transformers/pr_37082/en/model_doc/idefics.md

IDEFICS

Overview

The IDEFICS model was proposed in OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh

The abstract from the paper is the following:

Large multimodal models trained on natural documents, which interleave images and text, outperform models trained on image-text pairs on various multimodal benchmarks that require reasoning over one or multiple images to generate a text. However, the datasets used to train these models have not been released, and the collection process has not been fully specified. We introduce the OBELICS dataset, an open web-scale filtered dataset of interleaved image-text documents comprising 141 million web pages extracted from Common Crawl, 353 million associated images, and 115 billion text tokens. We describe the dataset creation process, present comprehensive filtering rules, and provide an analysis of the dataset's content. To show the viability of OBELISC, we train an 80 billion parameters vision and language model on the dataset and obtain competitive performance on various multimodal benchmarks. We release the code to reproduce the dataset along with the dataset itself.

This model was contributed by HuggingFaceM4. The original code can be found here. (TODO: don't have a public link yet).

IDEFICS modeling code in Transformers is for finetuning and inferencing the pre-trained IDEFICS models.

To train a new IDEFICS model from scratch use the m4 codebase (a link will be provided once it's made public)

IdeficsConfig[[transformers.IdeficsConfig]]

transformers.IdeficsConfig[[transformers.IdeficsConfig]]

Source

This is the configuration class to store the configuration of a IdeficsModel. It is used to instantiate an Idefics 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 Idefics-9B.

e.g. HuggingFaceM4/idefics-9b

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

Example:

>>> from transformers import IdeficsModel, IdeficsConfig

>>> # Initializing a Idefics idefics-9b style configuration
>>> configuration = IdeficsConfig()

>>> # Initializing a model from the idefics-9b style configuration
>>> model = IdeficsModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

Parameters:

additional_vocab_size (int, optional, defaults to 0) : Additional vocabulary size of the model, typically for the special "" token. Additional vocab tokens are always trainable whereas regular vocab tokens can be frozen or not.

vocab_size (int, optional, defaults to 32000) : Vocabulary size of the Idefics model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling ~IdeficsModel

hidden_size (int, optional, defaults to 4096) : Dimension of the hidden representations.

intermediate_size (int, optional, defaults to 11008) : Dimension of the MLP representations.

num_hidden_layers (int, optional, defaults to 32) : Number of hidden layers in the Transformer encoder.

num_attention_heads (int, optional, defaults to 32) : Number of attention heads for each attention layer in the Transformer encoder.

dropout (float, optional, defaults to 0.0) : The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.

hidden_act (str or function, optional, defaults to "silu") : The non-linear activation function (function or string) in the decoder.

initializer_range (float, optional, defaults to 0.02) : The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

alpha_initializer (str, optional, defaults to "zeros") : Initialization type for the alphas.

alphas_initializer_range (float, optional, defaults to 0.0) : The standard deviation of the truncated_normal_initializer for initializing the alphas in the Gated Cross Attention.

alpha_type (str, optional, defaults to "float") : Whether the gating alphas should be vectors or single floats.

rms_norm_eps (float, optional, defaults to 1e-6) : 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.

pad_token_id (int, optional, defaults to 0) : Padding token id.

bos_token_id (int, optional, defaults to 1) : Beginning of stream token id.

eos_token_id (int, optional, defaults to 2) : End of stream token id.

tie_word_embeddings(bool, optional, defaults to False) : Whether to tie weight embeddings

cross_layer_interval (int, optional, default to 1) : Interval for cross attention (from text to image) layers.

qk_layer_norms (bool, optional, defaults to False) : Whether to add layer norm after q and k

freeze_text_layers (bool, optional, defaults to True) : Whether to freeze text layers

freeze_text_module_exceptions (bool, optional, defaults to []) : Exceptions to freezing text layers when freeze_text_layers is True

freeze_lm_head (bool, optional, defaults to False) : Whether to freeze lm head

freeze_vision_layers (bool, optional, defaults to True) : Whether to freeze vision layers

freeze_vision_module_exceptions (bool, optional, defaults to []) : Exceptions to freezing vision layers when freeze_vision_layers is True

use_resampler (bool, optional, defaults to False) : Whether to use the Resampler

vision_config (IdeficsVisionConfig, optional) : Custom vision config or dict

perceiver_config (IdeficsPerceiverConfig, optional) : Custom perceiver config or dict

IdeficsModel[[transformers.IdeficsModel]]

transformers.IdeficsModel[[transformers.IdeficsModel]]

Source

The bare Idefics Model outputting raw hidden-states without any specific head on top.

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.

forwardtransformers.IdeficsModel.forwardhttps://github.com/huggingface/transformers/blob/vr_37082/src/transformers/models/idefics/modeling_idefics.py#L944[{"name": "input_ids", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "position_ids", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "past_key_values", "val": ": typing.Optional[transformers.cache_utils.Cache] = None"}, {"name": "inputs_embeds", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "pixel_values", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "image_encoder_embeddings", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "perceiver_embeddings", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "image_attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "use_cache", "val": ": typing.Optional[bool] = None"}, {"name": "interpolate_pos_encoding", "val": ": typing.Optional[bool] = False"}, {"name": "cache_position", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) -- Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

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?

• 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 (~cache_utils.Cache, optional) -- Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_values returned by the model at a previous stage of decoding, when use_cache=True or config.use_cache=True.

  Only Cache instance is allowed as input, see our kv cache guide. If no past_key_values are passed, DynamicCache will be initialized by default.

  The model will output the same cache format that is fed as input.

  If past_key_values are used, the user is expected to input only unprocessed input_ids (those that don't have their past key value states given to this model) of shape (batch_size, unprocessed_length) instead of all 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.

• pixel_values (torch.FloatTensor of shape (batch_size, num_channels, image_size, image_size), optional) -- The tensors corresponding to the input images. Pixel values can be obtained using IdeficsImageProcessor. See IdeficsImageProcessor.call() for details (IdeficsProcessor uses IdeficsImageProcessor for processing images).

• image_encoder_embeddings (torch.FloatTensor, optional) -- The output of the image encoder.

• perceiver_embeddings (torch.FloatTensor, optional) -- The output of the perceiver resampler.

• image_attention_mask (torch.LongTensor, optional) -- The attention mask for the image encoder.

• 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).

• interpolate_pos_encoding (bool, optional, defaults to False) -- Whether to interpolate the pre-trained position encodings.

• cache_position (torch.LongTensor of shape (sequence_length), optional) -- Indices depicting the position of the input sequence tokens in the sequence. Contrarily to position_ids, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length.0transformers.models.idefics.modeling_idefics.IdeficsBaseModelOutputWithPast or tuple(torch.FloatTensor)A transformers.models.idefics.modeling_idefics.IdeficsBaseModelOutputWithPast 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 (IdeficsConfig) and inputs.

• last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) -- Sequence of hidden-states at the output of the last layer of the model.

  If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.

• past_key_values (Cache, optional, returned when use_cache=True is passed or when config.use_cache=True) -- It is a Cache instance. For more details, see our kv cache guide.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if config.is_encoder_decoder=True in the cross-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.

• image_hidden_states (tuple(torch.FloatTensor), optional) -- Tuple of torch.FloatTensor (one for the output of the image embeddings, (batch_size, num_images, sequence_length, hidden_size).

  image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver

The IdeficsModel 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.

Parameters:

config (IdeficsConfig) : 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.

Returns:

transformers.models.idefics.modeling_idefics.IdeficsBaseModelOutputWithPast` or `tuple(torch.FloatTensor)

A transformers.models.idefics.modeling_idefics.IdeficsBaseModelOutputWithPast 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 (IdeficsConfig) and inputs.

• last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) -- Sequence of hidden-states at the output of the last layer of the model.

  If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.

• past_key_values (Cache, optional, returned when use_cache=True is passed or when config.use_cache=True) -- It is a Cache instance. For more details, see our kv cache guide.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if config.is_encoder_decoder=True in the cross-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.

• image_hidden_states (tuple(torch.FloatTensor), optional) -- Tuple of torch.FloatTensor (one for the output of the image embeddings, (batch_size, num_images, sequence_length, hidden_size).

  image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver

IdeficsForVisionText2Text[[transformers.IdeficsForVisionText2Text]]

transformers.IdeficsForVisionText2Text[[transformers.IdeficsForVisionText2Text]]

Source

forwardtransformers.IdeficsForVisionText2Text.forwardhttps://github.com/huggingface/transformers/blob/vr_37082/src/transformers/models/idefics/modeling_idefics.py#L1146[{"name": "input_ids", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "position_ids", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "past_key_values", "val": ": typing.Optional[transformers.cache_utils.Cache] = None"}, {"name": "inputs_embeds", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "pixel_values", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "image_encoder_embeddings", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "perceiver_embeddings", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "image_attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "labels", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "use_cache", "val": ": typing.Optional[bool] = None"}, {"name": "interpolate_pos_encoding", "val": ": typing.Optional[bool] = False"}, {"name": "cache_position", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "logits_to_keep", "val": ": typing.Union[int, torch.Tensor] = 0"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) -- Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

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?

• 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 (~cache_utils.Cache, optional) -- Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_values returned by the model at a previous stage of decoding, when use_cache=True or config.use_cache=True.

  Only Cache instance is allowed as input, see our kv cache guide. If no past_key_values are passed, DynamicCache will be initialized by default.

  The model will output the same cache format that is fed as input.

  If past_key_values are used, the user is expected to input only unprocessed input_ids (those that don't have their past key value states given to this model) of shape (batch_size, unprocessed_length) instead of all 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.

• pixel_values (torch.FloatTensor of shape (batch_size, num_channels, image_size, image_size), optional) -- The tensors corresponding to the input images. Pixel values can be obtained using IdeficsImageProcessor. See IdeficsImageProcessor.call() for details (IdeficsProcessor uses IdeficsImageProcessor for processing images).

• image_encoder_embeddings (torch.FloatTensor, optional) -- The output of the image encoder.

• perceiver_embeddings (torch.FloatTensor, optional) -- The output of the perceiver resampler.

• image_attention_mask (torch.LongTensor, optional) -- The attention mask for the image encoder.

• 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.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.vocab_size].

• 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).

• interpolate_pos_encoding (bool, optional, defaults to False) -- Whether to interpolate the pre-trained position encodings.

• cache_position (torch.LongTensor of shape (sequence_length), optional) -- Indices depicting the position of the input sequence tokens in the sequence. Contrarily to position_ids, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length.

• logits_to_keep (Union[int, torch.Tensor], defaults to 0) -- If an int, compute logits for the last logits_to_keep tokens. If 0, calculate logits for all input_ids (special case). Only last token logits are needed for generation, and calculating them only for that token can save memory, which becomes pretty significant for long sequences or large vocabulary size. If a torch.Tensor, must be 1D corresponding to the indices to keep in the sequence length dimension. This is useful when using packed tensor format (single dimension for batch and sequence length).0transformers.models.idefics.modeling_idefics.IdeficsCausalLMOutputWithPast or tuple(torch.FloatTensor)A transformers.models.idefics.modeling_idefics.IdeficsCausalLMOutputWithPast 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 (IdeficsConfig) 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 (Cache, optional, returned when use_cache=True is passed or when config.use_cache=True) -- It is a Cache instance. For more details, see our kv cache guide.

  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.

• image_hidden_states (tuple(torch.FloatTensor), optional) -- Tuple of torch.FloatTensor (one for the output of the image embeddings, (batch_size, num_images, sequence_length, hidden_size).

  image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver

The IdeficsForVisionText2Text 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.

Example:

>>> from transformers import AutoProcessor, IdeficsForVisionText2Text

>>> model = IdeficsForVisionText2Text.from_pretrained("HuggingFaceM4/idefics-9b")
>>> processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics-9b")

>>> dogs_image_url_1 = "https://huggingface.co/datasets/hf-internal-testing/fixtures_nlvr2/raw/main/image1.jpeg"
>>> dogs_image_url_2 = "https://huggingface.co/datasets/hf-internal-testing/fixtures_nlvr2/raw/main/image2.jpeg"

>>> prompts = [
...     [
...         "User:",
...         dogs_image_url_1,
...         "Describe this image.\nAssistant: An image of two dogs.\n",
...         "User:",
...         dogs_image_url_2,
...         "Describe this image.\nAssistant:",
...     ]
... ]
>>> inputs = processor(prompts, return_tensors="pt")
>>> generate_ids = model.generate(**inputs, max_new_tokens=6)
>>> processor.batch_decode(generate_ids, skip_special_tokens=True)

Parameters:

input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) : Indices of input sequence tokens in the vocabulary. Padding will be ignored by default. 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?

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 (~cache_utils.Cache, optional) : Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_values returned by the model at a previous stage of decoding, when use_cache=True or config.use_cache=True. Only Cache instance is allowed as input, see our kv cache guide. If no past_key_values are passed, DynamicCache will be initialized by default. The model will output the same cache format that is fed as input. If past_key_values are used, the user is expected to input only unprocessed input_ids (those that don't have their past key value states given to this model) of shape (batch_size, unprocessed_length) instead of all 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.

pixel_values (torch.FloatTensor of shape (batch_size, num_channels, image_size, image_size), optional) : The tensors corresponding to the input images. Pixel values can be obtained using IdeficsImageProcessor. See IdeficsImageProcessor.call() for details (IdeficsProcessor uses IdeficsImageProcessor for processing images).

image_encoder_embeddings (torch.FloatTensor, optional) : The output of the image encoder.

perceiver_embeddings (torch.FloatTensor, optional) : The output of the perceiver resampler.

image_attention_mask (torch.LongTensor, optional) : The attention mask for the image encoder.

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.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.vocab_size].

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).

interpolate_pos_encoding (bool, optional, defaults to False) : Whether to interpolate the pre-trained position encodings.

cache_position (torch.LongTensor of shape (sequence_length), optional) : Indices depicting the position of the input sequence tokens in the sequence. Contrarily to position_ids, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length.

logits_to_keep (Union[int, torch.Tensor], defaults to 0) : If an int, compute logits for the last logits_to_keep tokens. If 0, calculate logits for all input_ids (special case). Only last token logits are needed for generation, and calculating them only for that token can save memory, which becomes pretty significant for long sequences or large vocabulary size. If a torch.Tensor, must be 1D corresponding to the indices to keep in the sequence length dimension. This is useful when using packed tensor format (single dimension for batch and sequence length).

Returns:

transformers.models.idefics.modeling_idefics.IdeficsCausalLMOutputWithPast` or `tuple(torch.FloatTensor)

A transformers.models.idefics.modeling_idefics.IdeficsCausalLMOutputWithPast 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 (IdeficsConfig) 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 (Cache, optional, returned when use_cache=True is passed or when config.use_cache=True) -- It is a Cache instance. For more details, see our kv cache guide.

  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.

• image_hidden_states (tuple(torch.FloatTensor), optional) -- Tuple of torch.FloatTensor (one for the output of the image embeddings, (batch_size, num_images, sequence_length, hidden_size).

  image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver

IdeficsImageProcessor[[transformers.IdeficsImageProcessor]]

transformers.IdeficsImageProcessor[[transformers.IdeficsImageProcessor]]

Source

Constructs a Idefics image processor.

preprocesstransformers.IdeficsImageProcessor.preprocesshttps://github.com/huggingface/transformers/blob/vr_37082/src/transformers/models/idefics/image_processing_idefics.py#L114[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"}, {"name": "image_num_channels", "val": ": typing.Optional[int] = 3"}, {"name": "image_size", "val": ": typing.Optional[dict[str, int]] = None"}, {"name": "image_mean", "val": ": typing.Union[float, list[float], NoneType] = None"}, {"name": "image_std", "val": ": typing.Union[float, list[float], NoneType] = None"}, {"name": "transform", "val": ": typing.Optional[collections.abc.Callable] = None"}, {"name": "do_rescale", "val": ": typing.Optional[bool] = None"}, {"name": "rescale_factor", "val": ": typing.Optional[float] = None"}, {"name": "return_tensors", "val": ": typing.Union[str, transformers.utils.generic.TensorType, NoneType] = "}, {"name": "**kwargs", "val": ""}]- images (ImageInput) -- A list of images to preprocess.

• image_size (int, optional, defaults to self.image_size) -- Resize to image size
• image_num_channels (int, optional, defaults to self.image_num_channels) -- Number of image channels.
• image_mean (float or list[float], optional, defaults to IDEFICS_STANDARD_MEAN) -- Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the image_mean parameter in the preprocess method. Can be overridden by the image_mean parameter in the preprocess method.
• image_std (float or list[float], optional, defaults to IDEFICS_STANDARD_STD) -- Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the image_std parameter in the preprocess method. Can be overridden by the image_std parameter in the preprocess method.
• transform (Callable, optional, defaults to None) -- A custom transform function that accepts a single image can be passed for training. For example, torchvision.Compose can be used to compose multiple transforms. If None - an inference mode is assumed - and then a preset of inference-specific transforms will be applied to the images
• do_rescale (bool, optional, defaults to True) -- Whether to rescale the image by the specified scale rescale_factor. Can be overridden by do_rescale in the preprocess method.
• rescale_factor (int or float, optional, defaults to 1/255) -- Scale factor to use if rescaling the image. Can be overridden by rescale_factor in the preprocess method.0a PyTorch tensor of the processed images

Preprocess a batch of images.

Parameters:

image_size (int, optional, defaults to 224) : Resize to image size

image_mean (float or list[float], optional, defaults to IDEFICS_STANDARD_MEAN) : Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the image_mean parameter in the preprocess method. Can be overridden by the image_mean parameter in the preprocess method.

image_std (float or list[float], optional, defaults to IDEFICS_STANDARD_STD) : Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the image_std parameter in the preprocess method. Can be overridden by the image_std parameter in the preprocess method.

image_num_channels (int, optional, defaults to 3) : Number of image channels.

do_rescale (bool, optional, defaults to True) : Whether to rescale the image by the specified scale rescale_factor. Can be overridden by do_rescale in the preprocess method.

rescale_factor (int or float, optional, defaults to 1/255) : Scale factor to use if rescaling the image. Can be overridden by rescale_factor in the preprocess method.

Returns:

a PyTorch tensor of the processed images

IdeficsProcessor[[transformers.IdeficsProcessor]]

transformers.IdeficsProcessor[[transformers.IdeficsProcessor]]

Source

Constructs a IDEFICS processor which wraps a LLama tokenizer and IDEFICS image processor into a single processor.

IdeficsProcessor offers all the functionalities of IdeficsImageProcessor and LlamaTokenizerFast. See the docstring of call() and decode() for more information.

__call__transformers.IdeficsProcessor.__call__https://github.com/huggingface/transformers/blob/vr_37082/src/transformers/models/idefics/processing_idefics.py#L174[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor'], list[typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]], str, list[str], list[list[str]]] = None"}, {"name": "text", "val": ": typing.Union[str, list[str], list[list[str]], list[list[list[str]]]] = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.models.idefics.processing_idefics.IdeficsProcessorKwargs]"}]- images (Union[ImageInput, list[ImageInput], str, list[str], list[list[str]]]) -- either a single image or a batched list of images - can be passed in when text contains only text prompts, in order to use the image-text-to-text behavior.

• text (Union[list[TextInput], [list[list[TextInput]]]]) -- either a single prompt or a batched list of prompts - see the detailed description immediately after the end of the arguments doc section.
• return_tensors (str or TensorType, optional, defaults to TensorType.PYTORCH) -- The type of tensors to return. Can be one of:
  • TensorType.PYTORCH or 'pt': Return a batch of type torch.Tensor.0a dict with entriesinput_ids, attention_mask, pixel_values, image_attention_mask which can be directly passed to model.generate This method takes batched or non-batched prompts made of text and images and converts them into prompts that the model was trained on and prepares the image pixel values for the model to process.

Detailed explanation:

Each entry in text is either a text to be passed as is or an image that will be processed.

An image can be either an image object (PIL.Image) or a url from which the image can be retrieved.

When the processor encounters an image it'll inject `` entry into the prompt.

Example:

checkpoint = "HuggingFaceM4/idefics-9b"
processor = AutoProcessor.from_pretrained(checkpoint)
url = "https://hips.hearstapps.com/hmg-prod/images/cute-photos-of-cats-in-grass-1593184777.jpg"
img = processor.image_processor.fetch_images([url])[0]

prompts = [
    "User:",
    img,
    "Describe this image.
t: An image of two kittens in grass.

    "User:",
    "https://hips.hearstapps.com/hmg-prod/images/dog-puns-1581708208.jpg",
    "Describe this image.
t:",
]

inputs = processor(text=prompts, return_tensors="pt")
generated_ids = model.generate(**inputs, max_length=100)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

In this example the prompts will be converted into:

User:Describe this image.
Assistant: An image of two kittens in grass.
User:Describe this image.
Assistant:'

and the two images will be massaged using IdeficsImageProcessor.call() method and placed inside the pixel_values dict entry of the return value.

This example also exemplifies that images can be passed as objects or as text urls. It can be seen that the first image is passed as object and the second one as a url.

To do training do:

image_transform = transforms.Compose(
    [
        transforms.RandomResizedCrop(
            (w, h), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BICUBIC
        ),
        transforms.ToTensor(),
        transforms.Normalize(mean=self.image_mean, std=self.image_std),
    ]
)
inputs = processor(text=prompts, transform=image_transform, return_tensors="pt")

In order to help debug prompt generation enable debug=True which will show you what's happening.

Parameters:

image_processor (IdeficsImageProcessor) : An instance of IdeficsImageProcessor. The image processor is a required input.

tokenizer (LlamaTokenizerFast) : An instance of LlamaTokenizerFast. The tokenizer is a required input.

image_size (int, optional, defaults to 224) : Image size (assuming a square image)

add_end_of_utterance_token (str, optional) : The string representation of token representing end of utterance

Returns:

a dict with entries

input_ids, attention_mask, pixel_values, image_attention_mask which can be directly passed to model.generate