#
Fuyu

##
Overview

The Fuyu model was created by ADEPT, and authored by Rohan Bavishi, Erich Elsen, Curtis Hawthorne, Maxwell Nye, Augustus Odena, Arushi Somani, Sağnak Taşırlar.

The authors introduced Fuyu-8B, a decoder-only multimodal model based on the classic transformers architecture, with query and key normalization. A linear encoder is added to create multimodal embeddings from image inputs.

By treating image tokens like text tokens and using a special image-newline character, the model knows when an image line ends. Image positional embeddings are removed. This avoids the need for different training phases for various image resolutions. With 8 billion parameters and licensed under CC-BY-NC, Fuyu-8B is notable for its ability to handle both text and images, its impressive context size of 16K, and its overall performance.

The `Fuyu` models were trained using `bfloat16`, but the original inference uses `float16` The checkpoints uploaded on the hub use `torch_dtype = 'float16'` which will be used by the `AutoModel` API to cast the checkpoints from `torch.float32` to `torch.float16`.

The `dtype` of the online weights is mostly irrelevant, unless you are using `torch_dtype="auto"` when initializing a model using `model = AutoModelForCausalLM.from_pretrained("path", torch_dtype = "auto")`. The reason is that the model will first be downloaded ( using the `dtype` of the checkpoints online) then it will be cast to the default `dtype` of `torch` (becomes `torch.float32`). Users should specify the `torch_dtype` they want, and if they don’t it will be `torch.float32`.

Finetuning the model in `float16` is not recommended and known to produce `nan`, as such the model should be fine-tuned in `bfloat16`.

Tips:

- To convert the model, you need to clone the original repository using `git clone https://github.com/persimmon-ai-labs/adept-inference`, then get the checkpoints:

    git clone https://github.com/persimmon-ai-labs/adept-inference
    wget path/to/fuyu-8b-model-weights.tar
    tar -xvf fuyu-8b-model-weights.tar
    python src/transformers/models/fuyu/convert_fuyu_weights_to_hf.py  --input_dir /path/to/downloaded/fuyu/weights/ --output_dir /output/path \
        --pt_model_path /path/to/fuyu_8b_release/iter_0001251/mp_rank_00/model_optim_rng.pt
        --ada_lib_path /path/to/adept-inference

For the chat model:

    wget https://axtkn4xl5cip.objectstorage.us-phoenix-1.oci.customer-oci.com/n/axtkn4xl5cip/b/adept-public-data/o/8b_chat_model_release.tar
    tar -xvf 8b_base_model_release.tar

Then, model can be loaded via:

    from transformers import FuyuConfig, FuyuForCausalLM
    model_config = FuyuConfig()
    model = FuyuForCausalLM(model_config).from_pretrained('/output/path')

Inputs need to be passed through a specific Processor to have the correct formats. A processor requires an image_processor and a tokenizer. Hence, inputs can be loaded via:

    from PIL import Image
    from transformers import AutoTokenizer
    from transformers.models.fuyu.processing_fuyu import FuyuProcessor
    from transformers.models.fuyu.image_processing_fuyu import FuyuImageProcessor

    tokenizer = AutoTokenizer.from_pretrained('adept-hf-collab/fuyu-8b')
    image_processor = FuyuImageProcessor()

    processor = FuyuProcessor(image_processor=image_processor, tokenizer=tokenizer)
    text_prompt = "Generate a coco-style caption.\\n"

    bus_image_url = "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/bus.png"
    bus_image_pil = Image.open(io.BytesIO(requests.get(bus_image_url).content))
    inputs_to_model = processor(images=bus_image_pil, text=text_prompt)

This model was contributed by Molbap. The original code can be found here.

- Fuyu uses a `sentencepiece` based tokenizer, with a `Unigram` model. It supports bytefallback, which is only available in `tokenizers==0.14.0` for the fast tokenizer. The `LlamaTokenizer` is used as it is a standard wrapper around sentencepiece.

- The authors suggest to use the following prompt for image captioning: `f"Generate a coco-style caption.\\n"`

##
FuyuConfig

###
class transformers.FuyuConfig

< source >

( vocab_size = 262144 hidden_size = 4096 intermediate_size = 16384 num_hidden_layers = 36 num_attention_heads = 64 hidden_act = 'relu2' max_position_embeddings = 16384 image_size = 300 patch_size = 30 num_channels = 3 initializer_range = 0.02 layer_norm_eps = 1e-05 use_cache = True tie_word_embeddings = False rope_theta = 25000.0 rope_scaling = None qk_layernorm = True hidden_dropout = 0.0 attention_dropout = 0.0 partial_rotary_factor = 0.5 pad_token_id = None bos_token_id = 1 eos_token_id = 2 text_config = None **kwargs )

Parameters

- vocab_size (`int`, optional, defaults to 262144) — Vocabulary size of the Fuyu model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling FuyuForCausalLM
- hidden_size (`int`, optional, defaults to 4096) — Dimension of the hidden representations.
- intermediate_size (`int`, optional, defaults to 16384) — Dimension of the MLP representations.
- num_hidden_layers (`int`, optional, defaults to 36) — Number of hidden layers in the Transformer encoder.
- num_attention_heads (`int`, optional, defaults to 64) — Number of attention heads for each attention layer in the Transformer encoder.
- hidden_act (`str` or `function`, optional, defaults to `"relu2"`) — The non-linear activation function (function or string) in the decoder.
- max_position_embeddings (`int`, optional, defaults to 16384) — The maximum sequence length that this model might ever be used with.
- image_size (`int`, optional, defaults to 300) — The input image size.
- patch_size (`int`, optional, defaults to 30) — The input vision transformer encoding patch size.
- num_channels (`int`, optional, defaults to 3) — The input image number of channels.
- initializer_range (`float`, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
- layer_norm_eps (`float`, optional, defaults to 1e-05) — The epsilon used by the rms normalization layers.
- use_cache (`bool`, optional, defaults to `True`) — Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. Whether to tie weight embeddings
- tie_word_embeddings (`bool`, optional, defaults to `False`) — Whether to tie input and output embeddings.
- rope_theta (`float`, optional, defaults to 25000.0) — The base period of the RoPE embeddings.
- rope_scaling (`Dict`, optional) — Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don’t update `max_position_embeddings` to the expected new maximum. See the following thread for more information on how these scaling strategies behave: https://www.reddit.com/r/LocalFuyu/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an experimental feature, subject to breaking API changes in future versions.
- qk_layernorm (`bool`, optional, defaults to `True`) — Whether or not to normalize the Queries and Keys after projecting the hidden states
- hidden_dropout (`float`, optional, defaults to 0.0) — The dropout ratio after applying the MLP to the hidden states.
- attention_dropout (`float`, optional, defaults to 0.0) — The dropout ratio after computing the attention scores.
- partial_rotary_factor (`float`, optional, defaults to 0.5) — Percentage of the query and keys which will have rotary embedding.
- pad_token_id (`int`, optional) — The id of the padding token.
- bos_token_id (`int`, optional, defaults to 1) — The id of the beginning-of-sequence token.
- eos_token_id (`Union[int, List[int]]`, optional, defaults to 2) — The id of the end-of-sequence token. Optionally, use a list to set multiple end-of-sequence tokens.
- text_config (`dict`, optional) — Dictionary of configuration options used to initialize the `language```Aut`.

This is the configuration class to store the configuration of a FuyuForCausalLM. It is used to instantiate an Fuyu model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the adept/fuyu-8b.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

    >>> from transformers import FuyuConfig

    >>> # Initializing a Fuyu fuyu-7b style configuration
    >>> configuration = FuyuConfig()

##
FuyuForCausalLM

###
class transformers.FuyuForCausalLM

< source >

( config: FuyuConfig )

Parameters

- config (FuyuConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

Fuyu Model with a language modeling head on top for causal language model conditioned on image patches and text. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( input_ids: LongTensor = None image_patches: Tensor = None image_patches_indices: Tensor = None attention_mask: Optional = None position_ids: Optional = None past_key_values: Optional = None inputs_embeds: Optional = None use_cache: Optional = None labels: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None ) →
transformers.modeling_outputs.CausalLMOutputWithPast or `tuple(torch.FloatTensor)`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).

  If you want to change padding behavior, you should read `modeling_opt._prepare_decoder_attention_mask` and modify to your needs. See diagram 1 in the paper for more information on the default strategy.

  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- image_patches (`torch.FloatTensor` of shape `(batch_size, num_total_patches, patch_size_ x patch_size x num_channels)`, optional) — Image patches to be used as continuous embeddings. The patches are flattened and then projected to the hidden size of the model.
- image_patches_indices (`torch.LongTensor` of shape `(batch_size, num_total_patches + number_of_newline_tokens + number_of_text_tokens, patch_size_ x patch_size x num_channels )`, optional) — Indices indicating at which position the image_patches have to be inserted in input_embeds.
- position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.

  What are position IDs?

- past_key_values (`tuple(tuple(torch.FloatTensor))`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`) — Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

  If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don’t have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`.

- inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model’s internal embedding lookup matrix.
- use_cache (`bool`, optional) — If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.text_config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.text_config.vocab_size]`.

Returns

transformers.modeling_outputs.CausalLMOutputWithPast or `tuple(torch.FloatTensor)`

A transformers.modeling_outputs.CausalLMOutputWithPast or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (FuyuConfig) and inputs.

- loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `labels` is provided) — Language modeling loss (for next-token prediction).

- logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).

- past_key_values (`tuple(tuple(torch.FloatTensor))`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`) — Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`)

  Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

- hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The FuyuForCausalLM forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

    >>> from transformers import FuyuProcessor, FuyuForCausalLM
    >>> from PIL import Image
    >>> import requests

    >>> processor = FuyuProcessor.from_pretrained("adept/fuyu-8b")
    >>> model = FuyuForCausalLM.from_pretrained("adept/fuyu-8b")

    >>> url = "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/bus.png"
    >>> image = Image.open(requests.get(url, stream=True).raw)
    >>> prompt = "Generate a coco-style caption.\n"

    >>> inputs = processor(images=image, text=prompt, return_tensors="pt")
    >>> outputs = model(**inputs)

    >>> generated_ids = model.generate(**inputs, max_new_tokens=7)
    >>> generation_text = processor.batch_decode(generated_ids[:, -7:], skip_special_tokens=True)
    >>> print(generation_text[0])
    A blue bus parked on the side of a road.

##
FuyuImageProcessor

###
class transformers.FuyuImageProcessor

< source >

( do_resize: bool = True size: Optional = None resample: Resampling = <Resampling.BILINEAR: 2> do_pad: bool = True padding_value: float = 1.0 padding_mode: str = 'constant' do_normalize: bool = True image_mean: Union = 0.5 image_std: Union = 0.5 do_rescale: bool = True rescale_factor: float = 0.00392156862745098 patch_size: Optional = None **kwargs )

Parameters

- do_resize (`bool`, optional, defaults to `True`) — Whether to resize the image to `size`.
- size (`Dict[str, int]`, optional, defaults to `{"height" -- 1080, "width": 1920}`): Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
- resample (`PILImageResampling`, optional, defaults to `Resampling.BILINEAR`) — `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
- do_pad (`bool`, optional, defaults to `True`) — Whether to pad the image to `size`.
- padding_value (`float`, optional, defaults to 1.0) — The value to pad the image with.
- padding_mode (`str`, optional, defaults to `"constant"`) — The padding mode to use when padding the image.
- do_normalize (`bool`, optional, defaults to `True`) — Whether to normalize the image.
- image_mean (`float`, optional, defaults to 0.5) — The mean to use when normalizing the image.
- image_std (`float`, optional, defaults to 0.5) — The standard deviation to use when normalizing the image.
- do_rescale (`bool`, optional, defaults to `True`) — Whether to rescale the image.
- rescale_factor (`float`, optional, defaults to `1 / 255`) — The factor to use when rescaling the image.
- patch_size (`Dict[str, int]`, optional, defaults to `{"height" -- 30, "width": 30}`): Dictionary in the format `{"height": int, "width": int}` specifying the size of the patches.

This class should handle the image processing part before the main FuyuForCausalLM. In particular, it should handle:

- Processing Images: Taking a batch of images as input. If the images are variable-sized, it resizes them based on the desired patch dimensions. The image output is always img_h, img_w of (1080, 1920)

  Then, it patches up these images using the patchify_image function.

- Creating Image Input IDs: For each patch, a placeholder ID is given to identify where these patches belong in a token sequence. For variable-sized images, each line of patches is terminated with a newline ID.

- Image Patch Indices: For each image patch, the code maintains an index where these patches should be inserted in a token stream.

####
__call__

< source >

( images **kwargs )

Preprocess an image or a batch of images.

##
FuyuProcessor

###
class transformers.FuyuProcessor

< source >

( image_processor tokenizer **kwargs )

Parameters

- image_processor (FuyuImageProcessor) — The image processor is a required input.
- tokenizer (LlamaTokenizerFast) — The tokenizer is a required input.

Constructs a Fuyu processor which wraps a Fuyu image processor and a Llama tokenizer into a single processor.

FuyuProcessor offers all the functionalities of FuyuImageProcessor and LlamaTokenizerFast. See the call() and `decode()` for more information.

####
__call__

< source >

( images: Union = None text: Union = None audio = None videos = None **kwargs: Unpack ) →
`FuyuBatchEncoding`

Parameters

- images (`PIL.Image.Image`, `List[PIL.Image.Image]`) — The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch tensor. Both channels-first and channels-last formats are supported.
- text (`str`, `List[str]`) — The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).

Returns

`FuyuBatchEncoding`

A `FuyuBatchEncoding` with the following fields:

- input_ids — Tensor of token ids to be fed to a model. Returned when `text` is not `None`.
- image_patches — List of Tensor of image patches. Returned when `images` is not `None`.
- image_patches_indices — Tensor of indices where patch embeddings have to be inserted by the model.
- attention_mask — List of indices specifying which tokens should be attended to by the model when `return_attention_mask=True`.

Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text` and `kwargs` arguments to LlamaTokenizerFast’s call() if `text` is not `None` to encode the text. To prepare the image(s), this method forwards the `images` and `kwargs` arguments to FuyuImageProcessor’s call() if `images` is not `None`. Please refer to the doctsring of the above two methods for more information.

< > Update on GitHub