[ET-VK] Quantized Int8 Convolution + Linear #13811
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Title says it all! This PR adds implementations for int8 quantized convolution and linear layers. Convolution is implemented as matrix multiplication under the hood by using the im2col procedure. For both linear and convolution, two versions are implemented: 1. `q8ta_q8csw` variant which quantized the input tensor and then performs integer accumulation via the int8 dot product extension 2. `q8csw` variant which dequantized the weight tensor in-shader and performs floating point accumulation. The second one is needed to provide an alternative path for executing quantized models if the target GPU does not support int8 dot product extension. These new ops are tested via the custom op testing + benchmarking framework introduced in the previous diff. Differential Revision: [D81323424](https://our.internmc.facebook.com/intern/diff/D81323424/) [ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/13811
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Stack from ghstack (oldest at bottom):
Title says it all!
This PR adds implementations for int8 quantized convolution and linear layers. Convolution is implemented as matrix multiplication under the hood by using the im2col procedure.
For both linear and convolution, two versions are implemented:
q8ta_q8cswvariant which quantized the input tensor and then performs integer accumulation via the int8 dot product extensionq8cswvariant which dequantized the weight tensor in-shader and performs floating point accumulation.The second one is needed to provide an alternative path for executing quantized models if the target GPU does not support int8 dot product extension.
These new ops are tested via the custom op testing + benchmarking framework introduced in the previous diff.
Differential Revision: D81323424