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} | [IFRT] Add an IFRT API for Topology.
Also add a (currently unused) Compile() method to ifrt::Compiler that produces an ifrt::Executable given a ifrt::Topology.
At the moment this is a thin wrapper around the the PJRT TopologyDescription API, and does not attempt to change that API or its semantics. The change prepares for allowing the two to diverge.
Add a PJRT-IFRT implementation of ifrt::Topology that is for now a direct wrapper of PjRtTopologyDescription.
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" history = model.fit([X_word_train,np.array(X_char_train).reshape((len(X_char_train),max_len,max_len_char)),np.array(pos_tag_train), max_len], np.array(y_train).reshape(len(y_train),max_len,1), batch_size=5, epochs=60, validation_split=0.2, verbose=1)\r\n File \"C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\", line 66, in _method_wrapper\r\n return method(self, *args, **kwargs)\r\n File \"C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\", line 797, in fit\r\n shuffle=False))\r\n File \"C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\data_adapter.py\", line 1311, in train_validation_split\r\n \"arrays, found: {}\".format(arrays))\r\nValueError: `validation_split` is only supported for Tensors or NumPy arrays, found: ([array([[ 481, 8, 1128, ..., 0, 0, 0],\r\n [ 19, 693, 3, ..., 0, 0, 0],\r\n [2044, 31, 1254, ..., 0, 0, 0],\r\n ...,\r\n [ 54, 1545, 31, ..., 0, 0, 0],\r\n [ 699, 2, 1068, ..., 0, 0, 0],\r\n [ 230, 40, 18, ..., 0, 0, 0]]), array([[[ 92, 83, 51, ..., 0, 0, 0],\r\n [ 29, 83, 0, ..., 0, 0, 0],\r\n [ 29, 38, 16, ..., 0, 0, 0],\r\n ...,\r\n [ 0, 0, 0, ..., 0, 0, 0],\r\n [ 0, 0, 0, ..., 0, 0, 0],\r\n [ 0, 0, 0, ..., 0, 0, 0]],\r\n\r\n [[ 2, 52, 0, ..., 0, 0, 0],\r\n [ 33, 83, 33, ..., 0, 0, 0],\r\n [ 33, 14, 4, ..., 0, 0, 0],\r\n ...,\r\n [ 0, 0, 0, ..., 0, 0, 0],\r\n [ 0, 0, 0, ..., 0, 0, 0],\r\n [ 0, 0, 0, ..., 0, 0, 0]],\r\n\r\n [[ 59, 33, 47, ..., 0, 0, 0],\r\n [ 22, 0, 0, ..., 0, 0, 0],\r\n [ 29, 71, 47, ..., 0, 0, 0],\r\n ...,\r\n [ 0, 0, 0, ..., 0, 0, 0],\r\n [ 0, 0, 0, ..., 0, 0, 0],\r\n [ 0, 0, 0, ..., 0, 0, 0]],\r\n\r\n ...,\r\n\r\n [[105, 51, 47, ..., 0, 0, 0],\r\n [ 84, 7, 7, ..., 0, 0, 0],\r\n [ 22, 0, 0, ..., 0, 0, 0],\r\n ...,\r\n [ 0, 0, 0, ..., 0, 0, 0],\r\n [ 0, 0, 0, ..., 0, 0, 0],\r\n [ 0, 0, 0, ..., 0, 0, 0]],\r\n\r\n [[105, 38, 83, ..., 0, 0, 0],\r\n [ 29, 14, 0, ..., 0, 0, 0],\r\n [ 93, 38, 0, ..., 0, 0, 0],\r\n ...,\r\n [ 0, 0, 0, ..., 0, 0, 0],\r\n [ 0, 0, 0, ..., 0, 0, 0],\r\n [ 0, 0, 0, ..., 0, 0, 0]],\r\n\r\n [[ 40, 38, 83, ..., 0, 0, 0],\r\n [ 88, 29, 0, ..., 0, 0, 0],\r\n [ 29, 39, 0, ..., 0, 0, 0],\r\n ...,\r\n [ 0, 0, 0, ..., 0, 0, 0],\r\n [ 0, 0, 0, ..., 0, 0, 0],\r\n [ 0, 0, 0, ..., 0, 0, 0]]]), array([[28, 21, 15, ..., 9, 9, 9],\r\n [27, 28, 21, ..., 9, 9, 9],\r\n [31, 9, 16, ..., 9, 9, 9],\r\n ...,\r\n [28, 16, 9, ..., 9, 9, 9],\r\n [18, 21, 8, ..., 9, 9, 9],\r\n [18, 21, 17, ..., 9, 9, 9]]), 100], array([[[2],\r\n [3],\r\n [3],\r\n ...,\r\n [0],\r\n [0],\r\n [0]],\r\n\r\n [[2],\r\n [2],\r\n [3],\r\n ...,\r\n [0],\r\n [0],\r\n [0]],\r\n\r\n [[2],\r\n [1],\r\n [1],\r\n ...,\r\n [0],\r\n [0],\r\n [0]],\r\n\r\n ...,\r\n\r\n [[2],\r\n [2],\r\n [1],\r\n ...,\r\n [0],\r\n [0],\r\n [0]],\r\n\r\n [[2],\r\n [3],\r\n [2],\r\n ...,\r\n [0],\r\n [0],\r\n [0]],\r\n\r\n [[2],\r\n [3],\r\n [3],\r\n ...,\r\n [0],\r\n [0],\r\n [0]]]), None)",
"@kusumlata123 Could you please try to upgrade to the latest TF version as you are using an older TF version which is not actively supported. You might be using the validation_split argument in an unsupported format with your machine learning model. Kindly share the standalone code to replicate the issue if you still have any concern in the latest TF version?\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."
] | 2024-05-30T14:53:08 | 2024-06-11T01:51:05 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf2.9
### Custom code
Yes
### OS platform and distribution
window
### Mobile device
_No response_
### Python version
3.7.6
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
cuda_12.4.r12.4/compiler.34097967_0
### GPU model and memory
_No response_
### Current behavior?
File "MDLSTM7_CRF.py", line 381, in <module>
model_build(input_file)
File "MDLSTM7_CRF.py", line 275, in model_build
history = model.fit([np.array(X_word_train),np.array(X_char_train).reshape((len(X_char_train),max_len,max_len_char)),np.array(pos_tag_train), max_len], np.array(y_train).reshape(len(y_train),max_len,1), batch_size=5, epochs=60, validation_split=0.2, verbose=1)
File "C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\keras\engine\data_adapter.py", line 1482, in train_validation_split
"arrays, found following types in the input: {}".format(unsplitable))
ValueError: `validation_split` is only supported for Tensors or NumPy arrays, found following types in the input: [<class 'int'>]
### Standalone code to reproduce the issue
```shell
history = model.fit([np.array(X_word_train),np.array(X_char_train).reshape((len(X_char_train),max_len,max_len_char)),np.array(pos_tag_train), max_len], np.array(y_train).reshape(len(y_train),max_len,1), batch_size=5, epochs=60, validation_split=0.2, verbose=1)
```
### Relevant log output
```shell
File "MDLSTM7_CRF.py", line 381, in <module>
model_build(input_file)
File "MDLSTM7_CRF.py", line 275, in model_build
history = model.fit([np.array(X_word_train),np.array(X_char_train).reshape((len(X_char_train),max_len,max_len_char)),np.array(pos_tag_train), max_len], np.array(y_train).reshape(len(y_train),max_len,1), batch_size=5, epochs=60, validation_split=0.2, verbose=1)
File "C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\keras\engine\data_adapter.py", line 1482, in train_validation_split
"arrays, found following types in the input: {}".format(unsplitable))
ValueError: `validation_split` is only supported for Tensors or NumPy arrays, found following types in the input: [<class 'int'>]
```
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} | Update OpenXLA's Triton dependency to include the AMD backend.
- https://github.com/openxla/triton/pull/7
- https://github.com/openxla/triton/commit/25e4e02dcaf57f01ba49608345a9bbbbc20152bf
- https://github.com/triton-lang/triton/pull/4031
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} | [XLA:GPU] Fix logic for symbol lookup
Currently, we return/log bad Status when symbol is not found, but the logic for
resolving the constants does not expect it to be always found. This leads to
two issues:
1. Log spam from "Failed to found symbol" error messages.
2. Hiding actual errors, if the symbol failed to resolve in some other ways.
Returning StatusOr<optional<>> achieves both goals.
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} | PR #13109: Add flag for termination on nccl error
Imported from GitHub PR https://github.com/openxla/xla/pull/13109
This introduces a flag for termination on NCCL async error. With the flag on, XLA will terminate the process on NCCL error. With the flag off, the existing behavior should remain unchanged.
The patch is motivated by several problems:
- Without this patch, the heartbeat monitor only checks communicators that are currently not use by the running executable (because it obtains the communicators with TryAcquire). Since NCCL errors cause a hang in the running communicator, most failing communicators are locked, so their async errors just go undetected. As a result, XLA often hangs until Grpc timeout even in cases when ncclCommGetAsyncError would report an error.
- Ideally we would recover by aborting the faulty communicators, but that seems to be unreliable (aborts can cause hangs if NCCL currently hangs on a different communicator than the one being aborted). NCCL team is aware of this and working on a fix (https://github.com/NVIDIA/nccl/issues/1013). At the moment, there does not seem to be a reliable fast recovery mechanism short of process termination.
We propose to expose a flag for terminating the process on failure so that there is some way to detect and recover from a NCCL failure. Once the comm-abort works reliably, we will use that and propagate the error to the API user.
The patch is based on a PoC from pshamis@nvidia.com and vincentz@nvidia.com.
Copybara import of the project:
--
858aeacb2d689e4b03f4e3bcc0595223119143d5 by Jaroslav Sevcik <jsevcik@nvidia.com>:
Add flag for termination on nccl error
Merging this change closes #13109
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13109 from jaro-sevcik:terminate-on-nccl-error 858aeacb2d689e4b03f4e3bcc0595223119143d5
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"I too am failing to build TensorFlow for GPU (with or without the container).\r\n\r\nThe problem I see for you is that TensorRT 10 is not supported, the latest supported version is 8.6.1, see https://github.com/tensorflow/tensorflow/issues/66473. You have to install an older version and hopefully get past that error.\r\n\r\nThe problems I am getting are:\r\n* When using GCC it gets all the way to near the end and fails to link saying some symbols cannot be found (they are symbols of template classes). This occurs even with `--noincompatible_do_not_split_linking_cmdline` as recommended by https://github.com/tensorflow/tensorflow/issues/35623.\r\n* When using Clang it failed with `unused-command-line-argument` errors, but that was fixed by adding `--host_copt=-Wno-error=unused-command-line-argument`. After that it gets further but fails with messages about `__float128` not being a supported type on the target (the target being the GPU).\r\n* In the container it cannot find nvml.h - that header does not exist in the container at all.",
"> The problem I see for you is that TensorRT 10 is not supported, the latest supported version is 8.6.1,\n\nIdk why the TensorFlow project is not updated frequently as PyTorch.\n\nPyTorch even added support to the latest Ubuntu 24.04 Release and both Nvidia and TensorFlow are lagging behind."
] | 2024-05-30T07:56:54 | 2024-06-05T06:43:31 | null | NONE | null | null | null | ### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.16
### Custom code
Yes
### OS platform and distribution
Ubuntu LTS 22.04
### Mobile device
_No response_
### Python version
3.10.12
### Bazel version
6.5.0
### GCC/compiler version
Clang 17.0.6
### CUDA/cuDNN version
12.4/8.9.7
### GPU model and memory
RTX 4060 Laptop 8GB
### Current behavior?
I am builing TF r2.16 so that it supports CUDA and TensorRT. It is failing I don't know why. I used to use TF on windows bbut recently switched to linux to get GPU support.
`nvidia-smi`
```
Thu May 30 13:23:41 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.54.14 Driver Version: 550.54.14 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 4060 ... Off | 00000000:01:00.0 On | N/A |
| N/A 43C P8 4W / 80W | 342MiB / 8188MiB | 4% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| 0 N/A N/A 1098 G /usr/lib/xorg/Xorg 133MiB |
| 0 N/A N/A 1536 G budgie-wm 61MiB |
| 0 N/A N/A 2150 G ...irefox/3779/usr/lib/firefox/firefox 0MiB |
+-----------------------------------------------------------------------------------------+
```
All the code which I ran and all the error outputs are shown below.
Thanks
### Standalone code to reproduce the issue
```shell
sudo apt install python3-dev python3-pip
wget https://github.com/llvm/llvm-project/releases/download/llvmorg-17.0.2/clang+llvm-17.0.2-x86_64-linux-gnu-ubuntu-22.04.tar.xz
tar -xvf clang+llvm-17.0.2-x86_64-linux-gnu-ubuntu-22.04.tar.xz
cp -r clang+llvm-17.0.2-x86_64-linux-gnu-ubuntu-22.04/* /usr
git clone https://github.com/tensorflow/tensorflow.git
cd tensorflow
git checkout r2.16
./configure
bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package --jobs=20
```
### Relevant log output
```shell
./configure
"""
You have bazel 6.5.0 installed.
Please specify the location of python. [Default is /usr/bin/python3]:
Found possible Python library paths:
/usr/lib/python3.10/dist-packages
/usr/lib/python3/dist-packages
/usr/local/lib/python3.10/dist-packages
Please input the desired Python library path to use. Default is [/usr/lib/python3.10/dist-packages]
Do you wish to build TensorFlow with ROCm support? [y/N]: n
No ROCm support will be enabled for TensorFlow.
Do you wish to build TensorFlow with CUDA support? [y/N]: y
CUDA support will be enabled for TensorFlow.
Do you wish to build TensorFlow with TensorRT support? [y/N]: y
TensorRT support will be enabled for TensorFlow.
Found CUDA 12.4 in:
/usr/local/cuda-12.4/targets/x86_64-linux/lib
/usr/local/cuda-12.4/targets/x86_64-linux/include
Found cuDNN 8 in:
/usr/lib/x86_64-linux-gnu
/usr/include
Found TensorRT 10.0.1 in:
/usr/lib/x86_64-linux-gnu
/usr/include/x86_64-linux-gnu
Please specify a list of comma-separated CUDA compute capabilities you want to build with.
You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus. Each capability can be specified as "x.y" or "compute_xy" to include both virtual and binary GPU code, or as "sm_xy" to only include the binary code.
Please note that each additional compute capability significantly increases your build time and binary size, and that TensorFlow only supports compute capabilities >= 3.5 [Default is: 3.5,7.0]:
Do you want to use clang as CUDA compiler? [Y/n]: y
Clang will be used as CUDA compiler.
Please specify clang path that to be used as host compiler. [Default is /usr/lib/llvm-17/bin/clang]:
You have Clang 17.0.6 installed.
Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -Wno-sign-compare]:
Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: n
Not configuring the WORKSPACE for Android builds.
Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See .bazelrc for more details.
--config=mkl # Build with MKL support.
--config=mkl_aarch64 # Build with oneDNN and Compute Library for the Arm Architecture (ACL).
--config=monolithic # Config for mostly static monolithic build.
--config=numa # Build with NUMA support.
--config=dynamic_kernels # (Experimental) Build kernels into separate shared objects.
--config=v1 # Build with TensorFlow 1 API instead of TF 2 API.
Preconfigured Bazel build configs to DISABLE default on features:
--config=nogcp # Disable GCP support.
--config=nonccl # Disable NVIDIA NCCL support.
Configuration finished
"""
bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package --jobs=20
"""
WARNING: The following configs were expanded more than once: [tensorrt, cuda_clang, cuda]. For repeatable flags, repeats are counted twice and may lead to unexpected behavior.
INFO: Reading 'startup' options from /home/manveer/Downloads/tensorflow/.bazelrc: --windows_enable_symlinks
INFO: Options provided by the client:
Inherited 'common' options: --isatty=1 --terminal_columns=99
INFO: Reading rc options for 'build' from /home/manveer/Downloads/tensorflow/.bazelrc:
Inherited 'common' options: --experimental_repo_remote_exec
INFO: Reading rc options for 'build' from /home/manveer/Downloads/tensorflow/.bazelrc:
'build' options: --define framework_shared_object=true --define tsl_protobuf_header_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 --noincompatible_remove_legacy_whole_archive --features=-force_no_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility
INFO: Reading rc options for 'build' from /home/manveer/Downloads/tensorflow/.tf_configure.bazelrc:
'build' options: --action_env PYTHON_BIN_PATH=/usr/bin/python3 --action_env PYTHON_LIB_PATH=/usr/lib/python3.10/dist-packages --python_path=/usr/bin/python3 --config=tensorrt --action_env TF_CUDA_PATHS=/usr/local/cuda-12.4,/usr --action_env CUDA_TOOLKIT_PATH=/usr/local/cuda-12.4 --action_env TF_CUDA_COMPUTE_CAPABILITIES=3.5,7.0 --action_env LD_LIBRARY_PATH=/usr/local/cuda-12.4/lib64:/usr/lib/x86_64-linux-gnu:/lib/x86_64-linux-gnu:/usr/lib:/lib --config=cuda_clang --action_env CLANG_CUDA_COMPILER_PATH=/usr/lib/llvm-17/bin/clang --copt=-Wno-gnu-offsetof-extensions --config=cuda_clang
INFO: Found applicable config definition build:short_logs in file /home/manveer/Downloads/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file /home/manveer/Downloads/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:tensorrt in file /home/manveer/Downloads/tensorflow/.bazelrc: --repo_env TF_NEED_TENSORRT=1
INFO: Found applicable config definition build:cuda_clang in file /home/manveer/Downloads/tensorflow/.bazelrc: --config=cuda --config=tensorrt --action_env=TF_CUDA_CLANG=1 --@local_config_cuda//:cuda_compiler=clang --repo_env=TF_CUDA_COMPUTE_CAPABILITIES=sm_50,sm_60,sm_70,sm_80,compute_90
INFO: Found applicable config definition build:cuda in file /home/manveer/Downloads/tensorflow/.bazelrc: --repo_env TF_NEED_CUDA=1 --crosstool_top=@local_config_cuda//crosstool:toolchain --@local_config_cuda//:enable_cuda
INFO: Found applicable config definition build:tensorrt in file /home/manveer/Downloads/tensorflow/.bazelrc: --repo_env TF_NEED_TENSORRT=1
INFO: Found applicable config definition build:cuda_clang in file /home/manveer/Downloads/tensorflow/.bazelrc: --config=cuda --config=tensorrt --action_env=TF_CUDA_CLANG=1 --@local_config_cuda//:cuda_compiler=clang --repo_env=TF_CUDA_COMPUTE_CAPABILITIES=sm_50,sm_60,sm_70,sm_80,compute_90
INFO: Found applicable config definition build:cuda in file /home/manveer/Downloads/tensorflow/.bazelrc: --repo_env TF_NEED_CUDA=1 --crosstool_top=@local_config_cuda//crosstool:toolchain --@local_config_cuda//:enable_cuda
INFO: Found applicable config definition build:tensorrt in file /home/manveer/Downloads/tensorflow/.bazelrc: --repo_env TF_NEED_TENSORRT=1
INFO: Found applicable config definition build:opt in file /home/manveer/Downloads/tensorflow/.tf_configure.bazelrc: --copt=-Wno-sign-compare --host_copt=-Wno-sign-compare
INFO: Found applicable config definition build:linux in file /home/manveer/Downloads/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-bounds --copt=-Wunused-result --copt=-Werror=unused-result --copt=-Wswitch --copt=-Werror=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=dynamic_kernels --experimental_guard_against_concurrent_changes
INFO: Found applicable config definition build:dynamic_kernels in file /home/manveer/Downloads/tensorflow/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS
WARNING: The following configs were expanded more than once: [tensorrt, cuda_clang, cuda]. For repeatable flags, repeats are counted twice and may lead to unexpected behavior.
INFO: Build options --@local_config_cuda//:cuda_compiler, --action_env, --copt, and 2 more have changed, discarding analysis cache.
INFO: Analyzed target //tensorflow/tools/pip_package:build_pip_package (710 packages loaded, 51636 targets configured).
INFO: Found 1 target...
ERROR: /home/manveer/.cache/bazel/_bazel_manveer/795c5c27cbf66ab1777c71686be75148/external/local_config_tensorrt/BUILD:69:8: Executing genrule @local_config_tensorrt//:tensorrt_include failed: (Exit 1): bash failed: error executing command (from target @local_config_tensorrt//:tensorrt_include) /bin/bash -c ... (remaining 1 argument skipped)
cp: cannot stat '/usr/include/x86_64-linux-gnu/NvUtils.h': No such file or directory
Target //tensorflow/tools/pip_package:build_pip_package failed to build
Use --verbose_failures to see the command lines of failed build steps.
ERROR: /home/manveer/Downloads/tensorflow/tensorflow/core/common_runtime/BUILD:79:23 Middleman _middlemen/_S_Stensorflow_Score_Scommon_Uruntime_Ccore_Ucpu_Uheaders_Ulib-cc_library-compile failed: (Exit 1): bash failed: error executing command (from target @local_config_tensorrt//:tensorrt_include) /bin/bash -c ... (remaining 1 argument skipped)
INFO: Elapsed time: 3.792s, Critical Path: 0.24s
INFO: 74 processes: 40 internal, 34 local.
FAILED: Build did NOT complete successfully
"""
```
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As per the code [tensorflow/tensorflow/lite/python/interpreter.py](https://github.com/tensorflow/tensorflow/blob/b297295711589c2d71880a046333e6ec095c6d7d/tensorflow/lite/python/interpreter.py#L460-L464)
Lines 460 to 464 in [b297295](https://github.com/tensorflow/tensorflow/commit/b297295711589c2d71880a046333e6ec095c6d7d) , code num_threads is greater than equal to 1 but in description it was given as `-1`. This PR fixes description of num_threads in `tf.lite.interpreter` doc.
This PR fixes: TFLite issue [#68862](https://github.com/tensorflow/tensorflow/issues/68862)
Thank You | {
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} | Add DynamicApproxTopKOp to StableHLO
This CL adds a DynamicApproxTopKOp to StableHLO. This op is a dynamic version of the ApproxTopKOp. The op is represented via a custom call to `stablehlo.dynamic_approx_top_k`. The custom call has the regular operand of ApproxTopKOp plus an additional `k` operand that determines the shape of the output.
The semantics of DynamicApproxTopKOp are inherited from semantics of ApproxTopKOp.
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"@illian01,\r\nThank you for reporting the documentation issue. We have raised the PR for the changes which were suggested. The pr has been assigned for reviewing and once it is merged this issue will move to closed status.\r\nhttps://github.com/tensorflow/tensorflow/pull/68866\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."
] | 2024-05-30T05:44:57 | 2024-06-08T01:49:14 | null | NONE | null | null | null | ### Issue type
Documentation Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
Current master branch
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I was using `tensorflow.lite.Interpreter`, and reading the comments to understand the parameters.
According to the comments, the `num_threads` value can be set to -1 or any non-negative value (or `None`).
https://github.com/tensorflow/tensorflow/blob/b297295711589c2d71880a046333e6ec095c6d7d/tensorflow/lite/python/interpreter.py#L409-L416
However, the actual code restricts that the `num_threads` value is set to 1 or higher (or `None`).
https://github.com/tensorflow/tensorflow/blob/b297295711589c2d71880a046333e6ec095c6d7d/tensorflow/lite/python/interpreter.py#L460-L464
### Standalone code to reproduce the issue
```shell
N/A
```
### Relevant log output
_No response_ | {
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[765206e05045](https://github.com/llvm/llvm-project/commit/765206e05045)
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} | PR #13065: [NVIDIA] Disable the bias reuse for fp8 dot
Imported from GitHub PR https://github.com/openxla/xla/pull/13065
For some operations, we might want to do the `output to operand aliasing` to reuse the memory space. For example, in the dot operation, we can alias the output to the bias operand, when they have the same shape.
However, this aliasing is not always safe especially when the dot outputs fp8 outputs. In this case the bias is 2x larger than expected output and the HLO verifier will complain.
This PR fixes this issue by disabling the `output to operand aliasing` for fp8 dot.
cc. @philipphack @hx89 @nluehr
Copybara import of the project:
--
711e3097a80d9ff6e48b80924a1e81ff940ac240 by kaixih <kaixih@nvidia.com>:
Disable bias reuse for fp8 dot
--
7d86b99396385f85e6e04a28dc8d824fd53e91f3 by kaixih <kaixih@nvidia.com>:
Update to a general solution
--
15f774667013045c7d8d9e38ac80a45645313b1e by kaixih <kaixih@nvidia.com>:
Address comments
Merging this change closes #13065
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13065 from kaixih:disable_bias_reuse_in_fp8 15f774667013045c7d8d9e38ac80a45645313b1e
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} | Include stream_executor.h in a few files that were missing its inclusion.
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} | PR #13107: [ROCm] Fix reduce_row_vectorized.hlo.test
Imported from GitHub PR https://github.com/openxla/xla/pull/13107
Copybara import of the project:
--
6555b6d216bb18b1f98eb36c5dac3f58f4d09c05 by mmakevic <Milica.Makevic@amd.com>:
Fix reduce_row_vectorized.hlo.test
Merging this change closes #13107
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13107 from ROCm:ci_reduce_row_vectorized 6555b6d216bb18b1f98eb36c5dac3f58f4d09c05
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} | [XLA] Limit DCE MoF cleanup to GPU backend
We've had other backends blowup due to modifying the fusion body post-fusion, as now the fusion metadata is incorrect.
Reverts c82138f5a838ca67b55697d12a137b4662c08525
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} | [IFRT] Add several commonly used APIs to `xla::ifrt::Sharding`
This CL adds commonly used APIs for sharding.
* Equality test (`==`, `!=`): Checks if two shardings have the same logical partitioning (see below) and same device assignment.
* `GetShardShape`: Returns a shard shape if this sharding always returns a single shard shape. A faste version of taking the first shard's shape from `Disassemble()` result.
* `HasSamePartitioning`: Tests if two shardings have the same logical partitioning, which is when two shardings have the same type and their own partitioning scheme is equivalent.
* `WithDeviceAssignment`: Returns a new `Sharding` of the same type, with devices and/or memory_kind replaced.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13065 from kaixih:disable_bias_reuse_in_fp8 15f774667013045c7d8d9e38ac80a45645313b1e
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http://github.com/tensorflow/runtime/commit/4662f7552dc2e420ee20361c077b7aeb334a1087.
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Previously, the converter's Python API was intertwined with TOCO. This CL
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signatures are left untouched.
We copy rather than move to decouple migrating away from TOCO and deletion of
TOCO artifacts. TOCO APIs will be removed/deleted in subsequent CLs.
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HostOffloader will do this as needed.
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Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
_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?
When setting the `nbins` to a large value and the value range to [-np.inf, np.inf], the API `tf.histogram_fixed_width` will raises a segmentation fault.
Interestingly, I found this issue only occurs on CPU backend while this API works fine on GPU.
### Standalone code to reproduce the issue
```shell
import os
os.environ['CUDA_VISIBLE_DEVICES'] = ''
import numpy as np
import tensorflow as tf
input = tf.constant([1.,2,3])
bins = 23
out = tf.histogram_fixed_width(input, [-np.inf, np.inf], nbins=bins) # Segmentation fault (core dumped)
print(out)
```
```
### Relevant log output
_No response_ | {
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+ added a few more tests that now pass with thunks runtime
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The current trip count calculation is based on ```GetWhileNestMultiplier```, which assumes all layers of nested loops have the same trip count. Instead, the new function ```CalculateComputationNestTripCount``` uses the ```ComputeWhileLoopTripCount``` to get the estimated trip count for each individual while loop, which is expected to make more accurate trip count estimation.
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} | PR #13194: [ROCm] Fix build break in xla/service/gpu/ir_emitter_triton_rocm.cc
Imported from GitHub PR https://github.com/openxla/xla/pull/13194
Issue is present after following commit:
https://github.com/openxla/triton/commit/6d54ba2fba40073a21c22f5fac2bdc5fb385a655
Copybara import of the project:
--
c870abf922ae32592a693938f2ea829712e57347 by Zoran Jovanovic <zjovanov@amd.com>:
[ROCm] Fix build break in xla/service/gpu/ir_emitter_triton_rocm.cc
Merging this change closes #13194
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13194 from ROCm:ci_rocm_fix-ir_emitter_triton_rocm c870abf922ae32592a693938f2ea829712e57347
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"@Ralle1998 Could you try cleaning your Bazel build directory and rebuilding the project. Sometimes cached files can cause compilation issues.\r\nThank you!",
"@sushreebarsa Thanks for the answer! I cleaned the directory multiple times by using the command \"bazel clean\" and \"bazel clean --expunge\" and still have the same error. ",
"@Ralle1998 Thank you for the confirmation. Could you please search for header files included in state_helpers.h or other relevant files that might define final_packet. Also ensure the correct header file is included in the compilation process for `highwayhash/sip_hash.cc` using `#include` directives.\r\nThank you!",
"@sushreebarsa I looked through the files by using the projects in \"external\\highwayhash\\msvc\". Everything seems to be in order. I even was able to build all of the projects except for \"Benchmark.vcxproj\". Then I took a look at the properties of the projects.\r\n\r\nI saw that the C++ Language Standard was ISO C++14. So I changed it to ISO C++20 like I did in the .bazelrc file, build it again and BAM! Same ERROR! /std:c++20 hates the line 49 in state_helpers.h for some reason."
] | 2024-05-29T17:21:59 | 2024-06-06T03:00:19 | null | NONE | null | null | null | ### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16.1
### Custom code
No
### OS platform and distribution
Microsoft Windows 10 Home 10.0.19045
### Mobile device
_No response_
### Python version
3.12
### Bazel version
6.5.0
### GCC/compiler version
MSVC 14.38.33130
### CUDA/cuDNN version
Not used
### GPU model and memory
Not used
### Current behavior?
I tried to compile the source code of Tensorflow to get access to the C++ API. I followed the instructions on https://www.tensorflow.org/install/source_windows, cloned the repository to C:/tensorflow, set the configurations with configure.py and started to compile. Then I got the first error:
```
.\tensorflow/lite/delegates/xnnpack/weight_cache.h(276): error C7555: use of designated initializers requires at least '/std:c++20'
tensorflow/lite/delegates/xnnpack/weight_cache.cc(206): error C7555: use of designated initializers requires at least '/std:c++20'
tensorflow/lite/delegates/xnnpack/weight_cache.cc(210): error C7555: use of designated initializers requires at least '/std:c++20'
tensorflow/lite/delegates/xnnpack/weight_cache.cc(380): error C7555: use of designated initializers requires at least '/std:c++20'
tensorflow/lite/delegates/xnnpack/weight_cache.cc(383): error C7555: use of designated initializers requires at least '/std:c++20'
tensorflow/lite/delegates/xnnpack/weight_cache.cc(518): error C7555: use of designated initializers requires at least '/std:c++20'
```
This was an easy fix by changing the lines 389 and 390 in .bazelrc from
```
389 build:windows --cxxopt=/std:c++17
390 build:windows --host_cxxopt=/std:c++17
```
to
```
389 build:windows --cxxopt=/std:c++20
390 build:windows --host_cxxopt=/std:c++20
```
Now I came pretty far with the compilation until bazel tried to compile the file "sip_hash.cc". Now it throws this error:
```
ERROR: C:/users/ralle/_bazel_ralle/xv6zejqw/external/highwayhash/BUILD.bazel:111:11: Compiling highwayhash/sip_hash.cc failed: (Exit 2): cl.exe failed: error executing command (from target @highwayhash//:sip_hash)
[...]
# Configuration: 3d8ee62d8d62b08774dc7f4e97d832e9e7d26d50ed850f9d5b41083047ae81d8
# Execution platform: @local_execution_config_platform//:platform
external/highwayhash\highwayhash/state_helpers.h(49): error C2760: syntax error: 'alignas' was unexpected here; expected 'declaration'
external/highwayhash\highwayhash/state_helpers.h(64): error C2065: 'final_packet': undeclared identifier
external/highwayhash\highwayhash/state_helpers.h(65): error C2065: 'final_packet': undeclared identifier
external/highwayhash\highwayhash/state_helpers.h(67): error C2065: 'final_packet': undeclared identifier
Target //tensorflow/tools/pip_package:wheel failed to build
INFO: Elapsed time: 2.067s, Critical Path: 0.55s
INFO: 22 processes: 17 internal, 5 local.
FAILED: Build did NOT complete successfully
```
### Standalone code to reproduce the issue
Changed lines in .bazelrc:
```
389 build:windows --cxxopt=/std:c++20
390 build:windows --host_cxxopt=/std:c++20
```
configure.py:
```
You have bazel 6.5.0 installed.
Please specify the location of python. [Default is C:\Python312\python.exe]: Default
Please input the desired Python library path to use. Default is [C:\Python312\Lib\site-packages]: Default
Do you want to use Clang to build TensorFlow? [Y/n]: N
Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is /arch:AVX]: Default
Would you like to override eigen strong inline for some C++ compilation to reduce the compilation time? [Y/n]: Y
Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: N
```
Start of compilation in console:
```
C:\tensorflow>bazel build --config=opt --repo_env=TF_PYTHON_VERSION=3.12 //tensorflow/tools/pip_package:wheel --repo_env=WHEEL_NAME=tensorflow_cpu
```
### Relevant log output
```
ERROR: C:/users/ralle/_bazel_ralle/xv6zejqw/external/highwayhash/BUILD.bazel:111:11: Compiling highwayhash/sip_hash.cc failed: (Exit 2): cl.exe failed: error executing command (from target @highwayhash//:sip_hash)
[...]
# Configuration: 3d8ee62d8d62b08774dc7f4e97d832e9e7d26d50ed850f9d5b41083047ae81d8
# Execution platform: @local_execution_config_platform//:platform
external/highwayhash\highwayhash/state_helpers.h(49): error C2760: syntax error: 'alignas' was unexpected here; expected 'declaration'
external/highwayhash\highwayhash/state_helpers.h(64): error C2065: 'final_packet': undeclared identifier
external/highwayhash\highwayhash/state_helpers.h(65): error C2065: 'final_packet': undeclared identifier
external/highwayhash\highwayhash/state_helpers.h(67): error C2065: 'final_packet': undeclared identifier
Target //tensorflow/tools/pip_package:wheel failed to build
INFO: Elapsed time: 2.067s, Critical Path: 0.55s
INFO: 22 processes: 17 internal, 5 local.
FAILED: Build did NOT complete successfully
```
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Imported from GitHub PR https://github.com/openxla/xla/pull/13165
Copybara import of the project:
--
0a2b2ea90005d8c00e2afd77dac3a34be8673661 by Harsha HS <Harsha.HavanurShamsundara@amd.com>:
[ROCm] Update ROCm version to 6.0.2
Merging this change closes #13165
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13165 from ROCm:ci_amd_ci_dir_20240529 0a2b2ea90005d8c00e2afd77dac3a34be8673661
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BufferBase is too opaque to guess that it should be used for arguments which
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} | [xla:gpu] Exclude collectives from command buffers if CUDA < 12.3
Tracing NCCL collectives relies on CUDA feature that was added in 12.3
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} | PR #13018: [ROCm] Distinguish between AMD and NVIDIA GPUs with relevant tags
Imported from GitHub PR https://github.com/openxla/xla/pull/13018
Add `requires-gpu-amd` for AMD gpus and unit tests that require AMD backend. Use `requires-gpu-nvidia` for any nvidia gpu backend.
Copybara import of the project:
--
8c3c52760c37b6c9fc17910174df6bfa396b5b5e by Harsha HS <Harsha.HavanurShamsundara@amd.com>:
[ROCm] Distinguish between AMD and NVIDIA GPUs with relevant tags
--
0bf9ab80d06284883df19f17f421eb6a691c0dd8 by Harsha HS <Harsha.HavanurShamsundara@amd.com>:
Change ALL_GPU_BACKENDS to GPU_BACKENDS
Merging this change closes #13018
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13018 from ROCm:ci_amdtags_20240523 0bf9ab80d06284883df19f17f421eb6a691c0dd8
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* Use float32 accumulation for the gradients of
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"Hi **@Calabashypr** ,\r\n- It looks like you are using an older Version of Tensorflow (2.0). Many bugs have been fixed in the latest version. Can you please execute your code using Latest Version (2.15.0 or 2.16.1) and let us know if the issue still persists?\r\nThank you!",
"> Hi **@Calabashypr** ,\r\n> \r\n> * It looks like you are using an older Version of Tensorflow (2.0). Many bugs have been fixed in the latest version. Can you please execute your code using Latest Version (2.15.0 or 2.16.1) and let us know if the issue still persists?\r\n> Thank you!\r\n\r\nHello,I have updated the Version of Tensorflow to 2.15.0,but I got an ERROR below:\r\n`[ERROR] Crash when training model with tensorflow\r\nTraceback (most recent call last):\r\n File \"train.py\", line 254, in <module>\r\n model, input_objects_names, output_layers_names, x, y, ins, ins_value = __prepare(flags.loss, flags.optimizer,\r\n File \"train.py\", line 69, in __prepare\r\n ins = model._feed_inputs + model._feed_targets + model._feed_sample_weights\r\nAttributeError: 'Functional' object has no attribute '_feed_targets'\r\n`\r\nCould you help me solve the problem?"
] | 2024-05-29T14:06:36 | 2024-06-05T07:34:04 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.0.0
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04.4 LTS
### Mobile device
_No response_
### Python version
3.6.5
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
CUDA Version: 12.2
### GPU model and memory
_No response_
### Current behavior?
When I tried to train model with the Poisson loss function and the Adam optimizer,the loss value of the model became NaN.
The model file and test data are attached here.In order to simplify the process of identifying the bug, I have only included a portion of the code for the forward and backward propagation.
[000116.zip](https://github.com/tensorflow/tensorflow/files/15485500/000116.zip)
### Standalone code to reproduce the issue
```shell
# trainer.py
from typing import List, Optional
import numpy as np
import random
from pathlib import Path
import subprocess
import platform
class CmdProcess(object):
def __init__(self, cmd: str):
super().__init__()
self.__cmd = cmd
self.__result = None
self.__sys = platform.system()
def run(self, timeout):
p = subprocess.Popen(self.__cmd, shell=True)
try:
return p.wait(timeout)
except subprocess.TimeoutExpired:
p.kill()
return -1
def get_result(self):
return self.__result if self.__sys == "Windows" else (self.__result >> 8)
losses = [
'mean_squared_error',
'mean_absolute_error',
'mean_absolute_percentage_error',
'mean_squared_logarithmic_error',
'squared_hinge',
'categorical_hinge',
'logcosh',
'huber_loss',
'categorical_crossentropy',
'binary_crossentropy',
'kullback_leibler_divergence',
'poisson',
'cosine_proximity',
]
optimizers = [
'sgd',
'rmsprop',
'adagrad',
'adadelta',
'adam',
'adamax',
'nadam',
]
class Trainer(object):
def __init__(self, timeout=1000):
super().__init__()
self.__timeout = timeout
def train(self, model_id: int, exp_dir: str, ok_backends: List[str], loss: Optional[str] = None,
optimizer: Optional[str] = None):
model_dir = Path(exp_dir) / 'models'
training_inputs_path = Path(exp_dir) / 'dataset' / 'inputs.npz'
ground_truths_path = Path(exp_dir) / 'dataset' / 'ground_truths.npz'
outputs_dir = Path(exp_dir) / 'layer_outputs'
loss_dir = Path(exp_dir) / 'loss'
loss_grads_dir = Path(exp_dir) / 'loss_gradients'
gradients_dir = Path(exp_dir) / 'layer_gradients'
# weights_dir = Path(exp_dir) / 'layer_weights'
outputs_dir.mkdir(parents=True, exist_ok=True)
loss_dir.mkdir(parents=True, exist_ok=True)
loss_grads_dir.mkdir(parents=True, exist_ok=True)
gradients_dir.mkdir(parents=True, exist_ok=True)
if loss is None:
loss = random.choice(losses)
if optimizer is None:
optimizer = random.choice(optimizers)
crash_backends, nan_backends, inf_backends = [], [], []
backends_outputs, backends_losses, backends_loss_grads, backends_grads, backends_weights = {}, {}, {}, {}, {}
cmd_processes = {
bk: CmdProcess(f"/root/anaconda3/envs/{bk}/bin/python -m train"
f" --backend {bk}"
f" --loss {loss}"
f" --optimizer {optimizer}"
f" --model_path {str(model_dir / f'{bk}.h5')}"
f" --model_info_path {str(model_dir / 'model.json')}"
f" --training_instances_path {str(training_inputs_path)}"
f" --ground_truths_path {str(ground_truths_path)}"
f" --outputs_dir {str(outputs_dir / bk)}"
f" --loss_path {str(loss_dir / f'{bk}.txt')}"
f" --loss_grads_dir {str(loss_grads_dir / bk)}"
f" --gradients_dir {str(gradients_dir / bk)}")
# f" --weights_dir {str(weights_dir / bk)}")
for bk in ok_backends
}
status = {}
for bk, p in cmd_processes.items():
extract_status = p.run(self.__timeout)
print(f"{bk}_status: {extract_status}")
status[bk] = extract_status
if extract_status and extract_status in [255, 1, -1]:
crash_backends.append(bk)
else:
outputs_data = loss_value = loss_grads_data = grads_data = None # weights_data = None
if extract_status == 0 or extract_status >= 2:
outputs_data = {fn.stem: np.load(str(fn)) for fn in (outputs_dir / bk).glob("*.npy")}
if extract_status == 0 or extract_status >= 3:
with open(str(loss_dir / f'{bk}.txt'), 'r') as f:
loss_value = float(f.read())
if extract_status == 0 or extract_status >= 4:
loss_grads_data = {fn.stem: np.load(str(fn)) for fn in (loss_grads_dir / bk).glob("*.npy")}
if extract_status == 0 or extract_status >= 5:
grads_data = {fn.stem: np.load(str(fn)) for fn in (gradients_dir / bk).glob("*.npy")}
# if extract_status == 0 or extract_status >= 6:
# weights_data = {fn.stem: np.load(str(fn)) for fn in (weights_dir / bk).glob("*.npy")}
backends_outputs[bk] = outputs_data
backends_losses[bk] = loss_value
backends_loss_grads[bk] = loss_grads_data
backends_grads[bk] = grads_data
# backends_weights[bk] = weights_data
if extract_status == 0 or (extract_status != 255 and extract_status >= 2):
if self.__check(outputs_data, np.isnan):
nan_backends.append(bk)
if self.__check(outputs_data, np.isinf):
inf_backends.append(bk)
return status, backends_outputs, backends_losses, backends_loss_grads, backends_grads, [bk for bk in ok_backends
if
bk not in crash_backends], loss, optimizer
def __check(self, weights, f):
for w in weights.values():
if f(w).any():
return True
return False
if __name__ == '__main__':
tr = Trainer(timeout=1000)
model_id = 116
exp_dir = './dataset/000116'
ok_backends = ['tensorflow']
loss = 'poisson'
optimizer = 'adam'
print("---------------Train---------------")
status, backends_outputs, backends_losses, backends_loss_grads, backends_grads, ok_backends, loss, optimizer = tr.train(
model_id=model_id,
exp_dir=exp_dir,
ok_backends=ok_backends,
loss=loss,
optimizer=optimizer)
print("---------------Finish---------------")
print("---------------Calculate---------------")
from calculate import Comparator
cmp = Comparator()
is_nan = cmp.compare(backends_outputs=backends_outputs,
backends_losses=backends_losses,
backends_loss_grads=backends_loss_grads,
backends_grads=backends_grads,
ok_backends=ok_backends)
if is_nan:
print("loss:", loss)
print("optimizer:", optimizer)
print("---------------Finish---------------")
#######################################################
# train.py
import argparse
import sys
import json
from pathlib import Path
import numpy as np
import warnings
import os
warnings.filterwarnings("ignore")
use_gpu = True
def switch_backend(bk):
os.environ['KMP_WARNINGS'] = '0'
os.environ['KERAS_BACKEND'] = bk
# import keras.backend as K
# K.set_image_data_format("channels_last")
if bk == 'tensorflow':
os.environ["TF_CPP_MIN_LOG_LEVEL"] = '3' # 只显示 Error
import tensorflow as tf
# tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
print(f"tensorflow version: {tf.__version__}")
if use_gpu:
if str(tf.__version__)[0] == '1':
from tensorflow.compat.v1 import GPUOptions
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import Session
from keras.backend.tensorflow_backend import set_session
gpu_options = GPUOptions(allow_growth=True)
set_session(Session(config=ConfigProto(gpu_options=gpu_options)))
else:
gpus = tf.config.experimental.list_physical_devices(device_type='GPU')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
elif bk == 'theano':
os.environ['THEANO_FLAGS'] = "device=cuda,contexts=dev0->cuda0," \
f"force_device=True,floatX=float32,lib.cnmem=1"
import theano as th
print(f"theano version: {th.__version__}")
else:
from cntk.device import try_set_default_device, gpu
# try_set_default_device(gpu(0))
import cntk as ck
print(f"cntk version: {ck.__version__}")
def __prepare(loss_type: str, optimizer_type: str, training_instances_path: str, ground_truths_path: str,
model_path: str, model_info_path: str):
model = keras.models.load_model(model_path)
with open(model_info_path, "r") as f:
model_info = json.load(f)
input_objects_names = [model_info['model_structure'][str(idx)]['args']['name'] for idx in
model_info["input_id_list"]]
output_layers_names = [model_info['model_structure'][str(idx)]['args']['name'] for idx in
model_info["output_id_list"]]
tmp = np.load(training_instances_path)
training_instances = [*tmp.values()]
ground_truths = [*np.load(ground_truths_path).values()]
model.compile(loss=loss_type, optimizer=optimizer_type)
ins = model._feed_inputs + model._feed_targets + model._feed_sample_weights
x, y, sample_weight = model._standardize_user_data(training_instances, ground_truths)
ins_value = x + y + sample_weight
return model, input_objects_names, output_layers_names, training_instances, ground_truths, ins, ins_value
def __get_outputs(model, input_objects_names, x,
output_dir: str):
get_layer_output = K.function(model._feed_inputs + [K.learning_phase()],
[layer.output for layer in model.layers if layer.name not in input_objects_names])
layers_names = [layer.name for layer in model.layers if layer.name not in input_objects_names]
layers_outputs = get_layer_output(x + [1])
def save_outputs(layers_names, layers_outputs, output_dir):
for name, output in zip(layers_names, layers_outputs):
save_path = Path(output_dir) / f'{name}.npy'
np.save(save_path, output)
Path(output_dir).mkdir(parents=True, exist_ok=True)
save_outputs(input_objects_names + layers_names, x + layers_outputs, output_dir)
return {layer_name: output for layer_name, output in zip(input_objects_names + layers_names, x + layers_outputs)}
def __get_loss(model, ins, ins_value,
loss_path: str):
get_loss = K.function(
ins + [K.learning_phase()],
[model.total_loss]
)
loss_value = get_loss(ins_value + [1])[0]
def save_loss(loss, output_path):
with open(output_path, 'w') as f:
f.write(str(loss))
save_loss(loss_value, loss_path)
def __get_loss_gradients(model, output_layers_names, ins, ins_value, layers_outputs_value, y,
loss_grads_dir: str, model_info_path: str):
layer_outputs = [model.get_layer(layer_name).output for layer_name in output_layers_names]
if K.backend() == 'cntk':
loss_grads_value = [__cntk_get_gradients(model, layer_name, layers_outputs_value, y, model_info_path) for
layer_name in output_layers_names]
else:
get_loss_grads = K.function(
ins + [K.learning_phase()],
K.gradients(model.total_loss, layer_outputs)
)
loss_grads_value = get_loss_grads(ins_value + [1])
def save_loss_grad(layer_names, grads_value, output_dir):
for layer_name, g in zip(layer_names, grads_value):
save_path = Path(output_dir) / f'{layer_name}.npy'
np.save(save_path, g)
Path(loss_grads_dir).mkdir(parents=True, exist_ok=True)
save_loss_grad(output_layers_names, loss_grads_value, loss_grads_dir)
def __get_gradients(model, input_objects_names, ins, ins_value, layers_outputs_value, y,
grads_dir: str, model_info_path: str):
layer_names = input_objects_names + [layer.name for layer in model.layers if layer.name not in input_objects_names]
if K.backend() == 'cntk':
grads = [__cntk_get_gradients(model, layer_name, layers_outputs_value, y, model_info_path) for layer_name in
layer_names]
else:
layer_outputs = model.inputs + [layer.output for layer in model.layers if layer.name not in input_objects_names]
get_gradients = K.function(
ins + [K.learning_phase()],
K.gradients(model.total_loss, layer_outputs)
)
grads = get_gradients(ins_value + [1])
def save_gradients(layer_names, grads, output_dir):
for name, g in zip(layer_names, grads):
save_path = Path(output_dir) / f'{name}.npy'
np.save(save_path, g)
Path(grads_dir).mkdir(parents=True, exist_ok=True)
save_gradients(layer_names, grads, grads_dir)
def __cntk_get_gradients(model, layer_name, layer_outputs_value, y_true_list, model_info_path):
with open(str(model_info_path), "r") as f:
model_info = json.load(f)
import cntk as C
_output = C.input_variable(model.get_layer(layer_name).output_shape[1:], needs_gradient=True)
tmp_input = keras.Input(tensor=_output)
tmp_inputs = []
extra_inputs = []
extra_input_datas = []
layer_outputs = {}
def get_inbound_layers(layer):
ids = model_info['model_structure'][str(int(layer.name[:2]))]['pre_layers']
names = [model_info['model_structure'][str(idx)]['args']['name'] for idx in ids]
return [model.get_layer(name) for name in names]
def get_output_of_layer(layer):
if layer.name in layer_outputs:
return layer_outputs[layer.name]
if layer.name == layer_name:
tmp_inputs.append(tmp_input)
layer_outputs[layer.name] = tmp_input
return tmp_input
if int(layer.name[:2]) < int(layer_name[:2]):
_input = C.input_variable(layer.output_shape[1:], needs_gradient=False)
tmp = keras.Input(tensor=_input)
extra_inputs.append(_input)
extra_input_datas.append(layer_outputs_value[layer.name])
tmp_inputs.append(tmp)
layer_outputs[layer.name] = tmp
return tmp
inbound_layers = get_inbound_layers(layer)
layer_ins = [get_output_of_layer(layer) for layer in inbound_layers]
out = layer(layer_ins[0] if len(layer_ins) == 1 else layer_ins)
layer_outputs[layer.name] = out
return out
tmp_outputs = []
for output_id in model_info['output_id_list']:
name = model_info['model_structure'][str(output_id)]['args']['name']
tmp_outputs.append(get_output_of_layer(model.get_layer(name)))
tmp_model = keras.models.Model(inputs=tmp_inputs, outputs=tmp_outputs)
x = [layer_outputs_value[layer_name], *extra_input_datas]
y_true = y_true_list
tmp_model.compile(loss=model.loss, optimizer=model.optimizer)
ins = [_output, *extra_inputs] + tmp_model._feed_targets + tmp_model._feed_sample_weights
_, y, sample_weight = tmp_model._standardize_user_data([], y_true)
ins_value = x + y + sample_weight
grads = tmp_model.total_loss.grad({
k: v
for k, v in zip(ins, ins_value)
}, [_output])
return grads
if __name__ == "__main__":
parse = argparse.ArgumentParser()
parse.add_argument("--backend", type=str)
parse.add_argument("--loss", type=str)
parse.add_argument("--optimizer", type=str)
parse.add_argument("--model_path", type=str)
parse.add_argument("--model_info_path", type=str)
parse.add_argument("--training_instances_path", type=str)
parse.add_argument("--ground_truths_path", type=str)
parse.add_argument("--outputs_dir", type=str)
parse.add_argument("--loss_path", type=str)
parse.add_argument("--loss_grads_dir", type=str)
parse.add_argument("--gradients_dir", type=str)
flags, _ = parse.parse_known_args(sys.argv[1:])
FLAG = 0
try:
switch_backend(flags.backend)
import keras
from keras import backend as K
FLAG = -1
model, input_objects_names, output_layers_names, x, y, ins, ins_value = __prepare(flags.loss, flags.optimizer,
flags.training_instances_path,
flags.ground_truths_path,
flags.model_path,
flags.model_info_path)
FLAG = 1
layers_outputs_value = __get_outputs(model, input_objects_names, x, flags.outputs_dir)
FLAG = 2
__get_loss(model, ins, ins_value, flags.loss_path)
FLAG = 3
__get_loss_gradients(model, output_layers_names, ins, ins_value, layers_outputs_value, y, flags.loss_grads_dir,
flags.model_info_path)
FLAG = 4
__get_gradients(model, input_objects_names, ins, ins_value, layers_outputs_value, y, flags.gradients_dir,
flags.model_info_path)
FLAG = 5
# __get_weights(model, x, y, flags.weights_dir)
# FLAG = 6
if K.backend() in ['tensorflow', 'cntk']:
K.clear_session()
except Exception:
import traceback
log_dir = Path(flags.outputs_dir).parent.parent / 'logs'
log_dir.mkdir(parents=True, exist_ok=True)
with (log_dir / 'detection.log').open(mode='a', encoding='utf-8') as f:
f.write(f"[ERROR] Crash when training model with {flags.backend}\n")
traceback.print_exc(file=f)
f.write("\n\n")
sys.exit(FLAG)
#######################################################
#calculate.py
from itertools import combinations
import numpy as np
from pathlib import Path
def isnan_func(lis: list):
for arr in lis:
if np.isnan(arr).any() or np.isinf(arr).any():
return True
return False
class Comparator(object):
def __init__(self):
super().__init__()
self.__log_dir = Path(".")
def compare(self, backends_outputs: dict, backends_losses: dict,
backends_loss_grads: dict, backends_grads: dict, ok_backends: list):
for bk in ok_backends:
outputs1 = backends_outputs.get(bk, None)
loss1 = backends_losses.get(bk, None)
loss_grads1 = backends_loss_grads.get(bk, None)
grads1 = backends_grads.get(bk, None)
print(f"bk:{bk}")
of1 = isnan_func(list(outputs1.values()))
lgf1 = isnan_func(list(loss_grads1.values()))
gf1 = isnan_func(list(grads1.values()))
ls1=np.isnan(loss1).any()
print(f"output1:{of1}")
print(f"loss1:{ls1}")
print(f"loss_grads1:{lgf1}")
print(f"grads1:{gf1}")
if of1 or lgf1 or gf1 or ls1:
return True
return False
You can run the 'trainer.py' to reproduce the bug
```
### Relevant log output
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} | Add simple loop peeling for imperfect tiling.
This is very ad-hoc and ugly, but it works for now.
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We don't care about whether trivial 1-sized dimensions change their position.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/69033 from tensorflow:tilakrayal-patch-2 63a0146c62a5c5199abfb054dee9d4e9d084eb59
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} | [XLA:GPU] Add a "draft" version of a generic Triton Fusion Emitter.
This change introduces a new Triton emitter that should eventually replace the existing `MatMul` and `SoftMax` emitters.
Highlights:
- The new emitter is a replacement of the existing `TiledSoftMax` emitter and covers all of those cases.
- For now the new emitter can also generate some non-SoftMax fusions, e.g. I've added a simple reduction fusion test.
TODO in future changes:
- Add more tests.
- The new emitter replaces `TiledSoftMax` but is otherwise unused, because no HLO-passes set it as a backend yet. We should hook it up.
- Extend the new emitter to support 2D and 3D launch grids. At the moment it only supports a 1D grid.
- Compute the `output_tile_sizes` in a generic way or pipe them in from the Tiling Analysis.
- Extend the launch-dimensions computation to support generic fusions. Not quite sure how to do this yet.
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Replaced by
%s/in \[\(\d\+\), \(\d\+\)\]/\='in ['.(submatch(1)).', '.(submatch(2)+1).')'/g
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"@sonofisp,\r\nBy default the Tensorflow v2.16 contains the keras3.0 which might be the cause for the error. I suggest using the tf_keras 2.16 version for executing the code. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/f66a1510824cbab5199e3efe3992910e/untitled1922.ipynb). \r\n\r\nAlso add the below code for the execution.\r\n```python\r\n!pip install -U tf_keras # Keras 2\r\nimport os\r\nos.environ[\"TF_USE_LEGACY_KERAS\"] = \"1\"\r\n```\r\n\r\nThank you!\r\n",
"That works! Thanks a lot!",
"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/68797\">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/68797\">No</a>\n"
] | 2024-05-29T11:44:02 | 2024-05-30T07:06:18 | 2024-05-30T07:06:16 | NONE | null | null | null | ### 1. System information
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 10/Debian
- TensorFlow installation (pip package or built from source): pip package
- TensorFlow library (version, if pip package or github SHA, if built from source): 2.16.1
### 2. Code
A minimum example that produced a tflite model when using tf 2.15.
```
import tensorflow as tf
import numpy as np
# generate random train data (32 samples, 1 time step, 10 features)
NUM_FEAT = 10
rand_train_data_x = np.array(np.random.randn(32, 1, NUM_FEAT), dtype=np.float32)
# generate targets of 3 classes
NUM_CLASSES = 3
rand_train_data_y = np.ones(32)
rand_train_data_y[0:11] = 0
rand_train_data_y[20:31] = 2
rand_train_data_y = np.array(tf.one_hot(rand_train_data_y, NUM_CLASSES)).reshape(
-1, NUM_CLASSES
)
norm_layer = tf.keras.layers.Normalization(axis=-1)
norm_layer.adapt(rand_train_data_x)
x_train = norm_layer(rand_train_data_x)
y_train = tf.constant(rand_train_data_y)
# Build net
input_layer = tf.keras.Input(shape=(1, NUM_FEAT))
lstm_layer = tf.keras.layers.LSTM(
NUM_FEAT, input_shape=(1, NUM_FEAT), dropout=0.2, recurrent_dropout=0.2
)(input_layer)
dense_layer = tf.keras.layers.Dense(15, activation="relu")(lstm_layer)
outputs = tf.keras.layers.Dense(NUM_CLASSES, activation="softmax")(dense_layer)
model = tf.keras.Model(inputs=[input_layer], outputs=outputs)
loss_fn = tf.keras.losses.MeanSquaredLogarithmicError()
model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"])
print(model.summary())
model.fit(
x_train,
y_train,
epochs=10,
)
model.export("test_model") # , format="tf_saved_model")
converter = tf.lite.TFLiteConverter.from_saved_model("test_model")
converter.experimental_enable_resource_variables = True
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS,
tf.lite.OpsSet.SELECT_TF_OPS,
]
model_lite = converter.convert()
```
This leads to the following errors:
```
error: 'tfl.assign_variable' op operand #1 must be tensor of 32-bit float or 64-bit float or 1-bit signless integer or 8-bit unsigned integer or 8-bit signless integer or QI8 type or QUI8 type or 32-bit signless integer or 64-bit signless integer or QI16 type or complex type with 32-bit float elements or complex type with 64-bit float elements values, but got 'tensor<2xui32>'
loc(callsite(callsite(fused["ReadVariableOp:", "functional_1_1/lstm_1/ReadVariableOp_1@__inference___call___3175"] at fused["StatefulPartitionedCall:", "StatefulPartitionedCall@__inference_signature_wrapper___call___3218"]) at fused["StatefulPartitionedCall:", "StatefulPartitionedCall_1"])): error: 'tfl.read_variable' op result #0 must be tensor of 32-bit float or 64-bit
float or 1-bit signless integer or 8-bit unsigned integer or 8-bit signless integer or QI8 type or QUI8 type or 32-bit signless integer or 64-bit signless integer or QI16 type or complex
type with 32-bit float elements or complex type with 64-bit float elements values, but got 'tensor<2xui32>'
loc(callsite(callsite(fused["AssignVariableOp:", "functional_1_1/lstm_1/AssignVariableOp@__inference___call___3175"] at fused["StatefulPartitionedCall:", "StatefulPartitionedCall@__inference_signature_wrapper___call___3218"]) at fused["StatefulPartitionedCall:", "StatefulPartitionedCall_1"])): error: 'tfl.assign_variable' op operand #1 must be tensor of 32-bit float or 64-bit float or 1-bit signless integer or 8-bit unsigned integer or 8-bit signless integer or QI8 type or QUI8 type or 32-bit signless integer or 64-bit signless integer or QI16 type or complex type with 32-bit float elements or complex type with 64-bit float elements values, but got 'tensor<2xui32>'
loc(callsite(callsite(fused["ReadVariableOp:", "functional_1_1/lstm_1/ReadVariableOp_3@__inference___call___3175"] at fused["StatefulPartitionedCall:", "StatefulPartitionedCall@__inference_signature_wrapper___call___3218"]) at fused["StatefulPartitionedCall:", "StatefulPartitionedCall_1"])): error: 'tfl.read_variable' op result #0 must be tensor of 32-bit float or 64-bit
float or 1-bit signless integer or 8-bit unsigned integer or 8-bit signless integer or QI8 type or QUI8 type or 32-bit signless integer or 64-bit signless integer or QI16 type or complex
type with 32-bit float elements or complex type with 64-bit float elements values, but got 'tensor<2xui32>'
loc(callsite(callsite(fused["AssignVariableOp:", "functional_1_1/lstm_1/AssignVariableOp_1@__inference___call___3175"] at fused["StatefulPartitionedCall:", "StatefulPartitionedCall@__inference_signature_wrapper___call___3218"]) at fused["StatefulPartitionedCall:", "StatefulPartitionedCall_1"])): error: 'tfl.assign_variable' op operand #1 must be tensor of 32-bit float or
64-bit float or 1-bit signless integer or 8-bit unsigned integer or 8-bit signless integer or QI8 type or QUI8 type or 32-bit signless integer or 64-bit signless integer or QI16 type or complex type with 32-bit float elements or complex type with 64-bit float elements values, but got 'tensor<2xui32>'
Traceback (most recent call last):
File ".\lstm_minimum_bug.py", line 48, in <module>
model_lite = converter.convert()
^^^^^^^^^^^^^^^^^^^
File "C:\Python311\Lib\site-packages\tensorflow\lite\python\lite.py", line 1175, in wrapper
return self._convert_and_export_metrics(convert_func, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Python311\Lib\site-packages\tensorflow\lite\python\lite.py", line 1129, in _convert_and_export_metrics
result = convert_func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Python311\Lib\site-packages\tensorflow\lite\python\lite.py", line 1500, in convert
return self._convert_from_saved_model(graph_def)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Python311\Lib\site-packages\tensorflow\lite\python\lite.py", line 1367, in _convert_from_saved_model
result = _convert_saved_model(**converter_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Python311\Lib\site-packages\tensorflow\lite\python\convert_phase.py", line 212, in wrapper
raise converter_error from None # Re-throws the exception.
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Python311\Lib\site-packages\tensorflow\lite\python\convert_phase.py", line 205, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "C:\Python311\Lib\site-packages\tensorflow\lite\python\convert.py", line 1012, in convert_saved_model
data = convert(
^^^^^^^^
File "C:\Python311\Lib\site-packages\tensorflow\lite\python\convert.py", line 367, in convert
raise converter_error
tensorflow.lite.python.convert_phase.ConverterError: Variable constant folding is failed. Please consider using enabling `experimental_enable_resource_variables` flag in the TFLite converter object. For example, converter.experimental_enable_resource_variables = True<unknown>:0: error: 'tfl.assign_variable' op operand #1 must be tensor of 32-bit float or 64-bit float or 1-bit signless integer or 8-bit unsigned integer or 8-bit signless integer or QI8 type or QUI8 type or 32-bit signless integer or 64-bit signless integer or QI16 type or complex type with
32-bit float elements or complex type with 64-bit float elements values, but got 'tensor<2xui32>'
<unknown>:0: error: loc(callsite(callsite(fused["ReadVariableOp:", "functional_1_1/lstm_1/ReadVariableOp_1@__inference___call___3175"] at fused["StatefulPartitionedCall:", "StatefulPartitionedCall@__inference_signature_wrapper___call___3218"]) at fused["StatefulPartitionedCall:", "StatefulPartitionedCall_1"])): 'tfl.read_variable' op result #0 must be tensor of 32-bit float or 64-bit float or 1-bit signless integer or 8-bit unsigned integer or 8-bit signless integer or QI8 type or QUI8 type or 32-bit signless integer or 64-bit signless integer or QI16 type or complex type with 32-bit float elements or complex type with 64-bit float elements values, but got 'tensor<2xui32>'
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall_1"]): called from
<unknown>:0: error: loc(callsite(callsite(fused["AssignVariableOp:", "functional_1_1/lstm_1/AssignVariableOp@__inference___call___3175"] at fused["StatefulPartitionedCall:", "StatefulPartitionedCall@__inference_signature_wrapper___call___3218"]) at fused["StatefulPartitionedCall:", "StatefulPartitionedCall_1"])): 'tfl.assign_variable' op operand #1 must be tensor of 32-bit float or 64-bit float or 1-bit signless integer or 8-bit unsigned integer or 8-bit signless integer or QI8 type or QUI8 type or 32-bit signless integer or 64-bit signless integer or QI16 type or complex type with 32-bit float elements or complex type with 64-bit float elements values, but got 'tensor<2xui32>'
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall_1"]): called from
<unknown>:0: error: loc(callsite(callsite(fused["ReadVariableOp:", "functional_1_1/lstm_1/ReadVariableOp_3@__inference___call___3175"] at fused["StatefulPartitionedCall:", "StatefulPartitionedCall@__inference_signature_wrapper___call___3218"]) at fused["StatefulPartitionedCall:", "StatefulPartitionedCall_1"])): 'tfl.read_variable' op result #0 must be tensor of 32-bit float or 64-bit float or 1-bit signless integer or 8-bit unsigned integer or 8-bit signless integer or QI8 type or QUI8 type or 32-bit signless integer or 64-bit signless integer or QI16 type or complex type with 32-bit float elements or complex type with 64-bit float elements values, but got 'tensor<2xui32>'
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall_1"]): called from
<unknown>:0: error: loc(callsite(callsite(fused["AssignVariableOp:", "functional_1_1/lstm_1/AssignVariableOp_1@__inference___call___3175"] at fused["StatefulPartitionedCall:", "StatefulPartitionedCall@__inference_signature_wrapper___call___3218"]) at fused["StatefulPartitionedCall:", "StatefulPartitionedCall_1"])): 'tfl.assign_variable' op operand #1 must be tensor of 32-bit float or 64-bit float or 1-bit signless integer or 8-bit unsigned integer or 8-bit signless integer or QI8 type or QUI8 type or 32-bit signless integer or 64-bit signless integer or QI16 type or complex type with 32-bit float elements or complex type with 64-bit float elements values, but got 'tensor<2xui32>'
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall_1"]): called from
```
### 5. (optional) Any other info / logs
I'm aware that this is using experimental settings, but I wanted to share the error I got and I'm wondering if there is a way to export a LSTM model to tflite that uses dropout? Or are these not supported for tflite?
I never really tested the .tflite models that were exported with tf 2.15, because I had trouble with building the tensorflowlite_c.dll for tf 2.15 (this got solved with 2.16.1).
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https://api.github.com/repos/tensorflow/tensorflow/issues/68796 | https://api.github.com/repos/tensorflow/tensorflow | https://api.github.com/repos/tensorflow/tensorflow/issues/68796/labels{/name} | https://api.github.com/repos/tensorflow/tensorflow/issues/68796/comments | https://api.github.com/repos/tensorflow/tensorflow/issues/68796/events | https://github.com/tensorflow/tensorflow/pull/68796 | 2,323,092,906 | PR_kwDOArmXAs5w3493 | 68,796 | Sort summands by distance of symbols to insertion point. | {
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} | [] | closed | false | null | [] | null | [] | 2024-05-29T11:33:26 | 2024-05-29T13:29:59 | 2024-05-29T13:29:58 | NONE | null | true | {
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} | Sort summands by distance of symbols to insertion point.
By emitting more distant summands first, we maximize opportunities for LICM.
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} | Create a simple loop unswitching pass.
In some cases, LLVM's loop unswitcher doesn't detect our
conditions as trivial and doesn't optimize.
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