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[ONNX] remove outdated ImplicitCastType QA in onnx.rst (#81268)

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Extend work from: https://github.com/pytorch/pytorch/pull/80596
This PR removes outdated QA of ImplicitCastType , as the coverage is greatly increased with the introduction of onnx shape inference and scalar type analysis.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81268
Approved by: https://github.com/justinchuby, https://github.com/BowenBao
This commit is contained in:
titaiwang 2022-07-15 16:18:26 +00:00 committed by PyTorch MergeBot
parent d68fed56ef
commit 69608fc598
1 changed files with 3 additions and 21 deletions
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24
docs/source/onnx.rst
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@ -570,27 +570,9 @@ Q: How to export models with primitive type inputs (e.g. int, float)?
Q: Does ONNX support implicit scalar datatype casting?
No, but the exporter will try to handle that part. Scalars are exported as constant tensors.
The exporter will try to figure out the right datatype for scalars. However when it is unable
to do so, you will need to manually specify the datatype. This often happens with
scripted models, where the datatypes are not recorded. For example::
class ImplicitCastType(torch.jit.ScriptModule):
@torch.jit.script_method
def forward(self, x):
# Exporter knows x is float32, will export "2" as float32 as well.
y = x + 2
# Currently the exporter doesn't know the datatype of y, so
# "3" is exported as int64, which is wrong!
return y + 3
# To fix, replace the line above with:
# return y + torch.tensor([3], dtype=torch.float32)
x = torch.tensor([1.0], dtype=torch.float32)
torch.onnx.export(ImplicitCastType(), x, "implicit_cast.onnx",
example_outputs=ImplicitCastType()(x))
We are trying to improve the datatype propagation in the exporter such that implicit casting
is supported in more cases.
The exporter will figure out the right data type for scalars. In rare cases when it is unable
to do so, you will need to manually specify the datatype with e.g. `dtype=torch.float32`.
If you see any errors, please [create a GitHub issue](https://github.com/pytorch/pytorch/issues).
Q: Are lists of Tensors exportable to ONNX?