[torchfuzz] remove supports_variable_inputs for now (#163553)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163553
Approved by: https://github.com/laithsakka
ghstack dependencies: #163547

tools/experimental/dynamic_shapes/torchfuzz/operators/add.py

@@ -21,10 +21,6 @@ class AddOperator(Operator):
         """Add can produce tensors but not scalars."""
         return isinstance(output_spec, TensorSpec)
 
-    def supports_variable_inputs(self) -> bool:
-        """Add operator supports variable number of inputs."""
-        return True
-
     def decompose(self, output_spec: Spec, num_inputs: int = 2) -> list[Spec]:
         """Decompose tensor into input tensors for addition with type promotion."""
         if not isinstance(output_spec, TensorSpec):

tools/experimental/dynamic_shapes/torchfuzz/operators/arg.py

@@ -14,10 +14,6 @@ class ArgOperator(Operator):
         """Arg can produce any type of output."""
         return True
 
-    def supports_variable_inputs(self) -> bool:
-        """Arg operator does not require inputs."""
-        return False
-
     def decompose(self, output_spec: Spec, num_inputs: int = 0) -> list[Spec]:
         """Arg requires no inputs."""
         return []

tools/experimental/dynamic_shapes/torchfuzz/operators/base.py

@@ -16,10 +16,6 @@ class Operator(ABC):
     def can_produce(self, output_spec: Spec) -> bool:
         """Check if this operator can produce the given output spec."""
 
-    @abstractmethod
-    def supports_variable_inputs(self) -> bool:
-        """Check if this operator supports variable number of inputs."""
-
     @abstractmethod
     def decompose(self, output_spec: Spec, num_inputs: int = 2) -> list[Spec]:
         """Decompose output spec into input specs."""

tools/experimental/dynamic_shapes/torchfuzz/operators/constant.py

@@ -20,10 +20,6 @@ class ConstantOperator(Operator):
         """Constant can produce any type of output."""
         return True
 
-    def supports_variable_inputs(self) -> bool:
-        """Constant operator does not require inputs."""
-        return False
-
     def decompose(self, output_spec: Spec, num_inputs: int = 0) -> list[Spec]:
         """Constant requires no inputs."""
         return []

tools/experimental/dynamic_shapes/torchfuzz/operators/item.py

@@ -14,10 +14,6 @@ class ItemOperator(Operator):
         """Item can only produce scalars."""
         return isinstance(output_spec, ScalarSpec)
 
-    def supports_variable_inputs(self) -> bool:
-        """Item operator does not support variable number of inputs."""
-        return False
-
     def decompose(self, output_spec: Spec, num_inputs: int = 1) -> list[Spec]:
         """Decompose scalar into a single-element tensor for item operation."""
         if not isinstance(output_spec, ScalarSpec):

tools/experimental/dynamic_shapes/torchfuzz/operators/mul.py

@@ -16,10 +16,6 @@ class MulOperator(Operator):
         """Mul can produce tensors but not scalars."""
         return isinstance(output_spec, TensorSpec)
 
-    def supports_variable_inputs(self) -> bool:
-        """Mul operator supports variable number of inputs."""
-        return True
-
     def decompose(self, output_spec: Spec, num_inputs: int = 2) -> list[Spec]:
         """Decompose tensor into input tensors for multiplication with type promotion."""
         if not isinstance(output_spec, TensorSpec):

tools/experimental/dynamic_shapes/torchfuzz/operators/scalar_add.py

@@ -16,10 +16,6 @@ class ScalarAddOperator(Operator):
         """Scalar add can only produce scalars."""
         return isinstance(output_spec, ScalarSpec)
 
-    def supports_variable_inputs(self) -> bool:
-        """Scalar add operator does not support variable number of inputs."""
-        return False
-
     def decompose(self, output_spec: Spec, num_inputs: int = 2) -> list[Spec]:
         """Decompose scalar into input scalars for addition with type promotion."""
         if not isinstance(output_spec, ScalarSpec):

tools/experimental/dynamic_shapes/torchfuzz/operators/scalar_multiply.py

@@ -16,10 +16,6 @@ class ScalarMultiplyOperator(Operator):
         """Scalar multiply can only produce scalars."""
         return isinstance(output_spec, ScalarSpec)
 
-    def supports_variable_inputs(self) -> bool:
-        """Scalar multiply operator does not support variable number of inputs."""
-        return False
-
     def decompose(self, output_spec: Spec, num_inputs: int = 2) -> list[Spec]:
         """Decompose scalar into input scalars for multiplication with type promotion."""
         if not isinstance(output_spec, ScalarSpec):