From bee7517219ad082fc6463c04bf9e7203c11f6f03 Mon Sep 17 00:00:00 2001 From: Frank Yu Date: Thu, 6 Mar 2025 07:21:49 -0800 Subject: [PATCH] Fix typo in executorch documentation Summary: Fix a typo in executorch documentation https://pytorch.org/executorch/main/backends-xnnpack.html Reviewed By: cccclai Differential Revision: D70645356 --- docs/source/backends-xnnpack.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/source/backends-xnnpack.md b/docs/source/backends-xnnpack.md index b07543f5c68..99780f17648 100644 --- a/docs/source/backends-xnnpack.md +++ b/docs/source/backends-xnnpack.md @@ -1,6 +1,6 @@ # XNNPACK Backend -The XNNPACK delegate is the ExecuTorch solution for CPU execution on mobile CPUs. XNNPACK is a library that provides optimized kernels for machine learning operators on Arm and x86 CPUs. +The XNNPACK delegate is the ExecuTorch solution for CPU execution on mobile CPUs. XNNPACK is a library that provides optimized kernels for machine learning operators on Arm and x86 CPUs. ## Features @@ -51,7 +51,7 @@ The XNNPACK partitioner API allows for configuration of the model delegation to ### Quantization -The XNNPACK delegate can also be used as a backend to execute symmetrically quantized models. To quantize a PyTorch model for the XNNPACK backend, use the `XNNPACKQuantizer`. `Quantizers` are backend specific, which means the `XNNPACKQuantizer` is configured to quantize models to leverage the quantized operators offered by the XNNPACK Library. +The XNNPACK delegate can also be used as a backend to execute symmetrically quantized models. To quantize a PyTorch model for the XNNPACK backend, use the `XNNPACKQuantizer`. `Quantizers` are backend specific, which means the `XNNPACKQuantizer` is configured to quantize models to leverage the quantized operators offered by the XNNPACK Library. ### Configuring the XNNPACKQuantizer @@ -95,7 +95,7 @@ for cal_sample in cal_samples: # Replace with representative model inputs quantized_model = convert_pt2e(prepared_model) ``` -For static, post-training quantization (PTQ), the post-prepare\_pt2e model should beS run with a representative set of samples, which are used to determine the quantization parameters. +For static, post-training quantization (PTQ), the post-prepare\_pt2e model should be run with a representative set of samples, which are used to determine the quantization parameters. After `convert_pt2e`, the model can be exported and lowered using the normal ExecuTorch XNNPACK flow. For more information on PyTorch 2 quantization [here](https://pytorch.org/tutorials/prototype/pt2e_quant_ptq.html).