[GitHub] [incubator-mxnet] xinyu-intel commented on a change in pull request #15448: [MKLDNN]Enhance Quantization APIs and Tutorial

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xinyu-intel commented on a change in pull request #15448: [MKLDNN]Enhance Quantization APIs and Tutorial
URL: https://github.com/apache/incubator-mxnet/pull/15448#discussion_r299812483
 
 

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 File path: docs/tutorials/mkldnn/MKLDNN_QUANTIZATION.md
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+
+# Quantize custom models for production-level inference with MKL-DNN backend
+
+This document is to introduce how to quantize the customer models from FP32 to INT8 with Apache/MXNet toolkit and APIs under Intel CPU.
+
+If you are not familiar with Apache/MXNet quantizaiton flow, please reference [quantization blog](https://medium.com/apache-mxnet/model-quantization-for-production-level-neural-network-inference-f54462ebba05) first, and the perforamnce data is shown in [Apache/MXNet C++ interface](https://github.com/apache/incubator-mxnet/tree/master/cpp-package/example/inference) and [GluonCV](https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html). 
+
+## Installation and Prerequisites
+
+Installing MXNet with MKLDNN backend is an easy and essential process. You can follow [How to build and install MXNet with MKL-DNN backend](https://mxnet.incubator.apache.org/tutorials/mkldnn/MKLDNN_README.html) to build and install MXNet from source. Also, you can install the release or nightly version via PyPi and pip directly by running:
+
+```
+# release version
+pip install mxnet-mkl
+# nightly version
+pip install mxnet-mkl --pre
+```
+
+## Image Classification Demo
+
+A quantization script [imagenet_gen_qsym_mkldnn.py](https://github.com/apache/incubator-mxnet/blob/master/example/quantization/imagenet_gen_qsym_mkldnn.py) has been designed to launch quantization for image-classification models. This script is  integrated with [Gluon-CV modelzoo](https://gluon-cv.mxnet.io/model_zoo/classification.html), so that all pre-trained models can be downloaded from Gluon-CV and then converted for quantization. For details, you can refer [Model Quantization with Calibration Examples](https://github.com/apache/incubator-mxnet/blob/master/example/quantization/README.md).
+
+## Integrate Quantization Flow to Your Project
+
+Quantization flow works for both symbolic and Gluon models. If you're using Gluon, you can first refer [Saving and Loading Gluon Models](https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/save_load_params.html) to hybridize your computation graph and export it as a symbol before running quantization.
+
+In general, the quantization flow includes 4 steps. The user can get the acceptable accuracy from step 1 to 3 with minimum effort. Most of thing in this stage is out-of-box and the data scientists and researchers only need to focus on how to represent data and layers in their model. After a quantized model is generated, you may want to deploy it online and the performance will be the next key point. Thus, step 4, calibration, can improve the performance a lot by reducing lots of runtime calculation.
+
+![quantization flow](quantization.png)
+
+Now, we are going to take Gluon ResNet18 as an example to show how each step work.
+
+### Initialize Model
+
+```python
+import logging
+import mxnet as mx
+from mxnet.gluon.model_zoo import vision
+from mxnet.contrib.quantization import *
+
+logging.basicConfig()
+logger = logging.getLogger('logger')
+logger.setLevel(logging.INFO)
+
+batch_shape = (1, 3, 224, 224)
+resnet18 = vision.resnet18_v1(pretrained=True)
+resnet18.hybridize()
+resnet18.forward(mx.nd.zeros(batch_shape))
+resnet18.export('resnet18_v1')
+sym, arg_params, aux_params = mx.model.load_checkpoint('resnet18_v1', 0)
+# (optional) visualize float32 model
+mx.viz.plot_network(sym)
+```
+First, we download resnet18-v1 model from gluon modelzoo and export it as a symbol. You can visualize float32 model. Below is a raw residual block.
+
+![float32 model](fp32_raw.png)
 
 Review comment:
   thanks:)

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