vinx13 commented on a change in pull request #4667: [Tutorial] Deploy Quantized Model on CUDA URL: https://github.com/apache/incubator-tvm/pull/4667#discussion_r365245824
########## File path: tutorials/frontend/deploy_quantized.py ########## @@ -0,0 +1,154 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +""" +Deploy a Quantized Model on Cuda +================================ +**Author**: `Wuwei Lin <https://github.com/vinx13>`_ + +This article is an introductory tutorial of automatic quantization with TVM. +We will import a GluonCV pre-trained model on ImageNet to Relay, quantize the Relay model +and then perform the inference. +""" + +import tvm +from tvm import relay +from tvm.relay import quantize as qtz +import mxnet as mx +from tvm.contrib.download import download_testdata +from mxnet import gluon +import logging +import os + +batch_size = 1 +model_name = "resnet18_v1" +target = 'cuda' +ctx = tvm.context(target) + +############################################################################### +# Prepare the Dataset +# ------------------- +# We will demonstrate how to prepare the calibration dataset for quantization. +# We first download the validate set of ImageNet and pre-process the dataset. +calibration_rec = download_testdata( + 'http://data.mxnet.io.s3-website-us-west-1.amazonaws.com/data/val_256_q90.rec', + 'val_256_q90.rec') + +def get_val_data(num_workers=4): + mean_rgb = [123.68, 116.779, 103.939] + std_rgb = [58.393, 57.12, 57.375] + + def batch_fn(batch): + return batch.data[0].asnumpy(), batch.label[0].asnumpy() + + img_size = 299 if model_name == 'inceptionv3' else 224 + val_data = mx.io.ImageRecordIter( + path_imgrec=calibration_rec, + preprocess_threads=num_workers, + shuffle=False, + batch_size=batch_size, + resize=256, + data_shape=(3, img_size, img_size), + mean_r=mean_rgb[0], + mean_g=mean_rgb[1], + mean_b=mean_rgb[2], + std_r=std_rgb[0], + std_g=std_rgb[1], + std_b=std_rgb[2], + ) + return val_data, batch_fn + + +############################################################################### +# The calibration dataset should be a iterable object. We define the +# calibration dataset as a generator object in Python. In this tutorials, we +# only use a few samples for calibration. + +calibration_samples = 10 + +def calibrate_dataset(): + val_data, batch_fn = get_val_data() + val_data.reset() + for i, batch in enumerate(val_data): + if i * batch_size >= calibration_samples: + break + data, _ = batch_fn(batch) + yield {'data': data} + + +############################################################################### +# Import the model +# ---------------- +# We use the Relay MxNet frontent to import a model from the Gluon model zoo. +def get_model(): + gluon_model = gluon.model_zoo.vision.get_model(model_name, pretrained=True) + img_size = 299 if model_name == 'inceptionv3' else 224 + data_shape = (batch_size, 3, img_size, img_size) + mod, params = relay.frontend.from_mxnet(gluon_model, {"data": data_shape}) + return mod, params + + +############################################################################### +# Quantize the Model +# ------------------ +# In quantization, we need to find the scale for each weight and output tensor. +# For weights, the scales are directly calculated based on the value of the +# weights. Two modes are supported: `power2` and `max`. Both modes find the +# maximum value within the weight tensor first. In `power2` mode, the maximum +# is rounded down to power of two. If the scales of both weights and outputs +# are power of two, we can leverage bit shifting for multiplications. This make +# it computationally more efficient. In `max` mode, the maximum is used as the +# scale. Without rounding, `max` mode might have better accuracy in some cases. +# When the scales are not power of two, fixed point multiplications will +# be used. +# +# For outputs, we can find the scales with data-aware quantization. +# Data-aware quantization takes a calibration dataset as the input argument. +# Scales are calculated by minimizing the KL divergence of between the data Review comment: it is not necessarily 'activation', so i updated to 'output of each layer' ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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