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[xla:pjrt:cpu] Correctly create PjRt memory for constant allocations
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"2024-05-30T16:10:29"
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NONE
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[xla:pjrt:cpu] Correctly create PjRt memory for constant allocations
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Stop using xla/status.h in favor of absl/status/status.h.
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Stop using xla/status.h in favor of absl/status/status.h.
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Stop using xla/status.h in favor of absl/status/status.h.
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NONE
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Stop using xla/status.h in favor of absl/status/status.h.
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Stop using xla/status.h in favor of absl/status/status.h.
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NONE
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Stop using xla/status.h in favor of absl/status/status.h.
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Stop using xla/status.h in favor of absl/status/status.h.
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NONE
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Stop using xla/status.h in favor of absl/status/status.h.
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[XLA] Allow reusing channel IDs between sync/async variants of collectives.
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[XLA] Allow reusing channel IDs between sync/async variants of collectives.
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Stop using xla/status.h in favor of absl/status/status.h.
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Stop using xla/status.h in favor of absl/status/status.h.
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Stop using xla/status.h in favor of absl/status/status.h.
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Stop using xla/status.h in favor of absl/status/status.h.
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Stop using xla/status.h in favor of absl/status/status.h.
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NONE
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Stop using xla/status.h in favor of absl/status/status.h.
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Remove compatibility constraint.
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NONE
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Remove compatibility constraint.
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[IFRT] Add an IFRT API for Topology.
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"2024-05-30T15:37:23"
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NONE
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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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Right now, the shape inference code only looks at `_Arg`s to match user-given `arg_shapes`, as `_Arg`s are what placeholder ops are represented during runtime. To run shape inference ahead-of-time (with static graph and input shapes), we need the shape inference code to examine `Placeholder` ops as well; thus this cl
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"2024-05-30T14:56:55"
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Right now, the shape inference code only looks at `_Arg`s to match user-given `arg_shapes`, as `_Arg`s are what placeholder ops are represented during runtime. To run shape inference ahead-of-time (with static graph and input shapes), we need the shape inference code to examine `Placeholder` ops as well; thus this cl
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ValueError: `validation_split` is only supported for Tensors or NumPy arrays, found following types in the input: [<class 'int'>]
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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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[xla:cpu] Add support for kWhile thunk
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"2024-05-30T14:46:17"
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[xla:cpu] Add support for kWhile thunk
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[xla:cpu] Add support for emitting fusions with thread local computations
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[xla:cpu] Add support for emitting fusions with thread local computations
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Add flag to disable autotune for hlo compile tests.
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Add flag to disable autotune for hlo compile tests.
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Automated Code Change
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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
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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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[MHLO] Add support for batching dims when legalizing `mhlo.gather` to linalg
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[MHLO] Add support for batching dims when legalizing `mhlo.gather` to linalg
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Integrate StableHLO at openxla/stablehlo@c44d9af8
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Integrate StableHLO at openxla/stablehlo@c44d9af8
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Not able to build TensorFlow with GPU support
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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"
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### 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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Fixed description of num_threads in `tf.lite.interpreter` doc
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"2024-05-30T07:33:04"
"2024-06-03T04:21:43"
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Hi 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
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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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Wrong explanation about an argument of tflite interpreter
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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"
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### 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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Allow sharing the same IFRT client across multiple IFRT Proxy sessions
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Add profiling_info_pb2 to lite.py
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Disable MLIR-based TF graph optimizer
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Disable MLIR-based TF graph optimizer
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Fixes source list after schema utility functions relocation.
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Fixes source list after schema utility functions relocation.
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[xla:gpu] Add GemvRewriter pass
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[xla:gpu] Add GemvRewriter pass Currently this is unused but will be needed to fix matrix-vector dot performance
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Integrate LLVM at llvm/llvm-project@765206e05045
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Integrate LLVM at llvm/llvm-project@765206e05045 Updates LLVM usage to match [765206e05045](https://github.com/llvm/llvm-project/commit/765206e05045)
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PR #13065: [NVIDIA] Disable the bias reuse for fp8 dot
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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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Add note on TensorFlow NumPy 2.0 upgrade in the next release
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Add note on TensorFlow NumPy 2.0 upgrade in the next release
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Include stream_executor.h in a few files that were missing its inclusion.
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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
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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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Make Stream a pure virtual base class to start eliminating circular dependencies between and StreamExecutor.
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Make Stream a pure virtual base class to start eliminating circular dependencies between and StreamExecutor.
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[XLA] Limit DCE MoF cleanup to GPU backend
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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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Reverts changelist 638422085
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Reverts changelist 638422085
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[IFRT] Add several commonly used APIs to `xla::ifrt::Sharding`
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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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Allow model builder to build a model based on a file descriptor.
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Allow model builder to build a model based on a file descriptor. FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13065 from kaixih:disable_bias_reuse_in_fp8 15f774667013045c7d8d9e38ac80a45645313b1e
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Fix test build.
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NONE
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Fix test build.
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Update TFRT dependency to use revision
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NONE
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Update TFRT dependency to use revision http://github.com/tensorflow/runtime/commit/4662f7552dc2e420ee20361c077b7aeb334a1087.
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[TOCO Removal] Copy converter's Python API wrapper to a more appropriate dir.
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"2024-05-29T20:30:46"
"2024-05-31T18:18:42"
"2024-05-31T18:18:41"
NONE
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[TOCO Removal] Copy converter's Python API wrapper to a more appropriate dir. Previously, the converter's Python API was intertwined with TOCO. This CL copies it to a new location and does some light renaming in preparating of further disentangling of TOCO artifacts from the TFL converter. Method 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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[xla:gpu] Add support for u32 and u64 offset constants
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"2024-05-29T20:19:45"
"2024-05-29T21:33:23"
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NONE
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[xla:gpu] Add support for u32 and u64 offset constants
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Load variable concurrently
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"2024-05-29T20:17:51"
"2024-05-29T21:23:08"
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Load variable concurrently FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13018 from ROCm:ci_amdtags_20240523 0bf9ab80d06284883df19f17f421eb6a691c0dd8
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Replace `tensorflow::Status` with `absl::Status` in `next_pluggable_device`
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"2024-05-29T20:02:44"
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Replace `tensorflow::Status` with `absl::Status` in `next_pluggable_device`
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[XLA] Stop duplicating broadcasts before host memory offloading.
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"2024-05-29T19:56:14"
"2024-05-29T22:24:12"
"2024-05-29T22:24:12"
NONE
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[XLA] Stop duplicating broadcasts before host memory offloading. HostOffloader will do this as needed.
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segmentation fault when tf.histogram_fixed_width receives large `value_range` and `nbins` on CPU mode
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"2024-05-29T19:33:25"
"2024-05-31T03:34:44"
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### Issue type 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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move loop-invariant code outside the loop
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[xla:cpu] Add support for kCall thunks
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"2024-05-29T19:16:15"
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[xla:cpu] Pack constants of sub-byte element type into dense storage compatible with XLA format + added a few more tests that now pass with thunks runtime
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[XLA:MSA] Add a new function to estimate the total trip counts of instructions.
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[XLA:MSA] Add a new function to estimate the total trip counts of instructions. 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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[XLA:LHS] Add a rule for async ops that release nonextendable resources so that their overlap is closed right after their estimated time has passed. Also improve logging levels.
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"2024-05-29T18:36:11"
"2024-05-30T04:05:42"
"2024-05-30T04:05:41"
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[XLA:LHS] Add a rule for async ops that release nonextendable resources so that their overlap is closed right after their estimated time has passed. Also improve logging levels.
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[XLA] Update fusion kind after DCE
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[XLA] Update fusion kind after DCE
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copy the legalize tf passes under lite/stablehlo
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copy the legalize tf passes under lite/stablehlo
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Cap utilization at 100% when accurate bandwidth cannot be determined.
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"2024-05-29T18:28:27"
"2024-06-06T18:29:13"
"2024-06-06T18:29:10"
NONE
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Cap utilization at 100% when accurate bandwidth cannot be determined.
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Add additional overloaded versions of the BufferFromHostLiteral function in PjRtClient which take a device_layout parameter.
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Add additional overloaded versions of the BufferFromHostLiteral function in PjRtClient which take a device_layout parameter.
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Stop using xla/status.h in favor of absl/status/status.h.
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"2024-05-29T17:49:28"
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Stop using xla/status.h in favor of absl/status/status.h.
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PR #13194: [ROCm] Fix build break in xla/service/gpu/ir_emitter_triton_rocm.cc
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"2024-05-29T17:42:14"
"2024-05-29T18:59:19"
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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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Bazel compiling source code failed because of highwayhash/sip_hash.cc
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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"
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### 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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[XLA:GPU] Add a GUnit matcher for TiledHloInstruction
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[XLA:GPU] Add a GUnit matcher for TiledHloInstruction
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[IFRT] Add function for converting from xla::ifrt::Sharding to OpSharding.
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"2024-05-29T17:13:23"
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[IFRT] Add function for converting from xla::ifrt::Sharding to OpSharding.
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PR #13165: [ROCm] Update ROCm version to 6.0.2
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PR #13165: [ROCm] Update ROCm version to 6.0.2 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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Fix diff command for bazelrc in XLA build script
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Fix diff command for bazelrc in XLA build script
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Use numpy as the source of truth for expm1 test.
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Use numpy as the source of truth for expm1 test.
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Change deprecated field always_print_primitive_fields to favored field always_print_fields_with_no_presence to avoid mis-assigning unset explicit-presence fields
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Change deprecated field always_print_primitive_fields to favored field always_print_fields_with_no_presence to avoid mis-assigning unset explicit-presence fields
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[xla:ffi] Renamed BufferBase to AnyBuffer
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[xla:ffi] Renamed BufferBase to AnyBuffer BufferBase is too opaque to guess that it should be used for arguments which accept any combination of dtype/rank.
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[xla:gpu] Exclude collectives from command buffers if CUDA < 12.3
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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
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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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* Improve the perf of tf.sparse.segment_(mean/sum/sqrt) ops when using
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* Improve the perf of tf.sparse.segment_(mean/sum/sqrt) ops when using bfloat16/float16 precision with float32 for aggregation. * Use float32 accumulation for the gradients of tf.sparse.segment_(mean/sum/sqrt) ops when using bfloat16/float16 precision.
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Remove Stream::platform_specific_stream method, and keep it on GpuStream where it makes sense.
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Remove Stream::platform_specific_stream method, and keep it on GpuStream where it makes sense.
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Stop using xla/status.h in favor of absl/status/status.h.
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Stop using xla/status.h in favor of absl/status/status.h.
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Stop using xla/status.h in favor of absl/status/status.h.
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Stop using xla/status.h in favor of absl/status/status.h.
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Stop using xla/status.h in favor of absl/status/status.h.
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Stop using xla/status.h in favor of absl/status/status.h.
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Stop using xla/status.h in favor of absl/status/status.h.
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Stop using xla/status.h in favor of absl/status/status.h.
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Training model with the Poisson loss function and the Adam optimizer resulted in NaN loss
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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
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### 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 _No response_
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68,805
Add simple loop peeling for imperfect tiling.
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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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68,804
[xla] Add `test_xla_cpu_thunks` tag to test XLA:CPU with thunks runtime
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"2024-05-29T13:23:33"
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[xla] Add `test_xla_cpu_thunks` tag to test XLA:CPU with thunks runtime
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[XLA:GPU] Use priority fusion in TritonGemmAutotunerExtractor.
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[XLA:GPU] Use priority fusion in TritonGemmAutotunerExtractor.
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expose "bad_indice_policy" attribute for tf.gather_nd (python API)
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expose "bad_indice_policy" attribute for tf.gather_nd (python API)
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Account for trivial dimensions in TransposesMinorDimension().
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"2024-05-29T12:47:14"
"2024-06-03T11:02:43"
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NONE
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Account for trivial dimensions in TransposesMinorDimension(). 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 ToString() methods to Tiled HLO instructions and computations.
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[XLA:GPU] Add ToString() methods to Tiled HLO instructions and computations.
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[XLA:GPU] Add a "draft" version of a generic Triton Fusion Emitter.
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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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[XLA:GPU] Print Interval as a semi-open interval.
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"2024-05-29T11:47:35"
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[XLA:GPU] Print Interval as a semi-open interval. Replaced by %s/in \[\(\d\+\), \(\d\+\)\]/\='in ['.(submatch(1)).', '.(submatch(2)+1).')'/g
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[RNN] LSTM Model conversion error after upgrading to tf 2.16.1 from 2.15
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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
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### 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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Sort summands by distance of symbols to insertion point.
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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.
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"2024-05-29T11:18:13"
"2024-05-29T13:50:00"
"2024-05-29T13:49:58"
NONE
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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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https://api.github.com/repos/tensorflow/tensorflow/issues/68795/timeline
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