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Models that are / aren't supported by 🤗 Exporters

Only models that have a ModelNameCoreMLConfig object are currently supported.

If a model is not supported, this is either because there is some problem with the actual conversion process, or because we simply did not get around to writing a CoreMLConfig object for it.

Supported models

Legend:

  • ✅ = fully supported
  • 😓 = works but with hacks
  • ⚠️ = partially supported (for example no "with past" version)
  • ❌ = errors during conversion
  • ➖ = not supported
  • ? = unknown

Text Models

BART

  • ⚠️ BartModel (currently supports only use_past=False)
  • ✅ BartForCausalLM
  • ⚠️ BartForConditionalGeneration (currently supports only use_past=False)
  • ? BartForQuestionAnswering
  • ? BartForSequenceClassification

BERT

  • ✅ BertModel
  • ➖ BertForPreTraining
  • ✅ BertForMaskedLM
  • ✅ BertForMultipleChoice
  • ✅ BertForNextSentencePrediction
  • ✅ BertForQuestionAnswering
  • ✅ BertForSequenceClassification
  • ✅ BertForTokenClassification
  • ⚠️ BertLMHeadModel: works OK with coremltools commit 50c5569, breaks with later versions

BigBird

  • ? BigBirdModel
  • ➖ BigBirdForPreTraining
  • ⚠️ BigBirdForCausalLM: works OK with coremltools commit 50c5569, breaks with later versions
  • ? BigBirdForMaskedLM
  • ? BigBirdForMultipleChoice
  • ? BigBirdForQuestionAnswering
  • ? BigBirdForSequenceClassification
  • ? BigBirdForTokenClassification

BigBirdPegasus

  • ⚠️ BigBirdPegasusModel (currently supports only use_past=False)
  • ✅ BigBirdPegasusForCausalLM
  • ⚠️ BigBirdPegasusForConditionalGeneration (currently supports only use_past=False)
  • ? BigBirdPegasusForQuestionAnswering
  • ? BigBirdPegasusForSequenceClassification

Blenderbot

  • ⚠️ BlenderbotModel (currently supports only use_past=False)
  • ? BlenderbotForCausalLM
  • ⚠️ BlenderbotForConditionalGeneration (currently supports only use_past=False)

Blenderbot Small

  • ⚠️ BlenderbotSmallModel (currently supports only use_past=False)
  • ? BlenderbotSmallForCausalLM
  • ⚠️ BlenderbotSmallForConditionalGeneration (currently supports only use_past=False)

CTRL

  • ✅ CTRLModel
  • ✅ CTRLLMHeadModel
  • ✅ CTRLForSequenceClassification

DistilBERT

  • ✅ DistilBertModel
  • ✅ DistilBertForMaskedLM
  • ✅ DistilBertForMultipleChoice
  • ✅ DistilBertForQuestionAnswering
  • ✅ DistilBertForSequenceClassification
  • ✅ DistilBertForTokenClassification

ERNIE

  • ? ErnieModel
  • ➖ ErnieForPreTraining
  • ⚠️ ErnieForCausalLM: works OK with coremltools commit 50c5569, breaks with later versions
  • ? ErnieForMaskedLM
  • ? ErnieForMultipleChoice
  • ? ErnieForNextSentencePrediction
  • ? ErnieForQuestionAnswering
  • ? ErnieForSequenceClassification
  • ? ErnieForTokenClassification

GPT2 / DistilGPT2

Does not work with flexible sequence length and therefore does not support use_past.

  • ✅ GPT2Model
  • ➖ GPT2DoubleHeadsModel
  • ✅ GPT2ForSequenceClassification
  • ✅ GPT2ForTokenClassification
  • ⚠️ GPT2LMHeadModel (no use_past)

M2M100

  • ⚠️ M2M100Model (currently supports only use_past=False)
  • ⚠️ M2M100ForConditionalGeneration (currently supports only use_past=False)

MarianMT

  • ⚠️ MarianModel (currently supports only use_past=False)
  • ? MarianForCausalLM
  • ⚠️ MarianMTModel (currently supports only use_past=False)

MobileBERT

  • ✅ MobileBertModel
  • ➖ MobileBertForPreTraining
  • ✅ MobileBertForMaskedLM
  • ✅ MobileBertForMultipleChoice
  • ✅ MobileBertForNextSentencePrediction
  • ✅ MobileBertForQuestionAnswering
  • ✅ MobileBertForSequenceClassification
  • ✅ MobileBertForTokenClassification

MVP

  • ⚠️ MvpModel (currently supports only use_past=False)
  • ? MvpForCausalLM
  • ⚠️ MvpForConditionalGeneration (currently supports only use_past=False)
  • ? MvpForSequenceClassification
  • ? MvpForQuestionAnswering

Pegasus

  • ⚠️ PegasusModel (currently supports only use_past=False)
  • ? PegasusForCausalLM
  • ⚠️ PegasusForConditionalGeneration (currently supports only use_past=False)

PLBart

  • ⚠️ PLBartModel (currently supports only use_past=False)
  • ? PLBartForCausalLM
  • ⚠️ PLBartForConditionalGeneration (currently supports only use_past=False)
  • ? PLBartForSequenceClassification

RoBERTa

  • ? RobertaModel
  • ⚠️ RobertaForCausalLM: works OK with coremltools commit 50c5569, breaks with later versions
  • ? RobertaForMaskedLM
  • ? RobertaForMultipleChoice
  • ? RobertaForQuestionAnswering
  • ? RobertaForSequenceClassification
  • ? RobertaForTokenClassification

RoFormer

  • ? RoFormerModel
  • ❌ RoFormerForCausalLM: Conversion may appear to work but the model does not actually run. Core ML takes forever to load the model, allocates 100+ GB of RAM and eventually crashes.
  • ? RoFormerForMaskedLM
  • ? RoFormerForSequenceClassification
  • ? RoFormerForMultipleChoice
  • ? RoFormerForTokenClassification
  • ? RoFormerForQuestionAnswering

Splinter

  • ❌ SplinterModel: Conversion may appear to work but the model does not actually run. Core ML takes forever to load the model, allocates 100+ GB of RAM and eventually crashes.
  • ➖ SplinterForPreTraining
  • SplinterForQuestionAnswering

SqueezeBERT

  • ✅ SqueezeBertModel
  • ✅ SqueezeBertForMaskedLM
  • ✅ SqueezeBertForMultipleChoice
  • ✅ SqueezeBertForQuestionAnswering
  • ✅ SqueezeBertForSequenceClassification
  • ✅ SqueezeBertForTokenClassification

T5

  • ⚠️ T5Model (currently supports only use_past=False)
  • ✅ T5EncoderModel
  • ⚠️ T5ForConditionalGeneration (currently supports only use_past=False)

Vision Models

BEiT

  • ✅ BeitModel
  • ✅ BeitForImageClassification
  • ✅ BeitForSemanticSegmentation
  • ✅ BeitForMaskedImageModeling. Note: this model does not work with AutoModelForMaskedImageModeling and therefore the conversion script cannot load it, but converting from Python is supported.

ConvNeXT

  • ✅ ConvNextModel
  • ✅ ConvNextForImageClassification

CvT

  • ✅ CvtModel
  • ✅ CvtForImageClassification

LeViT

  • ✅ LevitModel
  • ✅ LevitForImageClassification
  • ➖ LevitForImageClassificationWithTeacher

MobileViT

  • ✅ MobileViTModel
  • ✅ MobileViTForImageClassification
  • ✅ MobileViTForSemanticSegmentation

SegFormer

  • ✅ SegformerModel
  • ✅ SegformerForImageClassification
  • ✅ SegformerForSemanticSegmentation

Vision Transformer (ViT)

  • ✅ ViTModel
  • ✅ ViTForMaskedImageModeling
  • ✅ ViTForImageClassification

YOLOS

  • ✅ YolosModel
  • ✅ YolosForObjectDetection

Audio Models

None

Multimodal Models

Data2Vec Audio

  • ? Data2VecAudioModel: [TODO verify] The conversion completes without errors but the Core ML compiler cannot load the model.
  • ? Data2VecAudioForAudioFrameClassification
  • ? Data2VecAudioForCTC
  • ? Data2VecAudioForSequenceClassification
  • ? Data2VecAudioForXVector

Data2Vec Text

  • ? Data2VecTextModel
  • ⚠️ Data2VecTextForCausalLM: works OK with coremltools commit 50c5569, breaks with later versions
  • ? Data2VecTextForMaskedLM
  • ? Data2VecTextForMultipleChoice
  • ? Data2VecTextForQuestionAnswering
  • ? Data2VecTextForSequenceClassification
  • ? Data2VecTextForTokenClassification

Data2Vec Vision

  • ? Data2VecVisionModel
  • ? Data2VecVisionForImageClassification
  • ? Data2VecVisionForSemanticSegmentation

Models that currently don't work

The following models are known to give errors when attempting conversion to Core ML format, or simply have not been tried yet.

Text Models

ALBERT

BARThez

BARTpho

BertGeneration

BertJapanese

Bertweet

BLOOM [TODO verify] Conversion error on a slicing operation.

BORT

ByT5

CamemBERT

CANINE

CodeGen [TODO verify] Conversion error on einsum.

ConvBERT

CPM

DeBERTa

DeBERTa-v2

DialoGPT

DPR

ELECTRA

  • ❌ ElectraForCausalLM: "AttributeError: 'list' object has no attribute 'val'" in repeat op. Also, coreml_config.values_override doesn't work to set use_cache to True for this model.

Encoder Decoder Models

ESM

FlauBERT

FNet

FSMT

  • ❌ FSMTForConditionalGeneration. Encoder converts OK. For decoder, Wrapper outputs wrong size logits tensor; goes wrong somewhere in hidden states output from decoder when return_dict=False?

Funnel Transformer

GPT

GPT Neo. [TODO verify] Gives no errors during conversion but predicts wrong results, or NaN when use_legacy_format=True.

  • GPTNeoModel
  • GPTNeoForCausalLM
  • GPTNeoForSequenceClassification

GPT NeoX

GPT NeoX Japanese

GPT-J

HerBERT

I-BERT

LayoutLM

LED

  • ❌ LEDForConditionalGeneration: JIT trace fails with the error:
RuntimeError: 0INTERNAL ASSERT FAILED at "/Users/distiller/project/pytorch/torch/csrc/jit/ir/alias_analysis.cpp":607, please report a bug to PyTorch. We don't have an op for aten::constant_pad_nd but it isn't a special case.  Argument types: Tensor, int[], bool,

LiLT

Longformer

LongT5

  • ❌ LongT5ForConditionalGeneration: Conversion error:
ValueError: In op, of type not_equal, named 133, the named input `y` must have the same data type as the named input `x`. However, y has dtype fp32 whereas x has dtype int32.

LUKE

MarkupLM

MBart and MBart-50

MegatronBERT

MegatronGPT2

mLUKE

MPNet

MT5

  • ❌ MT5ForConditionalGeneration: Converter error "User defined pattern has more than one final operation"

NEZHA [TODO verify] Conversion error on a slicing operation.

NLLB

Nyströmformer

OPT [TODO verify] Conversion error on a slicing operation.

PEGASUS-X

  • ❌ PegasusXForConditionalGeneration: "AttributeError: 'list' object has no attribute 'val'" in pad op. Maybe: needs remainder op (added recently in coremltools dev version).

PhoBERT

ProphetNet

  • ❌ ProphetNetForConditionalGeneration. Conversion error:
ValueError: Op "input.3" (op_type: clip) Input x="position_ids" expects tensor or scalar of dtype from type domain ['fp16', 'fp32'] but got tensor[1,is4273,int32]

QDQBert

RAG

REALM

Reformer

  • ❌ ReformerModelWithLMHead: does not have past_key_values but past_buckets_states

RemBERT

  • ❌ RemBertForCausalLM. Conversion to MIL succeeds after a long time but running the model gives "Error in declaring network." When using legacy mode, the model is too large to fit into protobuf.

RetriBERT

T5v1.1

TAPAS

TAPEX

Transformer XL

UL2

XGLM [TODO verify] Conversion error on a slicing operation.

XLM

XLM-ProphetNet

  • XLMProphetNetForConditionalGeneration: Conversion error:
ValueError: Op "input.3" (op_type: clip) Input x="position_ids" expects tensor or scalar of dtype from type domain ['fp16', 'fp32'] but got tensor[1,is4506,int32]

XLM-RoBERTa

XLM-RoBERTa-XL

XLNet [TODO verify] Conversion error.

YOSO

Vision Models

Conditional DETR

Deformable DETR

DeiT

DETR [TODO verify] The conversion completes without errors but the Core ML compiler cannot load the model. "Invalid operation output name: got 'tensor' when expecting token of type 'ID'"

DiT

DPT

GLPN

ImageGPT

MaskFormer

PoolFormer

RegNet

ResNet

Swin Transformer [TODO verify] The PyTorch graph contains unsupported operations: remainder, roll, adaptive_avg_pool1d. (Some of these may be supported in latest dev version.)

Swin Transformer V2

VAN

VideoMAE

ViTMAE

ViTMSN

Audio Models

Hubert [TODO verify] Unsupported op for nn.GroupNorm (should be possible to solve), invalid broadcasting operations (will be harder to solve), and most likely additional issues.

MCTCT

SEW [TODO verify] Unsupported op for nn.GroupNorm (should be possible to solve), invalid broadcasting operations (will be harder to solve), and most likely additional issues.

SEW-D

Speech2Text [TODO verify] The "glu" op is not supported by coremltools. Should be possible to solve by defining a @register_torch_op function. (Update: should be supported in dev version now.)

Speech2Text2

UniSpeech [TODO verify] Missing op for _weight_norm (possible to work around), also same Core ML compiler error as DETR.

UniSpeech-SAT

Wav2Vec2 [TODO verify] Unsupported op for nn.GroupNorm (should be possible to solve), invalid broadcasting operations (will be harder to solve), and most likely additional issues.

Wav2Vec2-Conformer

Wav2Vec2Phoneme

WavLM [TODO verify] Missing ops for _weight_norm, add_, full_like.

Whisper

XLS-R

XLSR-Wav2Vec2

Multimodal Models

CLIP

Donut

FLAVA

GroupViT [TODO verify] Conversion issue with scatter_along_axis operation.

LayoutLMV2

LayoutLMV3

LayoutXLM

LXMERT

OWL-ViT

Perceiver

Speech Encoder Decoder Models

TrOCR

ViLT

Vision Encoder Decoder Models

Vision Text Dual Encoder

VisualBERT

X-CLIP