torch.escape-hatch

assume_constant_result

Note

Tags: torch.escape-hatch

Support Level: SUPPORTED

Original source code:

import torch
import torch._dynamo as torchdynamo



class AssumeConstantResult(torch.nn.Module):
    """
    Applying `assume_constant_result` decorator to burn make non-tracable code as constant.
    """

    def __init__(self):
        super().__init__()

    @torchdynamo.assume_constant_result
    def get_item(self, y):
        return y.int().item()

    def forward(self, x, y):
        return x[: self.get_item(y)]

Result:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, arg0_1: f32[3, 2], arg1_1: i64[]):
            #
            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)
            eq - sym_size_int_1 -- 2;  sym_size_int_1 - 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 arg0_1.shape[1] is specialized at 2');  scalar_tensor_default - None
            eq_1 - sym_size_int -- 3;  sym_size_int - 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[0] is specialized at 3');  scalar_tensor_default_1 - None
            slice_tensor: f32[3, 2] - torch.ops.aten.slice.Tensor(arg0_1, 0, 0, 4);  arg0_1 - None
            return (slice_tensor,)

Graph Signature: ExportGraphSignature(parameters-[], buffers-[], user_inputs-['arg0_1', 'arg1_1'], user_outputs-['slice_tensor'], inputs_to_parameters-{}, inputs_to_buffers-{}, buffers_to_mutate-{}, backward_signature-None, assertion_dep_token-None)
Symbol to range: {}