Qualcomm AI Engine Direct - alias_copy op

Differential Revision: D73417415

Pull Request resolved: pytorch/executorch#10319

Author: winskuo-quic

backends/qualcomm/_passes/remove_redundancy.py

@@ -22,6 +22,7 @@ def __init__(self):
             exir_ops.edge.aten.clone.default: self._default_condition,
             torch.ops.aten.alias.default: self._default_condition,
             exir_ops.edge.aten.alias.default: self._default_condition,
+            exir_ops.edge.aten.alias_copy.default: self._default_condition,
             exir_ops.edge.aten.lift_fresh_copy.default: self._default_condition,
             # remove this target if '_skip_dim_order' is set to False
             exir_ops.edge.dim_order_ops._to_dim_order_copy.default: self._dim_order_op_condition,

backends/qualcomm/tests/models.py

@@ -10,6 +10,17 @@
 # module with related operator only
 
 
+# Ensure alias_copy is removed in remove_redundancy pass
+class Alias(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.relu = torch.nn.ReLU()
+
+    def forward(self, x):
+        alias_x = torch.ops.aten.alias.default(x)
+        return self.relu(alias_x)
+
+
 class And(torch.nn.Module):
     def __init__(self, pos, neg):
         super().__init__()

backends/qualcomm/tests/test_qnn_delegate.py

@@ -124,6 +124,11 @@ def test_qnn_backend_adaptive_avg_pool2d(self):
         sample_input = (torch.randn(1, 512, 7, 7),)
         self.lower_module_and_test_output(module, sample_input)
 
+    def test_qnn_backend_alias(self):
+        module = Alias()  # noqa: F405
+        sample_input = (torch.randn(1, 10),)
+        self.lower_module_and_test_output(module, sample_input)
+
     def test_qnn_backend_amax(self):
         modules = [AMax(dim=1, keepdim=False), AMax(dim=1, keepdim=True)]  # noqa: F405
         sample_input = (torch.randn(4, 4),)
@@ -1162,6 +1167,12 @@ def test_qnn_backend_adaptive_avg_pool2d(self):
         module = self.get_qdq_module(module, sample_input)
         self.lower_module_and_test_output(module, sample_input)
 
+    def test_qnn_backend_alias(self):
+        module = Alias()  # noqa: F405
+        sample_input = (torch.randn(1, 10),)
+        module = self.get_qdq_module(module, sample_input)
+        self.lower_module_and_test_output(module, sample_input)
+
     def test_qnn_backend_amax(self):
         modules = [AMax(dim=1, keepdim=False), AMax(dim=1, keepdim=True)]  # noqa: F405
         sample_input = (torch.randn(4, 4),)