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README.md

@@ -1,120 +1,140 @@
----
-library_name: transformers
-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
-
-### 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, 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) 
-```
-
-### 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.
-```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 }
-}
+---
+library_name: transformers
+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
+
+### 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, 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) 
+```
+
+### 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
+    ],
+    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_type=['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 {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 }
+}
 ```