Make seeding more consistent

openfold/data/data_transforms.py

@@ -16,6 +16,7 @@
 import itertools
 from functools import reduce, wraps
 from operator import add
+import random
 
 import numpy as np
 import torch
@@ -183,12 +184,11 @@ def randomly_replace_msa_with_unknown(protein, replace_proportion):
 @curry1
 def sample_msa(protein, max_seq, keep_extra, seed=None):
     """Sample MSA randomly, remaining sequences are stored are stored as `extra_*`.""" 
+    if(seed is None):
+        seed = random.randint(0, 2147483647)
     num_seq = protein["msa"].shape[0]
     g = torch.Generator(device=protein["msa"].device)
-    if seed is not None:
-        g.manual_seed(seed)
-    else:
-        g.seed()
+    g.manual_seed(seed)
     shuffled = torch.randperm(num_seq - 1, generator=g) + 1
     index_order = torch.cat(
         (torch.tensor([0], device=shuffled.device), shuffled),