Update attention.py (#1416)

* Update attention.py

modify the code about bigcode. 
This modification makes the KV cache with multiple new tokens works well.

* consider batch size = 1

* Update attention.py

* def kv_seq_len

optimum/bettertransformer/models/attention.py

@@ -699,6 +699,7 @@ def gpt_bigcode_wrapped_scaled_dot_product(
     # MHA models: (batch_size, num_heads, query_length, head_dim)
     query_shape = query.shape
     batch_size = query_shape[0]
+    kv_seq_len = key.shape[-2]
 
     if self.multi_query:
         query_length = query_shape[1]
@@ -725,30 +726,34 @@ def gpt_bigcode_wrapped_scaled_dot_product(
     key = key.expand(-1, self.num_heads, -1, -1)
     value = value.expand(-1, self.num_heads, -1, -1)
 
-    if batch_size == 1 or self.training:
-        if query_length > 1:
-            sdpa_result = torch.nn.functional.scaled_dot_product_attention(
-                query, key, value, attn_mask=None, dropout_p=dropout_p, is_causal=True
-            )
-        else:
-            sdpa_result = torch.nn.functional.scaled_dot_product_attention(
-                query, key, value, attn_mask=None, dropout_p=dropout_p, is_causal=False
-            )
+    # We treat self.training and (batch_size == 1 and query_length == 1) cases separately to still allow the dispatch to Flash Attention.
+    if self.training:
+        is_causal = True
+        attn_mask = None
+    elif batch_size == 1 and query_length == 1:
+        is_causal = False
+        attn_mask = None
+    elif batch_size == 1 and kv_seq_len == query_length:
+        is_causal = True
+        attn_mask = None
+    elif attention_mask is not None:
+        mask_value = self._get_mask_value(query.device, query.dtype)
+
+        # gpt_bigcode has the bad taste to use a causal mask a
+        # [batch_size, target_length, 1, source_length] which is different from
+        # **all** other architectures and not compatible with SDPA.
+        # We could avoid this transpose by overriding the forward from GPTBigCodeModel,
+        # but it is probably not worth it.
+        attention_mask = attention_mask.transpose(1, 2)
+        attn_mask = torch.where(attention_mask, 0.0, mask_value)
+        is_causal = False
     else:
-        if attention_mask is not None:
-            mask_value = self._get_mask_value(query.device, query.dtype)
+        attn_mask = None
+        is_causal = True
 
-            # gpt_bigcode has the bad taste to use a causal mask a
-            # [batch_size, target_length, 1, source_length] which is different from
-            # **all** other architectures and not compatible with SDPA.
-            # We could avoid this transpose by overriding the forward from GPTBigCodeModel,
-            # but it is probably not worth it.
-            attention_mask = attention_mask.transpose(1, 2)
-            attention_mask = torch.where(attention_mask, 0.0, mask_value)
-
-        sdpa_result = torch.nn.functional.scaled_dot_product_attention(
-            query, key, value, attn_mask=attention_mask, dropout_p=dropout_p, is_causal=False
-        )
+    sdpa_result = torch.nn.functional.scaled_dot_product_attention(
+        query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal
+    )
 
     if self.multi_query:
         # (batch_size, num_heads, seq_len, head_dim) --> (batch_size, seq_len, num_heads, head_dim)