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Enable RANGE definition in shader YAML for repetitive shader generation #4196
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/4196
Note: Links to docs will display an error until the docs builds have been completed. ❗ 1 Active SEVsThere are 1 currently active SEVs. If your PR is affected, please view them below: This comment was automatically generated by Dr. CI and updates every 15 minutes. |
This pull request was exported from Phabricator. Differential Revision: D59588223 |
This pull request was exported from Phabricator. Differential Revision: D59588223 |
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…on (pytorch#4196) Summary: Pull Request resolved: pytorch#4196 GPUInfo requires the compilation of a ton of shaders with small variations to emulate OpenCL's kernel compilation on runtime with strings. This diff enables a RANGE implementation to generate these shaders dynamically without typing each value out. The format is - RANGE: [start, end] // Inclusive SUFFIX: string // Optional If a suffix is selected, the shaders will be called `name_suffix_K ` for each value K from the range. If no suffix is selected, the shaders will be simply called `name_K ` Differential Revision: D59588223
This pull request was exported from Phabricator. Differential Revision: D59588223 |
…on (pytorch#4196) Summary: Pull Request resolved: pytorch#4196 GPUInfo requires the compilation of a ton of shaders with small variations to emulate OpenCL's kernel compilation on runtime with strings. This diff enables a RANGE implementation to generate these shaders dynamically without typing each value out. The format is - RANGE: [start, end] // Inclusive SUFFIX: string // Optional If a suffix is selected, the shaders will be called `name_suffix_K ` for each value K from the range. If no suffix is selected, the shaders will be simply called `name_K ` Differential Revision: D59588223
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This pull request was exported from Phabricator. Differential Revision: D59588223 |
…on (pytorch#4196) Summary: Pull Request resolved: pytorch#4196 GPUInfo requires the compilation of a ton of shaders with small variations to emulate OpenCL's kernel compilation on runtime with strings. This diff enables a RANGE implementation to generate these shaders dynamically without typing each value out. The format is - RANGE: [start, end] // Inclusive SUFFIX: string // Optional If a suffix is selected, the shaders will be called `name_suffix_K ` for each value K from the range. If no suffix is selected, the shaders will be simply called `name_K ` Reviewed By: SS-JIA Differential Revision: D59588223
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Summary: Pull Request resolved: pytorch#4159 This adds an internal implementation of https://github.com/microsoft/ArchProbe. This stack introduces a kernel that can be used to get the number of available registers on a mobile GPU by gradually increasing the number of accessed elements and detecting dramatic drops in performance. See [this paper ](https://www.microsoft.com/en-us/research/uploads/prod/2022/02/mobigpu_mobicom22_camera.pdf), page 4, for more information. This first diff gets the number of iterations (NITER) that can run in 1000us, to be used in the following tests. The kernel looks like the following for any K number of registers: float reg_data0 = float(niter) + 0; float reg_data1 = float(niter) + 1; ... float reg_dataK = float(niter) + K; int i = 0; for (; i < niter; ++i) { reg_data0 *= reg_dataK; reg_data1 *= reg_data0; reg_data2 *= reg_data1; ... reg_dataK *= reg_data(K-1); } i = i >> 31; buffer_out.data[0 * i] = reg_data0; buffer_out.data[1 * i] = reg_data1; ... buffer_out.data[K * i] = reg_dataK; Differential Revision: D59405012 Reviewed By: SS-JIA
Summary: Pull Request resolved: pytorch#4171 This project adds an internal implementation of https://github.com/microsoft/ArchProbe. This stack introduces a kernel that can be used to get the number of available registers on a mobile GPU by gradually increasing the number of accessed elements and detecting dramatic drops in performance. See [this paper](https://www.microsoft.com/en-us/research/uploads/prod/2022/02/mobigpu_mobicom22_camera.pdf), page 4, for more information. This diffs finds the number of registers in a single thread by increasing the number of registers and finding changes in latency. For a Galaxy S22, the latency graph looks like this. {F1750619092} We can easily spot the moment where there is a spill into memory. Differential Revision: D59494644 Reviewed By: SS-JIA
Summary: Pull Request resolved: pytorch#4173 This project adds an internal implementation of https://github.com/microsoft/ArchProbe. This stack introduces a kernel that can be used to get the number of available registers on a mobile GPU by gradually increasing the number of accessed elements and detecting dramatic drops in performance. See [this paper](https://www.microsoft.com/en-us/research/uploads/prod/2022/02/mobigpu_mobicom22_camera.pdf), page 4, for more information. This diff finds the number of concurrency groups available for full registry usage, and half registry usage. On a Samsung Galaxy S22, the latency graphs for full and half registry usage look like this: Full: {F1750677545} Half: {F1750679467} Differential Revision: D59497314 Reviewed By: SS-JIA
This pull request was exported from Phabricator. Differential Revision: D59588223 |
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…on (pytorch#4196) Summary: Pull Request resolved: pytorch#4196 GPUInfo requires the compilation of a ton of shaders with small variations to emulate OpenCL's kernel compilation on runtime with strings. This diff enables a RANGE implementation to generate these shaders dynamically without typing each value out. The format is - RANGE: [start, end] // Inclusive SUFFIX: string // Optional If a suffix is selected, the shaders will be called `name_suffix_K ` for each value K from the range. If no suffix is selected, the shaders will be simply called `name_K ` Reviewed By: SS-JIA Differential Revision: D59588223
This pull request was exported from Phabricator. Differential Revision: D59588223 |
…on (pytorch#4196) Summary: Pull Request resolved: pytorch#4196 GPUInfo requires the compilation of a ton of shaders with small variations to emulate OpenCL's kernel compilation on runtime with strings. This diff enables a RANGE implementation to generate these shaders dynamically without typing each value out. The format is - RANGE: [start, end] // Inclusive SUFFIX: string // Optional If a suffix is selected, the shaders will be called `name_suffix_K ` for each value K from the range. If no suffix is selected, the shaders will be simply called `name_K ` Reviewed By: SS-JIA Differential Revision: D59588223
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…on (pytorch#4196) Summary: Pull Request resolved: pytorch#4196 GPUInfo requires the compilation of a ton of shaders with small variations to emulate OpenCL's kernel compilation on runtime with strings. This diff enables a RANGE implementation to generate these shaders dynamically without typing each value out. The format is - RANGE: [start, end] // Inclusive SUFFIX: string // Optional If a suffix is selected, the shaders will be called `name_suffix_K ` for each value K from the range. If no suffix is selected, the shaders will be simply called `name_K ` Reviewed By: SS-JIA Differential Revision: D59588223
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This pull request has been merged in c698791. |
Summary:
GPUInfo requires the compilation of a ton of shaders with small variations to emulate OpenCL's kernel compilation on runtime with strings. This diff enables a RANGE implementation to generate these shaders dynamically without typing each value out.
The format is
If a suffix is selected, the shaders will be called
name_suffix_K
for each value K from the range. If no suffix is selected, the shaders will be simply calledname_K
Differential Revision: D59588223