update openfold

openfold/evoformer.py

@@ -182,33 +182,28 @@ class EvoformerBlockCore(nn.Module):
         self,
         m: torch.Tensor,
         z: torch.Tensor,
-        msa_mask: torch.Tensor,
-        pair_mask: torch.Tensor,
         chunk_size: Optional[int] = None,
-        _mask_trans: bool = True,
     ) -> Tuple[torch.Tensor, torch.Tensor]: 
         # DeepMind doesn't mask these transitions in the source, so _mask_trans
         # should be disabled to better approximate the exact activations of
         # the original.
-        msa_trans_mask = msa_mask if _mask_trans else None
-        pair_trans_mask = pair_mask if _mask_trans else None
 
         m = m + self.msa_transition(
-            m, mask=msa_trans_mask, chunk_size=chunk_size
+            m, chunk_size=chunk_size
         )
         z = z + self.outer_product_mean(
-            m, mask=msa_mask, chunk_size=chunk_size
+            m, chunk_size=chunk_size
         )
-        z = z + self.ps_dropout_row_layer(self.tri_mul_out(z, mask=pair_mask))
+        z = z + self.ps_dropout_row_layer(self.tri_mul_out(z))
-        z = z + self.ps_dropout_row_layer(self.tri_mul_in(z, mask=pair_mask))
+        z = z + self.ps_dropout_row_layer(self.tri_mul_in(z))
         z = z + self.ps_dropout_row_layer(
-            self.tri_att_start(z, mask=pair_mask, chunk_size=chunk_size)
+            self.tri_att_start(z, chunk_size=chunk_size)
         )
         z = z + self.ps_dropout_col_layer(
-            self.tri_att_end(z, mask=pair_mask, chunk_size=chunk_size)
+            self.tri_att_end(z, chunk_size=chunk_size)
         )
         z = z + self.pair_transition(
-            z, mask=pair_trans_mask, chunk_size=chunk_size
+            z, chunk_size=chunk_size
         )
 
         return m, z
@@ -274,22 +269,16 @@ class EvoformerBlock(nn.Module):
     def forward(self,
         m: torch.Tensor,
         z: torch.Tensor,
-        msa_mask: torch.Tensor,
-        pair_mask: torch.Tensor,
         chunk_size: Optional[int] = None,
-        _mask_trans: bool = True,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         m = m + self.msa_dropout_layer(
-            self.msa_att_row(m, z=z, mask=msa_mask, chunk_size=chunk_size)
+            self.msa_att_row(m, z=z, chunk_size=chunk_size)
         )
-        m = m + self.msa_att_col(m, mask=msa_mask, chunk_size=chunk_size)
+        m = m + self.msa_att_col(m, chunk_size=chunk_size)
         m, z = self.core(
             m, 
             z, 
-            msa_mask=msa_mask, 
-            pair_mask=pair_mask, 
             chunk_size=chunk_size, 
-            _mask_trans=_mask_trans,
         )
 
         return m, z

openfold/msa.py

@@ -136,45 +136,6 @@ class MSAAttention(nn.Module):
 
         return m, mask_bias, z
 
-    @torch.jit.ignore
-    def _chunked_msa_attn(self,
-        m: torch.Tensor,
-        z: Optional[torch.Tensor],
-        mask: Optional[torch.Tensor],
-        chunk_logits: int,
-        checkpoint: bool,
-    ) -> torch.Tensor:
-        MSA_DIM = -4
-
-        def _get_qkv(m, z):
-            m, mask_bias, z = self._prep_inputs(m, z, mask)
-            q, k, v = self.mha._prep_qkv(m, m)
-            return m, q, k, v, mask_bias, z
-
-        checkpoint_fn = get_checkpoint_fn()
-
-        if(torch.is_grad_enabled() and checkpoint):
-            m, q, k, v, mask_bias, z = checkpoint_fn(_get_qkv, m, z)
-        else:
-            m, q, k, v, mask_bias, z = _get_qkv(m, z)
-       
-        o = _attention_chunked_trainable(
-            query=q, 
-            key=k, 
-            value=v, 
-            biases=[mask_bias, z], 
-            chunk_size=chunk_logits, 
-            chunk_dim=MSA_DIM,
-            checkpoint=checkpoint,
-        )
-
-        if(torch.is_grad_enabled() and checkpoint):
-            # Storing an additional m here is far from ideal
-            m = checkpoint_fn(self.mha._wrap_up, o, m)
-        else:
-            m = self.mha._wrap_up(o, m)
-
-        return m
 
     def forward(self, 
         m: torch.Tensor, 
@@ -199,12 +160,6 @@ class MSAAttention(nn.Module):
                 cost of slower execution. Chunking is not performed by default.
                 
         """
-        if(_chunk_logits is not None):
-            return self._chunked_msa_attn(
-                m=m, z=z, mask=mask, 
-                chunk_logits=_chunk_logits, checkpoint=_checkpoint_chunks
-            )           
-
         m, mask_bias, z = self._prep_inputs(m, z, mask)
 
         biases = [mask_bias]
@@ -306,15 +261,11 @@ class MSAColumnAttention(nn.Module):
         """ 
         # [*, N_res, N_seq, C_in]
         m = m.transpose(-2, -3)
-        if mask is not None:
-            mask = mask.transpose(-1, -2)
 
-        m = self._msa_att(m, mask=mask, chunk_size=chunk_size)
+        m = self._msa_att(m, chunk_size=chunk_size)
 
         # [*, N_seq, N_res, C_in]
         m = m.transpose(-2, -3)
-        if mask is not None:
-            mask = mask.transpose(-1, -2)
 
         return m
 
@@ -344,12 +295,10 @@ class MSAColumnGlobalAttention(nn.Module):
     @torch.jit.ignore
     def _chunk(self,
         m: torch.Tensor,
-        mask: torch.Tensor,
         chunk_size: int,
     ) -> torch.Tensor:
         mha_input = {
             "m": m,
-            "mask": mask,
         }
         return chunk_layer(
             self.global_attention,
@@ -361,30 +310,20 @@ class MSAColumnGlobalAttention(nn.Module):
     def forward(
         self, 
         m: torch.Tensor, 
-        mask: Optional[torch.Tensor] = None, 
         chunk_size: Optional[int] = None,
     ) -> torch.Tensor:
         n_seq, n_res, c_in = m.shape[-3:]
 
-        if mask is None:
-            # [*, N_seq, N_res]
-            mask = torch.ones(
-                m.shape[:-1],
-                dtype=m.dtype,
-                device=m.device,
-            ).detach()
-
         # [*, N_res, N_seq, C_in]
         m = m.transpose(-2, -3)
-        mask = mask.transpose(-1, -2)
 
         # [*, N_res, N_seq, C_in]
         m = self.layer_norm_m(m)
 
         if chunk_size is not None:
-            m = self._chunk(m, mask, chunk_size) 
+            m = self._chunk(m, chunk_size) 
         else:
-            m = self.global_attention(m=m, mask=mask)
+            m = self.global_attention(m=m)
 
         # [*, N_seq, N_res, C_in]
         m = m.transpose(-2, -3)