Arm backend: Support aten.full_like

backends/arm/_passes/arm_pass_manager.py

@@ -1,6 +1,6 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
-# Copyright 2024-2025 Arm Limited and/or its affiliates.
 # All rights reserved.
+# Copyright 2024-2025 Arm Limited and/or its affiliates.
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
@@ -18,6 +18,9 @@
 from executorch.backends.arm._passes.convert_expand_copy_to_repeat import (
     ConvertExpandCopyToRepeatPass,
 )
+from executorch.backends.arm._passes.convert_full_like_to_full_pass import (
+    ConvertFullLikeToFullPass,
+)
 from executorch.backends.arm._passes.convert_split_to_slice import (
     ConvertSplitToSlicePass,
 )
@@ -95,6 +98,7 @@ def _tosa_080_BI_pipeline(self, exported_program: ExportedProgram) -> GraphModul
         self.add_pass(ConvertMmToBmmPass())
         self.add_pass(DecomposeLinearPass())
         self.add_pass(ConvertMeanDimToAveragePoolPass())
+        self.add_pass(ConvertFullLikeToFullPass())
 
         self.add_pass(AnnotateDecomposedMatmulPass())
         self.add_pass(QuantizeOperatorArguments())
@@ -133,7 +137,7 @@ def _tosa_080_MI_pipeline(self, exported_program: ExportedProgram) -> GraphModul
         self.add_pass(ConvertMeanDimToAveragePoolPass())
         self.add_pass(DecomposeDivPass())
         self.add_pass(DecomposeSoftmaxesPass())
-
+        self.add_pass(ConvertFullLikeToFullPass())
         self.add_pass(AnnotateDecomposedMatmulPass())
         self.add_pass(QuantizeOperatorArguments())
         self.add_pass(FoldAndAnnotateQParamsPass())  # type: ignore[call-arg]

backends/arm/_passes/convert_full_like_to_full_pass.py

@@ -0,0 +1,33 @@
+# Copyright 2025 Arm Limited and/or its affiliates.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from executorch.exir.dialects._ops import ops as exir_ops
+from executorch.exir.pass_base import ExportPass
+
+
+class ConvertFullLikeToFullPass(ExportPass):
+    """As per the full_like pytorch documentation,
+    `torch.full_like(input, fill_value)` is equivalent to
+    `torch.full(input.size(),
+                fill_value,
+                dtype=input.dtype,
+                layout=input.layout,
+                device=input.device
+                )`
+    Skip layout and device since it's not relevant for our backend.
+    """
+
+    def call_operator(self, op, args, kwargs, meta):
+        if op not in [
+            exir_ops.edge.aten.full_like.default,
+        ]:
+            return super().call_operator(op, args, kwargs, meta)
+
+        tensor = args[0].data
+        full_args = (list(tensor.shape), args[1])
+        full_kwargs = {"dtype": tensor.dtype}
+        return super().call_operator(
+            exir_ops.edge.aten.full.default, full_args, full_kwargs, meta
+        )

backends/arm/operator_support/tosa_supported_operators.py

@@ -105,6 +105,7 @@ def is_node_supported(self, submodules, node: fx.Node) -> bool:
             exir_ops.edge.aten.linear.default,
             exir_ops.edge.aten.split_with_sizes_copy.default,
             exir_ops.edge.aten.full.default,
+            exir_ops.edge.aten.full_like.default,
             exir_ops.edge.aten.ge.Tensor,
             exir_ops.edge.aten.gt.Tensor,
             exir_ops.edge.aten.le.Tensor,

backends/arm/quantizer/quantization_annotator.py

@@ -134,6 +134,7 @@ def _match_pattern(
     torch.ops.aten.sum.dim_IntList,
     torch.ops.aten.hardsigmoid.default,
     torch.ops.aten.hardswish.default,
+    torch.ops.aten.full_like.default,
 ]
 
 _one_to_one_shared_input_qspec = [
@@ -379,3 +380,11 @@ def annotate_graph(  # type: ignore[return]
             _annotate_output(node, quant_properties.quant_output)
 
         arm_quantizer_utils.mark_node_as_annotated(node)  # type: ignore[attr-defined]
+
+        # Quantization does not allow kwargs for some reason.
+        # Remove from ops we know have and where we know it does not break anything.
+        if node.target in [
+            torch.ops.aten.full_like.default,
+            torch.ops.aten.full.default,
+        ]:
+            node.kwargs = {}

backends/arm/test/models/test_conformer.py

@@ -27,7 +27,6 @@ class TestConformer(unittest.TestCase):
     # .to_executorch step, i.e. after Arm partitioner.
     ops_after_partitioner = {
         "executorch_exir_dialects_edge__ops_aten_arange_start_step": 1,
-        "executorch_exir_dialects_edge__ops_aten_full_like_default": 4,
         "executorch_exir_dialects_edge__ops_aten_max_default": 1,
         "executorch_exir_dialects_edge__ops_aten_mul_Scalar": 4,
         "executorch_exir_dialects_edge__ops_aten_eq_Scalar": 2,

backends/arm/test/ops/test_full.py

@@ -1,5 +1,4 @@
 # Copyright 2024-2025 Arm Limited and/or its affiliates.
-# All rights reserved.
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
@@ -36,8 +35,8 @@ def forward(self, x: torch.Tensor):
             return torch.full((2, 2, 3, 3), 4.5, dtype=torch.float32) + x
 
     class AddVariableFull(torch.nn.Module):
-        sizes = [
-            (5),
+        sizes: list[tuple[int, ...]] = [
+            (5,),
             (5, 5),
             (5, 5, 5),
             (1, 5, 5, 5),
@@ -48,6 +47,21 @@ def forward(self, x: torch.Tensor, y):
             # Input + a full with the shape from the input and a given value 'y'.
             return x + torch.full(x.shape, y)
 
+    class FullLike(torch.nn.Module):
+        """Since full_like is replaced with full, we only need to test on reference model, not FVP."""
+
+        test_parameters = [
+            ((torch.randn(2, 2, 2, 2) * 50, 3.2),),
+            ((torch.randn(2, 2, 2, 2) * 50, 3),),
+            (((torch.randn(2, 2, 2, 2) * 50).to(torch.int32), 3.2),),
+            (((torch.randn(2, 2, 2, 2) * 50).to(torch.int32), 3),),
+        ]
+
+        def forward(self, input_tensor: torch.Tensor, value):
+            # Our backend can't handle tensors without users, which input_tensor doesn't have
+            # when the full_like is converted to a full. Therefore involve it in the output.
+            return input_tensor + torch.full_like(input_tensor, value)
+
     def _test_full_tosa_MI_pipeline(
         self,
         module: torch.nn.Module,
@@ -63,9 +77,7 @@ def _test_full_tosa_MI_pipeline(
                 compile_spec=common.get_tosa_compile_spec("TOSA-0.80+MI"),
             )
             .export()
-            .check_count({"torch.ops.aten.full.default": 1})
-            .to_edge()
-            .partition()
+            .to_edge_transform_and_lower()
             .check_not(["executorch_exir_dialects_edge__ops_aten_full_default"])
             .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
             .to_executorch()
@@ -85,9 +97,7 @@ def _test_full_tosa_BI_pipeline(
             )
             .quantize()
             .export()
-            .check_count({"torch.ops.aten.full.default": 1})
-            .to_edge()
-            .partition()
+            .to_edge_transform_and_lower()
             .check_not(["executorch_exir_dialects_edge__ops_aten_full_default"])
             .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
             .to_executorch()
@@ -101,9 +111,7 @@ def _test_full_tosa_ethos_pipeline(
             ArmTester(module, example_inputs=test_data, compile_spec=compile_spec)
             .quantize()
             .export()
-            .check_count({"torch.ops.aten.full.default": 1})
-            .to_edge()
-            .partition()
+            .to_edge_transform_and_lower()
             .check_not(["executorch_exir_dialects_edge__ops_aten_full_default"])
             .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
             .to_executorch()
@@ -129,6 +137,10 @@ def test_const_full_tosa_MI(self):
         _input = torch.rand((2, 2, 3, 3)) * 10
         self._test_full_tosa_MI_pipeline(self.AddConstFull(), (_input,))
 
+    @parameterized.expand(FullLike.test_parameters)
+    def test_full_like_tosa_MI(self, test_tensor: Tuple):
+        self._test_full_tosa_MI_pipeline(self.FullLike(), test_tensor)
+
     def test_const_full_nhwc_tosa_BI(self):
         _input = torch.rand((2, 2, 3, 3)) * 10
         self._test_full_tosa_BI_pipeline(self.AddConstFull(), (_input,))
@@ -143,6 +155,10 @@ def test_full_tosa_MI(self, test_tensor: Tuple):
     def test_full_tosa_BI(self, test_tensor: Tuple):
         self._test_full_tosa_BI_pipeline(self.AddVariableFull(), test_tensor)
 
+    @parameterized.expand(FullLike.test_parameters)
+    def test_full_like_tosa_BI(self, test_tensor: Tuple):
+        self._test_full_tosa_BI_pipeline(self.FullLike(), test_tensor)
+
     @parameterized.expand(AddVariableFull.test_parameters)
     @pytest.mark.corstone_fvp
     def test_full_u55_BI(self, test_tensor: Tuple):

examples/arm/setup.sh

@@ -61,7 +61,7 @@ ethos_u_base_rev="24.08"
 
 # tosa reference model
 tosa_reference_model_url="https://review.mlplatform.org/tosa/reference_model"
-tosa_reference_model_rev="v0.80.1"
+tosa_reference_model_rev="70ed0b40fa831387e36abdb4f7fb9670a3464f5a"
 
 # vela
 vela_repo_url="https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela"