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README.md

@@ -1,165 +1,165 @@
----
-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 backends
-
-`sdpa` (PyTorch Scaled Dot Product Attention) is the default. It is fast, memory-efficient, and numerically equivalent to naive attention. The backend is set via `config.attn_backend` before loading.
-
-| Backend | Key | Notes |
-| :--- | :--- | :--- |
-| PyTorch SDPA | `"sdpa"` | Default. Exact numerics, stable on all hardware. |
-| Flash Attention | `"kernels_flash"` | Fastest. Requires `pip install kernels` (pre-built — no hours-long compilation). Outputs are not bitwise identical to SDPA due to online softmax reordering; differences are often small but not guaranteed to be inconsequential — use `"sdpa"` if exact numerics matter. |
-| Flex Attention | `"flex"` | Skips padding tokens via block mask — faster on variable-length batches. Near-exact numerics. First use compiles a Triton kernel (30–120 s). |
-| Auto | `"auto"` | Picks the best available: `kernels_flash` → `flex` → `sdpa`. |
-
-```python
-from transformers import AutoConfig, AutoModel
-
-config = AutoConfig.from_pretrained("Synthyra/ESM2-150M", trust_remote_code=True)
-config.attn_backend = "flex"  # or "kernels_flash", "sdpa", "auto"
-model = AutoModel.from_pretrained("Synthyra/ESM2-150M", config=config, trust_remote_code=True)
-```
-
-`torch.compile(model)` is heavily recommended for sustained throughput, especially with Flex Attention.
-
-Attention maps (`output_attentions=True`) are supported with all backends. For SDPA, Flash, and Flex, the attention weights are computed via a separate naive pass, so there is no memory benefit to enabling it during normal inference.
-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 }
-}
+---
+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 backends
+
+`sdpa` (PyTorch Scaled Dot Product Attention) is the default. It is fast, memory-efficient, and numerically equivalent to naive attention. The backend is set via `config.attn_backend` before loading.
+
+| Backend | Key | Notes |
+| :--- | :--- | :--- |
+| PyTorch SDPA | `"sdpa"` | Default. Exact numerics, stable on all hardware. |
+| Flash Attention | `"kernels_flash"` | Fastest. Requires `pip install kernels` (pre-built — no hours-long compilation). Outputs are not bitwise identical to SDPA due to online softmax reordering; differences are often small but not guaranteed to be inconsequential — use `"sdpa"` if exact numerics matter. |
+| Flex Attention | `"flex"` | Skips padding tokens via block mask — faster on variable-length batches. Near-exact numerics. First use compiles a Triton kernel (30–120 s). |
+| Auto | `"auto"` | Picks the best available: `kernels_flash` → `flex` → `sdpa`. |
+
+```python
+from transformers import AutoConfig, AutoModel
+
+config = AutoConfig.from_pretrained("Synthyra/ESM2-150M", trust_remote_code=True)
+config.attn_backend = "flex"  # or "kernels_flash", "sdpa", "auto"
+model = AutoModel.from_pretrained("Synthyra/ESM2-150M", config=config, trust_remote_code=True)
+```
+
+`torch.compile(model)` is heavily recommended for sustained throughput, especially with Flex Attention.
+
+Attention maps (`output_attentions=True`) are supported with all backends. For SDPA, Flash, and Flex, the attention weights are computed via a separate naive pass, so there is no memory benefit to enabling it during normal inference.
+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 }
+}
 ```