Fix triu/tril CoreML lowering error in to_edge_transform_and_lower

backends/apple/coreml/partition/coreml_partitioner.py

@@ -110,17 +110,33 @@ def ops_to_not_decompose(
     ) -> Tuple[List[torch._ops.OpOverload], Optional[Callable[[torch.fx.Node], bool]]]:
         do_not_decompose = []
         op_support = OperatorsSupportedForCoreMLBackend()
+        _logged_warnings = set()
+
+        # CoreML prevents certain ops (like triu) from lowering to CoreML when put in the ExecuTorch op namespace
+        # TODO: upstream fixes, but pending ET consuming a new published version of coremltools with the
+        # desired changes, we need to manually block them here
+        do_not_decompose_blocklist = [
+            # https://github.com/apple/coremltools/blob/release/8.3/coremltools/converters/mil/frontend/torch/ops.py#L6965-L6966
+            torch.ops.aten.triu.default,
+            # https://github.com/apple/coremltools/blob/release/8.3/coremltools/converters/mil/frontend/torch/ops.py#L6997-L6998
+            torch.ops.aten.tril.default,
+        ]
         for node in ep.graph.nodes:
             if node.op == "call_function" and isinstance(
                 node.target, torch._ops.OpOverload
             ):
                 try:
-                    if op_support.is_node_supported(None, node):
+                    if (
+                        op_support.is_node_supported(None, node)
+                        and node.target not in do_not_decompose_blocklist
+                    ):
                         do_not_decompose.append(node.target)
                 except Exception as e:
                     # CoreML's op_support.is_node_supported will sometimes throw
                     # for unsupported ops, rather than returning False
-                    logger.warning(
-                        f"Encountered exception when checking node support: {e}"
-                    )
+                    warn_str = f"Encountered exception when checking node support: {e}"
+                    if warn_str not in _logged_warnings:
+                        logger.warning(warn_str)
+                        _logged_warnings.add(warn_str)
+
         return do_not_decompose, None

backends/apple/coreml/test/test_coreml_partitioner.py

@@ -90,6 +90,13 @@ def forward(self, q, k, v, mask):
                     q, k, v, attn_mask=mask
                 )
 
+                # triu/tril should be ignored by do_not_decompose
+                # because otherwise they fail during CoreML lowering
+                offset1 = torch.triu(mask, diagonal=1)
+                offset2 = torch.tril(mask)
+                offset = offset1 + offset2
+                offset = torch.sum(offset)
+
                 # Add non-functional and alias ops
                 # These will be removed by ExecuTorch in non-decomposition
                 # table because they cannot be functionalized
@@ -102,7 +109,7 @@ def forward(self, q, k, v, mask):
                 out = out.sub_(4.0)
                 out = torch.ops.aten.view_copy.default(out, (-1,))
                 out = out.select(0, 0)
-                return out
+                return out + offset
 
         model = Model()
         model.eval()
@@ -118,6 +125,13 @@ def forward(self, q, k, v, mask):
         mask = torch.randn(seq_len, max_seq_length)
         example_inputs = (q, k, v, mask)
         ep = torch.export.export(model, example_inputs, strict=True)
+        self.assertTrue(
+            "torch.ops.aten.triu.default" in ep.graph_module.code,
+        )
+        self.assertTrue(
+            "torch.ops.aten.tril.default" in ep.graph_module.code,
+        )
+
         coreml_partitioner = CoreMLPartitioner()
 
         # Using to_edge_transform_and_lower, we expect SDPA will be preserved and show up in delegated graph

exir/program/_program.py

@@ -1017,6 +1017,13 @@ def _sanity_check_graph_for_non_decomp_ops(
 def _remove_invalid_ops_for_not_decompose(
     ops_to_not_decompose: List[torch._ops.OpOverload],
 ) -> List[torch._ops.OpOverload]:
+    _logged_warnings = set()
+
+    def log_warning(warn_str):
+        if warn_str not in _logged_warnings:
+            logging.warn(warn_str)
+            _logged_warnings.add(warn_str)
+
     # To address https://github.com/pytorch/executorch/issues/8781
     def keep(op):
         # Explicit allow list
@@ -1034,18 +1041,18 @@ def keep(op):
         schema = op._schema
         native_schema = _pybind_schema_to_native_schema(schema)
         if native_schema is None:
-            logging.warn(
+            log_warning(
                 f"Torchgen is not able to parse the schema of {op._schema}.  This is not fatal."
             )
         else:
             if native_schema.is_mutable:
-                logging.warn(
+                log_warning(
                     f"Op {op} was requested for preservation by partitioner.  This request is ignored because it is mutable."
                 )
                 return False
 
             if native_schema.aliased_return_names() != [None]:
-                logging.warn(
+                log_warning(
                     f"Op {op} was requested for preservation by partitioner.  This request is ignored because it aliases output."
                 )
                 return False
@@ -1067,7 +1074,7 @@ def keep(op):
             torch.ops.aten.unbind.int,
             torch.ops.aten.split_with_sizes.default,
         ]:
-            logging.warn(
+            log_warning(
                 f"Op {op} was requested for preservation by partitioner.  This request is ignored because it is in a blocklist."
             )
             return False