torch.map

dynamic_shape_map

Note

Tags: torch.map, torch.dynamic-shape

Support Level: SUPPORTED

Original source code:

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:

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: {}