Merge pull request #5 from chaidiscovery/alex/chailab

move ESM to/from GPU once per complex, not once per chain

chai_lab/data/dataset/embeddings/esm.py

@@ -33,44 +33,58 @@ def esm_model(model_name: str, device):
     model.to(device)
     model.eval()
     yield model
-    # move model back to CPU
-    model.to("cpu")
+    model.to("cpu")  # move model back to CPU when done
 
 
-def embedding_context_from_sequence(seq: str, device) -> EmbeddingContext:
+def _get_esm_contexts_for_sequences(
+    prot_sequences: set[str], device
+) -> dict[str, EmbeddingContext]:
+    if len(prot_sequences) == 0:
+        return {}  # skip loading ESM
+
     # local import, requires huggingface transformers
     from transformers import EsmTokenizer
 
     model_name = "facebook/esm2_t36_3B_UR50D"
     tokenizer = EsmTokenizer.from_pretrained(model_name)
 
-    inputs = tokenizer(seq, return_tensors="pt")
-    inputs = move_data_to_device(dict(**inputs), device=device)
+    seq2embedding_context = {}
 
     with torch.no_grad():
         with esm_model(model_name=model_name, device=device) as model:
-            outputs = model(**inputs)
+            for seq in prot_sequences:
+                inputs = tokenizer(seq, return_tensors="pt")
+                inputs = move_data_to_device(dict(**inputs), device=device)
+                outputs = model(**inputs)
+                # remove BOS/EOS, back to CPU
+                esm_embeddings = outputs.last_hidden_state[0, 1:-1].to("cpu")
+                seq_len, _emb_dim = esm_embeddings.shape
+                assert seq_len == len(seq)
+
+                seq2embedding_context[seq] = EmbeddingContext(
+                    esm_embeddings=esm_embeddings
+                )
 
-    # remove BOS/EOS, back to CPU
-    esm_embeddings = outputs.last_hidden_state[0, 1:-1].to("cpu")
-    seq_len, _emb_dim = esm_embeddings.shape
-    assert seq_len == len(seq)
-    return EmbeddingContext(esm_embeddings=esm_embeddings)
+    return seq2embedding_context
 
 
 @typecheck
 def get_esm_embedding_context(chains: list[Chain], device) -> EmbeddingContext:
     # device is used for computing, but result is still on CPU
-    chain_embs = []
 
+    protein_seq2emb_context = _get_esm_contexts_for_sequences(
+        prot_sequences=set(
+            chain.entity_data.sequence
+            for chain in chains
+            if chain.entity_data.entity_type == EntityType.PROTEIN
+        ),
+        device=device,
+    )
+
+    chain_embs = []
     for chain in chains:
         if chain.entity_data.entity_type == EntityType.PROTEIN:
-            emb = embedding_context_from_sequence(
-                # modified residues represented as X
-                seq=chain.entity_data.sequence,
-                device=device,
-            )
-            chain_embs.append(emb)
+            chain_embs.append(protein_seq2emb_context[chain.entity_data.sequence])
         else:
             # embed non-proteins with zeros
             chain_embs.append(