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

titaiwangms · 2023-08-02T19:57:41Z

Stack from ghstack (oldest at bottom):

-> [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:

@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:

Check if the function signature of inputs number is the same as the inputs.
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)

Check if input dtype matches
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.

# 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.

# 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

# 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.

# 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

…ts and disallow mismatch attributes [ghstack-poisoned]

pytorch-bot · 2023-08-02T19:57:43Z

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…tional inputs and disallow mismatch attributes" [ghstack-poisoned]

…ts and disallow mismatch attributes ghstack-source-id: a981e3c Pull Request resolved: #106478

torch/onnx/_internal/fx/op_validation.py

torch/onnx/_internal/fx/onnxfunction_dispatcher.py

…tional inputs and disallow mismatch attributes" 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. [ghstack-poisoned]

…ts and disallow mismatch attributes ghstack-source-id: 4045607 Pull Request resolved: #106478

titaiwangms · 2023-08-04T20:36:14Z

@BowenBao PTAL

test/onnx/test_fx_op_consistency.py

torch/onnx/_internal/fx/onnxfunction_dispatcher.py

torch/onnx/_internal/fx/op_validation.py

torch/onnx/_internal/fx/type_utils.py

BowenBao · 2023-08-04T21:22:17Z

torch/onnx/_internal/fx/onnxfunction_dispatcher.py

+        r"""Calculate the inputs matching score of the OpSchema requirements to find the nearest match.
+
+        Only the functions which have the same number of inputs and attributes as the
+        OpSchema are eligible to be a nearest match candidate. Thus, we don't need to


maybe check and raise otherwise in the beginning of this function

It's guaranteed to have the same length of inputs and attributes because self._record_matching_score(function_inputs, function_attributes) is only invoked when the check are done in perfect_match function (step 1).

torch/onnx/_internal/fx/onnxfunction_dispatcher.py

BowenBao · 2023-08-04T21:34:15Z

torch/onnx/_internal/fx/onnxfunction_dispatcher.py

@@ -569,18 +610,10 @@ def perfect_match_inputs(
                    f"Actual {torch_input_compatible_types} vs\n"
                    f"expected {allowed_types}\n"
                )
+                # NOTE: This is still a candidate for nearest match, as it only mismatches on dtypes.
+                self._record_matching_score(function_inputs, function_attributes)
+                diagnostic.with_additional_message(f"match score: {self.match_score}\n")
                return False


nit: we are short circuiting the diagnostic message for attr dtype mismatches, if there is input dtype mismatch...

I use a flag is_perfect_match to postpone the return to let diagnostics catch all it needs.

…tional inputs and disallow mismatch attributes" 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. [ghstack-poisoned]

…ts and disallow mismatch attributes ghstack-source-id: ac01b53 Pull Request resolved: #106478

…tional inputs and disallow mismatch attributes" 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. [ghstack-poisoned]

…ts and disallow mismatch attributes ghstack-source-id: 107c0aa Pull Request resolved: #106478

…tional inputs and disallow mismatch attributes" 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 [ghstack-poisoned]

…ts and disallow mismatch attributes ghstack-source-id: be5eb50 Pull Request resolved: #106478

titaiwangms · 2023-08-09T18:53:40Z

@BowenBao added case study which should be enough to explain the situations that this PR improves. Essentially, most of the changes are to align with the expectations of attribute from torchlib.

BowenBao · 2023-08-09T20:12:36Z

Thanks this is great. Could you put the case studies into comment and create a [NOTE: title] for it? There are also some comments I want to make.

thiagocrepaldi

LGTM. Just make sure Bowen's comments are addressed before merging :)

…tional inputs and disallow mismatch attributes" 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 [ghstack-poisoned]

titaiwangms · 2023-08-09T22:41:16Z

Thanks this is great. Could you put the case studies into comment and create a [NOTE: title] for it? There are also some comments I want to make.

Done. I extend/modify the description on _OpschemaWrapper

BowenBao

LG w/ comments.

BowenBao · 2023-08-09T23:22:04Z

torch/onnx/_internal/fx/onnxfunction_dispatcher.py

+        Result: Perfect match.
+
+    2. [NOTE: Optional input]: The dispatcher recognizes optional inputs. However,
+        the input can't be ignored. None must be provided.


nit: to check my understanding. Is this due to onnxscript limitation that None cannot be set as default value for Optional[xxx] typed argument? Such that inputs = (Tensor([2, 3], ) will not be matched, but otherwise it should? If there is an issue to link here that will be great ...

I typed above but then I found convolution aten signature is - func: convolution(Tensor input, Tensor weight, Tensor? bias, .... So the optional input by design does not have default value. And in that case, the type is only meaning None | INT64 so it is syntactically incorrect to omit it.

I think PyTorch doesn't omit any inputs, because if they do, we should see a lot of breakings now in terms of the param_schema separating. Not sure if there will be a change on this, but currently, we don't fill in default value to input, nor omit None.

BowenBao · 2023-08-09T23:23:49Z

torch/onnx/_internal/fx/onnxfunction_dispatcher.py

+        Result: Perfect match.
+        Real example: `aten::clone`
+
+    5. [NOTE: Ignore attribute with None value]: The attributes with None value


I guess this is due to the ONNXScript limitation I typed above? Cannot define optional attribute?

I think it's something about AttributeProto, but yes, we don't have Optional attribute at the time, and this assumption is acknowledged by OpSchemaWrapper.

Semantically, besides dtype, another goal of creating ONNX variants is to overload the cases with different attributes, because they are usaully composed by different set of ops. Thus, attribute with None value simply means the function doesn't need this attribute.

cc @justinchuby

I will need to double check with Rama on optional attributes. IIRC we either provide them in the graph or not provide them in the graph. There is not a "None" as the attribute value. In the torchscript builder, we omit None attributes whenever we see them.

torch/onnx/_internal/fx/onnxfunction_dispatcher.py

…tional inputs and disallow mismatch attributes" 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 [ghstack-poisoned]

…ts and disallow mismatch attributes ghstack-source-id: ad5465e Pull Request resolved: #106478

titaiwangms · 2023-08-10T00:44:32Z

@pytorchbot merge

pytorchmergebot · 2023-08-10T00:46:32Z

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…ts and disallow mismatch attributes (pytorch#106478) Fix pytorch#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: pytorch#106478 Approved by: https://github.com/thiagocrepaldi, https://github.com/BowenBao

[ONNX] Refactor perfect/nearest match criteria to allow optional inpu…

b60ef37

…ts and disallow mismatch attributes [ghstack-poisoned]

titaiwangms requested review from BowenBao, abock and thiagocrepaldi as code owners August 2, 2023 19:57

pytorch-bot bot added the release notes: onnx torch.onnx related changes that should show up in the release notes label Aug 2, 2023

pytorchbot added the open source label Aug 2, 2023

titaiwangms marked this pull request as draft August 2, 2023 20:09

Update on "[ONNX] Refactor perfect/nearest match criteria to allow op…

8c777ff

…tional inputs and disallow mismatch attributes" [ghstack-poisoned]

This was referenced Aug 2, 2023

[ONNX] Preserve extra kwargs when aten name fallback is triggered #106410

Closed

[ONNX] Do not filter memory_format as it's kwargs in aten.clone #106411

Closed

titaiwangms linked an issue Aug 2, 2023 that may be closed by this pull request

[ONNX] Reduce 'onnxfunction_dispatcher' warnings on nearest match #106057

Closed

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Update on "[ONNX] Refactor perfect/nearest match criteria to allow op…

9ddd83c

…tional inputs and disallow mismatch attributes" [ghstack-poisoned]

titaiwangms added a commit that referenced this pull request Aug 3, 2023

[ONNX] Refactor perfect/nearest match criteria to allow optional inpu…

6231572

…ts and disallow mismatch attributes ghstack-source-id: a981e3c Pull Request resolved: #106478

titaiwangms commented Aug 3, 2023

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titaiwangms marked this pull request as ready for review August 3, 2023 00:28

titaiwangms added module: onnx Related to torch.onnx topic: bug fixes topic category labels Aug 3, 2023

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titaiwangms added a commit that referenced this pull request Aug 3, 2023

[ONNX] Refactor perfect/nearest match criteria to allow optional inpu…

47aac21

…ts and disallow mismatch attributes ghstack-source-id: 4045607 Pull Request resolved: #106478

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titaiwangms added a commit that referenced this pull request Aug 5, 2023

[ONNX] Refactor perfect/nearest match criteria to allow optional inpu…

72a499b

…ts and disallow mismatch attributes ghstack-source-id: ac01b53 Pull Request resolved: #106478

This was referenced Aug 8, 2023

[ONNX] Migrate to PT2 logging #106592

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Implement aten::div.Tensor_mode | feat(torchlib) microsoft/onnxscript#988

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titaiwangms added a commit that referenced this pull request Aug 8, 2023

[ONNX] Refactor perfect/nearest match criteria to allow optional inpu…

d6dfb94

…ts and disallow mismatch attributes ghstack-source-id: 107c0aa Pull Request resolved: #106478

titaiwangms added a commit that referenced this pull request Aug 9, 2023

[ONNX] Refactor perfect/nearest match criteria to allow optional inpu…

e0776e7

…ts and disallow mismatch attributes ghstack-source-id: be5eb50 Pull Request resolved: #106478

thiagocrepaldi approved these changes Aug 9, 2023

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BowenBao approved these changes Aug 9, 2023

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titaiwangms added a commit that referenced this pull request Aug 10, 2023

[ONNX] Refactor perfect/nearest match criteria to allow optional inpu…

a964d92

…ts and disallow mismatch attributes ghstack-source-id: ad5465e Pull Request resolved: #106478

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[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

titaiwangms commented Aug 2, 2023 •

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[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

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titaiwangms commented Aug 2, 2023 • edited Loading

Case Study

Optional Input

Different attributes

Default attribute

Ignore attribute with None value

pytorch-bot bot commented Aug 2, 2023 • edited Loading

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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/106478

❗ 1 Active SEVs

✅ 2 Unrelated Failures

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