Add some more dependencies

README.md

@@ -23,8 +23,10 @@ in the future.
 ## Installation (Linux)
 
 Python dependencies available through `pip` are provided in `requirements.txt`. 
-OpenFold also depends on `openmm==7.5.1` and `pdbfixer`, which are only
-available via `conda`. 
+OpenFold depends on `openmm==7.5.1` and `pdbfixer`, which are only available 
+via `conda`. For producing sequence alignments, you'll also need `jackhmmer`, 
+`kalign`, and the [HH-Suite](https://github.com/soedinglab/hh-suite) installed 
+on your system.
 
 For convenience, we provide a script that installs Miniconda locally, creates a 
 `conda` virtual environment, installs all Python dependencies, and downloads

openfold/model/evoformer.py

@@ -349,7 +349,7 @@ class EvoformerStack(nn.Module):
             z:
                 [*, N_res, N_res, C_z] pair embedding
             s:
-                [*, N_res, C_s] single embedding
+                [*, N_res, C_s] single embedding (or None if extra MSA stack)
         """
         m, z = checkpoint_blocks(
             blocks=[

openfold/model/msa.py

@@ -97,11 +97,15 @@ class MSAAttention(nn.Module):
         # [*, N_seq, 1, 1, N_res]
         bias = (self.inf * (mask - 1))[..., :, None, None, :]
 
+        # This step simply returns a larger view of the bias, and does not
+        # consume additional memory.
         # [*, N_seq, no_heads, N_res, N_res]
         bias = bias.expand(
             ((-1,) * len(bias.shape[:-4])) + (-1, self.no_heads, n_res, -1)
         )
+        
         biases = [bias]
+
         if self.pair_bias:
             # [*, N_res, N_res, C_z]
             z = self.layer_norm_z(z)

openfold/model/primitives.py

@@ -207,7 +207,7 @@ class Attention(nn.Module):
         self.gating = gating
 
         # DISCREPANCY: c_hidden is not the per-head channel dimension, as
-        # stated in the supplement, but the overall channel dimension
+        # stated in the supplement, but the overall channel dimension.
 
         self.linear_q = Linear(
             self.c_q, self.c_hidden * self.no_heads, bias=False, init="glorot"
@@ -271,9 +271,11 @@ class Attention(nn.Module):
 
         norm = 1 / math.sqrt(self.c_hidden)  # [1]
         a *= norm
+
         if biases is not None:
             for b in biases:
                 a = a + b
+
         a = self.softmax(a)
 
         # [*, H, V, C_hidden]