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tensorflow_tensorflow_model

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Model card Files Community
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  1. README.md +498 -0
  2. config.json +24 -0
  3. config_sentence_transformers.json +10 -0
  4. config_setfit.json +8 -0
  5. model.safetensors +3 -0
  6. model_head.pkl +3 -0
  7. modules.json +20 -0
  8. sentence_bert_config.json +4 -0
  9. special_tokens_map.json +51 -0
  10. tokenizer.json +0 -0
  11. tokenizer_config.json +72 -0
  12. vocab.txt +0 -0
README.md ADDED
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+ ---
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+ base_model: sentence-transformers/all-mpnet-base-v2
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+ library_name: setfit
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+ metrics:
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+ - accuracy
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+ pipeline_tag: text-classification
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+ tags:
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+ - setfit
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+ - sentence-transformers
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+ - text-classification
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+ - generated_from_setfit_trainer
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+ widget:
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+ - text: "How to get single UnidirectionalSequenceRnnOp in tflite model ### Issue Type\r\
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+ \n\r\nSupport\r\n\r\n### Source\r\n\r\nsource\r\n\r\n### Tensorflow Version\r\n\
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+ \r\n2.8\r\n\r\n### Custom Code\r\n\r\nYes\r\n\r\n### OS Platform and Distribution\r\
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+ \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\
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+ \ there is `kUnidirectionalSequenceRnnOp` as a single operation in tflite, could\
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+ \ you give a python code example - how can I get this? For example - this code\
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+ \ for LSTM gives tflite with one UnidirectionalSequenceLSTM Op.\r\n```py\r\n#\
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+ \ NOTE tested with TF 2.8.0\r\nimport tensorflow as tf\r\nimport numpy as np\r\
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+ \n\r\nfrom tensorflow import keras\r\n\r\n\r\nmodel = keras.Sequential()\r\nshape\
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+ \ = (4, 4)\r\n\r\nmodel.add(keras.layers.InputLayer(input_shape=shape, batch_size=1))\r\
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+ \nmodel.add(keras.layers.LSTM(2, input_shape=shape))\r\n```\r\n![image](https://user-images.githubusercontent.com/4616940/197647526-59c63de2-df61-46a1-bd61-75baa2688376.png)\r\
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+ \nHow can I do same for UnidirectionalSequenceRnn?"
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+ - text: "[Feature Request] GELU activation with the Hexagon delegate **System information**\r\
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+ \n- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 20.04\r\n\
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+ - TensorFlow installed from (source or binary): binary\r\n- TensorFlow version\
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+ \ (or github SHA if from source): 2.9.1\r\n\r\nI think I'd be able to implement\
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+ \ this myself, but wanted to see if there was any interest in including this upstream.\
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+ \ Most of this I'm writing out to make sure my own understanding is correct.\r\
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+ \n\r\n### The problem\r\n\r\nI'd like to add support for the GELU op to the Hexagon\
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+ \ Delegate. The motivation for this is mostly for use with [DistilBERT](https://huggingface.co/distilbert-base-multilingual-cased),\
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+ \ which uses this activation function in its feedforward network layers. (Also\
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+ \ used by BERT, GPT-3, RoBERTa, etc.)\r\n\r\nAdding this as a supported op for\
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+ \ the Hexagon delegate would avoid creating a graph partition/transferring between\
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+ \ DSP<-->CPU each time the GELU activation function is used.\r\n\r\n### How I'd\
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+ \ implement this\r\n\r\nGELU in TF Lite is implemented as a lookup table when\
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+ \ there are integer inputs ([here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/kernels/activations.cc#L120-L140)\
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+ \ and [here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/kernels/internal/reference/gelu.h#L37-L53)).\r\
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+ \n\r\nThis same approach could be used for the Hexagon delegate, as it has int8/uint8\
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+ \ data types and also supports lookup tables.\r\n\r\nI'd plan to do this by adding\
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+ \ a new op builder in the delegate, populating a lookup table for each node as\
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+ \ 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)\
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+ \ nnlib library function to do the lookup.\r\n\r\n### Possible workaround\r\n\
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+ \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)\
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+ \ for approximate GELU in MLIR, and then using the approximate version of GELU\
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+ \ (so that using tanh and not Erf). This will probably be slower, but should\
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+ \ let me keep execution on the DSP.\r\n\r\nSince this will then be tanh, addition,\
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+ \ multiplication ops instead of GELU they should all be runnable by the DSP."
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+ - text: "Data init API for TFLite Swift <details><summary>Click to expand!</summary>\
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+ \ \n \n ### Issue Type\n\nFeature Request\n\n### Source\n\nsource\n\n### Tensorflow\
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+ \ Version\n\n2.8+\n\n### Custom Code\n\nNo\n\n### OS Platform and Distribution\n\
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+ \n_No response_\n\n### Mobile device\n\n_No response_\n\n### Python version\n\n\
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+ _No response_\n\n### Bazel version\n\n_No response_\n\n### GCC/Compiler version\n\
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+ \n_No response_\n\n### CUDA/cuDNN version\n\n_No response_\n\n### GPU model and\
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+ \ memory\n\n_No response_\n\n### Current Behaviour?\n\n```shell\nThe current Swift\
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+ \ API only has `init` functions from files on disk unlike the Java (Android) API\
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+ \ which has a byte buffer initializer. It'd be convenient if the Swift API could\
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+ \ initialize `Interpreters` from `Data`.\n```\n\n\n### Standalone code to reproduce\
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+ \ the issue\n\n```shell\nNo code. This is a feature request\n```\n\n\n### Relevant\
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+ \ log output\n\n_No response_</details>"
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+ - text: "tf.distribute.MirroredStrategy for asynchronous training <details><summary>Click\
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+ \ to expand!</summary> \r\n \r\n ### Issue Type\r\n\r\nFeature Request\r\n\r\n\
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+ ### Tensorflow Version\r\n\r\n2.8.1\r\n\r\n### Python version\r\n\r\n3.8.13\r\n\
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+ \r\n### CUDA/cuDNN version\r\n\r\n11.8\r\n\r\n### Use Case\r\n\r\nI need to run\
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+ \ multiple asynchronous copies of the same model on different slices of the dataset\
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+ \ (e.g. with bootstrap sampling). There's no *good* way to do this in keras api\
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+ \ that I'm aware of, although a couple of hacks exist. Would this use case be\
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+ \ feasible with tf.distribute?\r\n\r\n### Feature Request\r\n\r\n`tf.distribute.MirroredStrategy`\
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+ \ is a synchronous, data parallel strategy for distributed training across multiple\
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+ \ devices on a single host worker.\r\n\r\nWould it be possible to modify this\
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+ \ strategy to allow for asynchronous training of all model replicas, without computing\
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+ \ the average gradient over all replicas to update weights? In this case each\
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+ \ replica would need its own un-mirrored copy of model weights, and the update\
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+ \ rule would depend only on the loss and gradients of each replica.\r\n\r\nThanks"
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+ - text: "Build TensorFlow Lite for iOS failed!!!! Please go to Stack Overflow for\
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+ \ help and support:\r\n\r\nhttps://stackoverflow.com/questions/tagged/tensorflow\r\
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+ \n\r\nIf you open a GitHub issue, here is our policy:\r\n\r\n1. `bazel build --config=ios_arm64\
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+ \ -c opt --cxxopt=--std=c++17 \\\\\r\n //tensorflow/lite/ios:TensorFlowLiteC_framework\r\
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+ \n❯ bazel build --incompatible_run_shell_command_string=false --verbose_failures\
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+ \ --config=ios_arm64 -c opt //tensorflow/lite/ios:TensorFlowLiteCMetal_framework\r\
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+ \nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1\
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+ \ --terminal_columns=170\r\nINFO: Reading rc options for 'build' from /Users/thao/Desktop/tensorflow/.bazelrc:\r\
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+ \n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading\
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+ \ rc options for 'build' from /Users/thao/Desktop/tensorflow/.bazelrc:\r\n 'build'\
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+ \ options: --define framework_shared_object=true --define tsl_protobuf_header_only=true\
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+ \ --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone\
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+ \ -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive\
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+ \ --enable_platform_specific_config --define=with_xla_support=true --config=short_logs\
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+ \ --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library\
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+ \ --experimental_link_static_libraries_once=false\r\nINFO: Reading rc options\
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+ \ for 'build' from /Users/thao/Desktop/tensorflow/.tf_configure.bazelrc:\r\n \
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+ \ 'build' options: --action_env PYTHON_BIN_PATH=/Users/thao/miniforge3/bin/python\
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+ \ --action_env PYTHON_LIB_PATH=/Users/thao/miniforge3/lib/python3.10/site-packages\
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+ \ --python_path=/Users/thao/miniforge3/bin/python\r\nINFO: Reading rc options\
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+ \ for 'build' from /Users/thao/Desktop/tensorflow/.bazelrc:\r\n 'build' options:\
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+ \ --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\
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+ \nINFO: Found applicable config definition build:short_logs in file /Users/thao/Desktop/tensorflow/.bazelrc:\
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+ \ --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition\
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+ \ build:v2 in file /Users/thao/Desktop/tensorflow/.bazelrc: --define=tf_api_version=2\
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+ \ --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:ios_arm64\
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+ \ in file /Users/thao/Desktop/tensorflow/.bazelrc: --config=ios --cpu=ios_arm64\r\
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+ \nINFO: Found applicable config definition build:ios in file /Users/thao/Desktop/tensorflow/.bazelrc:\
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+ \ --apple_platform_type=ios --apple_bitcode=embedded --copt=-fembed-bitcode --copt=-Wno-c++11-narrowing\
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+ \ --noenable_platform_specific_config --copt=-w --cxxopt=-std=c++17 --host_cxxopt=-std=c++17\
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+ \ --define=with_xla_support=false\r\nINFO: Build option --cxxopt has changed,\
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+ \ discarding analysis cache.\r\nERROR: /private/var/tmp/_bazel_thao/26d40dc75f2c247e7283b353a9ab184f/external/local_config_cc/BUILD:48:19:\
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+ \ in cc_toolchain_suite rule @local_config_cc//:toolchain: cc_toolchain_suite\
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+ \ '@local_config_cc//:toolchain' does not contain a toolchain for cpu 'ios_arm64'\r\
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+ \nERROR: /private/var/tmp/_bazel_thao/26d40dc75f2c247e7283b353a9ab184f/external/local_config_cc/BUILD:48:19:\
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+ \ Analysis of target '@local_config_cc//:toolchain' failed\r\nERROR: Analysis\
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+ \ of target '//tensorflow/lite/ios:TensorFlowLiteCMetal_framework' failed; build\
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+ \ aborted: \r\nINFO: Elapsed time: 45.455s\r\nINFO: 0 processes.\r\nFAILED: Build\
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+ \ did NOT complete successfully (66 packages loaded, 1118 targets configured)`\r\
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+ \n\r\n**Here's why we have that policy**: TensorFlow developers respond to issues.\
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+ \ We want to focus on work that benefits the whole community, e.g., fixing bugs\
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+ \ and adding features. Support only helps individuals. GitHub also notifies thousands\
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+ \ of people when issues are filed. We want them to see you communicating an interesting\
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+ \ problem, rather than being redirected to Stack Overflow.\r\n\r\n------------------------\r\
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+ \n\r\n### System information\r\nMacOS-M1Max : 13.3\r\nTensorflow:2.9.2\r\nPython:\
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+ \ 3.10.0\r\n\r\n\r\n\r\n### Describe the problem\r\nDescribe the problem clearly\
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+ \ here. Be sure to convey here why it's a bug in TensorFlow or a feature request.\r\
123
+ \n\r\n### Source code / logs\r\nInclude any logs or source code that would be\
124
+ \ helpful to diagnose the problem. If including tracebacks, please include the\
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+ \ full traceback. Large logs and files should be attached. Try to provide a reproducible\
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+ \ test case that is the bare minimum necessary to generate the problem.\r\n"
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+ inference: true
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+ ---
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+
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+ # SetFit with sentence-transformers/all-mpnet-base-v2
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+
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+ 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.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
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+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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+ - **Maximum Sequence Length:** 384 tokens
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+ - **Number of Classes:** 3 classes
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+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:---------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | question | <ul><li>"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![image](https://github.com/tensorflow/tensorflow/assets/92656601/7ee73cb9-52dc-47ef-a89f-d17843bd0f60)\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_"</li><li>'Tensorflow Lite library is crashing in WASM library at 3rd inference <details><summary>Click to expand!</summary> \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 <cstdlib>\r\n#include "tflite_model.h"\r\n#include <iostream>\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<char> 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<float>& lms_input) {\r\n\r\n\t\tfloat* model_in = _interpreter->typed_input_tensor<float>(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<float>(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<float>(0);\r\n\t}\r\n\r\n\tstd::vector<int> 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<int> 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_</details>'</li><li>'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<?x?x?x?xf32>, tensor<1x1x512x512xf32>) -> (tensor<?x?x?x512xf32>) : {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<?x?x?x?xf32>, tensor<3x3x128x128xf32>) -> (tensor<?x?x?x128xf32>) : {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<?x?x?x?xf32>, tensor<3x3x128x3xf32>) -> (tensor<?x?x?x3xf32>) : {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<?x?x?x?xf32>, tensor<3x3x256x128xf32>) -> (tensor<?x?x?x128xf32>) : {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<?x?x?x?xf32>, tensor<3x3x256x256xf32>) -> (tensor<?x?x?x256xf32>) : {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<?x?x?x?xf32>, tensor<3x3x512x256xf32>) -> (tensor<?x?x?x256xf32>) : {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<?x?x?x?xf32>, tensor<3x3x512x512xf32>) -> (tensor<?x?x?x512xf32>) : {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.**'</li></ul> |
161
+ | feature | <ul><li>'tf.keras.optimizers.experimental.AdamW only support constant weight_decay <details><summary>Click to expand!</summary> \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_</details>'</li><li>'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'</li><li>"How to calculate 45 degree standing position of body from camera in swift (Pose estimation) <details><summary>Click to expand!</summary> \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_</details>"</li></ul> |
162
+ | bug | <ul><li>'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'</li><li>"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```"</li><li>"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_"</li></ul> |
163
+
164
+ ## Uses
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+
166
+ ### Direct Use for Inference
167
+
168
+ First install the SetFit library:
169
+
170
+ ```bash
171
+ pip install setfit
172
+ ```
173
+
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+ Then you can load this model and run inference.
175
+
176
+ ```python
177
+ from setfit import SetFitModel
178
+
179
+ # Download from the 🤗 Hub
180
+ model = SetFitModel.from_pretrained("setfit_model_id")
181
+ # Run inference
182
+ preds = model("Data init API for TFLite Swift <details><summary>Click to expand!</summary>
183
+
184
+ ### Issue Type
185
+
186
+ Feature Request
187
+
188
+ ### Source
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+
190
+ source
191
+
192
+ ### Tensorflow Version
193
+
194
+ 2.8+
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+
196
+ ### Custom Code
197
+
198
+ No
199
+
200
+ ### OS Platform and Distribution
201
+
202
+ _No response_
203
+
204
+ ### Mobile device
205
+
206
+ _No response_
207
+
208
+ ### Python version
209
+
210
+ _No response_
211
+
212
+ ### Bazel version
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+
214
+ _No response_
215
+
216
+ ### GCC/Compiler version
217
+
218
+ _No response_
219
+
220
+ ### CUDA/cuDNN version
221
+
222
+ _No response_
223
+
224
+ ### GPU model and memory
225
+
226
+ _No response_
227
+
228
+ ### Current Behaviour?
229
+
230
+ ```shell
231
+ 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`.
232
+ ```
233
+
234
+
235
+ ### Standalone code to reproduce the issue
236
+
237
+ ```shell
238
+ No code. This is a feature request
239
+ ```
240
+
241
+
242
+ ### Relevant log output
243
+
244
+ _No response_</details>")
245
+ ```
246
+
247
+ <!--
248
+ ### Downstream Use
249
+
250
+ *List how someone could finetune this model on their own dataset.*
251
+ -->
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+
253
+ <!--
254
+ ### Out-of-Scope Use
255
+
256
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
257
+ -->
258
+
259
+ <!--
260
+ ## Bias, Risks and Limitations
261
+
262
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
263
+ -->
264
+
265
+ <!--
266
+ ### Recommendations
267
+
268
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
269
+ -->
270
+
271
+ ## Training Details
272
+
273
+ ### Training Set Metrics
274
+ | Training set | Min | Median | Max |
275
+ |:-------------|:----|:---------|:-----|
276
+ | Word count | 5 | 353.7433 | 6124 |
277
+
278
+ | Label | Training Sample Count |
279
+ |:---------|:----------------------|
280
+ | bug | 200 |
281
+ | feature | 200 |
282
+ | question | 200 |
283
+
284
+ ### Training Hyperparameters
285
+ - batch_size: (16, 2)
286
+ - num_epochs: (1, 1)
287
+ - max_steps: -1
288
+ - sampling_strategy: oversampling
289
+ - num_iterations: 20
290
+ - body_learning_rate: (2e-05, 1e-05)
291
+ - head_learning_rate: 0.01
292
+ - loss: CosineSimilarityLoss
293
+ - distance_metric: cosine_distance
294
+ - margin: 0.25
295
+ - end_to_end: False
296
+ - use_amp: False
297
+ - warmup_proportion: 0.1
298
+ - seed: 42
299
+ - eval_max_steps: -1
300
+ - load_best_model_at_end: False
301
+
302
+ ### Training Results
303
+ | Epoch | Step | Training Loss | Validation Loss |
304
+ |:------:|:----:|:-------------:|:---------------:|
305
+ | 0.0007 | 1 | 0.1719 | - |
306
+ | 0.0067 | 10 | 0.2869 | - |
307
+ | 0.0133 | 20 | 0.2513 | - |
308
+ | 0.02 | 30 | 0.1871 | - |
309
+ | 0.0267 | 40 | 0.2065 | - |
310
+ | 0.0333 | 50 | 0.2302 | - |
311
+ | 0.04 | 60 | 0.1645 | - |
312
+ | 0.0467 | 70 | 0.1887 | - |
313
+ | 0.0533 | 80 | 0.1376 | - |
314
+ | 0.06 | 90 | 0.1171 | - |
315
+ | 0.0667 | 100 | 0.1303 | - |
316
+ | 0.0733 | 110 | 0.121 | - |
317
+ | 0.08 | 120 | 0.1126 | - |
318
+ | 0.0867 | 130 | 0.1247 | - |
319
+ | 0.0933 | 140 | 0.1764 | - |
320
+ | 0.1 | 150 | 0.0401 | - |
321
+ | 0.1067 | 160 | 0.1571 | - |
322
+ | 0.1133 | 170 | 0.0186 | - |
323
+ | 0.12 | 180 | 0.0501 | - |
324
+ | 0.1267 | 190 | 0.1003 | - |
325
+ | 0.1333 | 200 | 0.0152 | - |
326
+ | 0.14 | 210 | 0.0784 | - |
327
+ | 0.1467 | 220 | 0.1423 | - |
328
+ | 0.1533 | 230 | 0.1313 | - |
329
+ | 0.16 | 240 | 0.0799 | - |
330
+ | 0.1667 | 250 | 0.0542 | - |
331
+ | 0.1733 | 260 | 0.0426 | - |
332
+ | 0.18 | 270 | 0.047 | - |
333
+ | 0.1867 | 280 | 0.0062 | - |
334
+ | 0.1933 | 290 | 0.0085 | - |
335
+ | 0.2 | 300 | 0.0625 | - |
336
+ | 0.2067 | 310 | 0.095 | - |
337
+ | 0.2133 | 320 | 0.0262 | - |
338
+ | 0.22 | 330 | 0.0029 | - |
339
+ | 0.2267 | 340 | 0.0097 | - |
340
+ | 0.2333 | 350 | 0.063 | - |
341
+ | 0.24 | 360 | 0.0059 | - |
342
+ | 0.2467 | 370 | 0.0016 | - |
343
+ | 0.2533 | 380 | 0.0025 | - |
344
+ | 0.26 | 390 | 0.0033 | - |
345
+ | 0.2667 | 400 | 0.0006 | - |
346
+ | 0.2733 | 410 | 0.0032 | - |
347
+ | 0.28 | 420 | 0.0045 | - |
348
+ | 0.2867 | 430 | 0.0013 | - |
349
+ | 0.2933 | 440 | 0.0011 | - |
350
+ | 0.3 | 450 | 0.001 | - |
351
+ | 0.3067 | 460 | 0.0044 | - |
352
+ | 0.3133 | 470 | 0.001 | - |
353
+ | 0.32 | 480 | 0.0009 | - |
354
+ | 0.3267 | 490 | 0.0004 | - |
355
+ | 0.3333 | 500 | 0.0006 | - |
356
+ | 0.34 | 510 | 0.001 | - |
357
+ | 0.3467 | 520 | 0.0003 | - |
358
+ | 0.3533 | 530 | 0.0008 | - |
359
+ | 0.36 | 540 | 0.0003 | - |
360
+ | 0.3667 | 550 | 0.0023 | - |
361
+ | 0.3733 | 560 | 0.0336 | - |
362
+ | 0.38 | 570 | 0.0004 | - |
363
+ | 0.3867 | 580 | 0.0003 | - |
364
+ | 0.3933 | 590 | 0.0006 | - |
365
+ | 0.4 | 600 | 0.0008 | - |
366
+ | 0.4067 | 610 | 0.0011 | - |
367
+ | 0.4133 | 620 | 0.0002 | - |
368
+ | 0.42 | 630 | 0.0004 | - |
369
+ | 0.4267 | 640 | 0.0005 | - |
370
+ | 0.4333 | 650 | 0.0601 | - |
371
+ | 0.44 | 660 | 0.0003 | - |
372
+ | 0.4467 | 670 | 0.0003 | - |
373
+ | 0.4533 | 680 | 0.0006 | - |
374
+ | 0.46 | 690 | 0.0005 | - |
375
+ | 0.4667 | 700 | 0.0003 | - |
376
+ | 0.4733 | 710 | 0.0006 | - |
377
+ | 0.48 | 720 | 0.0001 | - |
378
+ | 0.4867 | 730 | 0.0002 | - |
379
+ | 0.4933 | 740 | 0.0002 | - |
380
+ | 0.5 | 750 | 0.0002 | - |
381
+ | 0.5067 | 760 | 0.0002 | - |
382
+ | 0.5133 | 770 | 0.0016 | - |
383
+ | 0.52 | 780 | 0.0001 | - |
384
+ | 0.5267 | 790 | 0.0005 | - |
385
+ | 0.5333 | 800 | 0.0004 | - |
386
+ | 0.54 | 810 | 0.0039 | - |
387
+ | 0.5467 | 820 | 0.0031 | - |
388
+ | 0.5533 | 830 | 0.0008 | - |
389
+ | 0.56 | 840 | 0.0003 | - |
390
+ | 0.5667 | 850 | 0.0002 | - |
391
+ | 0.5733 | 860 | 0.0002 | - |
392
+ | 0.58 | 870 | 0.0002 | - |
393
+ | 0.5867 | 880 | 0.0001 | - |
394
+ | 0.5933 | 890 | 0.0004 | - |
395
+ | 0.6 | 900 | 0.0002 | - |
396
+ | 0.6067 | 910 | 0.0008 | - |
397
+ | 0.6133 | 920 | 0.0005 | - |
398
+ | 0.62 | 930 | 0.0005 | - |
399
+ | 0.6267 | 940 | 0.0002 | - |
400
+ | 0.6333 | 950 | 0.0001 | - |
401
+ | 0.64 | 960 | 0.0002 | - |
402
+ | 0.6467 | 970 | 0.0007 | - |
403
+ | 0.6533 | 980 | 0.0002 | - |
404
+ | 0.66 | 990 | 0.0002 | - |
405
+ | 0.6667 | 1000 | 0.0002 | - |
406
+ | 0.6733 | 1010 | 0.0002 | - |
407
+ | 0.68 | 1020 | 0.0002 | - |
408
+ | 0.6867 | 1030 | 0.0002 | - |
409
+ | 0.6933 | 1040 | 0.0004 | - |
410
+ | 0.7 | 1050 | 0.0076 | - |
411
+ | 0.7067 | 1060 | 0.0002 | - |
412
+ | 0.7133 | 1070 | 0.0002 | - |
413
+ | 0.72 | 1080 | 0.0001 | - |
414
+ | 0.7267 | 1090 | 0.0002 | - |
415
+ | 0.7333 | 1100 | 0.0001 | - |
416
+ | 0.74 | 1110 | 0.0365 | - |
417
+ | 0.7467 | 1120 | 0.0002 | - |
418
+ | 0.7533 | 1130 | 0.0002 | - |
419
+ | 0.76 | 1140 | 0.0003 | - |
420
+ | 0.7667 | 1150 | 0.0002 | - |
421
+ | 0.7733 | 1160 | 0.0002 | - |
422
+ | 0.78 | 1170 | 0.0004 | - |
423
+ | 0.7867 | 1180 | 0.0001 | - |
424
+ | 0.7933 | 1190 | 0.0001 | - |
425
+ | 0.8 | 1200 | 0.0001 | - |
426
+ | 0.8067 | 1210 | 0.0001 | - |
427
+ | 0.8133 | 1220 | 0.0002 | - |
428
+ | 0.82 | 1230 | 0.0002 | - |
429
+ | 0.8267 | 1240 | 0.0001 | - |
430
+ | 0.8333 | 1250 | 0.0001 | - |
431
+ | 0.84 | 1260 | 0.0002 | - |
432
+ | 0.8467 | 1270 | 0.0002 | - |
433
+ | 0.8533 | 1280 | 0.0 | - |
434
+ | 0.86 | 1290 | 0.0002 | - |
435
+ | 0.8667 | 1300 | 0.032 | - |
436
+ | 0.8733 | 1310 | 0.0001 | - |
437
+ | 0.88 | 1320 | 0.0001 | - |
438
+ | 0.8867 | 1330 | 0.0001 | - |
439
+ | 0.8933 | 1340 | 0.0003 | - |
440
+ | 0.9 | 1350 | 0.0001 | - |
441
+ | 0.9067 | 1360 | 0.0001 | - |
442
+ | 0.9133 | 1370 | 0.0001 | - |
443
+ | 0.92 | 1380 | 0.0001 | - |
444
+ | 0.9267 | 1390 | 0.0001 | - |
445
+ | 0.9333 | 1400 | 0.0001 | - |
446
+ | 0.94 | 1410 | 0.0001 | - |
447
+ | 0.9467 | 1420 | 0.0001 | - |
448
+ | 0.9533 | 1430 | 0.031 | - |
449
+ | 0.96 | 1440 | 0.0001 | - |
450
+ | 0.9667 | 1450 | 0.0003 | - |
451
+ | 0.9733 | 1460 | 0.0001 | - |
452
+ | 0.98 | 1470 | 0.0001 | - |
453
+ | 0.9867 | 1480 | 0.0001 | - |
454
+ | 0.9933 | 1490 | 0.0001 | - |
455
+ | 1.0 | 1500 | 0.0001 | - |
456
+
457
+ ### Framework Versions
458
+ - Python: 3.10.12
459
+ - SetFit: 1.0.3
460
+ - Sentence Transformers: 3.0.1
461
+ - Transformers: 4.39.0
462
+ - PyTorch: 2.3.0+cu121
463
+ - Datasets: 2.20.0
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+ - Tokenizers: 0.15.2
465
+
466
+ ## Citation
467
+
468
+ ### BibTeX
469
+ ```bibtex
470
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
471
+ doi = {10.48550/ARXIV.2209.11055},
472
+ url = {https://arxiv.org/abs/2209.11055},
473
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
474
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
475
+ title = {Efficient Few-Shot Learning Without Prompts},
476
+ publisher = {arXiv},
477
+ year = {2022},
478
+ copyright = {Creative Commons Attribution 4.0 International}
479
+ }
480
+ ```
481
+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
494
+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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