Transformers
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    • Transformers
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  • 🌍TASK GUIDES
    • 🌍NATURAL LANGUAGE PROCESSING
      • Text classification
      • Token classification
      • Question answering
      • Causal language modeling
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      • Translation
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      • Multiple choice
    • 🌍AUDIO
      • Audio classification
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    • 🌍COMPUTER VISION
      • Image classification
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      • Object detection
      • Zero-shot object detection
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      • Depth estimation
    • 🌍MULTIMODAL
      • Image captioning
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    • 🌍GENERATION
      • Customize the generation strategy
    • 🌍PROMPTING
      • Image tasks with IDEFICS
  • 🌍DEVELOPER GUIDES
    • Use fast tokenizers from BOINC AI Tokenizers
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    • Templates for chat models
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  • 🌍PERFORMANCE AND SCALABILITY
    • Overview
    • 🌍EFFICIENT TRAINING TECHNIQUES
      • Methods and tools for efficient training on a single GPU
      • Multiple GPUs and parallelism
      • Efficient training on CPU
      • Distributed CPU training
      • Training on TPUs
      • Training on TPU with TensorFlow
      • Training on Specialized Hardware
      • Custom hardware for training
      • Hyperparameter Search using Trainer API
    • 🌍OPTIMIZING INFERENCE
      • Inference on CPU
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      • Inference on many GPUs
      • Inference on Specialized Hardware
    • Instantiating a big model
    • Troubleshooting
    • XLA Integration for TensorFlow Models
    • Optimize inference using `torch.compile()`
  • 🌍CONTRIBUTE
    • How to contribute to transformers?
    • How to add a model to BOINC AI Transformers?
    • How to convert a BOINC AI Transformers model to TensorFlow?
    • How to add a pipeline to BOINC AI Transformers?
    • Testing
    • Checks on a Pull Request
  • 🌍CONCEPTUAL GUIDES
    • Philosophy
    • Glossary
    • What BOINC AI Transformers can do
    • How BOINC AI Transformers solve tasks
    • The Transformer model family
    • Summary of the tokenizers
    • Attention mechanisms
    • Padding and truncation
    • BERTology
    • Perplexity of fixed-length models
    • Pipelines for webserver inference
    • Model training anatomy
  • 🌍API
    • 🌍MAIN CLASSES
      • Agents and Tools
      • 🌍Auto Classes
        • Extending the Auto Classes
        • AutoConfig
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        • AutoImageProcessor
        • AutoProcessor
        • Generic model classes
          • AutoModel
          • TFAutoModel
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          • FlaxAutoModelForPreTraining
        • Natural Language Processing
          • AutoModelForCausalLM
          • TFAutoModelForCausalLM
          • FlaxAutoModelForCausalLM
          • AutoModelForMaskedLM
          • TFAutoModelForMaskedLM
          • FlaxAutoModelForMaskedLM
          • AutoModelForMaskGenerationge
          • TFAutoModelForMaskGeneration
          • AutoModelForSeq2SeqLM
          • TFAutoModelForSeq2SeqLM
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          • AutoModelForSequenceClassification
          • TFAutoModelForSequenceClassification
          • FlaxAutoModelForSequenceClassification
          • AutoModelForMultipleChoice
          • TFAutoModelForMultipleChoice
          • FlaxAutoModelForMultipleChoice
          • AutoModelForNextSentencePrediction
          • TFAutoModelForNextSentencePrediction
          • FlaxAutoModelForNextSentencePrediction
          • AutoModelForTokenClassification
          • TFAutoModelForTokenClassification
          • FlaxAutoModelForTokenClassification
          • AutoModelForQuestionAnswering
          • TFAutoModelForQuestionAnswering
          • FlaxAutoModelForQuestionAnswering
          • AutoModelForTextEncoding
          • TFAutoModelForTextEncoding
        • Computer vision
          • AutoModelForDepthEstimation
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          • TFAutoModelForImageClassification
          • FlaxAutoModelForImageClassification
          • AutoModelForVideoClassification
          • AutoModelForMaskedImageModeling
          • TFAutoModelForMaskedImageModeling
          • AutoModelForObjectDetection
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          • AutoModelForImageToImage
          • AutoModelForSemanticSegmentation
          • TFAutoModelForSemanticSegmentation
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          • AutoModelForZeroShotImageClassification
          • TFAutoModelForZeroShotImageClassification
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        • Audio
          • AutoModelForAudioClassification
          • AutoModelForAudioFrameClassification
          • TFAutoModelForAudioFrameClassification
          • AutoModelForCTC
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          • TFAutoModelForSpeechSeq2Seq
          • FlaxAutoModelForSpeechSeq2Seq
          • AutoModelForAudioXVector
          • AutoModelForTextToSpectrogram
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        • Multimodal
          • AutoModelForTableQuestionAnswering
          • TFAutoModelForTableQuestionAnswering
          • AutoModelForDocumentQuestionAnswering
          • TFAutoModelForDocumentQuestionAnswering
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          • FlaxAutoModelForVision2Seq
      • Callbacks
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    • 🌍MODELS
      • 🌍TEXT MODELS
        • ALBERT
        • BART
        • BARThez
        • BARTpho
        • BERT
        • BertGeneration
        • BertJapanese
        • Bertweet
        • BigBird
        • BigBirdPegasus
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        • Blenderbot
        • Blenderbot Small
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        • Encoder Decoder Models
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        • Funnel Transformer
        • GPT
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        • GPT NeoX Japanese
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        • Jukebox
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        • LLaMA
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        • Longformer
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        • M2M100
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      • 🌍VISION MODELS
        • BEiT
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        • Vision Transformer (ViT)
        • ViT Hybrid
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      • 🌍AUDIO MODELS
        • Audio Spectrogram Transformer
        • Bark
        • CLAP
        • EnCodec
        • Hubert
        • MCTCT
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        • MusicGen
        • Pop2Piano
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        • Speech2Text2
        • SpeechT5
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        • VITS
        • Wav2Vec2
        • Wav2Vec2-Conformer
        • Wav2Vec2Phoneme
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      • 🌍MULTIMODAL MODELS
        • ALIGN
        • AltCLIP
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        • BLIP-2
        • BridgeTower
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        • Chinese-CLIP
        • CLIP
        • CLIPSeg
        • Data2Vec
        • DePlot
        • Donut
        • FLAVA
        • GIT
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        • IDEFICS
        • InstructBLIP
        • LayoutLM
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        • LayoutXLM
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        • MatCha
        • MGP-STR
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        • OWL-ViT
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        • Pix2Struct
        • Segment Anything
        • Speech Encoder Decoder Models
        • TAPAS
        • TrOCR
        • TVLT
        • ViLT
        • Vision Encoder Decoder Models
        • Vision Text Dual Encoder
        • VisualBERT
        • X-CLIP
      • 🌍REINFORCEMENT LEARNING MODELS
        • Decision Transformer
        • Trajectory Transformer
      • 🌍TIME SERIES MODELS
        • Autoformer
        • Informer
        • Time Series Transformer
      • 🌍GRAPH MODELS
        • Graphormer
  • 🌍INTERNAL HELPERS
    • Custom Layers and Utilities
    • Utilities for pipelines
    • Utilities for Tokenizers
    • Utilities for Trainer
    • Utilities for Generation
    • Utilities for Image Processors
    • Utilities for Audio processing
    • General Utilities
    • Utilities for Time Series
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On this page
  • Firewalled environments
  • CUDA out of memory
  • Unable to load a saved TensorFlow model
  • ImportError
  • CUDA error: device-side assert triggered
  • Incorrect output when padding tokens aren’t masked
  • ValueError: Unrecognized configuration class XYZ for this kind of AutoModel
  1. DEVELOPER GUIDES

Troubleshoot

PreviousCustom Tools and PromptsNextPERFORMANCE AND SCALABILITY

Last updated 1 year ago

Sometimes errors occur, but we are here to help! This guide covers some of the most common issues we’ve seen and how you can resolve them. However, this guide isn’t meant to be a comprehensive collection of every 🌍 Transformers issue. For more help with troubleshooting your issue.

  1. Asking for help on the . There are specific categories you can post your question to, like or 🌍. Make sure you write a good descriptive forum post with some reproducible code to maximize the likelihood that your problem is solved!

  2. Create an on the 🌍 Transformers repository if it is a bug related to the library. Try to include as much information describing the bug as possible to help us better figure out what’s wrong and how we can fix it.

  3. Check the guide if you use an older version of 🌍 Transformers since some important changes have been introduced between versions.

For more details about troubleshooting and getting help, take a look at of the BOINC AI course.

Firewalled environments

Some GPU instances on cloud and intranet setups are firewalled to external connections, resulting in a connection error. When your script attempts to download model weights or datasets, the download will hang and then timeout with the following message:

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ValueError: Connection error, and we cannot find the requested files in the cached path.
Please try again or make sure your Internet connection is on.

In this case, you should try to run 🌍 Transformers on to avoid the connection error.

CUDA out of memory

Training large models with millions of parameters can be challenging without the appropriate hardware. A common error you may encounter when the GPU runs out of memory is:

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CUDA out of memory. Tried to allocate 256.00 MiB (GPU 0; 11.17 GiB total capacity; 9.70 GiB already allocated; 179.81 MiB free; 9.85 GiB reserved in total by PyTorch)

Here are some potential solutions you can try to lessen memory use:

Unable to load a saved TensorFlow model

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>>> from transformers import TFPreTrainedModel
>>> from tensorflow import keras

>>> model.save_weights("some_folder/tf_model.h5")
>>> model = TFPreTrainedModel.from_pretrained("some_folder")

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>>> from transformers import TFPreTrainedModel

>>> model.save_pretrained("path_to/model")
>>> model = TFPreTrainedModel.from_pretrained("path_to/model")

ImportError

Another common error you may encounter, especially if it is a newly released model, is ImportError:

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ImportError: cannot import name 'ImageGPTImageProcessor' from 'transformers' (unknown location)

For these error types, check to make sure you have the latest version of 🌍 Transformers installed to access the most recent models:

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pip install transformers --upgrade

CUDA error: device-side assert triggered

Sometimes you may run into a generic CUDA error about an error in the device code.

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RuntimeError: CUDA error: device-side assert triggered

You should try to run the code on a CPU first to get a more descriptive error message. Add the following environment variable to the beginning of your code to switch to a CPU:

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>>> import os

>>> os.environ["CUDA_VISIBLE_DEVICES"] = ""

Another option is to get a better traceback from the GPU. Add the following environment variable to the beginning of your code to get the traceback to point to the source of the error:

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>>> import os

>>> os.environ["CUDA_LAUNCH_BLOCKING"] = "1"

Incorrect output when padding tokens aren’t masked

In some cases, the output hidden_state may be incorrect if the input_ids include padding tokens. To demonstrate, load a model and tokenizer. You can access a model’s pad_token_id to see its value. The pad_token_id may be None for some models, but you can always manually set it.

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>>> from transformers import AutoModelForSequenceClassification
>>> import torch

>>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
>>> model.config.pad_token_id
0

The following example shows the output without masking the padding tokens:

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>>> input_ids = torch.tensor([[7592, 2057, 2097, 2393, 9611, 2115], [7592, 0, 0, 0, 0, 0]])
>>> output = model(input_ids)
>>> print(output.logits)
tensor([[ 0.0082, -0.2307],
        [ 0.1317, -0.1683]], grad_fn=<AddmmBackward0>)

Here is the actual output of the second sequence:

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>>> input_ids = torch.tensor([[7592]])
>>> output = model(input_ids)
>>> print(output.logits)
tensor([[-0.1008, -0.4061]], grad_fn=<AddmmBackward0>)

Most of the time, you should provide an attention_mask to your model to ignore the padding tokens to avoid this silent error. Now the output of the second sequence matches its actual output:

By default, the tokenizer creates an attention_mask for you based on your specific tokenizer’s defaults.

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>>> attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 0, 0, 0, 0, 0]])
>>> output = model(input_ids, attention_mask=attention_mask)
>>> print(output.logits)
tensor([[ 0.0082, -0.2307],
        [-0.1008, -0.4061]], grad_fn=<AddmmBackward0>)

🌍 Transformers doesn’t automatically create an attention_mask to mask a padding token if it is provided because:

  • Some models don’t have a padding token.

  • For some use-cases, users want a model to attend to a padding token.

ValueError: Unrecognized configuration class XYZ for this kind of AutoModel

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>>> from transformers import AutoProcessor, AutoModelForQuestionAnswering

>>> processor = AutoProcessor.from_pretrained("gpt2-medium")
>>> model = AutoModelForQuestionAnswering.from_pretrained("gpt2-medium")
ValueError: Unrecognized configuration class <class 'transformers.models.gpt2.configuration_gpt2.GPT2Config'> for this kind of AutoModel: AutoModelForQuestionAnswering.
Model type should be one of AlbertConfig, BartConfig, BertConfig, BigBirdConfig, BigBirdPegasusConfig, BloomConfig, ...

Reduce the value in .

Try using in to effectively increase overall batch size.

Refer to the Performance for more details about memory-saving techniques.

TensorFlow’s method will save the entire model - architecture, weights, training configuration - in a single file. However, when you load the model file again, you may run into an error because 🌍 Transformers may not load all the TensorFlow-related objects in the model file. To avoid issues with saving and loading TensorFlow models, we recommend you:

Save the model weights as a h5 file extension with and then reload the model with :

Save the model with ~TFPretrainedModel.save_pretrained and load it again with :

Generally, we recommend using the class to load pretrained instances of models. This class can automatically infer and load the correct architecture from a given checkpoint based on the configuration. If you see this ValueError when loading a model from a checkpoint, this means the Auto class couldn’t find a mapping from the configuration in the given checkpoint to the kind of model you are trying to load. Most commonly, this happens when a checkpoint doesn’t support a given task. For instance, you’ll see this error in the following example because there is no GPT2 for question answering:

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AutoModel