transformers/docs/source/en/model_doc/auto.md
Jaeyong Sung 583db52bc6
Add Dia model (#38405)
* add dia model

* add tokenizer files

* cleanup some stuff

* brut copy paste code

* rough cleanup of the modeling code

* nuke some stuff

* more nuking

* more cleanups

* updates

* add mulitLayerEmbedding vectorization

* nits

* more modeling simplifications

* updates

* update rope

* update rope

* just fixup

* update configuration files

* more cleanup!

* default config values

* update

* forgotten comma

* another comma!

* update, more cleanups

* just more nits

* more config cleanups

* time for the encoder

* fix

* sa=mall nit

* nits

* n

* refacto a bit

* cleanup

* update cv scipt

* fix last issues

* fix last nits

* styling

* small fixes

* just run 1 generation

* fixes

* nits

* fix conversion

* fix

* more fixes

* full generate

* ouf!

* fixes!

* updates

* fix

* fix cvrt

* fixup

* nits

* delete wrong test

* update

* update

* test tokenization

* let's start changing things bit by bit - fix encoder step

* removing custom generation, moving to GenerationMixin

* add encoder decoder attention masks for generation

* mask changes, correctness checked against ad29837 in dia repo

* refactor a bit already --> next cache

* too important not to push :)

* minimal cleanup + more todos

* make main overwrite modeling utils

* add cfg filter & eos filter

* add eos countdown & delay pattern

* update eos countdown

* add max step eos countdown

* fix tests

* fix some things

* fix generation with testing

* move cfg & eos stuff to logits processor

* make RepetitionPenaltyLogitsProcessor flexible

- can accept 3D scores like (batch_size, channel, vocab)

* fix input_ids concatenation dimension in GenerationMixin for flexibility

* Add DiaHangoverLogitsProcessor and DiaExponentialDecayLengthPenalty classes; refactor logits processing in DiaForConditionalGeneration to utilize new configurations and improve flexibility.

* Add stopping criteria

* refactor

* move delay pattern from processor to modeling like musicgen.

- add docs
- change eos countdown to eos delay pattern

* fix processor & fix tests

* refactor types

* refactor imports

* format code

* fix docstring to pass ci

* add docstring to DiaConfig & add DiaModel to test

* fix docstring

* add docstring

* fix some bugs

* check

* porting / merging results from other branch - IMPORTANT: it very likely breaks generation, the goal is to have a proper forward path first

* experimental testing of left padding for first channel

* whoops

* Fix merge to make generation work

* fix cfg filter

* add position ids

* add todos, break things

* revert changes to generation --> we will force 2d but go 3d on custom stuff

* refactor a lot, change prepare decoder ids to work with left padding (needs testing), add todos

* some first fixes to get to 10. in generation

* some more generation fixes / adjustment

* style + rope fixes

* move cfg out, simplify a few things, more todos

* nit

* start working on custom logit processors

* nit

* quick fixes

* cfg top k

* more refactor of logits processing, needs a decision if gen config gets the new attributes or if we move it to config or similar

* lets keep changes to core code minimal, only eos scaling is questionable atm

* simpler eos delay logits processor

* that was for debugging :D

* proof of concept rope

* small fix on device mismatch

* cfg fixes + delay logits max len

* transformers rope

* modular dia

* more cleanup

* keep modeling consistently 3D, generate handles 2D internally

* decoder starts with bos if nothing

* post processing prototype

* style

* lol

* force sample / greedy + fixes on padding

* style

* fixup tokenization

* nits

* revert

* start working on dia tests

* fix a lot of tests

* more test fixes

* nit

* more test fixes + some features to simplify code more

* more cleanup

* forgot that one

* autodocs

* small consistency fixes

* fix regression

* small fixes

* dia feature extraction

* docs

* wip processor

* fix processor order

* processing goes brrr

* transpose before

* small fix

* fix major bug but needs now a closer look into the custom processors esp cfg

* small thing on logits

* nits

* simplify indices and shifts

* add simpler version of padding tests back (temporarily)

* add logit processor tests

* starting tests on processor

* fix mask application during generation

* some fixes on the weights conversion

* style + fixup logits order

* simplify conversion

* nit

* remove padding tests

* nits on modeling

* hmm

* fix tests

* trigger

* probably gonna be reverted, just a quick design around audio tokenizer

* fixup typing

* post merge + more typing

* initial design for audio tokenizer

* more design changes

* nit

* more processor tests and style related things

* add to init

* protect import

* not sure why tbh

* add another protect

* more fixes

* wow

* it aint stopping :D

* another missed type issue

* ...

* change design around audio tokenizer to prioritize init and go for auto - in regards to the review

* change to new causal mask function + docstrings

* change ternary

* docs

* remove todo, i dont think its essential tbh

* remove pipeline as current pipelines do not fit in the current scheme, same as csm

* closer to wrapping up the processor

* text to audio, just for demo purposes (will likely be reverted)

* check if it's this

* save audio function

* ensure no grad

* fixes on prefixed audio, hop length is used via preprocess dac, device fixes

* integration tests (tested locally on a100) + some processor utils / fixes

* style

* nits

* another round of smaller things

* docs + some fixes (generate one might be big)

* msytery solved

* small fix on conversion

* add abstract audio tokenizer, change init check to abstract class

* nits

* update docs + fix some processing :D

* change inheritance scheme for audio tokenizer

* delete dead / unnecessary code in copied generate loop

* last nits on new pipeline behavior (+ todo on tests) + style

* trigger

---------

Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Vasqu <antonprogamer@gmail.com>
2025-06-26 11:04:23 +00:00

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Markdown

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# Auto Classes
In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you
are supplying to the `from_pretrained()` method. AutoClasses are here to do this job for you so that you
automatically retrieve the relevant model given the name/path to the pretrained weights/config/vocabulary.
Instantiating one of [`AutoConfig`], [`AutoModel`], and
[`AutoTokenizer`] will directly create a class of the relevant architecture. For instance
```python
model = AutoModel.from_pretrained("google-bert/bert-base-cased")
```
will create a model that is an instance of [`BertModel`].
There is one class of `AutoModel` for each task, and for each backend (PyTorch, TensorFlow, or Flax).
## Extending the Auto Classes
Each of the auto classes has a method to be extended with your custom classes. For instance, if you have defined a
custom class of model `NewModel`, make sure you have a `NewModelConfig` then you can add those to the auto
classes like this:
```python
from transformers import AutoConfig, AutoModel
AutoConfig.register("new-model", NewModelConfig)
AutoModel.register(NewModelConfig, NewModel)
```
You will then be able to use the auto classes like you would usually do!
<Tip warning={true}>
If your `NewModelConfig` is a subclass of [`~transformers.PretrainedConfig`], make sure its
`model_type` attribute is set to the same key you use when registering the config (here `"new-model"`).
Likewise, if your `NewModel` is a subclass of [`PreTrainedModel`], make sure its
`config_class` attribute is set to the same class you use when registering the model (here
`NewModelConfig`).
</Tip>
## AutoConfig
[[autodoc]] AutoConfig
## AutoTokenizer
[[autodoc]] AutoTokenizer
## AutoFeatureExtractor
[[autodoc]] AutoFeatureExtractor
## AutoImageProcessor
[[autodoc]] AutoImageProcessor
## AutoVideoProcessor
[[autodoc]] AutoVideoProcessor
## AutoProcessor
[[autodoc]] AutoProcessor
## Generic model classes
The following auto classes are available for instantiating a base model class without a specific head.
### AutoModel
[[autodoc]] AutoModel
### TFAutoModel
[[autodoc]] TFAutoModel
### FlaxAutoModel
[[autodoc]] FlaxAutoModel
## Generic pretraining classes
The following auto classes are available for instantiating a model with a pretraining head.
### AutoModelForPreTraining
[[autodoc]] AutoModelForPreTraining
### TFAutoModelForPreTraining
[[autodoc]] TFAutoModelForPreTraining
### FlaxAutoModelForPreTraining
[[autodoc]] FlaxAutoModelForPreTraining
## Natural Language Processing
The following auto classes are available for the following natural language processing tasks.
### AutoModelForCausalLM
[[autodoc]] AutoModelForCausalLM
### TFAutoModelForCausalLM
[[autodoc]] TFAutoModelForCausalLM
### FlaxAutoModelForCausalLM
[[autodoc]] FlaxAutoModelForCausalLM
### AutoModelForMaskedLM
[[autodoc]] AutoModelForMaskedLM
### TFAutoModelForMaskedLM
[[autodoc]] TFAutoModelForMaskedLM
### FlaxAutoModelForMaskedLM
[[autodoc]] FlaxAutoModelForMaskedLM
### AutoModelForMaskGeneration
[[autodoc]] AutoModelForMaskGeneration
### TFAutoModelForMaskGeneration
[[autodoc]] TFAutoModelForMaskGeneration
### AutoModelForSeq2SeqLM
[[autodoc]] AutoModelForSeq2SeqLM
### TFAutoModelForSeq2SeqLM
[[autodoc]] TFAutoModelForSeq2SeqLM
### FlaxAutoModelForSeq2SeqLM
[[autodoc]] FlaxAutoModelForSeq2SeqLM
### AutoModelForSequenceClassification
[[autodoc]] AutoModelForSequenceClassification
### TFAutoModelForSequenceClassification
[[autodoc]] TFAutoModelForSequenceClassification
### FlaxAutoModelForSequenceClassification
[[autodoc]] FlaxAutoModelForSequenceClassification
### AutoModelForMultipleChoice
[[autodoc]] AutoModelForMultipleChoice
### TFAutoModelForMultipleChoice
[[autodoc]] TFAutoModelForMultipleChoice
### FlaxAutoModelForMultipleChoice
[[autodoc]] FlaxAutoModelForMultipleChoice
### AutoModelForNextSentencePrediction
[[autodoc]] AutoModelForNextSentencePrediction
### TFAutoModelForNextSentencePrediction
[[autodoc]] TFAutoModelForNextSentencePrediction
### FlaxAutoModelForNextSentencePrediction
[[autodoc]] FlaxAutoModelForNextSentencePrediction
### AutoModelForTokenClassification
[[autodoc]] AutoModelForTokenClassification
### TFAutoModelForTokenClassification
[[autodoc]] TFAutoModelForTokenClassification
### FlaxAutoModelForTokenClassification
[[autodoc]] FlaxAutoModelForTokenClassification
### AutoModelForQuestionAnswering
[[autodoc]] AutoModelForQuestionAnswering
### TFAutoModelForQuestionAnswering
[[autodoc]] TFAutoModelForQuestionAnswering
### FlaxAutoModelForQuestionAnswering
[[autodoc]] FlaxAutoModelForQuestionAnswering
### AutoModelForTextEncoding
[[autodoc]] AutoModelForTextEncoding
### TFAutoModelForTextEncoding
[[autodoc]] TFAutoModelForTextEncoding
## Computer vision
The following auto classes are available for the following computer vision tasks.
### AutoModelForDepthEstimation
[[autodoc]] AutoModelForDepthEstimation
### AutoModelForImageClassification
[[autodoc]] AutoModelForImageClassification
### TFAutoModelForImageClassification
[[autodoc]] TFAutoModelForImageClassification
### FlaxAutoModelForImageClassification
[[autodoc]] FlaxAutoModelForImageClassification
### AutoModelForVideoClassification
[[autodoc]] AutoModelForVideoClassification
### AutoModelForKeypointDetection
[[autodoc]] AutoModelForKeypointDetection
### AutoModelForMaskedImageModeling
[[autodoc]] AutoModelForMaskedImageModeling
### TFAutoModelForMaskedImageModeling
[[autodoc]] TFAutoModelForMaskedImageModeling
### AutoModelForObjectDetection
[[autodoc]] AutoModelForObjectDetection
### AutoModelForImageSegmentation
[[autodoc]] AutoModelForImageSegmentation
### AutoModelForImageToImage
[[autodoc]] AutoModelForImageToImage
### AutoModelForSemanticSegmentation
[[autodoc]] AutoModelForSemanticSegmentation
### TFAutoModelForSemanticSegmentation
[[autodoc]] TFAutoModelForSemanticSegmentation
### AutoModelForInstanceSegmentation
[[autodoc]] AutoModelForInstanceSegmentation
### AutoModelForUniversalSegmentation
[[autodoc]] AutoModelForUniversalSegmentation
### AutoModelForZeroShotImageClassification
[[autodoc]] AutoModelForZeroShotImageClassification
### TFAutoModelForZeroShotImageClassification
[[autodoc]] TFAutoModelForZeroShotImageClassification
### AutoModelForZeroShotObjectDetection
[[autodoc]] AutoModelForZeroShotObjectDetection
## Audio
The following auto classes are available for the following audio tasks.
### AutoModelForAudioClassification
[[autodoc]] AutoModelForAudioClassification
### AutoModelForAudioFrameClassification
[[autodoc]] TFAutoModelForAudioClassification
### TFAutoModelForAudioFrameClassification
[[autodoc]] AutoModelForAudioFrameClassification
### AutoModelForCTC
[[autodoc]] AutoModelForCTC
### AutoModelForSpeechSeq2Seq
[[autodoc]] AutoModelForSpeechSeq2Seq
### TFAutoModelForSpeechSeq2Seq
[[autodoc]] TFAutoModelForSpeechSeq2Seq
### FlaxAutoModelForSpeechSeq2Seq
[[autodoc]] FlaxAutoModelForSpeechSeq2Seq
### AutoModelForAudioXVector
[[autodoc]] AutoModelForAudioXVector
### AutoModelForTextToSpectrogram
[[autodoc]] AutoModelForTextToSpectrogram
### AutoModelForTextToWaveform
[[autodoc]] AutoModelForTextToWaveform
### AutoModelForAudioTokenization
[[autodoc]] AutoModelForAudioTokenization
## Multimodal
The following auto classes are available for the following multimodal tasks.
### AutoModelForTableQuestionAnswering
[[autodoc]] AutoModelForTableQuestionAnswering
### TFAutoModelForTableQuestionAnswering
[[autodoc]] TFAutoModelForTableQuestionAnswering
### AutoModelForDocumentQuestionAnswering
[[autodoc]] AutoModelForDocumentQuestionAnswering
### TFAutoModelForDocumentQuestionAnswering
[[autodoc]] TFAutoModelForDocumentQuestionAnswering
### AutoModelForVisualQuestionAnswering
[[autodoc]] AutoModelForVisualQuestionAnswering
### AutoModelForVision2Seq
[[autodoc]] AutoModelForVision2Seq
### TFAutoModelForVision2Seq
[[autodoc]] TFAutoModelForVision2Seq
### FlaxAutoModelForVision2Seq
[[autodoc]] FlaxAutoModelForVision2Seq
### AutoModelForImageTextToText
[[autodoc]] AutoModelForImageTextToText
## Time Series
### AutoModelForTimeSeriesPrediction
[[autodoc]] AutoModelForTimeSeriesPrediction