[xnnpack][on-device] executor class #88778
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# Executor Class Executor object used to wrap our xnn_runtime object. The ideal flow of this object looks as such: ``` executor.set_inputs(vector<tensor> inputs, vector<tensor> outputs) executor.forward() ``` This will likely be returned by our delegate compile and given over to execute in order to run inference using the xnn runtime ##### Executorch Considerations ``` #include <ATen/Functions.h> #include <ATen/Utils.h> ``` These Aten functions are included in order to use at::Tensor when setting the inputs, this will change when used for Executorch because we will be switching from at::Tensor to whatever tensor abstraction is used for ET. Seems like they have the same call for `.data_ptr<float>()`, so realistically all logic here will be the same. ATen/Utils is used for TORCH_CHECK. We will switch to ET_CHECK_MESSAGE for executorch. Differential Revision: [D40733121](https://our.internmc.facebook.com/intern/diff/D40733121/) [ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/88778
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# Executor Class Executor object used to wrap our xnn_runtime object. The ideal flow of this object looks as such: ``` executor.set_inputs(vector<tensor> inputs, vector<tensor> outputs) executor.forward() ``` This will likely be returned by our delegate compile and given over to execute in order to run inference using the xnn runtime ##### Executorch Considerations ``` #include <ATen/Functions.h> #include <ATen/Utils.h> ``` These Aten functions are included in order to use at::Tensor when setting the inputs, this will change when used for Executorch because we will be switching from at::Tensor to whatever tensor abstraction is used for ET. Seems like they have the same call for `.data_ptr<float>()`, so realistically all logic here will be the same. ATen/Utils is used for TORCH_CHECK. We will switch to ET_CHECK_MESSAGE for executorch. Differential Revision: [D40733121](https://our.internmc.facebook.com/intern/diff/D40733121/) ghstack-source-id: 172010559 Pull Request resolved: #88778
# Executor Class Executor object used to wrap our xnn_runtime object. The ideal flow of this object looks as such: ``` executor.set_inputs(vector<tensor> inputs, vector<tensor> outputs) executor.forward() ``` This will likely be returned by our delegate compile and given over to execute in order to run inference using the xnn runtime ##### Executorch Considerations ``` #include <ATen/Functions.h> #include <ATen/Utils.h> ``` These Aten functions are included in order to use at::Tensor when setting the inputs, this will change when used for Executorch because we will be switching from at::Tensor to whatever tensor abstraction is used for ET. Seems like they have the same call for `.data_ptr<float>()`, so realistically all logic here will be the same. ATen/Utils is used for TORCH_CHECK. We will switch to ET_CHECK_MESSAGE for executorch. Differential Revision: [D40733121](https://our.internmc.facebook.com/intern/diff/D40733121/) [ghstack-poisoned]
mcr229
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Pull Request resolved: #88778 # Executor Class Executor object used to wrap our xnn_runtime object. The ideal flow of this object looks as such: ``` executor.set_inputs(vector<tensor> inputs, vector<tensor> outputs) executor.forward() ``` This will likely be returned by our delegate compile and given over to execute in order to run inference using the xnn runtime ##### Executorch Considerations ``` #include <ATen/Functions.h> #include <ATen/Utils.h> ``` These Aten functions are included in order to use at::Tensor when setting the inputs, this will change when used for Executorch because we will be switching from at::Tensor to whatever tensor abstraction is used for ET. Seems like they have the same call for `.data_ptr<float>()`, so realistically all logic here will be the same. ATen/Utils is used for TORCH_CHECK. We will switch to ET_CHECK_MESSAGE for executorch. ghstack-source-id: 173215553 Differential Revision: [D40733121](https://our.internmc.facebook.com/intern/diff/D40733121/)
This was referenced Nov 9, 2022
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# Executor Class Executor object used to wrap our xnn_runtime object. The ideal flow of this object looks as such: ``` executor.set_inputs(vector<tensor> inputs, vector<tensor> outputs) executor.forward() ``` This will likely be returned by our delegate compile and given over to execute in order to run inference using the xnn runtime ##### Executorch Considerations ``` #include <ATen/Functions.h> #include <ATen/Utils.h> ``` These Aten functions are included in order to use at::Tensor when setting the inputs, this will change when used for Executorch because we will be switching from at::Tensor to whatever tensor abstraction is used for ET. Seems like they have the same call for `.data_ptr<float>()`, so realistically all logic here will be the same. ATen/Utils is used for TORCH_CHECK. We will switch to ET_CHECK_MESSAGE for executorch. Differential Revision: [D40733121](https://our.internmc.facebook.com/intern/diff/D40733121/) Pull Request resolved: pytorch#88778 Approved by: https://github.com/digantdesai
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Stack from ghstack (oldest at bottom):
Executor Class
Executor object used to wrap our xnn_runtime object. The ideal flow of this object looks as such:
This will likely be returned by our delegate compile and given over to execute in order to run inference using the xnn runtime
Executorch Considerations
These Aten functions are included in order to use at::Tensor when setting the inputs, this will change when used for Executorch because we will be switching from at::Tensor to whatever tensor abstraction is used for ET. Seems like they have the same call for
.data_ptr<float>(), so realistically all logic here will be the same.ATen/Utils is used for TORCH_CHECK. We will switch to ET_CHECK_MESSAGE for executorch.
Differential Revision: D40733121