pytorch

e93a90bd - [ONNX] Refactor perfect/nearest match criteria to allow optional inputs and disallow mismatch attributes (#106478)

[ONNX] Refactor perfect/nearest match criteria to allow optional inputs and disallow mismatch attributes (#106478) Fix #106057, except **Attribute dtype mismatch. E.g., alpha of aten.add.Tensor. -> Attribute: alpha INT vs FLOAT**. Summarized the change * Fill in defaults of attribute when `param_schema` is applied. This relaxes the matching on default attributes. * Fill in None to optional input when `param_schema` is applied. * Keep extra kwargs in attributes to make matching strictly. * Allow input to be None when its dtype is `optiona[INPUT]` The change comes with the guarantee from torchlib that attribute would never be None. For example, if `memory_format` is needed. The function should specify like this: ```python @torch_op("aten::clone") def aten_clone( self: TTensor, memory_format: str = "" # pylint: disable=unused-argument ) -> TTensor: """clone(Tensor self, *, MemoryFormat? memory_format=None) -> Tensor""" return op.Identity(self) ``` Previous to this PR, opSchema matching didn't strictly guard the number of inputs/attributes to allow nearest match, which introduces the bug of dispatching `aten::div.Tensor` to `aten::div.default` disregarding the fact that `aten::div.Tensor` has an extra attibute `rounding_mode`. This PR fixes the issue with the new logic to perfect/nearest match. Particularly, strictly restrict the qualification of being nearest match candidate. For each ONNX variants, we check these step by step: 1. Check if the function signature of inputs number is the same as the inputs. 2. Check if the function signature of attribute names is the same set of inputs. If either of the above two criteria fails to meet, the ONNX variant is not a perfect match, nor a nearest match candidate (match_score=None) 3. Check if input dtype matches 4. Check if attribute dtype matches If 3 and 4 are met, then this is a perfect match, otherwise, it's still considered a candidate of nearest match with a matching score. ## Case Study ### Optional Input The dispatcher recognizes optional inputs. However, the input can't be ignored. None must be provided. ```python # Perfect match is found inputs = (Tensor([2, 3]), None) aten_op(X: TTensor, Y: Optional[INT64]): ... ``` Real Case: aten::convolution NOTE: There is/will not optional attribute in torchlib. ### Different attributes If an attribute is provided with value, it's a must to match the attribute in function signature. ```python # Not perfect match, nor nearest match inputs = (Tensor([2, 3]),) attributes = {"a":1, "b":2} aten_op(X: TTensor, a: int): ... ``` Real Case: aten::div and aten::div.Tensor_mode ### Default attribute Default attribute will fill in the value into inputs/attributes ```python # Perfect match is found inputs = (Tensor([2, 3]),) attributes = {} aten_op(X: TTensor, a: int = 3): ... ``` Real case: aten::clone ### Ignore attribute with None value The attributes with None value will be ignored in matching. ```python # Perfect match inputs = (Tensor([2, 3]),) attributes = {"a": None} aten_op(X: TTensor): ... # Not perfect match, but eligible for nearest match inputs = (Tensor([2, 3]),) attributes = {"a": None} aten_op(X: TTensor, a: int = 3): ... ``` Real case: aten::div and aten::div.Tensor_mode Pull Request resolved: https://github.com/pytorch/pytorch/pull/106478 Approved by: https://github.com/thiagocrepaldi, https://github.com/BowenBao