Add logical and op (pytorch#13342)

Summary:
As title, add the logical and op for an internal model

Rollback Plan:

Differential Revision: D80122607

Author: cccclai

backends/qualcomm/_passes/layout_transform.py

@@ -93,6 +93,7 @@ class LayoutTransform(ExportPass):
         exir_ops.edge.aten.le.Tensor,
         exir_ops.edge.aten.linear.default,
         exir_ops.edge.aten.log.default,
+        exir_ops.edge.aten.logical_and.default,
         exir_ops.edge.aten.logical_not.default,
         exir_ops.edge.aten.lt.Scalar,
         exir_ops.edge.aten.lt.Tensor,

backends/qualcomm/builders/op_and.py

@@ -16,7 +16,7 @@
 
 @register_node_visitor
 class OpAnd(NodeVisitor):
-    target = ["aten.bitwise_and.Tensor"]
+    target = ["aten.bitwise_and.Tensor", "aten.logical_and.default"]
 
     def __init__(self, *args) -> None:
         super().__init__(*args)

backends/qualcomm/quantizer/annotators.py

@@ -839,7 +839,7 @@ def annotate_sigmoid(node: Node, quantization_config: QuantizationConfig) -> Non
         )
 
 
-@register_annotator([torch.ops.aten.__and__.Tensor])
+@register_annotator([torch.ops.aten.__and__.Tensor, torch.ops.aten.logical_and.default])
 def annotate_and(node: Node, quantization_config: QuantizationConfig) -> None:
     annotate_binary(node, quantization_config)
 

backends/qualcomm/tests/models.py

@@ -1223,6 +1223,14 @@ def forward(self, x):
         return torch.log(x)
 
 
+class LogicalAnd(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x, y):
+        return torch.logical_and(x != 0, y != 0).float()
+
+
 class LogicalNot(torch.nn.Module):
     def __init__(self):
         super().__init__()

backends/qualcomm/tests/test_qnn_delegate.py

@@ -924,6 +924,13 @@ def test_qnn_backend_log(self):
         sample_input = (torch.rand([1, 2, 3, 4]),)
         self.lower_module_and_test_output(module, sample_input)
 
+    def test_qnn_backend_logical_and(self):
+        module = LogicalAnd()  # noqa: F405
+        input1 = torch.tensor([True, False, True, False])
+        input2 = torch.tensor([True, True, False, False])
+        sample_input = (input1, input2)
+        self.lower_module_and_test_output(module, sample_input)
+
     def test_qnn_backend_logical_not(self):
         module = LogicalNot()  # noqa: F405
         sample_input = (torch.rand([1, 2, 3, 4]),)
@@ -2484,6 +2491,14 @@ def test_qnn_backend_log(self):
         module = self.get_qdq_module(module, sample_input)
         self.lower_module_and_test_output(module, sample_input)
 
+    def test_qnn_backend_logical_and(self):
+        module = LogicalAnd()  # noqa: F405
+        input1 = torch.tensor([0.0])
+        input2 = torch.tensor([1.0])
+        sample_input = (input1, input2)
+        module = self.get_qdq_module(module, sample_input)
+        self.lower_module_and_test_output(module, sample_input)
+
     def test_qnn_backend_logical_not(self):
         module = LogicalNot()  # noqa: F405
         sample_input = (torch.rand([1, 2, 3, 4]),)