Esmplusplus Small

🚀 ESM++

ESM++是ESMC（許可證）的忠實實現，它支持批量處理，並且無需ESM Python包即可與標準的Huggingface兼容。小版本對應於ESMC的3億參數版本。該項目解決了在不依賴ESM Python包的情況下，實現與Huggingface的標準兼容以及批量處理的問題，為相關研究和開發提供了便利。

🚀 快速開始

之前Huggingface權重綁定存在一個bug，導致ESM++的對數幾率與ESMC不同。該bug現已修復。

✨ 主要特性

• 忠實實現ESMC，支持批量處理。
• 無需ESM Python包，與標準Huggingface兼容。
• 支持序列和標記級別的分類任務。
• 提供不同浮點精度的加載選項。
• 可返回注意力圖。
• 相比ESMC和其他模型，推理速度更快。

📦 安裝指南

文檔未提及安裝步驟，暫不展示。

💻 使用示例

基礎用法

from transformers import AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained('Synthyra/ESMplusplus_small', trust_remote_code=True)
tokenizer = model.tokenizer

sequences = ['MPRTEIN', 'MSEQWENCE']
tokenized = tokenizer(sequences, padding=True, return_tensors='pt')

# tokenized['labels'] = tokenized['input_ids'].clone() # correctly mask input_ids and set unmasked instances of labels to -100 for MLM training

output = model(**tokenized) # get all hidden states with output_hidden_states=True
print(output.logits.shape) # language modeling logits, (batch_size, seq_len, vocab_size), (2, 11, 64)
print(output.last_hidden_state.shape) # last hidden state of the model, (batch_size, seq_len, hidden_size), (2, 11, 960)
print(output.loss) # language modeling loss if you passed labels
#print(output.hidden_states) # all hidden states if you passed output_hidden_states=True (in tuple)

高級用法

支持序列和標記級別的分類任務

from transformers import AutoModelForSequenceClassification, AutoModelForTokenClassification

model = AutoModelForSequenceClassification.from_pretrained('Synthyra/ESMplusplus_small', num_labels=2, trust_remote_code=True)
logits = model(**tokenized).logits
print(logits.shape) # (batch_size, num_labels), (2, 2)

以不同浮點精度加載權重

import torch
model = AutoModelForMaskedLM.from_pretrained('Synthyra/ESMplusplus_small', trust_remote_code=True, torch_dtype=torch.float16) # or torch.bfloat16

嵌入整個數據集

embedding_dict = model.embed_dataset(
    sequences=[
        'MALWMRLLPLLALLALWGPDPAAA', ... # list of protein sequences
    ],
    tokenizer=model.tokenizer,
    batch_size=2, # adjust for your GPU memory
    max_len=512, # adjust for your needs
    full_embeddings=False, # if True, no pooling is performed
    embed_dtype=torch.float32, # cast to what dtype you want
    pooling_types=['mean', 'cls'], # more than one pooling type will be concatenated together
    num_workers=0, # if you have many cpu cores, we find that num_workers = 4 is fast for large datasets
    sql=False, # if True, embeddings will be stored in SQLite database
    sql_db_path='embeddings.db',
    save=True, # if True, embeddings will be saved as a .pth file
    save_path='embeddings.pth',
)
# embedding_dict is a dictionary mapping sequences to their embeddings as tensors for .pth or numpy arrays for sql

model.embed_dataset()
Args:
    sequences: List of protein sequences
    batch_size: Batch size for processing
    max_len: Maximum sequence length
    full_embeddings: Whether to return full residue-wise (True) embeddings or pooled (False)
    pooling_type: Type of pooling ('mean' or 'cls')
    num_workers: Number of workers for data loading, 0 for the main process
    sql: Whether to store embeddings in SQLite database - will be stored in float32
    sql_db_path: Path to SQLite database
    
Returns:
    Dictionary mapping sequences to embeddings, or None if sql=True

Note:
    - If sql=True, embeddings can only be stored in float32
    - sql is ideal if you need to stream a very large dataset for training in real-time
    - save=True is ideal if you can store the entire embedding dictionary in RAM
    - sql will be used if it is True and save is True or False
    - If your sql database or .pth file is already present, they will be scanned first for already embedded sequences
    - Sequences will be truncated to max_len and sorted by length in descending order for faster processing

使用🤗 peft進行微調

model = AutoModelForSequenceClassification.from_pretrained('Synthyra/ESMplusplus_small', num_labels=2, trust_remote_code=True)
# these modules handle ESM++ and ESM2 attention layers
target_modules = ["layernorm_qkv.1", "out_proj", "query", "key", "value", "dense"]

lora_config = LoraConfig(
    r=8, # choose lora parameters to your liking
    lora_alpha=16,
    lora_dropout=0.01,
    bias="none",
    target_modules=target_modules,
)

# Apply LoRA to the model
model = get_peft_model(model, lora_config)

# Unfreeze the classifier head
for param in model.classifier.parameters():
    param.requires_grad = True

如需更全面的微調示例，請查看我們的示例腳本此處。

返回注意力圖

output = model(**tokenized, output_attentions=True)
att = output.attentions
len(att) # 30, one for each layer, size (batch_size, num_heads, seq_len, seq_len) each

📚 詳細文檔

不同浮點精度和實現方式的比較

我們測量了fp32權重與fp16或bf16的最後隱藏狀態的差異。發現fp16更接近fp32的輸出，因此建議以fp16加載。請注意，ESM包也以fp32加載ESMC，但默認轉換為bf16，這在推理/訓練中各有優缺點，因此可以根據需要選擇半精度加載方式。

• FP32與FP16的平均均方誤差（MSE）：0.00000003
• FP32與BF16的平均均方誤差（MSE）：0.00000140

我們還測量了ESM++與ESMC（均為bfloat16）在1000個隨機序列上的輸出差異，以確保與ESM包兼容。

• 最後隱藏狀態的平均均方誤差（MSE）：7.74e - 10

你可以通過將.from_pretrained(...)替換為.from_pretrained_esm('esmc_300m')，從ESM包而不是transformers加載權重。

模型探針

我們在各種PLM和標準數據集上採用線性探測技術，類似於我們之前的論文，以評估池化隱藏狀態與有價值屬性之間的內在相關性。ESMC（以及ESM++）表現出色。

下圖展示了在負控制（隨機向量嵌入）和最佳表現者之間進行歸一化後的性能。分類任務得分在MCC和F1（或多標籤的F1max）之間平均，迴歸任務在Spearman rho和R2之間平均。

推理速度

我們研究了各種ESM模型在H100上的吞吐量。在ESMC和ESM++之間添加高效的批量處理顯著提高了吞吐量，即使在批量大小為1的情況下，ESM++也比ESMC更快。ESM++小版本在處理長序列時甚至比ESM2 - 35M更快！在Linux機器上使用PyTorch > 2.5時，提升最為明顯。

🔧 技術細節

文檔未提及技術實現細節，暫不展示。

📄 許可證

如果你使用此實現或相關工作，請引用它（以及ESMC預印本）。

@misc {ESMPlusPlus,
	author       = { Hallee, L. and Bichara, D. and Gleghorn, J, P. },
	title        = { ESMPlusPlus },
	year         = 2024,
	url          = { https://huggingface.co/Synthyra/ESMplusplus_small },
	doi          = { 10.57967/hf/3725 },
	publisher    = { Hugging Face }
}