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- """This file exports ONNX ops for opset 15.
- Note [ONNX operators that are added/updated in opset 15]
- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- https://github.com/onnx/onnx/blob/master/docs/Changelog.md#version-15-of-the-default-onnx-operator-set
- New operators:
- Bernoulli
- CastLike
- Optional
- OptionalGetElement
- OptionalHasElement
- Updated operators:
- BatchNormalization https://github.com/onnx/onnx/pull/3545
- Backwards compatible
- TODO: test coverage for mixed types inputs.
- Pow https://github.com/onnx/onnx/pull/3412
- Backwards compatible
- TODO: bfloat16 support.
- Shape https://github.com/onnx/onnx/pull/3580
- Backwards compatible
- TODO: optional start/end attribute.
- """
- # EDITING THIS FILE? READ THIS FIRST!
- # see Note [Edit Symbolic Files] in symbolic_helper.py
- import torch
- from torch import _C
- from torch.onnx import symbolic_helper
- from torch.onnx import symbolic_opset9 as opset9
- def __is_(g, self, other):
- if symbolic_helper._is_none(other):
- if isinstance(self.type(), _C.OptionalType):
- none = g.op("OptionalHasElement", self)
- return g.op("Not", none)
- else:
- return g.op("Constant", value_t=torch.BoolTensor([0]))
- return opset9.eq(g, self, other)
- @opset9.wrap_logical_op_with_negation
- def __isnot_(g, self, other):
- return __is_(g, self, other)
- class Prim:
- domain = "prim"
- @staticmethod
- def unchecked_cast(g, self):
- # exists to refine the type of the Value
- # if x is Optional[Tensor], unchecked_cast will cast
- # x to Tensor, so the rest of the graph knows that x is a Tensor.
- if isinstance(self.type(), _C.OptionalType):
- return g.op("OptionalGetElement", self)
- return self
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