Add impl for aten.bitwise_and.Tensor and lowering.

PiperOrigin-RevId: 762031114

Author: junjiang-lab

ai_edge_torch/odml_torch/experimental/torch_tfl/_decomps.py

@@ -96,6 +96,20 @@ def _aten_pow_tensor_tensor_decomp(x, y):
   return torch.ops.tfl.pow(x, y)
 
 
+@register_decomp(torch.ops.aten.bitwise_and.Tensor)
+def _aten_bitwise_and_tensor_decomp(x, y):
+  if not (
+      isinstance(x, torch.Tensor)
+      and x.dtype == torch.bool
+      and isinstance(y, torch.Tensor)
+      and y.dtype == torch.bool
+  ):
+    raise TypeError(
+        "Input tensors for aten.bitwise_and only supports bool for now."
+    )
+  return torch.ops.tfl.logical_and(x, y)
+
+
 @register_decomp(torch.ops.aten.gt.Tensor)
 def _aten_gt_tensor_decomp(x, y):
   return torch.ops.tfl.greater(x, y)

ai_edge_torch/odml_torch/experimental/torch_tfl/_lowerings.py

@@ -164,6 +164,19 @@ def _tfl_pow_lowering(
   )
 
 
+@lower(torch.ops.tfl.logical_and.default)
+def _tfl_logical_and_lowering(
+    lctx: LoweringContext,
+    lhs: ir.Value,
+    rhs: ir.Value,
+) -> ir.Value:
+  return _ir_operation(
+      "tfl.logical_and",
+      results=lowering_utils.node_meta_to_ir_types(lctx.node),
+      operands=[lhs, rhs],
+  )
+
+
 @lower(torch.ops.tfl.greater.default)
 def _tfl_greater_lowering(
     lctx: LoweringContext,

ai_edge_torch/odml_torch/experimental/torch_tfl/_ops.py

@@ -63,6 +63,11 @@ def tfl_pow(x: Any, y: Any) -> torch.Tensor:
   return torch.pow(x, y)
 
 
+@custom_op_with_fake("tfl::logical_and")
+def tfl_logical_and(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+  return torch.logical_and(x, y)
+
+
 @custom_op_with_fake("tfl::greater")
 def tfl_greater(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
   return torch.gt(x, y)

ai_edge_torch/odml_torch/experimental/torch_tfl/test/test_torch_tfl_impls.py

@@ -133,6 +133,7 @@ def _assert_export_and_close(
       ("aten_pow_Scalar_0", torch.ops.aten.pow.Scalar, (np.random.rand(), rnd(torch.float32, (10, 10)),), dict()),
       ("aten_pow_Tensor_Scalar_0", torch.ops.aten.pow.Tensor_Scalar, (rnd(torch.float32, (10, 10)), np.random.rand(),), dict()),
       ("aten_pow_Tensor_Tensor_0", torch.ops.aten.pow.Tensor_Tensor, (rnd(torch.float32, (10, 10)), rnd(torch.float32, (10, 10)),), dict()),
+      ("aten_bitwise_and_Tensor_0", torch.ops.aten.bitwise_and.Tensor, (rnd(torch.bool, (10, 10)), rnd(torch.bool, (10, 10)),), dict()),
       ("aten_gt_Tensor_0", torch.ops.aten.gt.Tensor, (rnd(torch.float32, (10, 10)), rnd(torch.float32, (10, 10)),), dict()),
       ("aten_gt_Tensor_1", torch.ops.aten.gt.Tensor, (rnd(torch.float32, (1, 10)), rnd(torch.float32, (10, 1)),), dict()),
       ("aten_lt_Tensor_0", torch.ops.aten.lt.Tensor, (rnd(torch.float32, (10, 10)), rnd(torch.float32, (10, 10)),), dict()),