Synthyra
/

ESM2-650M

@@ -3,197 +3,94 @@ library_name: transformers
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 tags: []
 ---
+# 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.
+Outputting attention maps (or the contact prediction head) is not natively possible with SDPA. 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
+### For working with embeddings
+```python
+import torch
+from transformers import AutoModel, AutoTokenizer
+model_path = 'Synthyra/ESM2-650M'
+model = AutoModel.from_pretrained(model_path, torch_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, torch_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, torch_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)
+```
+## 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.
+```python
+embeddings = model.embed_dataset(
+    sequences=sequences, # list of protein strings
+    batch_size=16, # embedding batch size
+    max_len=2048, # truncate to max_len
+    full_embeddings=True, # return residue-wise embeddings
+    full_precision=False, # store as float32
+    pooling_type='mean', # use mean pooling if protein-wise embeddings
+    num_workers=0, # data loading num workers
+    sql=False, # return dictionary of sequences and embeddings
+)
+_ = model.embed_dataset(
+    sequences=sequences, # list of protein strings
+    batch_size=16, # embedding batch size
+    max_len=2048, # truncate to max_len
+    full_embeddings=True, # return residue-wise embeddings
+    full_precision=False, # store as float32
+    pooling_type='mean', # use mean pooling if protein-wise embeddings
+    num_workers=0, # data loading num workers
+    sql=True, # store sequences in local SQL database
+    sql_db_path='embeddings.db', # path to .db file of choice
+)
+```
+### 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 {FastESM2,
+	author       = { Hallee, L. and Bichara, D. and Gleghorn, J, P. },
+	title        = { FastESM2 },
+	year         = 2024,
+	url          = { https://huggingface.co/Synthyra/FastESM2_650 },
+	doi          = { 10.57967/hf/3729 },
+	publisher    = { Hugging Face }
+}
+```