torch.map
=============
dynamic_shape_map
^^^^^^^^^^^^^^^^^

.. note::

    Tags: :doc:`torch.map <torch.map>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`

    Support Level: SUPPORTED

Original source code:

.. code-block:: python

    import torch
    
    from functorch.experimental.control_flow import map
    
    
    def dynamic_shape_map(xs, y):
        """
        functorch map() maps a function over the first tensor dimension.
        """
    
        def body(x, y):
            return x + y
    
        return map(body, xs, y)
    

Result:

.. code-block::

    ExportedProgram:
        class GraphModule(torch.nn.Module):
            def forward(self, arg0_1: f32[3, 2], arg1_1: f32[2]):
                # 
                sym_size_int - torch.ops.aten.sym_size.int(arg0_1, 0)
                sym_size_int_1 - torch.ops.aten.sym_size.int(arg0_1, 1)
                sym_size_int_2 - torch.ops.aten.sym_size.int(arg1_1, 0)
                eq - sym_size_int_2 -- 2;  sym_size_int_2 - None
                scalar_tensor_default: f32[] - torch.ops.aten.scalar_tensor.default(eq);  eq - None
                _assert_async_msg - torch.ops.aten._assert_async.msg(scalar_tensor_default, 'Input arg1_1.shape[0] is specialized at 2');  scalar_tensor_default - None
                eq_1 - sym_size_int_1 -- 2;  sym_size_int_1 - None
                scalar_tensor_default_1: f32[] - torch.ops.aten.scalar_tensor.default(eq_1);  eq_1 - None
                _assert_async_msg_1 - torch.ops.aten._assert_async.msg(scalar_tensor_default_1, 'Input arg0_1.shape[1] is specialized at 2');  scalar_tensor_default_1 - None
                eq_2 - sym_size_int -- 3;  sym_size_int - None
                scalar_tensor_default_2: f32[] - torch.ops.aten.scalar_tensor.default(eq_2);  eq_2 - None
                _assert_async_msg_2 - torch.ops.aten._assert_async.msg(scalar_tensor_default_2, 'Input arg0_1.shape[0] is specialized at 3');  scalar_tensor_default_2 - None
                submodule_0 - self.submodule_0
                map_impl - torch.ops.map_impl(submodule_0, 1, arg0_1, arg1_1);  submodule_0 - arg0_1 - arg1_1 - None
                getitem: f32[3, 2] - map_impl[0];  map_impl - None
                return (getitem,)
                
            class GraphModule(torch.nn.Module):
                def forward(self, arg0_1: f32[3, 2], arg1_1: f32[2]):
                            add_tensor: f32[3, 2] - torch.ops.aten.add.Tensor(arg0_1, arg1_1);  arg0_1 - arg1_1 - None
                    return [add_tensor]
                    
    Graph Signature: ExportGraphSignature(parameters-[], buffers-[], user_inputs-['arg0_1', 'arg1_1'], user_outputs-['getitem'], inputs_to_parameters-{}, inputs_to_buffers-{}, buffers_to_mutate-{}, backward_signature-None, assertion_dep_token-None)
    Symbol to range: {}