Fix gradient shape error for DPMultiheadAttention (issue 650) (#651)

Summary:
Pull Request resolved: #651

When batch_first = True, the activation and partial gradient for each linear layer in DPMultiheadAttention still has batch_size in the second dimension, thus causing wrong gradient shape in optimizer.step().

Details in: #650

Reviewed By: EnayatUllah

Differential Revision: D57446245

fbshipit-source-id: c0f0e3643c802e51afe7ddb6bb054e5447845f32

opacus/layers/dp_multihead_attention.py

@@ -203,17 +203,12 @@ def forward(
         r"""
         Using the same logic with ``nn.MultiheadAttention`` (https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html).
         """
-        if self.batch_first:
-            if key is value:
-                if query is key:
-                    query = key = value = query.transpose(1, 0)
-                else:
-                    query, key = [x.transpose(1, 0) for x in (query, key)]
-                    value = key
-            else:
-                query, key, value = [x.transpose(1, 0) for x in (query, key, value)]
 
-        tgt_len, bsz, embed_dim = query.size()
+        if not self.batch_first:
+            tgt_len, bsz, embed_dim = query.size()
+        else:
+            bsz, tgt_len, embed_dim = query.size()
+
         if embed_dim != self.embed_dim:
             raise ValueError(
                 f"query has as size of {embed_dim} while the embedding"
@@ -234,6 +229,9 @@ def forward(
 
         q = q * scaling
 
+        if self.batch_first:
+            q, k, v = [x.transpose(0, 1) for x in (q, k, v)]
+
         if attn_mask is not None:
             if attn_mask.dtype not in (
                 torch.float32,
@@ -352,13 +350,14 @@ def forward(
 
         attn_output = torch.bmm(attn_output_weights, v)
         assert list(attn_output.size()) == [bsz * self.num_heads, tgt_len, head_dim]
-        attn_output = (
-            attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
-        )
-        attn_output = self.out_proj(attn_output)
 
         if self.batch_first:
-            attn_output = attn_output.transpose(1, 0)
+            attn_output = attn_output.contiguous().view(bsz, tgt_len, embed_dim)
+        else:
+            attn_output = (
+                attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
+            )
+        attn_output = self.out_proj(attn_output)
 
         if need_weights:
             # average attention weights over heads