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[ONNX] Support 'aten::randint' in torchscript onnx exporter #105089

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[ONNX] Support 'aten::randint' in torchscript onnx exporter #105089

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@BowenBao BowenBao commented Jul 12, 2023 •
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Stack from ghstack (oldest at bottom):

Export as 'ONNX::RandomUniform' which produces floating point result,
then round it to integer with 'ONNX::Cast'.

Fixes https://github.com/microsoft/onnx-converters-private/issues/173

@BowenBao
[ONNX] Support 'aten::randint' in torchscript onnx exporter
bccc4a0 
Export as 'ONNX::RandomUniform' which produces floating point result,
then round it to integer with 'ONNX::Cast'.

[ghstack-poisoned]
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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/105089

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@pytorch-bot pytorch-bot bot added the release notes: onnx torch.onnx related changes that should show up in the release notes label Jul 12, 2023
@BowenBao
randint_like as well on "[ONNX] Support 'aten::randint' in torchscrip…
ffd7663 
…t onnx exporter"

Export as 'ONNX::RandomUniform' which produces floating point result,
then round it to integer with 'ONNX::Cast'.

[ghstack-poisoned]
BowenBao added a commit that referenced this pull request Jul 12, 2023
@BowenBao
[ONNX] Support 'aten::randint' in torchscript onnx exporter
8bb7b6b 
Export as 'ONNX::RandomUniform' which produces floating point result,
then round it to integer with 'ONNX::Cast'.

ghstack-source-id: 07e0782
Pull Request resolved: #105089
@BowenBao BowenBao added topic: improvements topic category ciflow/trunk Trigger trunk jobs on your pull request labels Jul 12, 2023
thiagocrepaldi
thiagocrepaldi approved these changes Jul 12, 2023
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LGTM. I am assuming we don't care testing torch.jit.script scenarios at this point. scalar_type handling usually crash for script as opposed to tracing

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@justinchuby justinchuby mentioned this pull request Jul 14, 2023
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justinchuby
justinchuby reviewed Jul 14, 2023
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torch/onnx/symbolic_opset9.py
Comment on lines +5167 to +5169
randint = g.op("Cast", randn, to_i=int_dtype.onnx_type())
if int_dtype != scalar_type:
randint = g.op("Cast", randint, to_i=scalar_type.onnx_type())
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Trying to understand: why do we need to cast twice?

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first time to "floor" floating random number to integer.
second time the dtype is part of op argument, could be floating number.

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I see, thanks!

@facebook-github-bot facebook-github-bot deleted the gh/BowenBao/262/head branch July 16, 2023 14:15
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@thiagocrepaldi thiagocrepaldi thiagocrepaldi approved these changes

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