Update scaled_dot_product_attention to work with >6 inputs in latest torch version

coremltools/converters/mil/frontend/torch/ops.py

@@ -6437,6 +6437,7 @@ def scaled_dot_product_attention(context, node):
     - attn_mask : (target_seq, source_seq) or (B, target_seq, source_seq) or (B, h, target_seq, source_seq) or
                   (B, ..., target_seq, source_seq)
     - is_causal : bool
+    - scale : optional float
 
     Output shape: (target_seq, d_v) or (B,...,target_seq, d_v)
 
@@ -6448,14 +6449,22 @@ def scaled_dot_product_attention(context, node):
     https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
     """
     inputs = _get_inputs(context, node, min_expected=3)
-    q, k, v = inputs[: 3]
+    q, k, v = inputs[:3]
     attn_mask = None if len(inputs) < 4 else inputs[3]
     dropout = 0.0 if len(inputs) < 5 else inputs[4]
     is_causal = False if len(inputs) < 6 else inputs[5].val
+
+    # When len(inputs) == 7, the inputs are (q, k, v, attn_mask, dropout, is_causal, scale)
+    if len(inputs) == 7 and inputs[6] is not None:
+        raise NotImplementedError(
+            "scaled_dot_product_attention op: scale parameter is not handled."
+        )
+
     if attn_mask is not None and is_causal:
         raise ValueError(
             "scaled_dot_product_attention op: attn_mask cannot be provided when is_causal is set to True."
         )
+
     if dropout is not None and (dropout.val is None or dropout.val != 0.0):
         raise ValueError("scaled_dot_product_attention op: dropout is not supported yet")