Arm backend: Add support for le.Scalar

backends/arm/_passes/match_arg_ranks_pass.py

@@ -51,6 +51,7 @@ def __init__(self, exported_program):
         exir_ops.edge.aten.gt.Tensor,
         exir_ops.edge.aten.ge.Tensor,
         exir_ops.edge.aten.lt.Tensor,
+        exir_ops.edge.aten.le.Tensor,
         exir_ops.edge.aten.pow.Tensor_Tensor,
         exir_ops.edge.aten.where.self,
     ]

backends/arm/_passes/replace_scalar_with_tensor_pass.py

@@ -32,6 +32,7 @@
     exir_ops.edge.aten.gt.Scalar: exir_ops.edge.aten.gt.Tensor,
     exir_ops.edge.aten.ge.Scalar: exir_ops.edge.aten.ge.Tensor,
     exir_ops.edge.aten.lt.Scalar: exir_ops.edge.aten.lt.Tensor,
+    exir_ops.edge.aten.le.Scalar: exir_ops.edge.aten.le.Tensor,
     exir_ops.edge.aten.ne.Scalar: exir_ops.edge.aten.ne.Tensor,
     torch.ops.aten.add.Scalar: torch.ops.aten.add.Tensor,
     torch.ops.aten.sub.Scalar: torch.ops.aten.sub.Tensor,
@@ -43,6 +44,7 @@
     torch.ops.aten.gt.Scalar: torch.ops.aten.gt.Tensor,
     torch.ops.aten.ge.Scalar: torch.ops.aten.ge.Tensor,
     torch.ops.aten.lt.Scalar: torch.ops.aten.lt.Tensor,
+    torch.ops.aten.le.Scalar: torch.ops.aten.le.Tensor,
     torch.ops.aten.ne.Scalar: torch.ops.aten.ne.Tensor,
 }
 

backends/arm/operator_support/ethos_u55_support.py

@@ -138,6 +138,7 @@ class EthosU55NotSupported(OperatorSupportBase):
         exir_ops.edge.aten.gt.Tensor,
         exir_ops.edge.aten.gt.Scalar,
         exir_ops.edge.aten.le.Tensor,
+        exir_ops.edge.aten.le.Scalar,
         exir_ops.edge.aten.lt.Tensor,
         exir_ops.edge.aten.lt.Scalar,
         exir_ops.edge.aten.ne.Tensor,

backends/arm/operator_support/tosa_supported_operators.py

@@ -189,6 +189,7 @@ def is_node_supported(
             exir_ops.edge.aten.gt.Tensor,
             exir_ops.edge.aten.gt.Scalar,
             exir_ops.edge.aten.le.Tensor,
+            exir_ops.edge.aten.le.Scalar,
             exir_ops.edge.aten.lt.Tensor,
             exir_ops.edge.aten.lt.Scalar,
             exir_ops.edge.aten.mul.Tensor,

backends/arm/test/ops/test_le.py

@@ -15,13 +15,15 @@
     TosaPipelineMI,
 )
 
-aten_op = "torch.ops.aten.le.Tensor"
-exir_op = "executorch_exir_dialects_edge__ops_aten_le_Tensor"
 
 input_t = Tuple[torch.Tensor]
 
 
-class GreaterEqual(torch.nn.Module):
+class LessEqual(torch.nn.Module):
+    aten_op_tensor = "torch.ops.aten.le.Tensor"
+    aten_op_scalar = "torch.ops.aten.le.Scalar"
+    exir_op = "executorch_exir_dialects_edge__ops_aten_le_Tensor"
+
     def __init__(self, input, other):
         super().__init__()
         self.input_ = input
@@ -38,72 +40,151 @@ def get_inputs(self):
         return (self.input_, self.other_)
 
 
-op_le_rank1_ones = GreaterEqual(
+op_le_tensor_rank1_ones = LessEqual(
     torch.ones(5),
     torch.ones(5),
 )
-op_le_rank2_rand = GreaterEqual(
+op_le_tensor_rank2_rand = LessEqual(
     torch.rand(4, 5),
     torch.rand(1, 5),
 )
-op_le_rank3_randn = GreaterEqual(
+op_le_tensor_rank3_randn = LessEqual(
     torch.randn(10, 5, 2),
     torch.randn(10, 5, 2),
 )
-op_le_rank4_randn = GreaterEqual(
+op_le_tensor_rank4_randn = LessEqual(
     torch.randn(3, 2, 2, 2),
     torch.randn(3, 2, 2, 2),
 )
 
-test_data_common = {
-    "le_rank1_ones": lambda: op_le_rank1_ones,
-    "le_rank2_rand": lambda: op_le_rank2_rand,
-    "le_rank3_randn": lambda: op_le_rank3_randn,
-    "le_rank4_randn": lambda: op_le_rank4_randn,
+op_le_scalar_rank1_ones = LessEqual(torch.ones(5), 1.0)
+op_le_scalar_rank2_rand = LessEqual(torch.rand(4, 5), 0.2)
+op_le_scalar_rank3_randn = LessEqual(torch.randn(10, 5, 2), -0.1)
+op_le_scalar_rank4_randn = LessEqual(torch.randn(3, 2, 2, 2), 0.3)
+
+test_data_tensor = {
+    "le_tensor_rank1_ones": lambda: op_le_tensor_rank1_ones,
+    "le_tensor_rank2_rand": lambda: op_le_tensor_rank2_rand,
+    "le_tensor_rank3_randn": lambda: op_le_tensor_rank3_randn,
+    "le_tensor_rank4_randn": lambda: op_le_tensor_rank4_randn,
+}
+
+test_data_scalar = {
+    "le_scalar_rank1_ones": lambda: op_le_scalar_rank1_ones,
+    "le_scalar_rank2_rand": lambda: op_le_scalar_rank2_rand,
+    "le_scalar_rank3_randn": lambda: op_le_scalar_rank3_randn,
+    "le_scalar_rank4_randn": lambda: op_le_scalar_rank4_randn,
 }
 
 
-@common.parametrize("test_module", test_data_common)
+@common.parametrize("test_module", test_data_tensor)
 def test_le_tensor_tosa_MI(test_module):
     pipeline = TosaPipelineMI[input_t](
-        test_module(), test_module().get_inputs(), aten_op, exir_op
+        test_module(),
+        test_module().get_inputs(),
+        LessEqual.aten_op_tensor,
+        LessEqual.exir_op,
     )
     pipeline.run()
 
 
-@common.parametrize("test_module", test_data_common)
+@common.parametrize("test_module", test_data_scalar)
+def test_le_scalar_tosa_MI(test_module):
+    pipeline = TosaPipelineMI[input_t](
+        test_module(),
+        test_module().get_inputs(),
+        LessEqual.aten_op_scalar,
+        LessEqual.exir_op,
+    )
+    pipeline.run()
+
+
+@common.parametrize("test_module", test_data_tensor)
 def test_le_tensor_tosa_BI(test_module):
     pipeline = TosaPipelineBI[input_t](
-        test_module(), test_module().get_inputs(), aten_op, exir_op
+        test_module(),
+        test_module().get_inputs(),
+        LessEqual.aten_op_tensor,
+        LessEqual.exir_op,
     )
     pipeline.run()
 
 
-@common.parametrize("test_module", test_data_common)
+@common.parametrize("test_module", test_data_scalar)
+def test_le_scalar_tosa_BI(test_module):
+    pipeline = TosaPipelineBI[input_t](
+        test_module(),
+        test_module().get_inputs(),
+        LessEqual.aten_op_tensor,
+        LessEqual.exir_op,
+    )
+    pipeline.run()
+
+
+@common.parametrize("test_module", test_data_tensor)
+@common.XfailIfNoCorstone300
 def test_le_tensor_u55_BI_not_delegated(test_module):
     # GREATER_EQUAL is not supported on U55. LE uses the GREATER_EQUAL Tosa operator.
     pipeline = OpNotSupportedPipeline[input_t](
         test_module(),
         test_module().get_inputs(),
-        {exir_op: 1},
+        {LessEqual.exir_op: 1},
         quantize=True,
         u55_subset=True,
     )
     pipeline.run()
 
 
+@common.parametrize("test_module", test_data_scalar)
+@common.XfailIfNoCorstone300
+def test_le_scalar_u55_BI_not_delegated(test_module):
+    # GREATER_EQUAL is not supported on U55. LE uses the GREATER_EQUAL Tosa operator.
+    pipeline = OpNotSupportedPipeline[input_t](
+        test_module(),
+        test_module().get_inputs(),
+        {LessEqual.exir_op: 1},
+        n_expected_delegates=1,
+        quantize=True,
+        u55_subset=True,
+    )
+    pipeline.dump_operator_distribution("export")
+    pipeline.run()
+
+
 @common.parametrize(
     "test_module",
-    test_data_common,
-    xfails={"le_rank4_randn": "4D fails because boolean Tensors can't be subtracted"},
+    test_data_tensor,
+    xfails={
+        "le_tensor_rank4_randn": "4D fails because boolean Tensors can't be subtracted"
+    },
 )
 @common.XfailIfNoCorstone320
 def test_le_tensor_u85_BI(test_module):
     pipeline = EthosU85PipelineBI[input_t](
         test_module(),
         test_module().get_inputs(),
-        aten_op,
-        exir_op,
+        LessEqual.aten_op_tensor,
+        LessEqual.exir_op,
+        run_on_fvp=True,
+        use_to_edge_transform_and_lower=True,
+    )
+    pipeline.run()
+
+
+@common.parametrize(
+    "test_module",
+    test_data_scalar,
+    xfails={
+        "le_scalar_rank4_randn": "4D fails because boolean Tensors can't be subtracted"
+    },
+)
+@common.XfailIfNoCorstone320
+def test_le_scalar_u85_BI(test_module):
+    pipeline = EthosU85PipelineBI[input_t](
+        test_module(),
+        test_module().get_inputs(),
+        LessEqual.aten_op_tensor,
+        LessEqual.exir_op,
         run_on_fvp=True,
         use_to_edge_transform_and_lower=True,
     )