Evolla

Overview

The Evolla model was proposed in Decoding the Molecular Language of Proteins with Evolla by Zhou et al..

Evolla is an advanced 80-billion-parameter protein-language generative model designed to decode the molecular language of proteins. It integrates information from protein sequences, structures, and user queries to generate precise and contextually nuanced insights into protein function. Trained on an unprecedented AI-generated dataset of 546 million protein question-answer pairs and 150 billion word tokens, Evolla significantly advances research in proteomics and functional genomics, providing expert-level insights and shedding light on the molecular logic encoded in proteins.

The abstract from the paper is the following:

Proteins, nature’s intricate molecular machines, are the products of billions of years of evolution and play fundamental roles in sustaining life. Yet, deciphering their molecular language - that is, understanding how protein sequences and structures encode and determine biological functions - remains a corner-stone challenge in modern biology. Here, we introduce Evolla, an 80 billion frontier protein-language generative model designed to decode the molecular language of proteins. By integrating information from protein sequences, structures, and user queries, Evolla generates precise and contextually nuanced insights into protein function. A key innovation of Evolla lies in its training on an unprecedented AI-generated dataset: 546 million protein question-answer pairs and 150 billion word tokens, designed to reflect the immense complexity and functional diversity of proteins. Post-pretraining, Evolla integrates Direct Preference Optimization (DPO) to refine the model based on preference signals and Retrieval-Augmented Generation (RAG) for external knowledge incorporation, improving response quality and relevance. To evaluate its performance, we propose a novel framework, Instructional Response Space (IRS), demonstrating that Evolla delivers expert-level insights, advancing research in proteomics and functional genomics while shedding light on the molecular logic encoded in proteins. The online demo is available at http://www.chat-protein.com/.

Examples:

processor = EvollaProcessor.from_pretrained("westlake-repl/Evolla-10B-DPO-hf")
model = EvollaForProteinText2Text.from_pretrained("westlake-repl/Evolla-10B-DPO-hf")
# aa_seq should have same length as foldseek
protein_inputs = [
    {
        
        "aa_seq": "MATGGRRG...",
        "foldseek": "###lqpfd...", # hashtag means the low-confidence foldseek tokens
    },
    {
        "aa_seq": "MLPGLALL...",
        "foldseek": "dfwwkwad...",
    }
]
message_list = [
    [
        {
            "role": "system",
            "content": "You are an AI expert that can answer any questions about protein.",
        },
        {"role": "user", "content": "What is the function of this protein?"},
    ],
    [
        {
            "role": "system",
            "content": "You are an AI expert that can answer any questions about protein.",
        },
        {"role": "user", "content": "What is the function of this protein?"},
    ]
]
input_dict = processor(
    protein_informations, messages_list, return_tensors="pt", text_max_length=512, protein_max_length=1024
)
with torch.no_grad():
    generated_ids = hf_model.generate(**input_dict)
generated_texts = processor.batch_decode(
    generated_ids, skip_special_tokens=True
)

Tips:

This model was contributed by Xibin Bayes Zhou.
The original code can be found here.

EvollaConfig

class transformers.EvollaConfig

< source >

( protein_encoder_config = None vocab_size = 128256 hidden_size = 4096 intermediate_size = 14336 num_hidden_layers = 32 num_attention_heads = 32 num_key_value_heads = 8 hidden_act = 'silu' max_position_embeddings = 8192 rms_norm_eps = 1e-05 rope_theta = 500000.0 rope_scaling = None attention_bias = False attention_dropout = 0.0 mlp_bias = False aligner_ffn_mult = 4 aligner_enable_bias = True aligner_attention_probs_dropout_prob = 0.1 aligner_num_add_layers = 8 resampler_depth = 6 resampler_dim_head = 64 resampler_heads = 8 resampler_num_latents = 64 resampler_ff_mult = 4 initializer_range = 0.02 pad_token_id = None bos_token_id = 128000 eos_token_id = 128009 use_cache = False tie_word_embeddings = False **kwargs )

Parameters

protein_encoder_config (dict, optional) — Dictionary of configuration options used to initialize SaProtConfig.
vocab_size (int, optional, defaults to 128256) — Vocabulary size of the Evolla llama model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling EvollaModel.
hidden_size (int, optional, defaults to 4096) — Dimensionality of the llama layers and the pooler layer.
intermediate_size (int, optional, defaults to 14336) — Dimensionality of the intermediate layers in the llama model.
num_hidden_layers (int, optional, defaults to 32) — Number of hidden layers in the llama model.
num_attention_heads (int, optional, defaults to 32) — Number of attention heads for each attention layer in the llama model.
num_key_value_heads (int, optional, defaults to 8) — Number of key-value pairs for each attention layer in the llama model.
hidden_act (str or function, optional, defaults to "silu") — The non-linear activation function (function or string) in the llama model. If string, "gelu", "relu", "selu" and "silu" are supported.
max_position_embeddings (int, optional, defaults to 8192) — The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
rms_norm_eps (float, optional, defaults to 1e-05) — The epsilon value for the RMS-norm layer in the llama model.
rope_theta (float, optional, defaults to 500000.0) — The threshold value for the RoPE layer in the llama model.
rope_scaling (float, optional) — The scaling factor for the RoPE layer in the llama model.
attention_bias (bool, optional, defaults to False) — Whether to use bias in the attention layer.
attention_dropout (float, optional, defaults to 0.0) — The dropout ratio for the attention layer.
mlp_bias (bool, optional, defaults to False) — Whether to use bias in the MLP layer.
aligner_ffn_mult (int, optional, defaults to 4) — The FFN multiplier for the aligner layer.
aligner_enable_bias (bool, optional, defaults to True) — Whether to use bias in the aligner layer.
aligner_attention_probs_dropout_prob (float, optional, defaults to 0.1) — The dropout ratio for the attention probabilities in the aligner layer.
aligner_num_add_layers (int, optional, defaults to 8) — The number of additional layers for the aligner layer.
resampler_depth (int, optional, defaults to 6) — The depth of the resampler layer in the llama model.
resampler_dim_head (int, optional, defaults to 64) — The dimension of the heads in the resampler layer in the llama model.
resampler_heads (int, optional, defaults to 8) — The number of heads in the resampler layer in the llama model.
resampler_num_latents (int, optional, defaults to 64) — The number of latents in the resampler layer in the llama model.
resampler_ff_mult (int, optional, defaults to 4) — The FFN multiplier for the resampler layer.
initializer_range (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
pad_token_id (int, optional) — The id of the padding token.
bos_token_id (int, optional, defaults to 128000) — The id of the beginning-of-sequence token.
eos_token_id (int, optional, defaults to 128009) — The id of the end-of-sequence token.
use_cache (bool, optional, defaults to False) — Whether or not the model should return the last key/values attentions (not used by all models).
tie_word_embeddings (bool, optional, defaults to False) — Whether or not to tie the input and output word embeddings.

This is the configuration class to store the configuration of a EvollaModel. It is used to instantiate an Evolla 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 Evolla-10B.

e.g. westlake-repl/Evolla-10B-hf

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 EvollaModel, EvollaConfig

>>> # Initializing a Evolla evolla-10b style configuration
>>> configuration = EvollaConfig()

>>> # Initializing a model from the evolla-10b style configuration
>>> model = EvollaModel(configuration)

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

EvollaModel

class transformers.EvollaModel

< source >

( config: EvollaConfig )

forward

< source >

( input_ids: LongTensor = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None past_key_values: typing.Optional[transformers.cache_utils.Cache] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None use_cache: typing.Optional[bool] = None cache_position: typing.Optional[torch.LongTensor] = None protein_input_ids: typing.Optional[torch.LongTensor] = None protein_attention_mask: typing.Optional[torch.Tensor] = None structure_feats: typing.Optional[torch.FloatTensor] = None msa_feats: typing.Optional[torch.FloatTensor] = None structure_batch_mask: typing.Optional[torch.Tensor] = None msa_batch_mask: typing.Optional[torch.Tensor] = None **kwargs ) → transformers.modeling_outputs.BaseModelOutputWithPast 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.

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.
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).
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.
protein_input_ids (torch.LongTensor, optional) — The input IDs for the protein sequence in structure-aware tokens. Should be of shape (batch_size, protein_seq_length) and type torch.LongTensor.
protein_attention_mask (torch.Tensor, optional) — The attention mask for the protein sequence. Should be of shape (batch_size, protein_seq_length) and type torch.Tensor.
structure_feats (torch.FloatTensor, optional) — The input IDs for purely structure-based features. Should be of shape (batch_size, structure_seq_length, structure_feat_dim) and type torch.FloatTensor. Dummy input for now.
msa_feats (torch.FloatTensor, optional) — The input IDs for purely MSA-based features. Should be of shape (batch_size, msa_seq_length, msa_feat_dim) and type torch.FloatTensor. Dummy input for now.
structure_batch_mask (torch.Tensor, optional) — The batch mask to decide which protein sequences are purely structure-based. Should be of shape (batch_size) and type torch.Tensor. Should be paired with structure_feats. Dummpy input for now.
msa_batch_mask (torch.Tensor, optional) — The batch mask to decide which protein sequences are purely MSA-based. Should be of shape (batch_size) and type torch.Tensor. Should be paired with msa_feats. Dummpy input for now.

Returns

transformers.modeling_outputs.BaseModelOutputWithPast or tuple(torch.FloatTensor)

A transformers.modeling_outputs.BaseModelOutputWithPast 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 (None) 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.

The EvollaModel 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.

EvollaForProteinText2Text

class transformers.EvollaForProteinText2Text

< source >

( config )

forward

< source >

( input_ids: LongTensor = None attention_mask: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None protein_input_ids: LongTensor = None protein_attention_mask: typing.Optional[torch.Tensor] = None use_cache: typing.Optional[bool] = None **kwargs )

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.

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?
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.
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].
protein_input_ids (torch.LongTensor) — The input IDs for the protein sequence. Should be of shape (batch_size, protein_seq_length) and type torch.LongTensor.
protein_attention_mask (torch.Tensor, optional) — The attention mask for the protein sequence. Should be of shape (batch_size, protein_seq_length) and type torch.Tensor.
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).

The EvollaForProteinText2Text forward method, overrides the __call__ special method.

Example:

>>> from transformers import EvollaProcessor, EvollaForProteinText2Text
>>> model = EvollaForProteinText2Text.from_pretrained("westlake/Evolla-10B-hf")
>>> processor = EvollaProcessor.from_pretrained("westlake/Evolla-10B-hf")

>>> protein_information = {
    "aa_seq": "your amino acid sequence",
    "foldseek": "your foldseek sequence",
}
>>> question = "What is the function of this protein?"
>>> message = [
    {"role": "system", "content": "You are an AI expert that can answer any questions about protein."},
    {"role": "user", "content": question},
]

>>> inputs = processor(proteins=[protein_information], messages_list=[message], return_tensors="pt", padding="longest")
>>> outputs = model.generate(**inputs)

>>> print(processor.batch_decode(outputs, skip_special_tokens=True))

EvollaProcessor

class transformers.EvollaProcessor

< source >

( protein_tokenizer tokenizer = None protein_max_length = 1024 text_max_length = 512 **kwargs )

Parameters

protein_tokenizer (EsmTokenizer) — An instance of EsmTokenizer. The protein tokenizer is a required input.
tokenizer (LlamaTokenizerFast, optional) — An instance of LlamaTokenizerFast. The tokenizer is a required input.
protein_max_length (int, optional, defaults to 1024) — The maximum length of the sequence to be generated.
text_max_length (int, optional, defaults to 512) — The maximum length of the text to be generated.

Constructs a EVOLLA processor which wraps a LLama tokenizer and SaProt tokenizer (EsmTokenizer) into a single processor.

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

call

< source >

( proteins: typing.Union[list[dict], dict, NoneType] = None messages_list: typing.Union[list[list[dict]], list[dict], NoneType] = None protein_max_length: typing.Optional[int] = None text_max_length: typing.Optional[int] = None **kwargs ) → a dict with following keys

Parameters

proteins (Union[List[dict], dict]) — A list of dictionaries or a single dictionary containing the following keys:
- "aa_seq" (str) — The amino acid sequence of the protein.
- "foldseek" (str) — The foldseek string of the protein.
messages_list (Union[List[List[dict]], List[dict]]) — A list of lists of dictionaries or a list of dictionaries containing the following keys:
- "role" (str) — The role of the message.
- "content" (str) — The content of the message.
protein_max_length (int, optional, defaults to 1024) — The maximum length of the sequence to be generated.
text_max_length (int, optional, defaults to 512) — The maximum length of the text.

Returns

a dict with following keys

protein_input_ids (torch.Tensor of shape (batch_size, sequence_length)) — The input IDs for the protein sequence.
protein_attention_mask (torch.Tensor of shape (batch_size, sequence_length)) — The attention mask for the protein sequence.
text_input_ids (torch.Tensor of shape (batch_size, sequence_length)) — The input IDs for the text sequence.
text_attention_mask (torch.Tensor of shape (batch_size, sequence_length)) — The attention mask for the text sequence.

This method takes batched or non-batched proteins and messages_list and converts them into format that can be used by the model.

< > Update on GitHub

Transformers

Evolla

Overview

EvollaConfig

class transformers.EvollaConfig

EvollaModel

class transformers.EvollaModel

forward

EvollaForProteinText2Text

class transformers.EvollaForProteinText2Text

forward

EvollaProcessor

class transformers.EvollaProcessor

__call__

call