[xnnpack][lite-int][on-device] rebuild serialized modules at runtime #88780
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This is the on-device runtime work. We modify the compile and execute from our hacky solution from before to what will actually be running at runtime. First we rebuild our graph from the serialized flatbuffer string. We also introduce a runtime wrapper that inherits CustomClassHolder that allows us to forward along the built xnngraph runtime to our execute function Once the subgraph object has been rebuilt by our we pass it along to the runtime wrapper for us to forward along to execute At execute we prep the input/outputs and invoke the runtime using our runtime wrapper. Finally we forward those results to our execution Differential Revision: [D39413031](https://our.internmc.facebook.com/intern/diff/D39413031/) **NOTE FOR REVIEWERS**: This PR has internal Meta-specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D39413031/)! [ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/88780
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This was referenced Nov 9, 2022
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This is the on-device runtime work. We modify the compile and execute from our hacky solution from before to what will actually be running at runtime. First we rebuild our graph from the serialized flatbuffer string. We also introduce a runtime wrapper that inherits CustomClassHolder that allows us to forward along the built xnngraph runtime to our execute function Once the subgraph object has been rebuilt by our we pass it along to the runtime wrapper for us to forward along to execute At execute we prep the input/outputs and invoke the runtime using our runtime wrapper. Finally we forward those results to our execution Differential Revision: [D39413031](https://our.internmc.facebook.com/intern/diff/D39413031/) **NOTE FOR REVIEWERS**: This PR has internal Meta-specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D39413031/)! ghstack-source-id: 173215762 Pull Request resolved: #88780
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@pytorchbot merge (Initiating merge automatically since Phabricator Diff has merged) |
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@pytorchbot merge (Initiating merge automatically since Phabricator Diff has merged) |
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…ytorch#88780) This is the on-device runtime work. We modify the compile and execute from our hacky solution from before to what will actually be running at runtime. First we rebuild our graph from the serialized flatbuffer string. We also introduce a runtime wrapper that inherits CustomClassHolder that allows us to forward along the built xnngraph runtime to our execute function Once the subgraph object has been rebuilt by our we pass it along to the runtime wrapper for us to forward along to execute At execute we prep the input/outputs and invoke the runtime using our runtime wrapper. Finally we forward those results to our execution Differential Revision: [D39413031](https://our.internmc.facebook.com/intern/diff/D39413031/) **NOTE FOR REVIEWERS**: This PR has internal Meta-specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D39413031/)! Pull Request resolved: pytorch#88780 Approved by: https://github.com/digantdesai
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Stack from ghstack (oldest at bottom):
This is the on-device runtime work. We modify the compile and execute from our hacky solution from before to what will actually be running at runtime.
First we rebuild our graph from the serialized flatbuffer string. We also introduce a runtime wrapper that inherits CustomClassHolder that allows us to forward along the built xnngraph runtime to our execute function
Once the subgraph object has been rebuilt by our we pass it along to the runtime wrapper for us to forward along to execute
At execute we prep the input/outputs and invoke the runtime using our runtime wrapper. Finally we forward those results to our execution
Differential Revision: D39413031
NOTE FOR REVIEWERS: This PR has internal Meta-specific changes or comments, please review them on Phabricator!