tqchen commented on code in PR #16757:
URL: https://github.com/apache/tvm/pull/16757#discussion_r1536886548
##########
python/tvm/relax/frontend/nn/exporter.py:
##########
@@ -176,35 +183,26 @@ def _unwrap_ret(expr: typing.Any) -> typing.Any:
def _convert_input(arg):
if isinstance(arg, tir.Var):
return rx.Var(arg.name, struct_info=ShapeStructInfo(values=[arg]))
- if isinstance(arg, (core.Tensor, core.Object)):
+ elif isinstance(arg, (core.Tensor, core.Object)):
return arg._expr # pylint: disable=protected-access
- if isinstance(arg, _spec.Tuple):
+ elif isinstance(arg, _spec.Tuple):
return rx.Var(
arg.name,
struct_info=TupleStructInfo(
[_convert_input(arg_i).struct_info for arg_i in
arg.elements]
),
)
- raise TypeError(f"Unsupported input type: {type(arg)}")
+ elif isinstance(arg, rx.Expr):
+ return arg
+ else:
+ raise TypeError(f"Unsupported input type: {type(arg)}")
def _params(mode: str) -> typing.List[rx.Var]:
inputs: typing.List[rx.Var] = []
- def _get_var(shape_var: tir.Var) -> tir.Var:
- name = shape_var.name
- if name in str2var_params:
- return str2var_params[name]
Review Comment:
having ability to have common dynamic variables like `vocab_size` is a
pretty common need, so likely we will need to support this behavior. The
in_features and out_features are usually static in most cases
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