---
base_model: sentence-transformers/all-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: "How to get single UnidirectionalSequenceRnnOp in tflite model ### Issue Type\r\
\n\r\nSupport\r\n\r\n### Source\r\n\r\nsource\r\n\r\n### Tensorflow Version\r\n\
\r\n2.8\r\n\r\n### Custom Code\r\n\r\nYes\r\n\r\n### OS Platform and Distribution\r\
\n\r\nUbuntu 18.04\r\n\r\nAccording to https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc\
\ there is `kUnidirectionalSequenceRnnOp` as a single operation in tflite, could\
\ you give a python code example - how can I get this? For example - this code\
\ for LSTM gives tflite with one UnidirectionalSequenceLSTM Op.\r\n```py\r\n#\
\ NOTE tested with TF 2.8.0\r\nimport tensorflow as tf\r\nimport numpy as np\r\
\n\r\nfrom tensorflow import keras\r\n\r\n\r\nmodel = keras.Sequential()\r\nshape\
\ = (4, 4)\r\n\r\nmodel.add(keras.layers.InputLayer(input_shape=shape, batch_size=1))\r\
\nmodel.add(keras.layers.LSTM(2, input_shape=shape))\r\n```\r\n\r\
\nHow can I do same for UnidirectionalSequenceRnn?"
- text: "[Feature Request] GELU activation with the Hexagon delegate **System information**\r\
\n- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 20.04\r\n\
- TensorFlow installed from (source or binary): binary\r\n- TensorFlow version\
\ (or github SHA if from source): 2.9.1\r\n\r\nI think I'd be able to implement\
\ this myself, but wanted to see if there was any interest in including this upstream.\
\ Most of this I'm writing out to make sure my own understanding is correct.\r\
\n\r\n### The problem\r\n\r\nI'd like to add support for the GELU op to the Hexagon\
\ Delegate. The motivation for this is mostly for use with [DistilBERT](https://huggingface.co/distilbert-base-multilingual-cased),\
\ which uses this activation function in its feedforward network layers. (Also\
\ used by BERT, GPT-3, RoBERTa, etc.)\r\n\r\nAdding this as a supported op for\
\ the Hexagon delegate would avoid creating a graph partition/transferring between\
\ DSP<-->CPU each time the GELU activation function is used.\r\n\r\n### How I'd\
\ implement this\r\n\r\nGELU in TF Lite is implemented as a lookup table when\
\ there are integer inputs ([here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/kernels/activations.cc#L120-L140)\
\ and [here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/kernels/internal/reference/gelu.h#L37-L53)).\r\
\n\r\nThis same approach could be used for the Hexagon delegate, as it has int8/uint8\
\ data types and also supports lookup tables.\r\n\r\nI'd plan to do this by adding\
\ a new op builder in the delegate, populating a lookup table for each node as\
\ is currently done for the CPU version of the op, and then using the [Gather_8](https://source.codeaurora.org/quic/hexagon_nn/nnlib/tree/hexagon/ops/src/op_gather.c)\
\ nnlib library function to do the lookup.\r\n\r\n### Possible workaround\r\n\
\r\nA workaround I thought of:\r\n\r\nI'm going to try removing the [pattern matching](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/mlir/lite/transforms/optimize_patterns.td#L1034-L1095)\
\ for approximate GELU in MLIR, and then using the approximate version of GELU\
\ (so that using tanh and not Erf). This will probably be slower, but should\
\ let me keep execution on the DSP.\r\n\r\nSince this will then be tanh, addition,\
\ multiplication ops instead of GELU they should all be runnable by the DSP."
- text: "Data init API for TFLite Swift Click to expand!
\
\ \n \n ### Issue Type\n\nFeature Request\n\n### Source\n\nsource\n\n### Tensorflow\
\ Version\n\n2.8+\n\n### Custom Code\n\nNo\n\n### OS Platform and Distribution\n\
\n_No response_\n\n### Mobile device\n\n_No response_\n\n### Python version\n\n\
_No response_\n\n### Bazel version\n\n_No response_\n\n### GCC/Compiler version\n\
\n_No response_\n\n### CUDA/cuDNN version\n\n_No response_\n\n### GPU model and\
\ memory\n\n_No response_\n\n### Current Behaviour?\n\n```shell\nThe current Swift\
\ API only has `init` functions from files on disk unlike the Java (Android) API\
\ which has a byte buffer initializer. It'd be convenient if the Swift API could\
\ initialize `Interpreters` from `Data`.\n```\n\n\n### Standalone code to reproduce\
\ the issue\n\n```shell\nNo code. This is a feature request\n```\n\n\n### Relevant\
\ log output\n\n_No response_Click\
\ to expand!
\r\n \r\n ### Issue Type\r\n\r\nFeature Request\r\n\r\n\
### Tensorflow Version\r\n\r\n2.8.1\r\n\r\n### Python version\r\n\r\n3.8.13\r\n\
\r\n### CUDA/cuDNN version\r\n\r\n11.8\r\n\r\n### Use Case\r\n\r\nI need to run\
\ multiple asynchronous copies of the same model on different slices of the dataset\
\ (e.g. with bootstrap sampling). There's no *good* way to do this in keras api\
\ that I'm aware of, although a couple of hacks exist. Would this use case be\
\ feasible with tf.distribute?\r\n\r\n### Feature Request\r\n\r\n`tf.distribute.MirroredStrategy`\
\ is a synchronous, data parallel strategy for distributed training across multiple\
\ devices on a single host worker.\r\n\r\nWould it be possible to modify this\
\ strategy to allow for asynchronous training of all model replicas, without computing\
\ the average gradient over all replicas to update weights? In this case each\
\ replica would need its own un-mirrored copy of model weights, and the update\
\ rule would depend only on the loss and gradients of each replica.\r\n\r\nThanks"
- text: "Build TensorFlow Lite for iOS failed!!!! Please go to Stack Overflow for\
\ help and support:\r\n\r\nhttps://stackoverflow.com/questions/tagged/tensorflow\r\
\n\r\nIf you open a GitHub issue, here is our policy:\r\n\r\n1. `bazel build --config=ios_arm64\
\ -c opt --cxxopt=--std=c++17 \\\\\r\n //tensorflow/lite/ios:TensorFlowLiteC_framework\r\
\n❯ bazel build --incompatible_run_shell_command_string=false --verbose_failures\
\ --config=ios_arm64 -c opt //tensorflow/lite/ios:TensorFlowLiteCMetal_framework\r\
\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1\
\ --terminal_columns=170\r\nINFO: Reading rc options for 'build' from /Users/thao/Desktop/tensorflow/.bazelrc:\r\
\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading\
\ rc options for 'build' from /Users/thao/Desktop/tensorflow/.bazelrc:\r\n '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\
\ --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\r\nINFO: Reading rc options\
\ for 'build' from /Users/thao/Desktop/tensorflow/.tf_configure.bazelrc:\r\n \
\ 'build' options: --action_env PYTHON_BIN_PATH=/Users/thao/miniforge3/bin/python\
\ --action_env PYTHON_LIB_PATH=/Users/thao/miniforge3/lib/python3.10/site-packages\
\ --python_path=/Users/thao/miniforge3/bin/python\r\nINFO: Reading rc options\
\ for 'build' from /Users/thao/Desktop/tensorflow/.bazelrc:\r\n 'build' options:\
\ --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_jitrt,tensorflow/compiler/mlir/tfrt/tests/tf_to_corert,tensorflow/compiler/mlir/tfrt/tests/tf_to_tfrt_data,tensorflow/compiler/mlir/tfrt/tests/saved_model,tensorflow/compiler/mlir/tfrt/transforms/lhlo_gpu_to_tfrt_gpu,tensorflow/core/runtime_fallback,tensorflow/core/runtime_fallback/conversion,tensorflow/core/runtime_fallback/kernel,tensorflow/core/runtime_fallback/opdefs,tensorflow/core/runtime_fallback/runtime,tensorflow/core/runtime_fallback/util,tensorflow/core/tfrt/common,tensorflow/core/tfrt/eager,tensorflow/core/tfrt/eager/backends/cpu,tensorflow/core/tfrt/eager/backends/gpu,tensorflow/core/tfrt/eager/core_runtime,tensorflow/core/tfrt/eager/cpp_tests/core_runtime,tensorflow/core/tfrt/gpu,tensorflow/core/tfrt/run_handler_thread_pool,tensorflow/core/tfrt/runtime,tensorflow/core/tfrt/saved_model,tensorflow/core/tfrt/graph_executor,tensorflow/core/tfrt/saved_model/tests,tensorflow/core/tfrt/tpu,tensorflow/core/tfrt/utils\r\
\nINFO: Found applicable config definition build:short_logs in file /Users/thao/Desktop/tensorflow/.bazelrc:\
\ --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition\
\ build:v2 in file /Users/thao/Desktop/tensorflow/.bazelrc: --define=tf_api_version=2\
\ --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:ios_arm64\
\ in file /Users/thao/Desktop/tensorflow/.bazelrc: --config=ios --cpu=ios_arm64\r\
\nINFO: Found applicable config definition build:ios in file /Users/thao/Desktop/tensorflow/.bazelrc:\
\ --apple_platform_type=ios --apple_bitcode=embedded --copt=-fembed-bitcode --copt=-Wno-c++11-narrowing\
\ --noenable_platform_specific_config --copt=-w --cxxopt=-std=c++17 --host_cxxopt=-std=c++17\
\ --define=with_xla_support=false\r\nINFO: Build option --cxxopt has changed,\
\ discarding analysis cache.\r\nERROR: /private/var/tmp/_bazel_thao/26d40dc75f2c247e7283b353a9ab184f/external/local_config_cc/BUILD:48:19:\
\ in cc_toolchain_suite rule @local_config_cc//:toolchain: cc_toolchain_suite\
\ '@local_config_cc//:toolchain' does not contain a toolchain for cpu 'ios_arm64'\r\
\nERROR: /private/var/tmp/_bazel_thao/26d40dc75f2c247e7283b353a9ab184f/external/local_config_cc/BUILD:48:19:\
\ Analysis of target '@local_config_cc//:toolchain' failed\r\nERROR: Analysis\
\ of target '//tensorflow/lite/ios:TensorFlowLiteCMetal_framework' failed; build\
\ aborted: \r\nINFO: Elapsed time: 45.455s\r\nINFO: 0 processes.\r\nFAILED: Build\
\ did NOT complete successfully (66 packages loaded, 1118 targets configured)`\r\
\n\r\n**Here's why we have that policy**: TensorFlow developers respond to issues.\
\ We want to focus on work that benefits the whole community, e.g., fixing bugs\
\ and adding features. Support only helps individuals. GitHub also notifies thousands\
\ of people when issues are filed. We want them to see you communicating an interesting\
\ problem, rather than being redirected to Stack Overflow.\r\n\r\n------------------------\r\
\n\r\n### System information\r\nMacOS-M1Max : 13.3\r\nTensorflow:2.9.2\r\nPython:\
\ 3.10.0\r\n\r\n\r\n\r\n### Describe the problem\r\nDescribe the problem clearly\
\ here. Be sure to convey here why it's a bug in TensorFlow or a feature request.\r\
\n\r\n### Source code / logs\r\nInclude any logs or source code that would be\
\ helpful to diagnose the problem. If including tracebacks, please include the\
\ full traceback. Large logs and files should be attached. Try to provide a reproducible\
\ test case that is the bare minimum necessary to generate the problem.\r\n"
inference: true
---
# SetFit with sentence-transformers/all-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 384 tokens
- **Number of Classes:** 3 classes
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:---------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| question | - "Parse output of `mobile_ssd_v2_float_coco.tflite` ### Issue type\n\nSupport\n\n### Have you reproduced the bug with TensorFlow Nightly?\n\nNo\n\n### Source\n\nsource\n\n### TensorFlow version\n\nv2.11.1\n\n### Custom code\n\nYes\n\n### OS platform and distribution\n\nLinux Ubuntu 20.04\n\n### Mobile device\n\nAndroid\n\n### Python version\n\n_No response_\n\n### Bazel version\n\n6.2.0\n\n### GCC/compiler version\n\n12\n\n### CUDA/cuDNN version\n\n_No response_\n\n### GPU model and memory\n\n_No response_\n\n### Current behavior?\n\nI'm trying to use the model [mobile_ssd_v2_float_coco.tflite](https://storage.googleapis.com/download.tensorflow.org/models/tflite/gpu/mobile_ssd_v2_float_coco.tflite) on a C++ application, I'm able to execute the inference and get the results.\r\n\r\nBased on the Netron app I see that its output is:\r\n\r\n\r\nBut I couldn't find an example code showing how to parse this output.\r\n\r\nI tried to look into https://github.com/tensorflow/tensorflow/issues/29054 and https://github.com/tensorflow/tensorflow/issues/40298 but the output of the model is different from the one provided [here](https://storage.googleapis.com/download.tensorflow.org/models/tflite/gpu/mobile_ssd_v2_float_coco.tflite).\r\n\r\nDo you have any example code available in Java, Python, or even better in C++ to parse this model output?\n\n### Standalone code to reproduce the issue\n\n```shell\nNo example code is available to parse the output of mobile_ssd_v2_float_coco.tflite.\n```\n\n\n### Relevant log output\n\n_No response_"
- 'Tensorflow Lite library is crashing in WASM library at 3rd inference
Click to expand!
\r\n \r\n ### Issue Type\r\n\r\nSupport\r\n\r\n### Have you reproduced the bug with TF nightly?\r\n\r\nYes\r\n\r\n### Source\r\n\r\nsource\r\n\r\n### Tensorflow Version\r\n\r\n2.7.0\r\n\r\n### Custom Code\r\n\r\nYes\r\n\r\n### OS Platform and Distribution\r\n\r\nEmscripten, Ubuntu 18.04\r\n\r\n### Mobile device\r\n\r\n_No response_\r\n\r\n### Python version\r\n\r\n_No response_\r\n\r\n### Bazel version\r\n\r\n_No response_\r\n\r\n### GCC/Compiler version\r\n\r\n_No response_\r\n\r\n### CUDA/cuDNN version\r\n\r\n_No response_\r\n\r\n### GPU model and memory\r\n\r\n_No response_\r\n\r\n### Current Behaviour?\r\n\r\n```shell\r\nHello! I have C++ code that I want to deploy as WASM library and this code contains TFLite library. I have compiled TFLite library with XNNPack support using Emscripten toolchain quite easy, so no issue there. I have a leight-weight convolution+dense model that runs perfectly on Desktop, but I am starting having problems in the browser.\r\n\r\nIn 99% of cases I have an error on the third inference:\r\n\r\nUncaught RuntimeError: memory access out of bounds\r\n\r\nThrough some trivial debugging I have found out that the issue comes from _interpreter->Invoke() method. Does not matter if I put any input or not, I just need to call Invoke() three times and I have a crash.\r\n\r\nFirst thing first: I decided to add more memory to my WASM library by adding this line to CMake:\r\n\r\nSET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -s TOTAL_STACK=134217728 -s TOTAL_MEMORY=268435456")\r\nSET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -s TOTAL_STACK=134217728 -s TOTAL_MEMORY=268435456")\r\n\r\n128 MB and 256 MB in total for 1 MB model - I think this is more than enough. And on top of that, I am allowing Memory Growth. But unfortunately, I have exactly the same issue.\r\n\r\nI am beating on this problem for 2 weeks straight and at this stage I have no clue how to fix it. Also I have tried to set custom allocation using TfLiteCustomAllocation but in this case I have a crash on the very first inference. I guess I was not using it right, but unfortunately I couldn\'t find even one tutorial describing how to apply custom allocation in TFLite.\r\n\r\nI said that I have a crash in 99% of cases. There was one time when WASM library worked and inference worked as well. It happens just randomly once, and I couldn\'t reproduce it anymore.\r\n```\r\n\r\n\r\n### Standalone code to reproduce the issue\r\n\r\n```shell\r\nHere is the code that does TFLite inference\r\n\r\n\r\n#include \r\n#include "tflite_model.h"\r\n#include \r\n\r\n#include "tensorflow/lite/interpreter.h"\r\n#include "tensorflow/lite/util.h"\r\n\r\nnamespace tracker {\r\n\r\n#ifdef EMSCRIPTEN\r\n\tvoid TFLiteModel::init(std::stringstream& stream) {\r\n\r\n\t\tstd::string img_str = stream.str();\r\n\t\tstd::vector img_model_data(img_str.size());\r\n\t\tstd::copy(img_str.begin(), img_str.end(), img_model_data.begin());\r\n\r\n\t\t_model = tflite::FlatBufferModel::BuildFromBuffer(img_str.data(), img_str.size());\r\n#else\r\n\tvoid TFLiteModel::init(const std::string& path) {\r\n\t\t_model = tflite::FlatBufferModel::BuildFromFile(path.c_str());\r\n\r\n#endif\r\n\r\n\t\ttflite::ops::builtin::BuiltinOpResolver resolver;\r\n\t\ttflite::InterpreterBuilder(*_model, resolver)(&_interpreter);\r\n\r\n\t\t_interpreter->AllocateTensors();\r\n\r\n\t\t/*for (int i = 0; i < _interpreter->tensors_size(); i++) {\r\n\t\t\tTfLiteTensor* tensor = _interpreter->tensor(i);\r\n\r\n\t\t\tif (tensor->allocation_type == kTfLiteArenaRw || tensor->allocation_type == kTfLiteArenaRwPersistent) {\r\n\r\n\t\t\t\tint aligned_bytes = tensor->bytes + (tflite::kDefaultTensorAlignment - tensor->bytes % tflite::kDefaultTensorAlignment) % tflite::kDefaultTensorAlignment;\r\n\r\n\t\t\t\tTfLiteCustomAllocation customAlloc;\r\n\t\t\t\tint result = posix_memalign(&customAlloc.data, tflite::kDefaultTensorAlignment, tensor->bytes);\r\n\t\t\t\tif (result != 0 || customAlloc.data == NULL) {\r\n\t\t\t\t\tstd::cout << "posix_memalign does not work!\\\\n";\r\n\t\t\t\t}\r\n\r\n\t\t\t\tTfLiteStatus st = _interpreter->SetCustomAllocationForTensor(i, customAlloc);\r\n\t\t\t\tstd::cout << "status = " << st << std::endl;\r\n\t\t\t\tif (tensor->bytes % tflite::kDefaultTensorAlignment != 0) {\r\n\t\t\t\t\tstd::cout << "bad! i " << i << ", size " << tensor->bytes << std::endl;\r\n\t\t\t\t}\r\n\t\t\t\t_allocations.push_back(customAlloc);\r\n\t\t\t}\r\n\t\t}\r\n\t\texit(0);*/\r\n\t}\r\n\r\n\tvoid TFLiteModel::forward(const cv::Mat& img_input, const std::vector& lms_input) {\r\n\r\n\t\tfloat* model_in = _interpreter->typed_input_tensor(0);\r\n\t\tstd::memcpy(model_in, img_input.data, img_input.total() * img_input.elemSize());\r\n\r\n\t\tfloat* lms_in = _interpreter->typed_input_tensor(1);\r\n\t\tstd::memcpy(lms_in, lms_input.data(), sizeof(float) * lms_input.size());\r\n\t\t\r\n\t\t_interpreter->Invoke();\r\n\t}\r\n\r\n\tfloat* TFLiteModel::out() {\r\n\t\treturn _interpreter->typed_output_tensor(0);\r\n\t}\r\n\r\n\tstd::vector TFLiteModel::getOutputShape() const {\r\n\t\tTfLiteTensor* outtensor = _interpreter->output_tensor(0);\r\n\t\tTfLiteIntArray* dims = outtensor->dims;\r\n\r\n\t\tstd::vector sh;\r\n\t\tfor (int i = 0; i < dims->size; i++) {\r\n\t\t\tsh.push_back(dims->data[i]);\r\n\t\t}\r\n\r\n\t\treturn sh;\r\n\t}\r\n}\r\n```\r\n\r\n\r\n### Relevant log output\r\n\r\n_No response_ - 'error: \'tf.Conv2D\' op is neither a custom op nor a flex op ### 1. System information\r\n\r\n- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 20.04\r\n- TensorFlow installation (pip package or built from source): pip package\r\n- TensorFlow library (version, if pip package or github SHA, if built from source): v2.10\r\n\r\n### 2. Code\r\nCode for conversion\r\n```\r\nconverter = tf.lite.TFLiteConverter.from_saved_model(f\'savedmodel/decoder\')\r\ntflite_model = converter.convert()\r\n\r\n# save the model\r\nwith open(f\'{name}.tflite\', \'wb\') as f:\r\n f.write(tflite_model)\r\n```\r\nCode for the model\r\n```\r\nlatent = keras.layers.Input((n_h, n_w, 4))\r\ndecoder = Decoder()\r\ndecoder = keras.models.Model(latent, decoder(latent))\r\n```\r\n```\r\nclass Decoder(keras.Sequential):\r\n def __init__(self):\r\n super().__init__(\r\n [\r\n keras.layers.Lambda(lambda x: 1 / 0.18215 * x),\r\n PaddedConv2D(4, 1),\r\n PaddedConv2D(512, 3, padding=1),\r\n ResnetBlock(512, 512),\r\n AttentionBlock(512),\r\n ResnetBlock(512, 512),\r\n ResnetBlock(512, 512),\r\n ResnetBlock(512, 512),\r\n ResnetBlock(512, 512),\r\n keras.layers.UpSampling2D(size=(2, 2)),\r\n PaddedConv2D(512, 3, padding=1),\r\n ResnetBlock(512, 512),\r\n ResnetBlock(512, 512),\r\n ResnetBlock(512, 512),\r\n keras.layers.UpSampling2D(size=(2, 2)),\r\n PaddedConv2D(512, 3, padding=1),\r\n ResnetBlock(512, 256),\r\n ResnetBlock(256, 256),\r\n ResnetBlock(256, 256),\r\n keras.layers.UpSampling2D(size=(2, 2)),\r\n PaddedConv2D(256, 3, padding=1),\r\n ResnetBlock(256, 128),\r\n ResnetBlock(128, 128),\r\n ResnetBlock(128, 128),\r\n tfa.layers.GroupNormalization(epsilon=1e-5),\r\n keras.layers.Activation("swish"),\r\n PaddedConv2D(3, 3, padding=1),\r\n ]\r\n )\r\n```\r\n\r\n### 3. Failure after conversion\r\nconversion fails\r\n\r\n\r\n### 5. (optional) Any other info / logs\r\n[error.log](https://github.com/tensorflow/tensorflow/files/10302790/error.log)\r\n```\r\nSome ops are not supported by the native TFLite runtime, you can enable TF kernels fallback using TF Select. See instructions: https://www.tensorflow.org/lite/guide/ops_select \r\nTF Select ops: Conv2D\r\nDetails:\r\n\ttf.Conv2D(tensor, tensor<1x1x512x512xf32>) -> (tensor) : {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}\r\n\ttf.Conv2D(tensor, tensor<3x3x128x128xf32>) -> (tensor) : {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}\r\n\ttf.Conv2D(tensor, tensor<3x3x128x3xf32>) -> (tensor) : {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}\r\n\ttf.Conv2D(tensor, tensor<3x3x256x128xf32>) -> (tensor) : {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}\r\n\ttf.Conv2D(tensor, tensor<3x3x256x256xf32>) -> (tensor) : {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}\r\n\ttf.Conv2D(tensor, tensor<3x3x512x256xf32>) -> (tensor) : {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}\r\n\ttf.Conv2D(tensor, tensor<3x3x512x512xf32>) -> (tensor) : {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}\r\n```\r\nAccording to the error message, I suspect that it can not recognize the input shape. But as you can see on the above code, input is specified for the functional API for `decoder` model. \r\n(FYI, The inference code is called with `predict_on_batch` method. I found out other model with `predict_on_batch` is converted successfully, but that model doesn\'t contain `conv2d` block inside. Can using `predict_on_batch` together with `conv2d` be a problem?)\r\n\r\n**I\'m sure `conv2d` is on the allowlist for TFLite operators. Any suggestions for this problem? Thank you.**'
|
| feature | - 'tf.keras.optimizers.experimental.AdamW only support constant weight_decay
Click to expand!
\n \n ### Issue Type\n\nFeature Request\n\n### Source\n\nsource\n\n### Tensorflow Version\n\n2.8\n\n### Custom Code\n\nNo\n\n### OS Platform and Distribution\n\n_No response_\n\n### Mobile device\n\n_No response_\n\n### Python version\n\n_No response_\n\n### Bazel version\n\n_No response_\n\n### GCC/Compiler version\n\n_No response_\n\n### CUDA/cuDNN version\n\n_No response_\n\n### GPU model and memory\n\n_No response_\n\n### Current Behaviour?\n\n```shell\ntf.keras.optimizers.experimental.AdamW only supports constant weight decay. But usually we want the weight_decay value to decay with learning rate schedule.\n```\n\n\n### Standalone code to reproduce the issue\n\n```shell\nThe legacy tfa.optimizers.AdamW supports callable weight_decay, which is much better.\n```\n\n\n### Relevant log output\n\n_No response_ - 'RFE tensorflow-aarch64==2.6.0 build ? **System information**\r\n TensorFlow version (you are using): 2.6.0\r\n- Are you willing to contribute it (Yes/No): Yes\r\n\r\n**Describe the feature and the current behavior/state.**\r\n\r\nBrainchip Akida AKD1000 SNN neuromorphic MetaTF SDK support 2.6.0 on x86_64. They claim support for aarch64, but when creating a virtualenv it fails on aarch64 due to lacking tensorflow-aarc64==2.6.0 build.\r\n\r\n**Will this change the current api? How?**\r\n\r\nNA\r\n\r\n**Who will benefit with this feature?**\r\n\r\nCustomer of Brainchip Akida who run on Arm64 platforms.\r\n\r\n**Any Other info.**\r\n\r\nhttps://doc.brainchipinc.com/installation.html\r\n\r\n\r\n'
- "How to calculate 45 degree standing position of body from camera in swift (Pose estimation)
Click to expand!
\n \n ### Issue Type\n\nFeature Request\n\n### Source\n\nsource\n\n### Tensorflow Version\n\npod 'TensorFlowLiteSwift', '~> 0.0.1-nightly', :subspecs => ['CoreML', 'Metal']\n\n### Custom Code\n\nYes\n\n### OS Platform and Distribution\n\n_No response_\n\n### Mobile device\n\n_No response_\n\n### Python version\n\n_No response_\n\n### Bazel version\n\n_No response_\n\n### GCC/Compiler version\n\n_No response_\n\n### CUDA/cuDNN version\n\n_No response_\n\n### GPU model and memory\n\n_No response_\n\n### Current Behaviour?\n\n```shell\nHow to calculate 45 degree standing position of body from camera in swift.\n```\n\n\n### Standalone code to reproduce the issue\n\n```shell\nHow to calculate 45 degree standing position of body from camera in swift using the body keypoints. (Pose estimation)\n```\n\n\n### Relevant log output\n\n_No response_
|
| bug | - 'Abort when running tensorflow.python.ops.gen_array_ops.depth_to_space ### Issue type\n\nBug\n\n### Have you reproduced the bug with TensorFlow Nightly?\n\nNo\n\n### Source\n\nbinary\n\n### TensorFlow version\n\n2.11.0\n\n### Custom code\n\nYes\n\n### OS platform and distribution\n\n22.04\n\n### Mobile device\n\n_No response_\n\n### Python version\n\n3.9\n\n### Bazel version\n\n_No response_\n\n### GCC/compiler version\n\n_No response_\n\n### CUDA/cuDNN version\n\nnvidia-cudnn-cu11==8.6.0.163, cudatoolkit=11.8.0\n\n### GPU model and memory\n\n_No response_\n\n### Current behavior?\n\nDue to very large integer argument\n\n### Standalone code to reproduce the issue\n\n```shell\nimport tensorflow as tf\r\nimport os\r\nimport numpy as np\r\nfrom tensorflow.python.ops import gen_array_ops\r\ntry:\r\n arg_0_tensor = tf.random.uniform([3, 2, 3, 4], dtype=tf.float32)\r\n arg_0 = tf.identity(arg_0_tensor)\r\n arg_1 = 2147483647\r\n arg_2 = "NHWC"\r\n out = gen_array_ops.depth_to_space(arg_0,arg_1,arg_2,)\r\nexcept Exception as e:\r\n print("Error:"+str(e))\r\n\r\n```\n```\n\n\n### Relevant log output\n\n```shell\n023-08-13 00:23:53.644564: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\r\n2023-08-13 00:23:54.491071: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-08-13 00:23:54.510564: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-08-13 00:23:54.510736: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-08-13 00:23:54.511051: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-08-13 00:23:54.511595: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-08-13 00:23:54.511717: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-08-13 00:23:54.511830: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-08-13 00:23:54.572398: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-08-13 00:23:54.572634: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-08-13 00:23:54.572791: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2023-08-13 00:23:54.572916: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 153 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5\r\n2023-08-13 00:23:54.594062: I tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:735] failed to allocate 153.88M (161349632 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory\r\n2023-08-13 00:23:54.594484: I tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:735] failed to allocate 138.49M (145214720 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory\r\n2023-08-13 00:23:54.600623: F tensorflow/core/framework/tensor_shape.cc:201] Non-OK-status: InitDims(dim_sizes) status: INVALID_ARGUMENT: Expected a non-negative size, got -2\r\nAborted\r\n\r\n```\n```\n'
- "float8 (both e4m3fn and e5m2) missing from numbertype ### Issue Type\r\n\r\nBug\r\n\r\n### Have you reproduced the bug with TF nightly?\r\n\r\nNo\r\n\r\n### Source\r\n\r\nbinary\r\n\r\n### Tensorflow Version\r\n\r\n2.12.0\r\n\r\n### Custom Code\r\n\r\nYes\r\n\r\n### OS Platform and Distribution\r\n\r\nmacOS-13.2.1-arm64-arm-64bit\r\n\r\n### Mobile device\r\n\r\n_No response_\r\n\r\n### Python version\r\n\r\n3.9.6\r\n\r\n### Bazel version\r\n\r\n_No response_\r\n\r\n### GCC/Compiler version\r\n\r\n_No response_\r\n\r\n### CUDA/cuDNN version\r\n\r\n_No response_\r\n\r\n### GPU model and memory\r\n\r\n_No response_\r\n\r\n### Current Behaviour?\r\n\r\nFP8 datatypes are missing from `kNumberTypes` in `tensorflow/core/framework/types.h`, and also missing from `TF_CALL_FLOAT_TYPES(m)` in `tensorflow/core/framework/register_types.h`. This causes simple ops (like slice, transpose, split, etc.) to raise NotFoundError.\r\n\r\n### Standalone code to reproduce the issue\r\n\r\n```python\r\nimport tensorflow as tf\r\nfrom tensorflow.python.framework import dtypes\r\n\r\na = tf.constant([[1.2345678, 2.3456789, 3.4567891], [4.5678912, 5.6789123, 6.7891234]], dtype=dtypes.float16)\r\nprint(a)\r\n\r\na_fp8 = tf.cast(a, dtypes.float8_e4m3fn)\r\nprint(a_fp8)\r\n\r\nb = a_fp8[1:2] # tensorflow.python.framework.errors_impl.NotFoundError\r\nb = tf.transpose(a_fp8, [1, 0]) # tensorflow.python.framework.errors_impl.NotFoundError\r\n```\r\n\r\n\r\n### Relevant log output\r\n\r\n```\r\ntensorflow.python.framework.errors_impl.NotFoundError: Could not find device for node: {{node StridedSlice}} = StridedSlice[Index=DT_INT32, T=DT_FLOAT8_E4M3FN, begin_mask=0, ellipsis_mask=0, end_mask=0, new_axis_mask=0, shrink_axis_mask=0]\r\nAll kernels registered for op StridedSlice:\r\n device='XLA_CPU_JIT'; Index in [DT_INT32, DT_INT16, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT16, 930109355527764061, DT_HALF, DT_UINT32, DT_UINT64, DT_FLOAT8_E5M2, DT_FLOAT8_E4M3FN]\r\n device='CPU'; T in [DT_UINT64]\r\n device='CPU'; T in [DT_INT64]\r\n device='CPU'; T in [DT_UINT32]\r\n device='CPU'; T in [DT_UINT16]\r\n device='CPU'; T in [DT_INT16]\r\n device='CPU'; T in [DT_UINT8]\r\n device='CPU'; T in [DT_INT8]\r\n device='CPU'; T in [DT_INT32]\r\n device='CPU'; T in [DT_HALF]\r\n device='CPU'; T in [DT_BFLOAT16]\r\n device='CPU'; T in [DT_FLOAT]\r\n device='CPU'; T in [DT_DOUBLE]\r\n device='CPU'; T in [DT_COMPLEX64]\r\n device='CPU'; T in [DT_COMPLEX128]\r\n device='CPU'; T in [DT_BOOL]\r\n device='CPU'; T in [DT_STRING]\r\n device='CPU'; T in [DT_RESOURCE]\r\n device='CPU'; T in [DT_VARIANT]\r\n device='CPU'; T in [DT_QINT8]\r\n device='CPU'; T in [DT_QUINT8]\r\n device='CPU'; T in [DT_QINT32]\r\n device='DEFAULT'; T in [DT_INT32]\r\n [Op:StridedSlice] name: strided_slice/\r\n```\r\n\r\n```\r\ntensorflow.python.framework.errors_impl.NotFoundError: Could not find device for node: {{node Transpose}} = Transpose[T=DT_FLOAT8_E4M3FN, Tperm=DT_INT32]\r\nAll kernels registered for op Transpose:\r\n device='XLA_CPU_JIT'; Tperm in [DT_INT32, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT16, 930109355527764061, DT_HALF, DT_UINT32, DT_UINT64, DT_FLOAT8_E5M2, DT_FLOAT8_E4M3FN]\r\n device='CPU'; T in [DT_UINT64]\r\n device='CPU'; T in [DT_INT64]\r\n device='CPU'; T in [DT_UINT32]\r\n device='CPU'; T in [DT_UINT16]\r\n device='CPU'; T in [DT_INT16]\r\n device='CPU'; T in [DT_UINT8]\r\n device='CPU'; T in [DT_INT8]\r\n device='CPU'; T in [DT_INT32]\r\n device='CPU'; T in [DT_HALF]\r\n device='CPU'; T in [DT_BFLOAT16]\r\n device='CPU'; T in [DT_FLOAT]\r\n device='CPU'; T in [DT_DOUBLE]\r\n device='CPU'; T in [DT_COMPLEX64]\r\n device='CPU'; T in [DT_COMPLEX128]\r\n device='CPU'; T in [DT_BOOL]\r\n device='CPU'; T in [DT_STRING]\r\n device='CPU'; T in [DT_RESOURCE]\r\n device='CPU'; T in [DT_VARIANT]\r\n [Op:Transpose]\r\n```"
- "My customized OP gives incorrect outputs on GPUs since `tf-nightly 2.13.0.dev20230413` ### Issue type\n\nBug\n\n### Have you reproduced the bug with TensorFlow Nightly?\n\nYes\n\n### Source\n\nbinary\n\n### TensorFlow version\n\n2.13\n\n### Custom code\n\nYes\n\n### OS platform and distribution\n\nfedora 36\n\n### Mobile device\n\n_No response_\n\n### Python version\n\n3.11.4\n\n### Bazel version\n\n_No response_\n\n### GCC/compiler version\n\n_No response_\n\n### CUDA/cuDNN version\n\n_No response_\n\n### GPU model and memory\n\n_No response_\n\n### Current behavior?\n\nI have a complex program based on TensorFlow with several customized OPs. These OPs were created following https://www.tensorflow.org/guide/create_op. Yesterday TF 2.13.0 was released, but after I upgraded to 2.13.0, I found that one of my customized OP gives incorrect results on GPUs and still has the correct outputs on CPUs.\r\n\r\nThen I tested many `tf-nightly` versions and found that `tf-nightly 2.13.0.dev20230412` works but `tf-nightly 2.13.0.dev20230413` fails. So the situation is shown in the following table:\r\n| version | CPU | GPU |\r\n| -------- | --------- | ----------- |\r\n| tensorflow 2.12.0 | Correct | Correct |\r\n| tensorflow 2.13.0 | Correct | Incorrect |\r\n| tf-nightly 2.13.0.dev20230412 | Correct | Correct |\r\n| tf-nightly 2.13.0.dev20230413 | Correct | Incorrect |\r\n\r\nI'd like to know what changed between April 12th and 13th related to the customized OPs. This can be a breaking change to downstream applications or an internal bug. Thanks!\r\n\r\nHere is a quick link for commits between April 12th and 13th:\r\nhttps://github.com/tensorflow/tensorflow/commits/master?before=525da8a93eca846e32e5c41eddc0496b25a2ef5b+770\r\n\n\n### Standalone code to reproduce the issue\n\n```shell\nIndeed, the reason is still unclear to me, so it is hard to create a minimal example.\r\n\r\nThe code of our customized OPs is https://github.com/deepmodeling/deepmd-kit/blob/37fd8d193362f91c925cf7c2f3a58b97dc921b27/source/op/prod_force_multi_device.cc#L49-L166\n```\n\n\n### Relevant log output\n\n_No response_"
|
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("Data init API for TFLite Swift Click to expand!
### Issue Type
Feature Request
### Source
source
### Tensorflow Version
2.8+
### 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 Behaviour?
```shell
The current Swift API only has `init` functions from files on disk unlike the Java (Android) API which has a byte buffer initializer. It'd be convenient if the Swift API could initialize `Interpreters` from `Data`.
```
### Standalone code to reproduce the issue
```shell
No code. This is a feature request
```
### Relevant log output
_No response_