Arm backend: Add TOSA support for logical not (#9128)

Adds TOSA support for logical not in Arm backend.

cc @digantdesai @freddan80 @per @zingo @oscarandersson8218

Signed-off-by: Måns Nilsson <[email protected]>
Co-authored-by: Yufeng Shi <[email protected]>

Author: mansnils

backends/arm/operator_support/tosa_supported_operators.py

@@ -115,6 +115,7 @@ def is_node_supported(
             exir_ops.edge.aten.logical_and.default,
             exir_ops.edge.aten.logical_or.default,
             exir_ops.edge.aten.logical_xor.default,
+            exir_ops.edge.aten.logical_not.default,
             exir_ops.edge.aten.bitwise_and.Tensor,
             exir_ops.edge.aten.bitwise_or.Tensor,
             exir_ops.edge.aten.bitwise_xor.Tensor,
@@ -199,6 +200,7 @@ def is_node_supported(
                 exir_ops.edge.aten.logical_and.default,
                 exir_ops.edge.aten.logical_or.default,
                 exir_ops.edge.aten.logical_xor.default,
+                exir_ops.edge.aten.logical_not.default,
                 exir_ops.edge.aten.amax.default,
                 exir_ops.edge.aten.amin.default,
                 exir_ops.edge.aten.eq.Tensor,

backends/arm/operators/ops_unary.py

@@ -14,17 +14,17 @@
 )
 
 from executorch.backends.arm.tosa_mapping import TosaArg
-from executorch.backends.arm.tosa_specification import TosaSpecification
 from serializer.tosa_serializer import TosaOp
 
 
 def unary_operator_factory(unary_target: str, tosa_op):
     "Creates and registers NodeVisitors for operations that have one input and map directly into a TOSA op."
 
-    class UnaryOperator_080_MI(NodeVisitor):
-        target = unary_target
+    # Some TOSA unary operators only support float
+    fp_only_ops = ["aten.floor.default"]
 
-        tosa_specs = [TosaSpecification.create_from_string("TOSA-0.80+MI")]
+    class UnaryOperator(NodeVisitor):
+        target = unary_target
 
         def __init__(self, *args):
             super().__init__(*args)
@@ -43,15 +43,15 @@ def define_node(
                     f"Got {inputs[0].dtype=}, {output.dtype=}"
                 )
 
-            if not (inputs[0].dtype == ts.DType.FP32):
+            if self.target in fp_only_ops and not (inputs[0].dtype == ts.DType.FP32):
                 raise ValueError(
                     "All inputs need to be FP32." f"Got {inputs[0].dtype=}"
                 )
 
-            # MI lowering
             tosa_graph.addOperator(tosa_op, [inputs[0].name], [output.name])
 
-    register_node_visitor(UnaryOperator_080_MI)
+    register_node_visitor(UnaryOperator)
 
 
 unary_operator_factory("aten.floor.default", TosaOp.Op().FLOOR)
+unary_operator_factory("aten.logical_not.default", TosaOp.Op().LOGICAL_NOT)

backends/arm/test/models/test_conformer.py

@@ -34,7 +34,6 @@ class TestConformer(unittest.TestCase):
         "executorch_exir_dialects_edge__ops_aten_max_default": 1,
         "executorch_exir_dialects_edge__ops_aten_eq_Scalar": 2,
         "executorch_exir_dialects_edge__ops_aten_where_self": 4,
-        "executorch_exir_dialects_edge__ops_aten_logical_not_default": 4,
         "executorch_exir_dialects_edge__ops_aten_any_dim": 2,
         "torch.ops.aten._assert_scalar.default": 10,
         "torch.ops.aten._local_scalar_dense.default": 1,

backends/arm/test/ops/test_logical.py

@@ -40,6 +40,14 @@ def forward(self, tensor1: torch.Tensor, tensor2: torch.Tensor):
         return tensor1.logical_or(tensor2)
 
 
+class Not(torch.nn.Module):
+    aten_op = "torch.ops.aten.logical_not.default"
+    exir_op = "executorch_exir_dialects_edge__ops_aten_logical_not_default"
+
+    def forward(self, tensor: torch.Tensor):
+        return torch.logical_not(tensor)
+
+
 input_t2 = Tuple[torch.Tensor, torch.Tensor]  # Input x, y
 
 
@@ -64,6 +72,10 @@ def forward(self, tensor1: torch.Tensor, tensor2: torch.Tensor):
 
 
 test_data = {
+    "not_rank1": (Not(), test_input["rank1"][:-1]),
+    "not_rand_rank2": (Not(), test_input["rand_rank2"][:-1]),
+    "not_rand_rank3": (Not(), test_input["rand_rank3"][:-1]),
+    "not_rand_rank4": (Not(), test_input["rand_rank4"][:-1]),
     "and_rank1": (And(), test_input["rank1"]),
     "and_rand_rank2": (And(), test_input["rand_rank2"]),
     "and_rand_rank3": (And(), test_input["rand_rank3"]),
@@ -80,6 +92,10 @@ def forward(self, tensor1: torch.Tensor, tensor2: torch.Tensor):
 
 
 fvp_xfails = {
+    "not_rank1": "MLETORCH-706 Support ScalarType::Bool in EthosUBackend.",
+    "not_rand_rank2": "MLETORCH-706: Support ScalarType::Bool in EthosUBackend.",
+    "not_rand_rank3": "MLETORCH-706: Support ScalarType::Bool in EthosUBackend.",
+    "not_rand_rank4": "MLETORCH-706: Support ScalarType::Bool in EthosUBackend.",
     "and_rank1": "MLETORCH-706 Support ScalarType::Bool in EthosUBackend.",
     "and_rand_rank2": "MLETORCH-706: Support ScalarType::Bool in EthosUBackend.",
     "and_rand_rank3": "MLETORCH-706: Support ScalarType::Bool in EthosUBackend.",