Synthyra
/

ESMplusplus_large

@@ -1,188 +1,188 @@
----
-library_name: transformers
-tags: []
----
-# NOTE
-The GitHub with the implementation and requirements.txt can be found [here](https://github.com/Synthyra/FastPLMs.git)
-# ESM++
-[ESM++](https://github.com/Synthyra/ESMplusplus) is a faithful implementation of [ESMC](https://www.evolutionaryscale.ai/blog/esm-cambrian) ([license](https://www.evolutionaryscale.ai/policies/cambrian-non-commercial-license-agreement)) that allows for batching and standard Huggingface compatibility without requiring the ESM Python package.
-The large version corresponds to the 600 million parameter version of ESMC.
-## Attention backends
-`sdpa` (PyTorch Scaled Dot Product Attention) is the default. 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 on Ampere/Hopper GPUs. 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). Best combined with `torch.compile`. |
-| Auto | `"auto"` | Picks the best available: `kernels_flash` → `flex` → `sdpa`. |
-```python
-from transformers import AutoConfig, AutoModelForMaskedLM
-config = AutoConfig.from_pretrained('Synthyra/ESMplusplus_large', trust_remote_code=True)
-config.attn_backend = "flex"  # or "kernels_flash", "sdpa", "auto"
-model = AutoModelForMaskedLM.from_pretrained('Synthyra/ESMplusplus_large', config=config, trust_remote_code=True)
-```
-`torch.compile(model)` is heavily recommended for sustained throughput, especially with Flex Attention.
-## Use with 🤗 transformers
-```python
-from transformers import AutoModelForMaskedLM
-model = AutoModelForMaskedLM.from_pretrained('Synthyra/ESMplusplus_large', 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, 1152)
-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)
-```
-ESM++ also supports sequence and token level classification tasks like ESM2. Simply pass the number of labels during initialization.
-```python
-from transformers import AutoModelForSequenceClassification, AutoModelForTokenClassification
-model = AutoModelForSequenceClassification.from_pretrained('Synthyra/ESMplusplus_large', num_labels=2, trust_remote_code=True)
-logits = model(**tokenized).logits
-print(logits.shape) # (batch_size, num_labels), (2, 2)
-```
-ESM++ weights are fp32 by default. You can load them in fp16 or bf16 like this:
-```python
-import torch
-model = AutoModelForMaskedLM.from_pretrained('Synthyra/ESMplusplus_large', trust_remote_code=True, dtype=torch.float16) # or torch.bfloat16
-```
-## 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
-```
-## Fine-tuning with 🤗 peft
-```python
-model = AutoModelForSequenceClassification.from_pretrained('Synthyra/ESMplusplus_large', 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
-```
-For a more thourough example of fine-tuning, check out our example script [here](https://github.com/Synthyra/FastPLMs/blob/main/fine_tuning_example.py).
-## Returning attention maps
-When `attn_backend="flex"`, Flex Attention with a pad-token block mask is used for attention calculations. Optimized attention paths do not return attention maps directly.
-ESM++ has the option to ```output_attentions```, which will calculate attention manually. This is much slower, so do not use unless you need the attention maps.
-```python
-output = model(**tokenized, output_attentions=True)
-att = output.attentions
-len(att) # 33, one for each layer, size (batch_size, num_heads, seq_len, seq_len) each
-```
-## Comparison across floating-point precision and implementations
-We measured the difference of the last hidden states of the fp32 weights vs. fp16 or bf16. We find that the fp16 is closer to the fp32 outputs, so we recommend loading in fp16.
-Please note that the ESM package also loads ESMC in fp32 but casts to bf16 by default, which has its share of advantages and disadvantages in inference / training - so load whichever you like for half precision.
-Average MSE for FP16: 0.00000003
-Average MSE for BF16: 0.00000122
-We also measured the difference between the outputs of ESM++ vs. ESMC (both in bfloat16) on 1000 random sequences to ensure compliance with the ESM package.
-Average MSE of last hidden state: 2.46e-09
-You can load the weights from the ESM package instead of transformers by replacing .from_pretrained(...) to .from_pretrained_esm('esmc_600m')
-## Model probes
-We employ linear probing techniques on various PLMs and standard datasets, similar our previous [paper](https://www.biorxiv.org/content/10.1101/2024.07.30.605924v1), to assess the intrinsic correlation between pooled hidden states and valuable properties. ESMC (and thus ESM++) perform very well.
-The plot below showcases performance normalized between the negative control (random vector embeddings) and the best performer. Classification task scores are averaged between MCC and F1 (or F1max for multilabel) and regression tasks are averaged between Spearman rho and R2.
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/62f2bd3bdb7cbd214b658c48/uRAHYQcwkbgajylTIFbUb.png)
-## Inference speeds
-We look at various ESM models and their throughput on an H100. Adding efficient batching between ESMC and ESM++ significantly improves the throughput, although ESM++ is also faster than ESMC for batch size one. ESM++ small is even faster than ESM2-35M with long sequences! The most gains will be seen with PyTorch > 2.5 on linux machines.
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/62f2bd3bdb7cbd214b658c48/Lu6nWB9Fc-7YTql3Z1hVB.png)
-### Citation
-If you use any of this implementation or work please cite it (as well as the ESMC preprint).
-```
-@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)
+# ESM++
+[ESM++](https://github.com/Synthyra/ESMplusplus) is a faithful implementation of [ESMC](https://www.evolutionaryscale.ai/blog/esm-cambrian) ([license](https://www.evolutionaryscale.ai/policies/cambrian-non-commercial-license-agreement)) that allows for batching and standard Huggingface compatibility without requiring the ESM Python package.
+The large version corresponds to the 600 million parameter version of ESMC.
+## Attention backends
+`sdpa` (PyTorch Scaled Dot Product Attention) is the default. 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 on Ampere/Hopper GPUs. 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). Best combined with `torch.compile`. |
+| Auto | `"auto"` | Picks the best available: `kernels_flash` → `flex` → `sdpa`. |
+```python
+from transformers import AutoConfig, AutoModelForMaskedLM
+config = AutoConfig.from_pretrained('Synthyra/ESMplusplus_large', trust_remote_code=True)
+config.attn_backend = "flex"  # or "kernels_flash", "sdpa", "auto"
+model = AutoModelForMaskedLM.from_pretrained('Synthyra/ESMplusplus_large', config=config, trust_remote_code=True)
+```
+`torch.compile(model)` is heavily recommended for sustained throughput, especially with Flex Attention.
+## Use with 🤗 transformers
+```python
+from transformers import AutoModelForMaskedLM
+model = AutoModelForMaskedLM.from_pretrained('Synthyra/ESMplusplus_large', 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, 1152)
+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)
+```
+ESM++ also supports sequence and token level classification tasks like ESM2. Simply pass the number of labels during initialization.
+```python
+from transformers import AutoModelForSequenceClassification, AutoModelForTokenClassification
+model = AutoModelForSequenceClassification.from_pretrained('Synthyra/ESMplusplus_large', num_labels=2, trust_remote_code=True)
+logits = model(**tokenized).logits
+print(logits.shape) # (batch_size, num_labels), (2, 2)
+```
+ESM++ weights are fp32 by default. You can load them in fp16 or bf16 like this:
+```python
+import torch
+model = AutoModelForMaskedLM.from_pretrained('Synthyra/ESMplusplus_large', trust_remote_code=True, dtype=torch.float16) # or torch.bfloat16
+```
+## 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
+```
+## Fine-tuning with 🤗 peft
+```python
+model = AutoModelForSequenceClassification.from_pretrained('Synthyra/ESMplusplus_large', 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
+```
+For a more thourough example of fine-tuning, check out our example script [here](https://github.com/Synthyra/FastPLMs/blob/main/fine_tuning_example.py).
+## Returning attention maps
+When `attn_backend="flex"`, Flex Attention with a pad-token block mask is used for attention calculations. Optimized attention paths do not return attention maps directly.
+ESM++ has the option to ```output_attentions```, which will calculate attention manually. This is much slower, so do not use unless you need the attention maps.
+```python
+output = model(**tokenized, output_attentions=True)
+att = output.attentions
+len(att) # 33, one for each layer, size (batch_size, num_heads, seq_len, seq_len) each
+```
+## Comparison across floating-point precision and implementations
+We measured the difference of the last hidden states of the fp32 weights vs. fp16 or bf16. We find that the fp16 is closer to the fp32 outputs, so we recommend loading in fp16.
+Please note that the ESM package also loads ESMC in fp32 but casts to bf16 by default, which has its share of advantages and disadvantages in inference / training - so load whichever you like for half precision.
+Average MSE for FP16: 0.00000003
+Average MSE for BF16: 0.00000122
+We also measured the difference between the outputs of ESM++ vs. ESMC (both in bfloat16) on 1000 random sequences to ensure compliance with the ESM package.
+Average MSE of last hidden state: 2.46e-09
+You can load the weights from the ESM package instead of transformers by replacing .from_pretrained(...) to .from_pretrained_esm('esmc_600m')
+## Model probes
+We employ linear probing techniques on various PLMs and standard datasets, similar our previous [paper](https://www.biorxiv.org/content/10.1101/2024.07.30.605924v1), to assess the intrinsic correlation between pooled hidden states and valuable properties. ESMC (and thus ESM++) perform very well.
+The plot below showcases performance normalized between the negative control (random vector embeddings) and the best performer. Classification task scores are averaged between MCC and F1 (or F1max for multilabel) and regression tasks are averaged between Spearman rho and R2.
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/62f2bd3bdb7cbd214b658c48/uRAHYQcwkbgajylTIFbUb.png)
+## Inference speeds
+We look at various ESM models and their throughput on an H100. Adding efficient batching between ESMC and ESM++ significantly improves the throughput, although ESM++ is also faster than ESMC for batch size one. ESM++ small is even faster than ESM2-35M with long sequences! The most gains will be seen with PyTorch > 2.5 on linux machines.
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/62f2bd3bdb7cbd214b658c48/Lu6nWB9Fc-7YTql3Z1hVB.png)
+### Citation
+If you use any of this implementation or work please cite it (as well as the ESMC preprint).
+```
+@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 }
+}
 ```