Arm backend: support mean.default

backends/arm/_passes/decompose_meandim_pass.py

@@ -19,13 +19,13 @@
 
 
 def get_meandim_decomposition(op) -> tuple:
-    if op == exir_ops.edge.aten.mean.dim:
+    if op in (exir_ops.edge.aten.mean.dim, exir_ops.edge.aten.mean.default):
         return (
             exir_ops.edge.aten.sum.dim_IntList,
             exir_ops.edge.aten.full.default,
             exir_ops.edge.aten.mul.Tensor,
         )
-    if op == torch.ops.aten.mean.dim:
+    if op in (torch.ops.aten.mean.dim, torch.ops.aten.mean.default):
         return (
             torch.ops.aten.sum.dim_IntList,
             torch.ops.aten.full.default,
@@ -35,17 +35,17 @@ def get_meandim_decomposition(op) -> tuple:
 
 
 def get_avgpool(op):
-    if op == exir_ops.edge.aten.mean.dim:
+    if op in (exir_ops.edge.aten.mean.dim, exir_ops.edge.aten.mean.default):
         return exir_ops.edge.aten.avg_pool2d.default
-    if op == torch.ops.aten.mean.dim:
+    if op in (torch.ops.aten.mean.dim, torch.ops.aten.mean.default):
         return torch.ops.aten.avg_pool2d.default
     raise RuntimeError(f"Can't get meandim decomposition for op {op}")
 
 
 def get_view(op):
-    if op == exir_ops.edge.aten.mean.dim:
+    if op in (exir_ops.edge.aten.mean.dim, exir_ops.edge.aten.mean.default):
         return exir_ops.edge.aten.view_copy.default
-    if op == torch.ops.aten.mean.dim:
+    if op in (torch.ops.aten.mean.dim, torch.ops.aten.mean.default):
         return torch.ops.aten.view_copy.default
     raise RuntimeError(f"Can't get meandim decomposition for op {op}")
 
@@ -87,13 +87,18 @@ def __init__(self, graph_module, tosa_spec):
         )
 
     def call_operator(self, op, args, kwargs, meta):
-        if op not in (exir_ops.edge.aten.mean.dim, torch.ops.aten.mean.dim):
+        if op not in (
+            exir_ops.edge.aten.mean.dim,
+            torch.ops.aten.mean.dim,
+            exir_ops.edge.aten.mean.default,
+            torch.ops.aten.mean.default,
+        ):
             return super().call_operator(op, args, kwargs, meta)
 
         x = get_node_arg(args, 0)
         input_shape = list(x.data.shape)
         output_shape = list(meta["val"].shape)
-        dims_to_reduce = get_node_arg(args, 1)
+        dims_to_reduce = get_node_arg(args, 1, range(len(input_shape)))
         if dims_to_reduce is None:
             dims_to_reduce = range(len(input_shape))
         dims_to_reduce = [dim % len(input_shape) for dim in dims_to_reduce]

backends/arm/operator_support/tosa_profile_supported_op_lists.py

@@ -178,6 +178,7 @@
     exir_ops.edge.aten.native_group_norm.default,
     exir_ops.edge.aten.sigmoid.default,
     exir_ops.edge.aten.mean.dim,
+    exir_ops.edge.aten.mean.default,
     exir_ops.edge.aten.mm.default,
     exir_ops.edge.aten.minimum.default,
     exir_ops.edge.aten.maximum.default,

backends/arm/scripts/parse_test_names.py

@@ -14,6 +14,7 @@
     "hardswish.default",
     "linear.default",
     "maximum.default",
+    "mean.default",
     "multihead_attention.default",
     "adaptive_avg_pool2d.default",
     "bitwise_right_shift.Tensor",

backends/arm/test/ops/test_mean_dim.py

@@ -4,6 +4,8 @@
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
+from typing import Callable
+
 import torch
 from executorch.backends.arm.test import common
 from executorch.backends.arm.test.tester.test_pipeline import (
@@ -344,3 +346,43 @@ def test_mean_dim_vgf_INT(test_data):
         tosa_version="TOSA-1.0+INT",
     )
     pipeline.run()
+
+
+mean_input_t = tuple[torch.Tensor, bool]
+
+
+class MeanDefault(torch.nn.Module):
+    def forward(self, tensor: torch.Tensor, keepdim: bool):
+        return tensor.mean()
+
+    test_data_suite: dict[str, Callable[[], mean_input_t]] = {
+        "rank1": lambda: (
+            torch.rand(
+                1,
+            ),
+            False,
+        ),
+        "rank2": lambda: (torch.rand(5, 5), True),
+        "rank4": lambda: (torch.rand(5, 1, 10, 1), False),
+    }
+
+
+@common.parametrize("test_data", MeanDefault.test_data_suite)
+def test_mean_tosa_FP(test_data):
+    pipeline = TosaPipelineFP[mean_input_t](
+        MeanDefault(),
+        test_data(),
+        [],  # Might be sum, avgpool, or both
+    )
+    pipeline.run()
+
+
+@common.parametrize("test_data", MeanDefault.test_data_suite)
+def test_mean_tosa_INT(test_data):
+    pipeline = TosaPipelineINT[mean_input_t](
+        MeanDefault(),
+        test_data(),
+        [],  # Might be sum, avgpool, or both
+        symmetric_io_quantization=True,
+    )
+    pipeline.run()