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TorchScriptTensor to cache torch dtype for performance | feat(torchlib) #922

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Merged
merged 1 commit into from
Jul 26, 2023
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TorchScriptTensor to cache torch dtype for performance | feat(torchlib) #922

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7 changes: 6 additions & 1 deletion onnxscript/function_libs/torch_lib/graph_building.py
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Original file line number Diff line number Diff line change
Expand Up @@ -89,6 +89,7 @@ def __init__(self, value: torch.Value):
self._torch_value: torch.Value = value
self._concrete_value: Optional[np.ndarray] = None
self._shape: Optional[Tuple[int | None, ...]] = None
self._torch_dtype: Optional[torch.dtype] = None
self._name: Optional[str] = None
self._is_complex: bool = False

Expand Down Expand Up @@ -149,15 +150,19 @@ def shape(self, shape: Tuple[int | None, ...]):
@property # type: ignore[override]
def dtype(self) -> torch.dtype | None:
# TODO: Return numpy dtype
if self._torch_dtype is not None:
return self._torch_dtype
torch_dtype = _type_utils.JitScalarType.from_value( # type: ignore[attr-defined]
self._torch_value, default=_type_utils.JitScalarType.UNDEFINED
)
if torch_dtype == _type_utils.JitScalarType.UNDEFINED:
return None
return torch_dtype.dtype()
self._torch_dtype = torch_dtype.dtype()
return self._torch_dtype

@dtype.setter
def dtype(self, dtype: torch.dtype):
self._torch_dtype = dtype
self._torch_value.setType(self._torch_value.type().with_dtype(dtype))

@property
Expand Down