Issue Title,Description,Created At,Comments [xla:gpu] Extend collective-permute decomposer to also make decision for,"[xla:gpu] Extend collective-permute decomposer to also make decision for Send-Recv pipeling and record the decision with frontend attributes. We first use a simple heuristics to decide on the decomposition of which CollectivePermute operations will be pipelined. We will only pipeline CollectivePermute that sends loop input data, and pick the first pipelineable CollectivePermute for pipelining. Then, if there is another pipelineable CollectivePermute that forms a cycle with the to-be-pipelined CollectivePermute, we will pipeline both CollectivePermute. Otherwise, we will only pipeline one CollectivePermute. Then, when we decompose CollectivePermute operations, we add a frontend attribute to the Send/Recv operation to represent the pipelining decision. Add tests. ",2024-03-11T05:16:45Z,0 Microoptmize the conditions in IsArrayType.,"Microoptmize the conditions in IsArrayType. ",2024-03-11T04:30:26Z,0 Do not call Shape::is_static when unnecessary.,"Do not call Shape::is_static when unnecessary. ",2024-03-11T04:26:26Z,0 Eliminate unnecessary copies for HloSharding.,"Eliminate unnecessary copies for HloSharding. ",2024-03-11T04:25:26Z,0 Add Dynamic Range Quantized op support for `op_stat_pass.cc`.,"Add Dynamic Range Quantized op support for `op_stat_pass.cc`. - Cleanup header imports as well. ",2024-03-11T03:12:47Z,0 Add check conditions in `quantization_driver_test.cc`.,"Add check conditions in `quantization_driver_test.cc`. - Adds more rigorous checks for desired states in intermediate testing stages. - Renames and rewrites `IsEmpty` and `HasQuantParams` for clarity. ",2024-03-11T02:17:30Z,0 2.16.1 libtensorflow binary,"### Issue type Support ### Have you reproduced the bug with TensorFlow Nightly? Yes ### Source binary ### TensorFlow version 2.16.1 ### Custom code No ### OS platform and distribution Linux ### 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 Yes ### Current behavior? Hi! Tensorflow 2.16.1 has been [released](https://github.com/tensorflow/tensorflow/releases/tag/v2.16.1) recently. However, the latest archive with the `libtensorflow` on the official website [is still 2.15](https://www.tensorflow.org/install/lang_c). Where can I get the latest 2.16.1 `libtensorflow` with GPU support for Linux? ### Standalone code to reproduce the issue ```shell - ``` ### Relevant log output _No response_",2024-03-10T20:56:00Z,0 Make function loading more concurrent with `TF_ENABLE_EAGER_CLIENT_STREAMING_ENQUEUE` set to `false`,"Make function loading more concurrent with `TF_ENABLE_EAGER_CLIENT_STREAMING_ENQUEUE` set to `false` ",2024-03-10T19:12:58Z,0 Testing a temporary code change.,"Testing a temporary code change. ",2024-03-10T18:13:15Z,0 [XLA:Python] Port py_values to nanobind.,"[XLA:Python] Port py_values to nanobind. ",2024-03-10T15:11:31Z,0 tf.tensor_scatter_nd_add: Aborted (core dumped),"### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? Yes ### Source binary ### TensorFlow version tf 2.15 ### Custom code Yes ### OS platform and distribution Ubuntu 20.04 ### Mobile device _No response_ ### Python version 3.9 ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? Under specific input, `tf.tensor_scatter_nd_add` encounters ""Aborted (core dumped)"". ### Standalone code to reproduce the issue ```shell import tensorflow as tf # Generate input data input_tensor = tf.zeros([15, 15, 15]) indices = tf.constant([[[0, 0, 0], [1, 1, 1]], [[2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5]], [[6, 6, 6], [7, 7, 7]], [[8, 8, 8], [9, 9, 9]], [[10, 10, 10], [11, 11, 11]], [[12, 12, 12], [13, 13, 13]], [[14, 14, 14], [0, 0, 0]], [[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]], [[7, 7, 7], [8, 8, 8]], [[9, 9, 9], [10, 10, 10]], [[11, 11, 11], [12, 12, 12]], [[13, 13, 13], [14, 14, 14]]]) updates = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0]) # Cast updates to float # Invoke tf.tensor_scatter_nd_add result = tf.tensor_scatter_nd_add(input_tensor, indices, updates) # Print the result print(result) ``` ### Relevant log output ```shell 2024-03-10 14:59:51.853766: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (1 vs. 1) Aborted (core dumped) ``` ",2024-03-10T15:00:49Z,0 tf.raw_ops.UnicodeEncode: Segmentation fault (core dumped),"### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? Yes ### Source binary ### TensorFlow version tf 2.15 ### Custom code Yes ### OS platform and distribution Ubuntu 20.04 ### Mobile device _No response_ ### Python version 3.9 ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? Under specific input, `tf.raw_ops.UnicodeEncode` encounters ""Segmentation fault (core dumped)"". ### Standalone code to reproduce the issue ```shell import tensorflow as tf # Generate input data input_values = tf.constant([72, 101, 108, 108, 111, 32, 87, 111, 114, 108, 100]) # Unicode codepoints for ""Hello World"" input_splits = tf.constant([[0, 5, 11]]) # Split indices for the input_values with two dimensions output_encoding = ""UTF-8"" # Invoke tf.raw_ops.unicode_encode output = tf.raw_ops.UnicodeEncode(input_values=input_values, input_splits=input_splits, output_encoding=output_encoding) # Print the output print(output) ``` ### Relevant log output ```shell Segmentation fault (core dumped) ``` ",2024-03-10T14:59:08Z,0 tf.raw_ops.TensorScatterSub: Aborted (core dumped),"### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? Yes ### Source binary ### TensorFlow version tf 2.15 ### Custom code Yes ### OS platform and distribution Ubuntu 20.04 ### Mobile device _No response_ ### Python version 3.9 ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? Under specific input, `tf.raw_ops.TensorScatterSub` encounters ""Aborted (core dumped)"". ### Standalone code to reproduce the issue ```shell import tensorflow as tf # Generate input data tensor = tf.constant([1, 2, 3, 4, 5]) indices = tf.constant([[[1], [3]], [[0], [2]]]) # Nested structure for indices updates = tf.constant([10, 20]) # Invoke tf.raw_ops.TensorScatterSub result = tf.raw_ops.TensorScatterSub(tensor=tensor, indices=indices, updates=updates) # Print the result print(result) ``` ### Relevant log output ```shell 2024-03-10 14:55:41.958738: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (1 vs. 1) Aborted (core dumped) ``` ",2024-03-10T14:57:36Z,0 tf.raw_ops.SparseConcat: Overflow bug ,"### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? Yes ### Source binary ### TensorFlow version tf 2.15 ### Custom code Yes ### OS platform and distribution Ubuntu 20.04 ### Mobile device _No response_ ### Python version 3.9 ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? Under specific input, `tf.raw_ops.SparseConcat` encounters overflow bug. ### Standalone code to reproduce the issue ```shell import tensorflow as tf # Generate input data indices1 = tf.constant([[0, 0], [1, 2]], dtype=tf.int64) values1 = tf.constant([1, 2], dtype=tf.float32) shape1 = tf.constant([3, 4], dtype=tf.int64) indices2 = tf.constant([[0, 1], [2, 3]], dtype=tf.int64) values2 = tf.constant([3, 4], dtype=tf.float32) shape2 = tf.constant([-1, 4], dtype=tf.int64) # Mutated shape with the negative bit set # Invoke tf.raw_ops.SparseConcat concatenated_sparse = tf.raw_ops.SparseConcat( indices=[indices1, indices2], values=[values1, values2], shapes=[shape1, shape2], concat_dim=0 ) print(concatenated_sparse) ``` ### Relevant log output ```shell tensorflow.python.framework.errors_impl.InternalError: {{function_node __wrapped__SparseConcat_N_2_device_/job:localhost/replica:0/task:0/device:CPU:0}} Encountered overflow from large input shape. [Op:SparseConcat] name: ``` ",2024-03-10T14:55:13Z,0 tf.raw_ops.FusedPadConv2D: Aborted (core dumped),"### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? Yes ### Source binary ### TensorFlow version tf 2.15 ### Custom code Yes ### OS platform and distribution Ubuntu 20.04 ### Mobile device _No response_ ### Python version 3.9 ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? Under specific input, `tf.raw_ops.FusedPadConv2D` encounters ""Aborted (core dumped)"". ### Standalone code to reproduce the issue ```shell import tensorflow as tf # Generate input data input_data = tf.random.normal([3, 10, 10]) # Define paddings paddings = tf.constant([[0, 0], [1, 1], [1, 1]]) # Define filter filter = tf.random.normal([3, 3, 3, 16]) # Define mode mode = ""REFLECT"" # Change mode to ""REFLECT"" or ""SYMMETRIC"" # Define strides strides = [1, 1, 1, 1] # Define padding padding = ""VALID"" # Invoke tf.raw_ops.FusedPadConv2D output = tf.raw_ops.FusedPadConv2D(input=input_data, paddings=paddings, filter=filter, mode=mode, strides=strides, padding=padding) print(output) ``` ### Relevant log output ```shell 2024-03-10 14:49:28.555826: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (3 vs. 3) Aborted (core dumped) ``` ",2024-03-10T14:51:07Z,0 tf.tensor_scatter_nd_update: Aborted (core dumped),"### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? Yes ### Source binary ### TensorFlow version tf 2.15 ### Custom code Yes ### OS platform and distribution Ubuntu 20.04 ### Mobile device _No response_ ### Python version 3.9 ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? Under specific input, `tf.tensor_scatter_nd_update` encounters ""Aborted (core dumped)"". ### Standalone code to reproduce the issue ```shell import tensorflow as tf # Generate input data input_tensor = tf.zeros([2, 2, 2]) # A tensor that contains other tensors, creating a nested structure indices = tf.constant([[[0, 0, 0], [1, 1, 1]], [[1, 0, 1], [0, 1, 0]]]) updates = tf.constant([1, 2], dtype=tf.float32) # Cast updates to float # Invoke tf.tensor_scatter_nd_update result = tf.tensor_scatter_nd_update(input_tensor, indices, updates) # Print the result print(result) ``` ### Relevant log output ```shell 2024-03-10 14:36:43.315650: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (1 vs. 1) Aborted (core dumped) ``` ",2024-03-10T14:48:19Z,0 failed to compile a tensorflow C++ example. # Error incompatible with your Protocol Buffer headers ,"### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? No ### Source source ### TensorFlow version tf 2.15.0 ### Custom code No ### OS platform and distribution Linux Ubuntu 22.04 ### Mobile device _No response_ ### Python version 3.10.12 ### Bazel version 6.1.0 ### GCC/compiler version 11.4.0 ### CUDA/cuDNN version 12.2/8.9.7 ### GPU model and memory GTX 3090/24G ### Current behavior? I first compiled TensorFlow using Bazel according to the official documentation, these are my operations: `git clone https://github.com/tensorflow/tensorflow` `cd tensorflow` `git checkout r2.15` `./configure ` and information is: > > You have bazel 6.1.0 installed. > Please specify the location of python. [Default is /usr/bin/python3]: > > > Found possible Python library paths: > /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/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]: n > No TensorRT support will be enabled for TensorFlow. > > Found CUDA 12.2 in: > /usr/local/cuda-12.2/targets/x86_64-linux/lib > /usr/local/cuda-12.2/targets/x86_64-linux/include > Found cuDNN 8 in: > /usr/lib/x86_64-linux-gnu > /usr/include > > > 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: 8.6]: > > > Do you want to use clang as CUDA compiler? [Y/n]: n > nvcc will be used as CUDA compiler. > > Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]: > > > 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 and I then compile with bazel: `bazel build --config=cuda tensorflow:tensorflow_cc` `bazel build tensorflow:install_headers` There were no issues, I successfully compiled the header files and link libraries I wanted in the `bazel-bin` folder. But when I try to compile a C++ sample: ``` #include #include #include using namespace std; using namespace tensorflow; int main() { Session* session; Status status = NewSession(SessionOptions(), &session); if (!status.ok()) { cout << status.ToString() << ""\n""; return 1; } cout << ""Session successfully created.\n""; } ``` command is `g++ -std=c++14 -o tf_example -I/home/wangchen/tensorflow/bazel-bin/tensorflow/include -L/home/wangchen/tensorflow/bazel-bin/tensorflow/libtensorflow_cc -L/home/wangchen/tensorflow/bazel-bin/tensorflow/libtensorflow_framework -ltensorflow_framework -ltensorflow_cc tf_example.cpp ` I got an error #error This file was generated by an older version of protoc which is incompatible with your Protocol Buffer headers. Please regenerate this file with a newer version of protoc. My protobuf is compiled from official repo, the versions are: ``` { ""23.x"": { ""protoc_version"": ""23.4"", ""lts"": false, ""date"": ""2023-07-05"", ""languages"": { ""cpp"": ""4.23.4"", ""csharp"": ""3.23.4"", ""java"": ""3.23.4"", ""javascript"": ""3.23.4"", ""objectivec"": ""3.23.4"", ""php"": ""3.23.4"", ""python"": ""4.23.4"", ""ruby"": ""3.23.4"" } } } ``` I suspect there might be some protobuf versions that are incompatible with my TensorFlow. What methods should I use to obtain the correct version? I would greatly appreciate any proposed solutions. ### Standalone code to reproduce the issue ```shell #include #include #include using namespace std; using namespace tensorflow; int main() { Session* session; Status status = NewSession(SessionOptions(), &session); if (!status.ok()) { cout << status.ToString() << ""\n""; return 1; } cout << ""Session successfully created.\n""; } ``` ``` ### Relevant log output ```shell wangchen@wc:~/tfc++test$ g++ -std=c++14 -o tf_example -I/home/wangchen/tensorflow/bazel-bin/tensorflow/include -L/home/wangchen/tensorflow/bazel-bin/tensorflow/libtensorflow_cc -L/home/wangchen/tensorflow/bazel-bin/tensorflow/libtensorflow_framework -ltensorflow_framework -ltensorflow_cc tf_example.cpp In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tsl/platform/status.h:39, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/platform/status.h:23, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/platform/errors.h:27, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/platform/env.h:27, from tf_example.cpp:1: /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tsl/protobuf/error_codes.pb.h:17:2: error: #error This file was generated by an older version of protoc which is 17 | #error This file was generated by an older version of protoc which is | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tsl/protobuf/error_codes.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please 18 | #error incompatible with your Protocol Buffer headers. Please | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tsl/protobuf/error_codes.pb.h:19:2: error: #error regenerate this file with a newer version of protoc. 19 | #error regenerate this file with a newer version of protoc. | ^~~~~ In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:24, from tf_example.cpp:2: /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/device_attributes.pb.h:17:2: error: #error This file was generated by an older version of protoc which is 17 | #error This file was generated by an older version of protoc which is | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/device_attributes.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please 18 | #error incompatible with your Protocol Buffer headers. Please | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/device_attributes.pb.h:19:2: error: #error regenerate this file with a newer version of protoc. 19 | #error regenerate this file with a newer version of protoc. | ^~~~~ In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:25, from tf_example.cpp:2: /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:17:2: error: #error This file was generated by an older version of protoc which is 17 | #error This file was generated by an older version of protoc which is | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please 18 | #error incompatible with your Protocol Buffer headers. Please | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:19:2: error: #error regenerate this file with a newer version of protoc. 19 | #error regenerate this file with a newer version of protoc. | ^~~~~ In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:33, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:25, from tf_example.cpp:2: /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/function.pb.h:17:2: error: #error This file was generated by an older version of protoc which is 17 | #error This file was generated by an older version of protoc which is | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/function.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please 18 | #error incompatible with your Protocol Buffer headers. Please | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/function.pb.h:19:2: error: #error regenerate this file with a newer version of protoc. 19 | #error regenerate this file with a newer version of protoc. | ^~~~~ In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/function.pb.h:36, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:33, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:25, from tf_example.cpp:2: /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/attr_value.pb.h:17:2: error: #error This file was generated by an older version of protoc which is 17 | #error This file was generated by an older version of protoc which is | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/attr_value.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please 18 | #error incompatible with your Protocol Buffer headers. Please | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/attr_value.pb.h:19:2: error: #error regenerate this file with a newer version of protoc. 19 | #error regenerate this file with a newer version of protoc. | ^~~~~ In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/attr_value.pb.h:36, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/function.pb.h:36, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:33, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:25, from tf_example.cpp:2: /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/tensor.pb.h:17:2: error: #error This file was generated by an older version of protoc which is 17 | #error This file was generated by an older version of protoc which is | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/tensor.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please 18 | #error incompatible with your Protocol Buffer headers. Please | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/tensor.pb.h:19:2: error: #error regenerate this file with a newer version of protoc. 19 | #error regenerate this file with a newer version of protoc. | ^~~~~ In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/tensor.pb.h:33, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/attr_value.pb.h:36, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/function.pb.h:36, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:33, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:25, from tf_example.cpp:2: /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/resource_handle.pb.h:17:2: error: #error This file was generated by an older version of protoc which is 17 | #error This file was generated by an older version of protoc which is | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/resource_handle.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please 18 | #error incompatible with your Protocol Buffer headers. Please | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/resource_handle.pb.h:19:2: error: #error regenerate this file with a newer version of protoc. 19 | #error regenerate this file with a newer version of protoc. | ^~~~~ In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/resource_handle.pb.h:33, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/tensor.pb.h:33, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/attr_value.pb.h:36, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/function.pb.h:36, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:33, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:25, from tf_example.cpp:2: /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/tensor_shape.pb.h:17:2: error: #error This file was generated by an older version of protoc which is 17 | #error This file was generated by an older version of protoc which is | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/tensor_shape.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please 18 | #error incompatible with your Protocol Buffer headers. Please | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/tensor_shape.pb.h:19:2: error: #error regenerate this file with a newer version of protoc. 19 | #error regenerate this file with a newer version of protoc. | ^~~~~ In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/resource_handle.pb.h:34, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/tensor.pb.h:33, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/attr_value.pb.h:36, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/function.pb.h:36, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:33, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:25, from tf_example.cpp:2: /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/types.pb.h:17:2: error: #error This file was generated by an older version of protoc which is 17 | #error This file was generated by an older version of protoc which is | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/types.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please 18 | #error incompatible with your Protocol Buffer headers. Please | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/types.pb.h:19:2: error: #error regenerate this file with a newer version of protoc. 19 | #error regenerate this file with a newer version of protoc. | ^~~~~ In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/function.pb.h:37, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:33, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:25, from tf_example.cpp:2: /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/node_def.pb.h:17:2: error: #error This file was generated by an older version of protoc which is 17 | #error This file was generated by an older version of protoc which is | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/node_def.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please 18 | #error incompatible with your Protocol Buffer headers. Please | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/node_def.pb.h:19:2: error: #error regenerate this file with a newer version of protoc. 19 | #error regenerate this file with a newer version of protoc. | ^~~~~ In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/node_def.pb.h:37, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/function.pb.h:37, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:33, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:25, from tf_example.cpp:2: /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/full_type.pb.h:17:2: error: #error This file was generated by an older version of protoc which is 17 | #error This file was generated by an older version of protoc which is | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/full_type.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please 18 | #error incompatible with your Protocol Buffer headers. Please | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/full_type.pb.h:19:2: error: #error regenerate this file with a newer version of protoc. 19 | #error regenerate this file with a newer version of protoc. | ^~~~~ In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/function.pb.h:38, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:33, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:25, from tf_example.cpp:2: /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/op_def.pb.h:17:2: error: #error This file was generated by an older version of protoc which is 17 | #error This file was generated by an older version of protoc which is | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/op_def.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please 18 | #error incompatible with your Protocol Buffer headers. Please | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/op_def.pb.h:19:2: error: #error regenerate this file with a newer version of protoc. 19 | #error regenerate this file with a newer version of protoc. | ^~~~~ In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:34, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:25, from tf_example.cpp:2: /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph_debug_info.pb.h:17:2: error: #error This file was generated by an older version of protoc which is 17 | #error This file was generated by an older version of protoc which is | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph_debug_info.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please 18 | #error incompatible with your Protocol Buffer headers. Please | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph_debug_info.pb.h:19:2: error: #error regenerate this file with a newer version of protoc. 19 | #error regenerate this file with a newer version of protoc. | ^~~~~ In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/graph.pb.h:36, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:25, from tf_example.cpp:2: /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/versions.pb.h:17:2: error: #error This file was generated by an older version of protoc which is 17 | #error This file was generated by an older version of protoc which is | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/versions.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please 18 | #error incompatible with your Protocol Buffer headers. Please | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/versions.pb.h:19:2: error: #error regenerate this file with a newer version of protoc. 19 | #error regenerate this file with a newer version of protoc. | ^~~~~ In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:30, from tf_example.cpp:2: /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/config.pb.h:17:2: error: #error This file was generated by an older version of protoc which is 17 | #error This file was generated by an older version of protoc which is | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/config.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please 18 | #error incompatible with your Protocol Buffer headers. Please | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/config.pb.h:19:2: error: #error regenerate this file with a newer version of protoc. 19 | #error regenerate this file with a newer version of protoc. | ^~~~~ In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/config.pb.h:37, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:30, from tf_example.cpp:2: /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/cost_graph.pb.h:17:2: error: #error This file was generated by an older version of protoc which is 17 | #error This file was generated by an older version of protoc which is | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/cost_graph.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please 18 | #error incompatible with your Protocol Buffer headers. Please | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/cost_graph.pb.h:19:2: error: #error regenerate this file with a newer version of protoc. 19 | #error regenerate this file with a newer version of protoc. | ^~~~~ In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/config.pb.h:39, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:30, from tf_example.cpp:2: /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/step_stats.pb.h:17:2: error: #error This file was generated by an older version of protoc which is 17 | #error This file was generated by an older version of protoc which is | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/step_stats.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please 18 | #error incompatible with your Protocol Buffer headers. Please | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/step_stats.pb.h:19:2: error: #error regenerate this file with a newer version of protoc. 19 | #error regenerate this file with a newer version of protoc. | ^~~~~ In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/step_stats.pb.h:36, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/config.pb.h:39, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:30, from tf_example.cpp:2: /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/allocation_description.pb.h:17:2: error: #error This file was generated by an older version of protoc which is 17 | #error This file was generated by an older version of protoc which is | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/allocation_description.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please 18 | #error incompatible with your Protocol Buffer headers. Please | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/allocation_description.pb.h:19:2: error: #error regenerate this file with a newer version of protoc. 19 | #error regenerate this file with a newer version of protoc. | ^~~~~ In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/step_stats.pb.h:37, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/config.pb.h:39, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:30, from tf_example.cpp:2: /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/tensor_description.pb.h:17:2: error: #error This file was generated by an older version of protoc which is 17 | #error This file was generated by an older version of protoc which is | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/tensor_description.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please 18 | #error incompatible with your Protocol Buffer headers. Please | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/framework/tensor_description.pb.h:19:2: error: #error regenerate this file with a newer version of protoc. 19 | #error regenerate this file with a newer version of protoc. | ^~~~~ In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/config.pb.h:40, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:30, from tf_example.cpp:2: /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/cluster.pb.h:17:2: error: #error This file was generated by an older version of protoc which is 17 | #error This file was generated by an older version of protoc which is | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/cluster.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please 18 | #error incompatible with your Protocol Buffer headers. Please | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/cluster.pb.h:19:2: error: #error regenerate this file with a newer version of protoc. 19 | #error regenerate this file with a newer version of protoc. | ^~~~~ In file included from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/config.pb.h:41, from /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/public/session.h:30, from tf_example.cpp:2: /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/debug.pb.h:17:2: error: #error This file was generated by an older version of protoc which is 17 | #error This file was generated by an older version of protoc which is | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/debug.pb.h:18:2: error: #error incompatible with your Protocol Buffer headers. Please 18 | #error incompatible with your Protocol Buffer headers. Please | ^~~~~ /home/wangchen/tensorflow/bazel-bin/tensorflow/include/tensorflow/core/protobuf/debug.pb.h:19:2: error: #error regenerate this file with a newer version of protoc. 19 | #error regenerate this file with a newer version of protoc. | ^~~~~ ``` ",2024-03-10T04:22:46Z,0 Saved model won't load: Unable to synchronously open object (bad local heap signature),"### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? Yes ### Source binary ### TensorFlow version 2.16.1 ### Custom code Yes ### OS platform and distribution windows 10 ### Mobile device _No response_ ### Python version 3.12 ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? Model saved from Python 3.12 tensorflow 2.16.1 model.save('my_model.keras', overwrite=True) After this the model does not load ### Standalone code to reproduce the issue ```shell model=tf.keras.models.load_model('my_model.keras', custom_objects=None, compile=True, safe_mode=True) ``` ### Relevant log output ```shell Traceback (most recent call last): File ""D:\Project\main.py"", line 391, in model=tf.keras.models.load_model('my_model.keras', custom_objects=None, compile=True, safe_mode=True) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File ""D:\Project\venv\Lib\site-packages\keras\src\saving\saving_api.py"", line 176, in load_model return saving_lib.load_model( ^^^^^^^^^^^^^^^^^^^^^^ File ""D:\Project\venv\Lib\site-packages\keras\src\saving\saving_lib.py"", line 192, in load_model _raise_loading_failure(error_msgs) File ""D:\Project\venv\Lib\site-packages\keras\src\saving\saving_lib.py"", line 273, in _raise_loading_failure raise ValueError(msg) ValueError: A total of 13 objects could not be loaded. Example error message for object : 'Unable to synchronously open object (bad local heap signature)' List of objects that could not be loaded: [, , , , , , , , , , , , ] ``` ",2024-03-10T04:07:46Z,1 Tensorflow import error,"### Issue type Build/Install ### Have you reproduced the bug with TensorFlow Nightly? Yes ### Source source ### TensorFlow version tf 2.13.0 ### Custom code Yes ### OS platform and distribution Win 11 ### Mobile device _No response_ ### Python version 3.9.7 ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? I intalled tensorflow, but it gives an error when I try to import it. ### Standalone code to reproduce the issue ```shell import tensorflow as tf ``` ### Relevant log output ```shell runfile('X:/Nano-Photonics and Quantum Optics Lab!/ML Project/Tkinter learning/Tkinter Git - GitLab/Inverse_Design_Periodic_GUI_CustomModern.py', wdir='X:/Nano-Photonics and Quantum Optics Lab!/ML Project/Tkinter learning/Tkinter Git - GitLab') Traceback (most recent call last): File ""X:\Nano-Photonics and Quantum Optics Lab!\ML Project\Tkinter learning\Tkinter Git - GitLab\Inverse_Design_Periodic_GUI_CustomModern.py"", line 20, in import tensorflow as tf #print(tf.__version__) File ""C:\Users\athen\anaconda3\lib\site-packages\tensorflow\__init__.py"", line 469, in _keras._load() File ""C:\Users\athen\anaconda3\lib\site-packages\tensorflow\python\util\lazy_loader.py"", line 41, in _load module = importlib.import_module(self.__name__) File ""C:\Users\athen\anaconda3\lib\importlib\__init__.py"", line 127, in import_module return _bootstrap._gcd_import(name[level:], package, level) File ""C:\Users\athen\anaconda3\lib\site-packages\keras\__init__.py"", line 20, in from keras import distribute File ""C:\Users\athen\anaconda3\lib\site-packages\keras\distribute\__init__.py"", line 18, in from keras.distribute import sidecar_evaluator File ""C:\Users\athen\anaconda3\lib\site-packages\keras\distribute\sidecar_evaluator.py"", line 22, in from keras.optimizers.optimizer_experimental import ( File ""C:\Users\athen\anaconda3\lib\site-packages\keras\optimizers\__init__.py"", line 25, in from keras import backend File ""C:\Users\athen\anaconda3\lib\site-packages\keras\backend\__init__.py"", line 3, in from keras.backend import experimental File ""C:\Users\athen\anaconda3\lib\site-packages\keras\backend\experimental\__init__.py"", line 3, in from keras.src.backend import disable_tf_random_generator File ""C:\Users\athen\anaconda3\lib\site-packages\keras\src\__init__.py"", line 21, in from keras.src import applications File ""C:\Users\athen\anaconda3\lib\site-packages\keras\src\applications\__init__.py"", line 18, in from keras.src.applications.convnext import ConvNeXtBase File ""C:\Users\athen\anaconda3\lib\site-packages\keras\src\applications\convnext.py"", line 28, in from keras.src import backend File ""C:\Users\athen\anaconda3\lib\site-packages\keras\src\backend.py"", line 35, in from keras.src.engine import keras_tensor File ""C:\Users\athen\anaconda3\lib\site-packages\keras\src\engine\keras_tensor.py"", line 19, in from keras.src.utils import object_identity File ""C:\Users\athen\anaconda3\lib\site-packages\keras\src\utils\__init__.py"", line 20, in from keras.src.saving.serialization_lib import deserialize_keras_object File ""C:\Users\athen\anaconda3\lib\site-packages\keras\src\saving\serialization_lib.py"", line 28, in from keras.src.saving.legacy.saved_model.utils import in_tf_saved_model_scope File ""C:\Users\athen\anaconda3\lib\site-packages\keras\src\saving\legacy\saved_model\utils.py"", line 30, in from keras.src.utils.layer_utils import CallFunctionSpec File ""C:\Users\athen\anaconda3\lib\site-packages\keras\src\utils\layer_utils.py"", line 26, in from keras.src import initializers File ""C:\Users\athen\anaconda3\lib\site-packages\keras\src\initializers\__init__.py"", line 23, in from keras.src.initializers import initializers_v1 File ""C:\Users\athen\anaconda3\lib\site-packages\keras\src\initializers\initializers_v1.py"", line 32, in keras_export(v1=[""keras.initializers.Zeros"", ""keras.initializers.zeros""])( File ""C:\Users\athen\anaconda3\lib\site-packages\tensorflow\python\util\tf_export.py"", line 348, in __call__ self.set_attr(undecorated_func, api_names_attr, self._names) File ""C:\Users\athen\anaconda3\lib\site-packages\tensorflow\python\util\tf_export.py"", line 363, in set_attr raise SymbolAlreadyExposedError( SymbolAlreadyExposedError: Symbol Zeros is already exposed as (). ``` ",2024-03-10T01:09:44Z,2 TF 2.16.1 Fails to work with GPUs,"### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? No ### Source binary ### TensorFlow version TF 2.16.1 ### Custom code No ### OS platform and distribution Linux Ubuntu 22.04.4 LTS ### Mobile device _No response_ ### Python version 3.10.12 ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version 12.4 ### GPU model and memory _No response_ ### Current behavior? I created a python venv in which I installed TF 2.16.1 following your instructions: pip install tensorflow When I run python, import tf, and issue tf.config.list_physical_devices('GPU') I get an empty list [ ] I created another python venv, installed TF 2.16.1, only this time with the instructions: python3 -m pip install tensorflow[and-cuda] When I run that version, import tensorflow as tf, and issue tf.config.list_physical_devices('GPU') I also get an empty list. BTW, I have no problems running on my box TF 2.15.1 with GPUs. Julia also works just fine with GPUs and so does PyTorch. the ### Standalone code to reproduce the issue ```shell Python 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] on linux Type ""help"", ""copyright"", ""credits"" or ""license"" for more information. >>> import tensorflow as tf 2024-03-09 19:15:45.018171: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2024-03-09 19:15:50.412646: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT >>> tf.__version__ '2.16.1' tf.config.list_physical_devices('GPU') 2024-03-09 19:16:28.923792: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 2024-03-09 19:16:29.078379: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. Skipping registering GPU devices... [] >>> ``` ### Relevant log output _No response_",2024-03-10T00:17:36Z,6 Replace `RemoteTensorHandle` with `TensorProto` for scalars in an `EnqueueRequest` except for `DT_RESOURCE`,"Replace `RemoteTensorHandle` with `TensorProto` for scalars in an `EnqueueRequest` except for `DT_RESOURCE` ",2024-03-09T20:18:30Z,0 tensorflow 2.16.1 build error: Compiling xla/service/cpu/onednn_matmul.cc failed,"### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? No ### Source source ### TensorFlow version 2.16.1 ### Custom code No ### OS platform and distribution Linux Ubuntu 22.04 ### Mobile device _No response_ ### Python version 3.11.8 ### Bazel version 6.5.0 ### GCC/compiler version 11.4.0 ### CUDA/cuDNN version 12.4/9.0.0.312 ### GPU model and memory NVIDIA GeForce 940MX ### Current behavior? INFO: Reading 'startup' options from ~/Documents/dev/git/tensorflow/.bazelrc: --windows_enable_symlinks INFO: Options provided by the client: Inherited 'common' options: --isatty=1 --terminal_columns=211 INFO: Reading rc options for 'build' from ~/Documents/dev/git/tensorflow/.bazelrc: Inherited 'common' options: --experimental_repo_remote_exec INFO: Reading rc options for 'build' from ~/Documents/dev/git/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 ~/Documents/dev/git/tensorflow/.tf_configure.bazelrc: 'build' options: --action_env PYTHON_BIN_PATH=~/Documents/dev/programs/miniconda3/envs/tf/bin/python3 --action_env PYTHON_LIB_PATH=~/Documents/dev/programs/miniconda3/envs/tf/lib/python3.11/site-packages --python_path=~/Documents/dev/programs/miniconda3/envs/tf/bin/python3 --action_env CUDA_TOOLKIT_PATH=/usr/local/cuda-12.3 --action_env TF_CUDA_COMPUTE_CAPABILITIES=5.0 --action_env LD_LIBRARY_PATH=/usr/lib/libreoffice/program:/usr/local/cuda/targets/x86_64-linux/lib:/usr/lib/x86_64-linux-gnu --action_env GCC_HOST_COMPILER_PATH=/usr/bin/x86_64-linux-gnu-gcc-11 --config=cuda INFO: Found applicable config definition build:short_logs in file ~/Documents/dev/git/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING INFO: Found applicable config definition build:v2 in file ~/Documents/dev/git/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1 INFO: Found applicable config definition build:cuda in file ~/Documents/dev/git/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:mkl in file ~/Documents/dev/git/tensorflow/.bazelrc: --define=build_with_mkl=true --define=enable_mkl=true --define=tensorflow_mkldnn_contraction_kernel=0 --define=build_with_openmp=true -c opt INFO: Found applicable config definition build:opt in file ~/Documents/dev/git/tensorflow/.tf_configure.bazelrc: --copt=-Wno-sign-compare --host_copt=-Wno-sign-compare INFO: Found applicable config definition build:linux in file ~/Documents/dev/git/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 ~/Documents/dev/git/tensorflow/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS INFO: Analyzed target //tensorflow/tools/pip_package:build_pip_package (711 packages loaded, 51601 targets configured). INFO: Found 1 target... ERROR: ~/.cache/bazel/_bazel_vyepishov/cf67b2b2e967476eb2b1ee98e33ab5bd/external/local_xla/xla/service/cpu/BUILD:1638:11: Compiling xla/service/cpu/onednn_matmul.cc failed: (Exit 1): crosstool_wrapper_driver_is_not_gcc failed: error executing command (from target @local_xla//xla/service/cpu:onednn_matmul) external/local_config_cuda/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc -MD -MF bazel-out/k8-opt/bin/external/local_xla/xla/service/cpu/_objs/onednn_matmul/onednn_matmul.pic.d ... (remaining 229 arguments skipped) In file included from external/local_xla/xla/shape.h:28, from external/local_xla/xla/service/cpu/onednn_matmul.h:21, from external/local_xla/xla/service/cpu/onednn_matmul.cc:18: external/local_xla/xla/layout.h:377:18: warning: ‘xla::Layout::DimInfo::dim_level_type’ is too small to hold all values of ‘enum xla::DimLevelType’ 377 | DimLevelType dim_level_type : 6; | ^~~~~~~~~~~~~~ external/local_xla/xla/layout.h:389:17: warning: ‘xla::Layout::index_primitive_type_’ is too small to hold all values of ‘enum xla::PrimitiveType’ 389 | PrimitiveType index_primitive_type_ : 8; | ^~~~~~~~~~~~~~~~~~~~~ external/local_xla/xla/layout.h:390:17: warning: ‘xla::Layout::pointer_primitive_type_’ is too small to hold all values of ‘enum xla::PrimitiveType’ 390 | PrimitiveType pointer_primitive_type_ : 8; | ^~~~~~~~~~~~~~~~~~~~~~~ external/local_xla/xla/service/cpu/onednn_matmul.cc: In function ‘void xla::cpu::__xla_cpu_runtime_OneDnnMatMul(void*, void**)’: external/local_xla/xla/service/cpu/onednn_matmul.cc:186:68: error: cannot convert ‘std::unique_ptr::pointer’ {aka ‘tsl::OneDnnThreadPool*’} to ‘dnnl::threadpool_interop::threadpool_iface*’ 186 | auto onednn_stream = MakeOneDnnStream(cpu_engine, thread_pool.get()); | ~~~~~~~~~~~~~~~^~ | | | std::unique_ptr::pointer {aka tsl::OneDnnThreadPool*} external/local_xla/xla/service/cpu/onednn_matmul.cc:148:49: note: initializing argument 2 of ‘dnnl::stream xla::cpu::{anonymous}::MakeOneDnnStream(const dnnl::engine&, dnnl::threadpool_interop::threadpool_iface*)’ 148 | dnnl::threadpool_interop::threadpool_iface* thread_pool) { | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~ external/local_xla/xla/service/cpu/onednn_matmul.cc: In function ‘void xla::cpu::__xla_cpu_runtime_OneDnnMatMulReorder(void*, void**)’: external/local_xla/xla/service/cpu/onednn_matmul.cc:322:68: error: cannot convert ‘std::unique_ptr::pointer’ {aka ‘tsl::OneDnnThreadPool*’} to ‘dnnl::threadpool_interop::threadpool_iface*’ 322 | auto onednn_stream = MakeOneDnnStream(cpu_engine, thread_pool.get()); | ~~~~~~~~~~~~~~~^~ | | | std::unique_ptr::pointer {aka tsl::OneDnnThreadPool*} external/local_xla/xla/service/cpu/onednn_matmul.cc:148:49: note: initializing argument 2 of ‘dnnl::stream xla::cpu::{anonymous}::MakeOneDnnStream(const dnnl::engine&, dnnl::threadpool_interop::threadpool_iface*)’ 148 | dnnl::threadpool_interop::threadpool_iface* thread_pool) { | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~ Target //tensorflow/tools/pip_package:build_pip_package failed to build Use --verbose_failures to see the command lines of failed build steps. INFO: Elapsed time: 16142.186s, Critical Path: 328.40s INFO: 25824 processes: 8831 internal, 16993 local. FAILED: Build did NOT complete successfully ### Standalone code to reproduce the issue ```shell bazel build --config=mkl --config=opt //tensorflow/tools/pip_package:build_pip_package ``` ### Relevant log output ```shell INFO: Reading 'startup' options from ~/Documents/dev/git/tensorflow/.bazelrc: --windows_enable_symlinks INFO: Options provided by the client: Inherited 'common' options: --isatty=1 --terminal_columns=211 INFO: Reading rc options for 'build' from ~/Documents/dev/git/tensorflow/.bazelrc: Inherited 'common' options: --experimental_repo_remote_exec INFO: Reading rc options for 'build' from ~/Documents/dev/git/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 ~/Documents/dev/git/tensorflow/.tf_configure.bazelrc: 'build' options: --action_env PYTHON_BIN_PATH=~/Documents/dev/programs/miniconda3/envs/tf/bin/python3 --action_env PYTHON_LIB_PATH=~/Documents/dev/programs/miniconda3/envs/tf/lib/python3.11/site-packages --python_path=~/Documents/dev/programs/miniconda3/envs/tf/bin/python3 --action_env CUDA_TOOLKIT_PATH=/usr/local/cuda-12.3 --action_env TF_CUDA_COMPUTE_CAPABILITIES=5.0 --action_env LD_LIBRARY_PATH=/usr/lib/libreoffice/program:/usr/local/cuda/targets/x86_64-linux/lib:/usr/lib/x86_64-linux-gnu --action_env GCC_HOST_COMPILER_PATH=/usr/bin/x86_64-linux-gnu-gcc-11 --config=cuda INFO: Found applicable config definition build:short_logs in file ~/Documents/dev/git/tensorflow/.bazelrc: --output_filter=DONT_MATCH_ANYTHING INFO: Found applicable config definition build:v2 in file ~/Documents/dev/git/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1 INFO: Found applicable config definition build:cuda in file ~/Documents/dev/git/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:mkl in file ~/Documents/dev/git/tensorflow/.bazelrc: --define=build_with_mkl=true --define=enable_mkl=true --define=tensorflow_mkldnn_contraction_kernel=0 --define=build_with_openmp=true -c opt INFO: Found applicable config definition build:opt in file ~/Documents/dev/git/tensorflow/.tf_configure.bazelrc: --copt=-Wno-sign-compare --host_copt=-Wno-sign-compare INFO: Found applicable config definition build:linux in file ~/Documents/dev/git/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 ~/Documents/dev/git/tensorflow/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS INFO: Analyzed target //tensorflow/tools/pip_package:build_pip_package (711 packages loaded, 51601 targets configured). INFO: Found 1 target... ERROR: ~/.cache/bazel/_bazel_vyepishov/cf67b2b2e967476eb2b1ee98e33ab5bd/external/local_xla/xla/service/cpu/BUILD:1638:11: Compiling xla/service/cpu/onednn_matmul.cc failed: (Exit 1): crosstool_wrapper_driver_is_not_gcc failed: error executing command (from target @local_xla//xla/service/cpu:onednn_matmul) external/local_config_cuda/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc -MD -MF bazel-out/k8-opt/bin/external/local_xla/xla/service/cpu/_objs/onednn_matmul/onednn_matmul.pic.d ... (remaining 229 arguments skipped) In file included from external/local_xla/xla/shape.h:28, from external/local_xla/xla/service/cpu/onednn_matmul.h:21, from external/local_xla/xla/service/cpu/onednn_matmul.cc:18: external/local_xla/xla/layout.h:377:18: warning: ‘xla::Layout::DimInfo::dim_level_type’ is too small to hold all values of ‘enum xla::DimLevelType’ 377 | DimLevelType dim_level_type : 6; | ^~~~~~~~~~~~~~ external/local_xla/xla/layout.h:389:17: warning: ‘xla::Layout::index_primitive_type_’ is too small to hold all values of ‘enum xla::PrimitiveType’ 389 | PrimitiveType index_primitive_type_ : 8; | ^~~~~~~~~~~~~~~~~~~~~ external/local_xla/xla/layout.h:390:17: warning: ‘xla::Layout::pointer_primitive_type_’ is too small to hold all values of ‘enum xla::PrimitiveType’ 390 | PrimitiveType pointer_primitive_type_ : 8; | ^~~~~~~~~~~~~~~~~~~~~~~ external/local_xla/xla/service/cpu/onednn_matmul.cc: In function ‘void xla::cpu::__xla_cpu_runtime_OneDnnMatMul(void*, void**)’: external/local_xla/xla/service/cpu/onednn_matmul.cc:186:68: error: cannot convert ‘std::unique_ptr::pointer’ {aka ‘tsl::OneDnnThreadPool*’} to ‘dnnl::threadpool_interop::threadpool_iface*’ 186 | auto onednn_stream = MakeOneDnnStream(cpu_engine, thread_pool.get()); | ~~~~~~~~~~~~~~~^~ | | | std::unique_ptr::pointer {aka tsl::OneDnnThreadPool*} external/local_xla/xla/service/cpu/onednn_matmul.cc:148:49: note: initializing argument 2 of ‘dnnl::stream xla::cpu::{anonymous}::MakeOneDnnStream(const dnnl::engine&, dnnl::threadpool_interop::threadpool_iface*)’ 148 | dnnl::threadpool_interop::threadpool_iface* thread_pool) { | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~ external/local_xla/xla/service/cpu/onednn_matmul.cc: In function ‘void xla::cpu::__xla_cpu_runtime_OneDnnMatMulReorder(void*, void**)’: external/local_xla/xla/service/cpu/onednn_matmul.cc:322:68: error: cannot convert ‘std::unique_ptr::pointer’ {aka ‘tsl::OneDnnThreadPool*’} to ‘dnnl::threadpool_interop::threadpool_iface*’ 322 | auto onednn_stream = MakeOneDnnStream(cpu_engine, thread_pool.get()); | ~~~~~~~~~~~~~~~^~ | | | std::unique_ptr::pointer {aka tsl::OneDnnThreadPool*} external/local_xla/xla/service/cpu/onednn_matmul.cc:148:49: note: initializing argument 2 of ‘dnnl::stream xla::cpu::{anonymous}::MakeOneDnnStream(const dnnl::engine&, dnnl::threadpool_interop::threadpool_iface*)’ 148 | dnnl::threadpool_interop::threadpool_iface* thread_pool) { | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~ Target //tensorflow/tools/pip_package:build_pip_package failed to build Use --verbose_failures to see the command lines of failed build steps. INFO: Elapsed time: 16142.186s, Critical Path: 328.40s INFO: 25824 processes: 8831 internal, 16993 local. FAILED: Build did NOT complete successfully ``` ",2024-03-09T20:04:58Z,0 Fix SegFault in Python InterpreterWrapper,"If `InterpreterWrapper::TensorSparsityParameters` encounters Tensors which do not have a `block_map`, a `nullptr` is dereferenced causing AccViol/SegFault. Add a check for `nullptr`. Attempts to fix #62058",2024-03-09T19:57:47Z,0 Force an extra step from pred to u32 before then converting to f32 as that can fail on TGP,"Force an extra step from pred to u32 before then converting to f32 as that can fail on TGP ",2024-03-09T19:43:15Z,0 Build error related to XLA and absl,"### Issue type Build/Install ### Have you reproduced the bug with TensorFlow Nightly? No ### Source source ### TensorFlow version 2.16.1 ### Custom code No ### OS platform and distribution Linux Ubuntu 22.04 ### Mobile device _No response_ ### Python version 3.11.7 ### Bazel version 6.5.0 ### GCC/compiler version 11.4.0 ### CUDA/cuDNN version 11.8.0/8.9.7.29 ### GPU model and memory _No response_ ### Current behavior? When building TF from source using the Spack package manager, I see the following build failure: ``` ERROR: /tmp/spackkiy_sjk0/dfa266778fb055fec5b77ad2acb73759/external/local_xla/xla/service/gpu/kernels/BUILD:157:13: Compiling xla/service/gpu/kernels/topk_kernel_bfloat16.cu.cc failed: (Exit 1): crosstool_wrapper_driver_is_not_gcc failed: error executing command (from target @local_xla//xla/service/gpu/kernels:topk_kernel_cuda) ... external/com_google_absl/absl/strings/internal/str_format/bind.h: In constructor ‘absl::lts_20230802::str_format_internal::FormatSpecTemplate::FormatSpecTemplate(const absl::lts_20230802::str_format_internal::ExtendedParsedFormat&)’: external/com_google_absl/absl/strings/internal/str_format/bind.h:172:1: error: parse error in template argument list 172 | CheckArity(); | ^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ external/com_google_absl/absl/strings/internal/str_format/bind.h:172:63: error: expected ‘;’ before ‘)’ token 172 | CheckArity(); | ^ external/com_google_absl/absl/strings/internal/str_format/bind.h:173:147: error: template argument 1 is invalid 173 | CheckMatches(absl::make_index_sequence{}); | ^ external/com_google_absl/absl/strings/internal/str_format/bind.h:173:151: error: expected primary-expression before ‘{’ token 173 | CheckMatches(absl::make_index_sequence{}); | ^ external/com_google_absl/absl/strings/internal/str_format/bind.h:173:151: error: expected ‘;’ before ‘{’ token external/com_google_absl/absl/strings/internal/str_format/bind.h:173:153: error: expected primary-expression before ‘)’ token 173 | CheckMatches(absl::make_index_sequence{}); | ^ Target //tensorflow/tools/pip_package:build_pip_package failed to build INFO: Elapsed time: 1238.631s, Critical Path: 57.51s INFO: 17066 processes: 6004 internal, 11062 local. FAILED: Build did NOT complete successfully ``` ### Standalone code to reproduce the issue See the below build log for steps to reproduce the issue. ### Relevant log output * [build log](https://github.com/tensorflow/tensorflow/files/14547197/spack-build-out.txt) * [build env](https://github.com/tensorflow/tensorflow/files/14547196/spack-build-env-mods.txt) ",2024-03-09T17:20:29Z,1 core dumped with tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel,"### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? Yes ### Source binary ### TensorFlow version tf 2.15 ### Custom code Yes ### OS platform and distribution Ubuntu 20.04 ### Mobile device _No response_ ### Python version 3.9 ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? core dumped error with specific input parameters. ### Standalone code to reproduce the issue ```shell import tensorflow as tf # Generate input data input_data = tf.constant([[1.5, 2.5, 3.5], [4.5, 5.5, 6.5]]) # Define min and max values per channel min_per_channel = tf.constant([1.0, 2.0, 3.0]) max_per_channel = tf.constant([2.0, 3.0, 4.0]) # Invoke tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel with inputs as 0-dimensional tensor and max as a 1x3 tensor quantized_output = tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel(inputs=tf.constant(0.0), min=min_per_channel, max=max_per_channel, num_bits=8, narrow_range=False) # Print the quantized output print(quantized_output) ``` ### Relevant log output ```shell 2024-03-09 15:02:07.858055: F tensorflow/core/framework/tensor_shape.cc:356] Check failed: d >= 0 (0 vs. -1) Aborted (core dumped) ``` ",2024-03-09T15:03:18Z,0 core dumped with tf.raw_ops.DrawBoundingBoxes and tf.raw_ops.DrawBoundingBoxesV2,"### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? Yes ### Source binary ### TensorFlow version tf 2.15 ### Custom code Yes ### OS platform and distribution Ubuntu 20.04 ### Mobile device _No response_ ### Python version 3.9 ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? core dumped error with specific input parameters. ### Standalone code to reproduce the issue 1. The code of `tf.raw_ops.DrawBoundingBoxes`: ```shell import tensorflow as tf import numpy as np # Generate input data batch_size = 1 image_height = 100 image_width = 100 num_channels = 3 num_boxes = 2 images = np.random.rand(image_height, image_width, num_channels).astype(np.float32) # Remove the batch dimension boxes = np.random.rand(batch_size, num_boxes, 4).astype(np.float32) # Invoke tf.raw_ops.DrawBoundingBoxes with a zero-dimensional tensor for images drawn_images = tf.raw_ops.DrawBoundingBoxes(images=tf.convert_to_tensor(images), boxes=tf.convert_to_tensor(boxes)) # Print the result print(drawn_images) ``` 2. The code of `tf.raw_ops.DrawBoundingBoxesV2`: ``` import tensorflow as tf import numpy as np # Generate input data image_height = 100 image_width = 100 num_channels = 3 num_boxes = 2 images = tf.random.uniform((image_height, image_width, num_channels)) # Change the shape to satisfy the requirement of a zero-dimensional tensor boxes = tf.random.uniform((1, num_boxes, 4)) colors = tf.constant([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]]) # Define colors for each bounding box # Invoke tf.raw_ops.DrawBoundingBoxesV2 output_images = tf.raw_ops.DrawBoundingBoxesV2(images=images, boxes=boxes, colors=colors) # Print the output images print(output_images) ``` ### Relevant log output ```shell 2024-03-09 14:55:53.834849: F tensorflow/core/framework/tensor_shape.cc:357] Check failed: d < dims() (3 vs. 3) Aborted (core dumped) ``` ",2024-03-09T14:57:17Z,2 Aborted (core dumped) with tf.raw_ops.AvgPoolGrad,"### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? Yes ### Source binary ### TensorFlow version tf 2.15 ### Custom code Yes ### OS platform and distribution Ubuntu 20.04 ### Mobile device _No response_ ### Python version 3.9 ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? core dumped error with specific input parameters. ### Standalone code to reproduce the issue ```shell import tensorflow as tf # Generate input data input_data = tf.random.normal([1, 28, 28, 3]) grad = tf.random.normal([1, 14, 14, 6]) # Change the number of channels in grad tensor # Perform average pooling result = tf.nn.avg_pool2d(input_data, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID', data_format='NHWC') # Compute gradient grad_result = tf.raw_ops.AvgPoolGrad(orig_input_shape=tf.shape(input_data), grad=grad, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID', data_format='NHWC') print(grad_result) ``` ### Relevant log output ```shell free(): corrupted unsorted chunks Aborted (core dumped) ``` ",2024-03-09T14:54:40Z,0 Segmentation fault with tf.raw_ops.AudioSpectrogram,"### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? Yes ### Source binary ### TensorFlow version tf 2.15 ### Custom code Yes ### OS platform and distribution Ubuntu 20.04 ### Mobile device _No response_ ### Python version 3.9 ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? Segmentation fault error with specific input parameters. ### Standalone code to reproduce the issue ```shell import tensorflow as tf # Generate input data input_data = tf.random.normal([1, 44100], dtype=tf.float32) # Invoke tf.raw_ops.AudioSpectrogram with a negative window_size spectrogram = tf.raw_ops.AudioSpectrogram(input=input_data, window_size=-1024, stride=64, magnitude_squared=False) # Print the spectrogram print(spectrogram) ``` ### Relevant log output ```shell Segmentation fault (core dumped) ``` ",2024-03-09T14:50:26Z,1 core dumped with tf.quantization.fake_quant_with_min_max_vars_per_channel,"### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? Yes ### Source binary ### TensorFlow version tf 2.15 ### Custom code Yes ### OS platform and distribution Ubuntu 20.04 ### Mobile device _No response_ ### Python version 3.9 ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? core dumped error with specific input parameters. ### Standalone code to reproduce the issue ```shell import tensorflow as tf input_data = tf.constant(3.0) min_per_channel = tf.constant(2.0) max_per_channel = tf.constant(4.0) quantized_data = tf.quantization.fake_quant_with_min_max_vars_per_channel(input_data, min_per_channel, max_per_channel) print(quantized_data) ``` ### Relevant log output ```shell 2024-03-09 14:43:28.826225: F tensorflow/core/framework/tensor_shape.cc:356] Check failed: d >= 0 (0 vs. -1) Aborted (core dumped) ``` ",2024-03-09T14:47:46Z,0