---
library_name: transformers
tags: []
---

# NOTE
The GitHub with the implementation and requirements.txt can be found [here](https://github.com/Synthyra/FastPLMs.git)

# FastESM
FastESM is a Huggingface compatible plug in version of ESM2 rewritten with a newer PyTorch attention implementation.

Load any ESM2 models into a FastEsm model to dramatically speed up training and inference without **ANY** cost in performance.

## Attention backend defaults
`sdpa` is the default attention backend for FastESM.

To enable Flex Attention, set `attn_backend="flex"` on the config before model initialization/loading.

For throughput and memory efficiency, `torch.compile(...)` is heavily recommended, especially when using Flex Attention.

Outputting attention maps (or the contact prediction head) is not natively possible with the optimized attention backends (including Flex Attention). You can still pass ```output_attentions``` to have attention calculated manually and returned.
Various other optimizations also make the base implementation slightly different than the one in transformers.

## Use with 🤗 transformers

### Supported models
```python
model_dict = {
    # Synthyra/ESM2-8M
    'ESM2-8M': 'facebook/esm2_t6_8M_UR50D',
    # Synthyra/ESM2-35M
    'ESM2-35M': 'facebook/esm2_t12_35M_UR50D',
    # Synthyra/ESM2-150M
    'ESM2-150M': 'facebook/esm2_t30_150M_UR50D',
    # Synthyra/ESM2-650M
    'ESM2-650M': 'facebook/esm2_t33_650M_UR50D',
    # Synthyra/ESM2-3B
    'ESM2-3B': 'facebook/esm2_t36_3B_UR50D',
}
```

### For working with embeddings
```python
import torch
from transformers import AutoModel, AutoTokenizer

model_path = 'Synthyra/ESM2-8M'
model = AutoModel.from_pretrained(model_path, dtype=torch.float16, trust_remote_code=True).eval()
tokenizer = model.tokenizer

sequences = ['MPRTEIN', 'MSEQWENCE']
tokenized = tokenizer(sequences, padding=True, return_tensors='pt')
with torch.no_grad():
    embeddings = model(**tokenized).last_hidden_state

print(embeddings.shape) # (2, 11, 1280)
```

### For working with sequence logits
```python
import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer

model = AutoModelForMaskedLM.from_pretrained(model_path, dtype=torch.float16, trust_remote_code=True).eval()
with torch.no_grad():
    logits = model(**tokenized).logits

print(logits.shape) # (2, 11, 33)
```

### For working with attention maps
```python
import torch
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained(model_path, dtype=torch.float16, trust_remote_code=True).eval()
with torch.no_grad():
    attentions = model(**tokenized, output_attentions).attentions # tuples of (batch_size, num_heads, seq_len, seq_len)

print(attentions[-1].shape) # (2, 20, 11, 11) 
```

### Contact prediction
Because we can output attentions using the naive attention implementation, the contact prediction is also supported
```python
with torch.no_grad():
    contact_map = model.predict_contacts(**tokenized).squeeze().cpu().numpy() # (seq_len, seq_len)
```
![image/png](https://cdn-uploads.huggingface.co/production/uploads/62f2bd3bdb7cbd214b658c48/9707OSXZ3Wdgn0Ni-55T-.png)

## Embed entire datasets with no new code
To embed a list of protein sequences **fast**, just call embed_dataset. Sequences are sorted to reduce padding tokens, so the initial progress bar estimation is usually much longer than the actual time it will take.

Example:
```python
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
```


### Citation
If you use any of this implementation or work please cite it (as well as the [ESM2](https://www.science.org/doi/10.1126/science.ade2574) paper).
```
@misc {FastPLMs,
    author       = { Hallee, Logan and Bichara, David and Gleghorn, Jason P.},
    title        = { FastPLMs: Fast, efficient, protien language model inference from Huggingface AutoModel.},
    year         = {2024},
    url          = { https://huggingface.co/Synthyra/ESMplusplus_small },
    DOI          = { 10.57967/hf/3726 },
    publisher    = { Hugging Face }
}
```