Empty cache before we run folding (#47)

* Empty cache before we run folding

* More aggressively move data off GPU

* Delete modules after we are finished with them

* Remove redundant call to move inputs to cpu

chai_lab/chai1.py

@@ -327,6 +327,9 @@ def run_folding_on_context(
     if device is None:
         device = torch.device("cuda:0")
 
+    # Clear memory
+    torch.cuda.empty_cache()
+
     ##
     ## Validate inputs
     ##
@@ -443,6 +446,9 @@ def run_folding_on_context(
             token_single_mask=token_single_mask,
             token_pair_mask=token_pair_mask,
         )
+    # We won't be using the trunk anymore; remove it from memory
+    del trunk
+    torch.cuda.empty_cache()
 
     ##
     ## Denoise the trunk representation by passing it through the diffusion module
@@ -534,6 +540,10 @@ def _denoise(atom_pos: Tensor, sigma: Tensor, s: int) -> Tensor:
             d_i_prime = (atom_pos - denoised_pos) / sigma_next
             atom_pos = atom_pos + (sigma_next - sigma_hat) * ((d_i_prime + d_i) / 2)
 
+    # We won't be running diffusion anymore
+    del diffusion_module
+    torch.cuda.empty_cache()
+
     ##
     ## Run the confidence model
     ##
@@ -610,6 +620,11 @@ def avg_per_token_1d(x):
     ##
     ## Write the outputs
     ##
+    # Move data to the CPU so we don't hit GPU memory limits
+    inputs = move_data_to_device(inputs, torch.device("cpu"))
+    atom_pos = atom_pos.cpu()
+    plddt_logits = plddt_logits.cpu()
+    pae_logits = pae_logits.cpu()
 
     # Plot coverage of tokens by MSA, save plot
     output_dir.mkdir(parents=True, exist_ok=True)
@@ -671,7 +686,7 @@ def avg_per_token_1d(x):
         outputs_to_cif(
             coords=atom_pos[idx : idx + 1],
             bfactors=scaled_plddt_scores_per_atom,
-            output_batch=move_data_to_device(inputs, torch.device("cpu")),
+            output_batch=inputs,
             write_path=cif_out_path,
             entity_names={
                 c.entity_data.entity_id: c.entity_data.entity_name