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mila-intel/
ProtST-esm1b

Name: ProtST-esm1b
Availability: InStock

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Abstract

Current protein language models (PLMs) learn protein representations mainly based on their sequences, thereby well capturing co-evolutionary information, but they are unable to explicitly acquire protein functions, which is the end goal of protein representation learning. Fortunately, for many proteins, their textual property descriptions are available, where their various functions are also described. Motivated by this fact, we first build the ProtDescribe dataset to augment protein sequences with text descriptions of their functions and other important properties. Based on this dataset, we propose the ProtST framework to enhance Protein Sequence pre-training and understanding by biomedical Texts. During pre-training, we design three types of tasks, i.e., unimodal mask prediction, multimodal representation alignment and multimodal mask prediction, to enhance a PLM with protein property information with different granularities and, at the same time, preserve the PLM’s original representation power. On downstream tasks, ProtST enables both supervised learning and zeroshot prediction. We verify the superiority of ProtST-induced PLMs over previous ones on diverse representation learning benchmarks. Under the zero-shot setting, we show the effectiveness of ProtST on zero-shot protein classification, and ProtST also enables functional protein retrieval from a large-scale database without any function annotation. Source code and model weights are available at https://github.com/DeepGraphLearning/ProtST.

Performance

Zero-shot ProtST-ESM-1b outperforms few-shot classifiers.

Example

The following script shows how to run ProtST with Gaudi on zero-shot classification task.

diff

import logging
import functools
from tqdm import tqdm
import torch
from datasets import load_dataset
from transformers import AutoModel, AutoTokenizer, AutoConfig
+ import habana_frameworks.torch

logger = logging.getLogger(__name__)


def tokenize_protein(example, protein_tokenizer=None, padding=None):
    protein_seqs = example["prot_seq"]

-   protein_inputs = protein_tokenizer(protein_seqs, padding=padding, add_special_tokens=True)
+   protein_inputs = protein_tokenizer(protein_seqs, padding="max_length", truncation=True, add_special_tokens=True, max_length=1024)
    example["protein_input_ids"] = protein_inputs.input_ids
    example["protein_attention_mask"] = protein_inputs.attention_mask
    
    return example


def label_embedding(labels, text_tokenizer, text_model, device):
    # embed label descriptions
    label_feature = []
    with torch.inference_mode():
        for label in labels:
            label_input_ids = text_tokenizer.encode(label, max_length=128,
-                                                   truncation=True, add_special_tokens=False)
+                                                   truncation=True, add_special_tokens=False, padding="max_length")
            label_input_ids = [text_tokenizer.cls_token_id] + label_input_ids
            label_input_ids = torch.tensor(label_input_ids, dtype=torch.long, device=device).unsqueeze(0)
            attention_mask = label_input_ids != text_tokenizer.pad_token_id
            attention_mask = attention_mask.to(device)

            text_outputs = text_model(label_input_ids, attention_mask=attention_mask)

-           label_feature.append(text_outputs["text_feature"])
+           label_feature.append(text_outputs["text_feature"].clone())
    label_feature = torch.cat(label_feature, dim=0)
    label_feature = label_feature / label_feature.norm(dim=-1, keepdim=True)

    return label_feature

def zero_shot_eval(logger, device,                                          
                   test_dataset, target_field, protein_model, logit_scale, label_feature):

    # get prediction and target
    test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=False)
    preds, targets = [], []
    with torch.inference_mode():
        for data in tqdm(test_dataloader):
            target = data[target_field]
            targets.append(target)

            protein_input_ids = torch.tensor(data["protein_input_ids"], dtype=torch.long, device=device).unsqueeze(0)
            attention_mask = torch.tensor(data["protein_attention_mask"], dtype=torch.long, device=device).unsqueeze(0)
            
            protein_outputs = protein_model(protein_input_ids, attention_mask=attention_mask)

            protein_feature = protein_outputs["protein_feature"]
            protein_feature = protein_feature / protein_feature.norm(dim=-1, keepdim=True)
            pred = logit_scale * protein_feature @ label_feature.t()
            preds.append(pred)

    preds = torch.cat(preds, dim=0)
    targets = torch.tensor(targets, dtype=torch.long, device=device)
    accuracy = (preds.argmax(dim=-1) == targets).float().mean().item()
    logger.warning("Zero-shot accuracy: %.6f" % accuracy)


if __name__ == "__main__":
    # get datasets
    raw_datasets = load_dataset("mila-intel/ProtST-SubcellularLocalization", cache_dir="~/.cache/huggingface/datasets", split='test') # cache_dir defaults to "~/.cache/huggingface/datasets"

-   device = torch.device("cpu")
+   device = torch.device("hpu")
    
    protst_model = AutoModel.from_pretrained("mila-intel/ProtST-esm1b", trust_remote_code=True, torch_dtype=torch.bfloat16).to(device)
    protein_model = protst_model.protein_model
    text_model = protst_model.text_model
    logit_scale = protst_model.logit_scale
+   from habana_frameworks.torch.hpu import wrap_in_hpu_graph
+   protein_model = wrap_in_hpu_graph(protein_model)
+   text_model = wrap_in_hpu_graph(text_model)
    logit_scale.requires_grad = False
    logit_scale = logit_scale.to(device)
    logit_scale = logit_scale.exp()

    protein_tokenizer = AutoTokenizer.from_pretrained("facebook/esm1b_t33_650M_UR50S")
    text_tokenizer = AutoTokenizer.from_pretrained("microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract")

    func_tokenize_protein = functools.partial(tokenize_protein, protein_tokenizer=protein_tokenizer, padding=False)
    test_dataset = raw_datasets.map(
            func_tokenize_protein, batched=False,
            remove_columns=["prot_seq"],
            desc="Running tokenize_proteins on dataset",
        )

    labels = load_dataset("mila-intel/subloc_template", cache_dir="~/.cache/huggingface/datasets")["train"]["name"]

    text_tokenizer.encode(labels[0], max_length=128, truncation=True, add_special_tokens=False)
    label_feature = label_embedding(labels, text_tokenizer, text_model, device)
    zero_shot_eval(logger, device, test_dataset, "localization",
                   protein_model, logit_scale, label_feature)

Run ProtST on CPU with optimum-intel optimization.

diff

...
    protst_model = AutoModel.from_pretrained("mila-intel/ProtST-esm1b", trust_remote_code=True, torch_dtype=torch.bfloat16).to(device)
    protein_model = protst_model.protein_model
+   import intel_extension_for_pytorch as ipex
+   from optimum.intel.generation.modeling import jit_trace
+   protein_model = ipex.optimize(protein_model, dtype=torch.bfloat16, inplace=True)
+   protein_model = jit_trace(protein_model, "sequence-classification")
...