Introduce function-local settings for executor, expose in c++ #74012
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…c++" This allows setting an executor on a function. The first use case is use to decompositions in C++ without additional fusion passes etc which might not work with custom tensors like batched tensors/vmap. A subsequent use case might be taking advantage of invokees of JIT execution which guard on certain properties before invocation (such as complete shapes in AOT autograd, rank in lazy tensor). [ghstack-poisoned]
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Krovatkin
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Mar 10, 2022
| return *execution_plan_; | ||
| } | ||
| auto copy = graph->copy(); | ||
| runNooptPassPipeline(copy); |
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lmao why do we call this Noopt ? :)
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| graph_(std::move(graph)), | ||
| function_creator_(std::move(function_creator)) {} | ||
| function_creator_(std::move(function_creator)) { | ||
| executor_execution_mode_ = executor_execution_mode; |
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why is this initialized inside a c-tor vs doing it like this: graph_(std::move(graph)),
…c++" This allows setting an executor on a function. The first use case is use to decompositions in C++ without additional fusion passes etc which might not work with custom tensors like batched tensors/vmap. A subsequent use case might be taking advantage of invokees of JIT execution which guard on certain properties before invocation (such as complete shapes in AOT autograd, rank in lazy tensor). [ghstack-poisoned]
…c++" This allows setting an executor on a function. The first use case is use to decompositions in C++ without additional fusion passes etc which might not work with custom tensors like batched tensors/vmap. A subsequent use case might be taking advantage of invokees of JIT execution which guard on certain properties before invocation (such as complete shapes in AOT autograd, rank in lazy tensor). [ghstack-poisoned]
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…c++" This allows setting an executor on a function. The first use case is use to decompositions in C++ without additional fusion passes etc which might not work with custom tensors like batched tensors/vmap. A subsequent use case might be taking advantage of invokees of JIT execution which guard on certain properties before invocation (such as complete shapes in AOT autograd, rank in lazy tensor). Differential Revision: [D34938124](https://our.internmc.facebook.com/intern/diff/D34938124) [ghstack-poisoned]
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This was referenced Mar 16, 2022
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…c++" This allows setting an executor on a function. The first use case is use to decompositions in C++ without additional fusion passes etc which might not work with custom tensors like batched tensors/vmap. A subsequent use case might be taking advantage of invokees of JIT execution which guard on certain properties before invocation (such as complete shapes in AOT autograd, rank in lazy tensor). Differential Revision: [D34938124](https://our.internmc.facebook.com/intern/diff/D34938124) [ghstack-poisoned]
…c++" This allows setting an executor on a function. The first use case is use to decompositions in C++ without additional fusion passes etc which might not work with custom tensors like batched tensors/vmap. A subsequent use case might be taking advantage of invokees of JIT execution which guard on certain properties before invocation (such as complete shapes in AOT autograd, rank in lazy tensor). Differential Revision: [D34938124](https://our.internmc.facebook.com/intern/diff/D34938124) [ghstack-poisoned]
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…c++" This allows setting an executor on a function. The first use case is use to decompositions in C++ without additional fusion passes etc which might not work with custom tensors like batched tensors/vmap. A subsequent use case might be taking advantage of invokees of JIT execution which guard on certain properties before invocation (such as complete shapes in AOT autograd, rank in lazy tensor). Differential Revision: [D34938124](https://our.internmc.facebook.com/intern/diff/D34938124) [ghstack-poisoned]
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added 2 commits
March 22, 2022 09:32
…c++" This allows setting an executor on a function. The first use case is use to decompositions in C++ without additional fusion passes etc which might not work with custom tensors like batched tensors/vmap. A subsequent use case might be taking advantage of invokees of JIT execution which guard on certain properties before invocation (such as complete shapes in AOT autograd, rank in lazy tensor). Differential Revision: [D34938124](https://our.internmc.facebook.com/intern/diff/D34938124) [ghstack-poisoned]
…c++" This allows setting an executor on a function. The first use case is use to decompositions in C++ without additional fusion passes etc which might not work with custom tensors like batched tensors/vmap. A subsequent use case might be taking advantage of invokees of JIT execution which guard on certain properties before invocation (such as complete shapes in AOT autograd, rank in lazy tensor). Differential Revision: [D34938124](https://our.internmc.facebook.com/intern/diff/D34938124) [ghstack-poisoned]
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…c++" This allows setting an executor on a function. The first use case is use to decompositions in C++ without additional fusion passes etc which might not work with custom tensors like batched tensors/vmap. A subsequent use case might be taking advantage of invokees of JIT execution which guard on certain properties before invocation (such as complete shapes in AOT autograd, rank in lazy tensor). Differential Revision: [D34938124](https://our.internmc.facebook.com/intern/diff/D34938124) [ghstack-poisoned]
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…c++" This allows setting an executor on a function. The first use case is use to decompositions in C++ without additional fusion passes etc which might not work with custom tensors like batched tensors/vmap. A subsequent use case might be taking advantage of invokees of JIT execution which guard on certain properties before invocation (such as complete shapes in AOT autograd, rank in lazy tensor). Differential Revision: [D34938124](https://our.internmc.facebook.com/intern/diff/D34938124) [ghstack-poisoned]
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@eellison has imported this pull request. If you are a Facebook employee, you can view this diff on Phabricator. |
…c++" This allows setting an executor on a function. The first use case is use to decompositions in C++ without additional fusion passes etc which might not work with custom tensors like batched tensors/vmap. A subsequent use case might be taking advantage of invokees of JIT execution which guard on certain properties before invocation (such as complete shapes in AOT autograd, rank in lazy tensor). Differential Revision: [D34938124](https://our.internmc.facebook.com/intern/diff/D34938124) [ghstack-poisoned]
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@eellison has imported this pull request. If you are a Facebook employee, you can view this diff on Phabricator. |
…c++" This allows setting an executor on a function. The first use case is use to decompositions in C++ without additional fusion passes etc which might not work with custom tensors like batched tensors/vmap. A subsequent use case might be taking advantage of invokees of JIT execution which guard on certain properties before invocation (such as complete shapes in AOT autograd, rank in lazy tensor). Differential Revision: [D34938124](https://our.internmc.facebook.com/intern/diff/D34938124) [ghstack-poisoned]
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@eellison has imported this pull request. If you are a Facebook employee, you can view this diff on Phabricator. |
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Summary: Pull Request resolved: #74012 This allows setting an executor on a function. The first use case is use to decompositions in C++ without additional fusion passes etc which might not work with custom tensors like batched tensors/vmap. A subsequent use case might be taking advantage of invokees of JIT execution which guard on certain properties before invocation (such as complete shapes in AOT autograd, rank in lazy tensor). Test Plan: Imported from OSS Reviewed By: gchanan Differential Revision: D34938124 Pulled By: eellison fbshipit-source-id: cf7a45416457942b872322cab47d871a8336bdb5
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ghstack-source-id: 0f315ff Pull Request resolved: pytorch/pytorch#74012
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ghstack-source-id: cf25ac1 Pull Request resolved: pytorch/pytorch#74012
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Stack from ghstack:
This allows setting an executor on a function. The first use case is use to decompositions in C++ without additional fusion passes etc which might not work with custom tensors like batched tensors/vmap. A subsequent use case might be taking advantage of invokees of JIT execution which guard on certain properties before invocation (such as complete shapes in AOT autograd, rank in lazy tensor).
Differential Revision: D34938124