Merge branch 'main' into multimer

tests/test_primitives.py

@@ -15,33 +15,39 @@
 import torch
 import unittest
 
-from openfold.model.primitives import Attention
+from openfold.model.primitives import (
+    lecun_normal_init_,
+    Attention,
+)
 from tests.config import consts
+from tests.data_utils import random_attention_inputs
 
 
 class TestLMA(unittest.TestCase):
     def test_lma_vs_attention(self):
-        batch_size = consts.batch_size
         c_hidden = 32
-        n = 2 ** 12
         no_heads = 4
 
-        q = torch.rand(batch_size, n, c_hidden).cuda()
-        kv = torch.rand(batch_size, n, c_hidden).cuda()
-
-        bias = [torch.rand(no_heads, 1, n)]
-        bias = [b.cuda() for b in bias]
+        q, kv, _, biases = random_attention_inputs(batch_size=consts.batch_size,
+                                                   n_seq=consts.n_seq,
+                                                   n=2 ** 12,
+                                                   no_heads=no_heads,
+                                                   c_hidden=c_hidden)
 
         a = Attention(
             c_hidden, c_hidden, c_hidden, c_hidden, no_heads
         ).cuda()
 
         with torch.no_grad():
-            l = a(q, kv, biases=bias, use_lma=True)
-            real = a(q, kv, biases=bias)
+            lecun_normal_init_(a.linear_g.weight)
+            lecun_normal_init_(a.linear_o.weight)
+
+            l = a(q, kv, biases=biases, use_lma=True).cpu()
+            real = a(q, kv, biases=biases).cpu()
 
-        self.assertTrue(torch.max(torch.abs(l - real)) < consts.eps)
+        err = torch.max(torch.abs(l - real))
+        self.assertTrue(err < consts.eps, f'Error: {err}')
 
 
 if __name__ == "__main__":
-    unittest.main()
+    unittest.main()
\ No newline at end of file