutputTensorInfo(i));
}
}
```
In the second for loop the bound `inputCount` should be `outputCount` as it is looping through the output tensors
### Standalone code to reproduce the issue
```shell
Load in a model with input and output tensors of different length and you will find that either not all the tensors get logged or there is an error.
```
### Relevant log output
_No response_
|
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PR_kwDOArmXAs5oT0cw
| 63,092 |
Fix checkfail in tf.raw_ops.UnravelIndex
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Currently the API `tf.raw_ops.UnravelIndex` not checking whether the `indices` argument argument is empty. If user passes empty tensor it might lead to assertion failure.
May fix #63037 .
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Fix checkfail in tf.raw_ops.Substr
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[
"This is being rolled back. We need a different fix",
"Let's try again with `>= 0` instead of `> 0`"
] | 2024-02-29T15:08:23 | 2024-03-20T19:04:32 | 2024-03-20T08:57:05 |
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The API `tf.raw_ops.Substr` currently validates whether the input args `pos` and `len` are of same shape or not.Its not checking whether these tensors are empty or not and trying to access the Tensor values directly without validating.If a user passes empty tensors it will lead to` assertion failure` causing core dumped error.
May fixes #63036
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