Use EvoformerFusedAttention directly to avoid all-zero bias term in c…

…olumn attention

openfold/model/primitives.py

@@ -23,7 +23,7 @@
     import deepspeed
 
 if ds4s_is_installed:
-    from deepspeed.ops.deepspeed4science import DS4Sci_EvoformerAttention
+    from deepspeed.ops.deepspeed4science import EvoformerFusedAttention
 
 fa_is_installed = importlib.util.find_spec("flash_attn") is not None
 if fa_is_installed:
@@ -661,18 +661,19 @@ def reshape_dims(x):
         v = reshape_dims(v)
         biases = [reshape_dims(b) for b in biases]
 
+    biases.extend([None] * (2 - len(biases)))
+
     # DeepSpeed attn. kernel requires inputs to be type bf16 or fp16
     # Cast to bf16 so kernel can be used during inference
     orig_dtype = q.dtype
     if orig_dtype not in [torch.bfloat16, torch.float16]:
-        o = DS4Sci_EvoformerAttention(q.to(dtype=torch.bfloat16),
-                                      k.to(dtype=torch.bfloat16),
-                                      v.to(dtype=torch.bfloat16),
-                                      [b.to(dtype=torch.bfloat16) for b in biases])
+        inputs_bf16 = [x.to(dtype=torch.bfloat16) if x is not None else x
+                       for x in (q, k, v, biases[0], biases[1])]
+        o = EvoformerFusedAttention.apply(*inputs_bf16)
 
         o = o.to(dtype=orig_dtype)
     else:
-        o = DS4Sci_EvoformerAttention(q, k, v, biases)
+        o = EvoformerFusedAttention.apply(q, k, v, biases[0], biases[1])
 
     o = o.reshape(orig_shape)
     return o