[TOPI][PYTORCH]Logical & Bitwise operator support (apache#5341)

docs/api/python/topi.rst

@@ -99,6 +99,7 @@ List of operators
    topi.logical_and
    topi.logical_or
    topi.logical_not
+   topi.logical_xor
    topi.arange
    topi.stack
    topi.repeat
@@ -193,6 +194,7 @@ topi
 .. autofunction:: topi.logical_and
 .. autofunction:: topi.logical_or
 .. autofunction:: topi.logical_not
+.. autofunction:: topi.logical_xor
 
 topi.nn
 ~~~~~~~

docs/langref/relay_op.rst

@@ -150,6 +150,7 @@ This level enables additional math and transform operators.
    tvm.relay.logical_and
    tvm.relay.logical_or
    tvm.relay.logical_not
+   tvm.relay.logical_xor
    tvm.relay.maximum
    tvm.relay.minimum
    tvm.relay.power

python/tvm/relay/frontend/pytorch.py

@@ -1168,7 +1168,6 @@ def _impl(inputs, input_types):
 
 def _clamp():
     def _impl(inputs, input_types):
-        print(inputs, input_types)
         data = inputs[0]
         amin = inputs[1] if inputs[1] else np.finfo(np.float32).min
         amax = inputs[2] if inputs[2] else np.finfo(np.float32).max
@@ -1298,6 +1297,67 @@ def _impl(inputs, input_types):
     return _impl
 
 
+def _bitwise_not():
+    def _impl(inputs, input_types):
+        data = inputs[0]
+        # The input tensor must be of integral or Boolean types.
+        # For bool tensors, it computes the logical NOT
+        if input_types[0] == "bool":
+            out = _op.logical_not(_op.cast(data, "bool"))
+        else:
+            out = _op.bitwise_not(_op.cast(data, "int"))
+
+        return out
+    return _impl
+
+
+def _bitwise_xor():
+    def _impl(inputs, input_types):
+        lhs = inputs[0]
+
+        import torch
+        if isinstance(inputs[1], _expr.Var):
+            rhs = inputs[1]
+        elif isinstance(inputs[1], torch.Tensor):
+            rhs = _wrap_const(inputs[1].numpy())
+        else:
+            msg = "Data type %s could not be parsed in bitwise_xor operator." % (type(inputs[1]))
+            raise AssertionError(msg)
+
+        lhs = _op.cast(lhs, "bool") if input_types[0] == "bool" else _op.cast(lhs, "int")
+        rhs = _op.cast(rhs, "bool") if input_types[1] == "bool" else _op.cast(rhs, "int")
+
+        return _op.bitwise_xor(lhs, rhs)
+    return _impl
+
+
+def _logical_not():
+    def _impl(inputs, input_types):
+        data = inputs[0]
+
+        return _op.logical_not(_op.cast(data, "bool"))
+    return _impl
+
+
+def _logical_xor():
+    def _impl(inputs, input_types):
+        lhs = _op.cast(inputs[0], "bool")
+
+        import torch
+        if isinstance(inputs[1], _expr.Var):
+            rhs = inputs[1]
+        elif isinstance(inputs[1], torch.Tensor):
+            rhs = _wrap_const(inputs[1].numpy())
+        else:
+            msg = "Data type %s could not be parsed in logical_xor operator." % (type(inputs[1]))
+            raise AssertionError(msg)
+
+        rhs = _op.cast(rhs, "bool")
+
+        return _op.logical_xor(lhs, rhs)
+    return _impl
+
+
 def _isfinite():
     def _impl(inputs, input_types):
         return _op.isfinite(inputs[0])
@@ -1524,6 +1584,10 @@ def _get_convert_map(prelude):
         "aten::ge"                              : _elemwise("greater_equal"),
         "aten::ne"                              : _elemwise("not_equal"),
         "aten::eq"                              : _elemwise("equal"),
+        "aten::logical_not"                     : _logical_not(),
+        "aten::logical_xor"                     : _logical_xor(),
+        "aten::bitwise_not"                     : _bitwise_not(),
+        "aten::bitwise_xor"                     : _bitwise_xor(),
         "aten::isfinite"                        : _isfinite(),
         "aten::isnan"                           : _isnan(),
         "aten::Bool"                            : _Bool(),

python/tvm/relay/op/_tensor.py

@@ -53,6 +53,7 @@
 register_broadcast_schedule("logical_not")
 register_broadcast_schedule("logical_and")
 register_broadcast_schedule("logical_or")
+register_broadcast_schedule("logical_xor")
 register_broadcast_schedule("bitwise_not")
 register_broadcast_schedule("bitwise_and")
 register_broadcast_schedule("bitwise_or")
@@ -205,6 +206,7 @@ def elemwise_shape_func(attrs, inputs, _):
 register_shape_func("floor_mod", False, broadcast_shape_func)
 register_shape_func("logical_and", False, broadcast_shape_func)
 register_shape_func("logical_or", False, broadcast_shape_func)
+register_shape_func("logical_xor", False, broadcast_shape_func)
 register_shape_func("bitwise_not", False, broadcast_shape_func)
 register_shape_func("bitwise_and", False, broadcast_shape_func)
 register_shape_func("bitwise_or", False, broadcast_shape_func)

python/tvm/relay/op/tensor.py

@@ -537,6 +537,23 @@ def logical_or(lhs, rhs):
     return _make.logical_or(lhs, rhs)
 
 
+def logical_xor(lhs, rhs):
+    """logical XOR with numpy-style broadcasting.
+
+    Parameters
+    ----------
+    lhs : relay.Expr
+        The left hand side input data
+    rhs : relay.Expr
+        The right hand side input data
+
+    Returns
+    -------
+    result : relay.Expr
+        The computed result.
+    """
+    return _make.logical_xor(lhs, rhs)
+
 def bitwise_and(lhs, rhs):
     """bitwise AND with numpy-style broadcasting.
 

src/relay/op/tensor/binary.cc

@@ -123,6 +123,12 @@ RELAY_REGISTER_BINARY_OP("logical_or")
 .set_attr<FTVMCompute>("FTVMCompute", RELAY_BINARY_COMPUTE(topi::logical_or));
 
 
+RELAY_REGISTER_BINARY_OP("logical_xor")
+.describe("Elementwise logical XOR with broadcasting")
+.set_support_level(4)
+.set_attr<FTVMCompute>("FTVMCompute", RELAY_BINARY_COMPUTE(topi::logical_xor));
+
+
 RELAY_REGISTER_BINARY_OP("bitwise_and")
 .describe("Elementwise bitwise AND with broadcasting")
 .set_support_level(4)

tests/python/frontend/pytorch/test_forward.py

@@ -159,7 +159,7 @@ def verify_model(model_name, input_data=[],
     if isinstance(baseline_outputs, tuple):
         baseline_outputs = tuple(out.cpu().numpy() for out in baseline_outputs)
     else:
-        baseline_outputs = (baseline_outputs.float().cpu().numpy(),)
+        baseline_outputs = (baseline_outputs.cpu().numpy(),)
 
     trace = torch.jit.trace(baseline_model, baseline_input).float().eval()
 
@@ -1600,6 +1600,95 @@ def forward(self, *args):
     verify_model(Topk6().float().eval(), input_data=input_data)
 
 
+def test_forward_logical_not():
+    torch.set_grad_enabled(False)
+
+    class LogicalNot1(Module):
+        def forward(self, *args):
+            return torch.logical_not(args[0])
+
+    input_data = torch.tensor([True, False])
+    verify_model(LogicalNot1().float().eval(), input_data=input_data)
+
+    input_data = torch.tensor([0, 1, -10], dtype=torch.int8)
+    verify_model(LogicalNot1().float().eval(), input_data=input_data)
+
+    input_data = torch.tensor([0., 1.5, -10.], dtype=torch.double)
+    verify_model(LogicalNot1().float().eval(), input_data=input_data)
+
+    input_data = torch.tensor([0., 1., -10.], dtype=torch.int32)
+    verify_model(LogicalNot1().float().eval(), input_data=input_data)
+
+
+def test_forward_bitwise_not():
+    torch.set_grad_enabled(False)
+
+    class BitwiseNot1(Module):
+        def forward(self, *args):
+            return torch.bitwise_not(args[0])
+
+    input_data = torch.tensor([0, 1, -10], dtype=torch.int8)
+    verify_model(BitwiseNot1().float().eval(), input_data=input_data)
+
+    input_data = torch.tensor([0., 1., -10.], dtype=torch.int32)
+    verify_model(BitwiseNot1().float().eval(), input_data=input_data)
+
+    input_data = torch.tensor([True, False])
+    verify_model(BitwiseNot1().float().eval(), input_data=input_data)
+
+
+def test_forward_bitwise_xor():
+    torch.set_grad_enabled(False)
+
+    class BitwiseXor1(Module):
+        def forward(self, *args):
+            return torch.bitwise_xor(args[0], args[1])
+
+    class BitwiseXor2(Module):
+        def forward(self, *args):
+            rhs = torch.tensor([1, 0, 3], dtype=torch.int8)
+            if torch.cuda.is_available():
+                rhs = rhs.cuda()
+            return torch.bitwise_xor(args[0], rhs)
+
+    lhs = torch.tensor([-1, -2, 3], dtype=torch.int8)
+    rhs = torch.tensor([1, 0, 3], dtype=torch.int8)
+    verify_model(BitwiseXor1().float().eval(), input_data=[lhs, rhs])
+
+    lhs = torch.tensor([True, True, False])
+    rhs = torch.tensor([False, True, False])
+    verify_model(BitwiseXor1().float().eval(), input_data=[lhs, rhs])
+
+    lhs = torch.tensor([-1, -2, 3], dtype=torch.int8)
+    verify_model(BitwiseXor2().float().eval(), input_data=[lhs])
+
+
+def test_forward_logical_xor():
+    torch.set_grad_enabled(False)
+
+    class LogicalXor1(Module):
+        def forward(self, *args):
+            return torch.logical_xor(args[0], args[1])
+
+    class LogicalXor2(Module):
+        def forward(self, *args):
+            rhs = torch.tensor([1, 0, 3], dtype=torch.int8)
+            if torch.cuda.is_available():
+                rhs = rhs.cuda()
+            return torch.logical_xor(args[0], rhs)
+
+    lhs = torch.tensor([-1, -2, 3], dtype=torch.int8)
+    rhs = torch.tensor([1, 0, 3], dtype=torch.int8)
+    verify_model(LogicalXor1().float().eval(), input_data=[lhs, rhs])
+
+    lhs = torch.tensor([True, True, False])
+    rhs = torch.tensor([False, True, False])
+    verify_model(LogicalXor1().float().eval(), input_data=[lhs, rhs])
+
+    lhs = torch.tensor([-1, -2, 3], dtype=torch.int8)
+    verify_model(LogicalXor2().float().eval(), input_data=[lhs])
+
+
 if __name__ == "__main__":
     # Single operator tests
     test_forward_add()
@@ -1663,6 +1752,10 @@ def forward(self, *args):
     test_forward_clamp()
     test_forward_floor()
     test_forward_round()
+    test_forward_logical_not()
+    test_forward_bitwise_not()
+    test_forward_bitwise_xor()
+    test_forward_logical_xor()
     test_forward_isfinite()
     test_forward_isnan()
     test_forward_isinf()

topi/include/topi/broadcast.h

@@ -140,6 +140,19 @@ TOPI_DEFINE_OP_OVERLOAD(operator&&, logical_and);
 TOPI_DEFINE_BCAST_OP(logical_or, { return a || b; });
 TOPI_DEFINE_OP_OVERLOAD(operator||, logical_or);
 
+/*!
+ * \fn logical_xor
+ * \brief Compute A ^ B with auto-broadcasting.
+ *
+ * \param A The first tensor, or Expr
+ * \param B The second tensor, or Expr
+ * \param name The name of the operation
+ * \param tag The tag to mark the operation
+ *
+ * \return The result.
+ */
+TOPI_DEFINE_BCAST_OP(logical_xor, { return a ^ b; });
+
 /*!
  * \fn bitwise_and
  * \brief Compute A & B with auto-broadcasting.

topi/python/topi/broadcast.py

@@ -420,6 +420,25 @@ def logical_or(lhs, rhs):
     return _cpp.logical_or(lhs, rhs)
 
 
+def logical_xor(lhs, rhs):
+    """Compute element-wise logical xor of data.
+
+    Parameters
+    ----------
+    lhs : tvm.te.Tensor or Expr
+          The left operand
+    rhs : tvm.te.Tensor or Expr
+          The right operand
+
+    Returns
+    -------
+    ret : tvm.te.Tensor or Expr
+          Returns Expr if both operands are Expr.
+          Otherwise returns Tensor.
+    """
+    return _cpp.logical_xor(lhs, rhs)
+
+
 def bitwise_and(lhs, rhs):
     """Compute element-wise bitwise and of data.
 

topi/src/broadcast.cc

@@ -65,6 +65,7 @@ TOPI_REGISTER_BCAST_OP("topi.power", topi::power);
 TOPI_REGISTER_BCAST_OP("topi.left_shift", topi::left_shift);
 TOPI_REGISTER_BCAST_OP("topi.logical_and", topi::logical_and);
 TOPI_REGISTER_BCAST_OP("topi.logical_or", topi::logical_or);
+TOPI_REGISTER_BCAST_OP("topi.logical_xor", topi::logical_xor);
 TOPI_REGISTER_BCAST_OP("topi.bitwise_and", topi::bitwise_and);
 TOPI_REGISTER_BCAST_OP("topi.bitwise_or", topi::bitwise_or);
 TOPI_REGISTER_BCAST_OP("topi.bitwise_xor", topi::bitwise_xor);

topi/tests/python/test_topi_broadcast.py

@@ -355,6 +355,8 @@ def check_device(device):
     test_apply(topi.logical_and, "logical_and", np.logical_and, [True, False], [False, False])
     test_apply(topi.logical_or, "logical_or", np.logical_or, True, False)
     test_apply(topi.logical_or, "logical_or", np.logical_or, [True, False], [False, False])
+    test_apply(topi.logical_xor, "logical_xor", np.logical_xor, True, False)
+    test_apply(topi.logical_xor, "logical_xor", np.logical_xor, [True, False], [False, False])
 
 
 def test_bitwise_and():