Fix matmul inputs dtype (#18585)

src/transformers/models/deberta/modeling_deberta.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 """ PyTorch DeBERTa model."""
 
-import math
 from collections.abc import Sequence
 from typing import Optional, Tuple, Union
 
@@ -640,8 +639,8 @@ class DisentangledSelfAttention(nn.Module):
             qkvw = [torch.cat([ws[i * 3 + k] for i in range(self.num_attention_heads)], dim=0) for k in range(3)]
             qkvb = [None] * 3
 
-            q = linear(qkvw[0], qkvb[0], query_states)
-            k, v = [linear(qkvw[i], qkvb[i], hidden_states) for i in range(1, 3)]
+            q = linear(qkvw[0], qkvb[0], torch.tensor(query_states, dtype=qkvw[0].dtype))
+            k, v = [linear(qkvw[i], qkvb[i], torch.tensor(hidden_states, dtype=qkvw[i].dtype)) for i in range(1, 3)]
             query_layer, key_layer, value_layer = [self.transpose_for_scores(x) for x in [q, k, v]]
 
         query_layer = query_layer + self.transpose_for_scores(self.q_bias[None, None, :])
@@ -650,8 +649,8 @@ class DisentangledSelfAttention(nn.Module):
         rel_att = None
         # Take the dot product between "query" and "key" to get the raw attention scores.
         scale_factor = 1 + len(self.pos_att_type)
-        scale = math.sqrt(query_layer.size(-1) * scale_factor)
-        query_layer = query_layer / scale
+        scale = torch.sqrt(torch.tensor(query_layer.size(-1), dtype=torch.float) * scale_factor)
+        query_layer = query_layer / torch.tensor(scale, dtype=query_layer.dtype)
         attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
         if self.relative_attention:
             rel_embeddings = self.pos_dropout(rel_embeddings)
@@ -711,13 +710,13 @@ class DisentangledSelfAttention(nn.Module):
         if "p2c" in self.pos_att_type:
             pos_query_layer = self.pos_q_proj(rel_embeddings)
             pos_query_layer = self.transpose_for_scores(pos_query_layer)
-            pos_query_layer /= math.sqrt(pos_query_layer.size(-1) * scale_factor)
+            pos_query_layer /= torch.sqrt(torch.tensor(pos_query_layer.size(-1), dtype=torch.float) * scale_factor)
             if query_layer.size(-2) != key_layer.size(-2):
                 r_pos = build_relative_position(key_layer.size(-2), key_layer.size(-2), query_layer.device)
             else:
                 r_pos = relative_pos
             p2c_pos = torch.clamp(-r_pos + att_span, 0, att_span * 2 - 1)
-            p2c_att = torch.matmul(key_layer, pos_query_layer.transpose(-1, -2))
+            p2c_att = torch.matmul(key_layer, torch.tensor(pos_query_layer.transpose(-1, -2), dtype=key_layer.dtype))
             p2c_att = torch.gather(
                 p2c_att, dim=-1, index=p2c_dynamic_expand(p2c_pos, query_layer, key_layer)
             ).transpose(-1, -2)

src/transformers/models/deberta_v2/modeling_deberta_v2.py

@@ -717,7 +717,9 @@ class DisentangledSelfAttention(nn.Module):
         if "p2c" in self.pos_att_type:
             scale_factor += 1
         scale = torch.sqrt(torch.tensor(query_layer.size(-1), dtype=torch.float) * scale_factor)
-        attention_scores = torch.bmm(query_layer, key_layer.transpose(-1, -2)) / scale
+        attention_scores = torch.bmm(query_layer, key_layer.transpose(-1, -2)) / torch.tensor(
+            scale, dtype=query_layer.dtype
+        )
         if self.relative_attention:
             rel_embeddings = self.pos_dropout(rel_embeddings)
             rel_att = self.disentangled_attention_bias(
@@ -799,7 +801,7 @@ class DisentangledSelfAttention(nn.Module):
                 dim=-1,
                 index=c2p_pos.squeeze(0).expand([query_layer.size(0), query_layer.size(1), relative_pos.size(-1)]),
             )
-            score += c2p_att / scale
+            score += c2p_att / torch.tensor(scale, dtype=c2p_att.dtype)
 
         # position->content
         if "p2c" in self.pos_att_type:
@@ -822,7 +824,7 @@ class DisentangledSelfAttention(nn.Module):
                 dim=-1,
                 index=p2c_pos.squeeze(0).expand([query_layer.size(0), key_layer.size(-2), key_layer.size(-2)]),
             ).transpose(-1, -2)
-            score += p2c_att / scale
+            score += p2c_att / torch.tensor(scale, dtype=p2c_att.dtype)
 
         return score
 

src/transformers/models/sew_d/modeling_sew_d.py

@@ -791,7 +791,9 @@ class DisentangledSelfAttention(nn.Module):
         if "p2c" in self.pos_att_type:
             scale_factor += 1
         scale = torch.sqrt(torch.tensor(query_layer.size(-1), dtype=torch.float) * scale_factor)
-        attention_scores = torch.bmm(query_layer, key_layer.transpose(-1, -2)) / scale
+        attention_scores = torch.bmm(query_layer, key_layer.transpose(-1, -2)) / torch.tensor(
+            scale, dtype=query_layer.dtype
+        )
         if self.relative_attention:
             rel_embeddings = self.pos_dropout(rel_embeddings)
             rel_att = self.disentangled_attention_bias(
@@ -873,7 +875,7 @@ class DisentangledSelfAttention(nn.Module):
                 dim=-1,
                 index=c2p_pos.squeeze(0).expand([query_layer.size(0), query_layer.size(1), relative_pos.size(-1)]),
             )
-            score += c2p_att / scale
+            score += c2p_att / torch.tensor(scale, dtype=c2p_att.dtype)
 
         # position->content
         if "p2c" in self.pos_att_type:
@@ -896,7 +898,7 @@ class DisentangledSelfAttention(nn.Module):
                 dim=-1,
                 index=p2c_pos.squeeze(0).expand([query_layer.size(0), key_layer.size(-2), key_layer.size(-2)]),
             ).transpose(-1, -2)
-            score += p2c_att / scale
+            score += p2c_att / torch.tensor(scale, dtype=p2c_att.dtype)
 
         return score