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hello_nas.py
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hello_nas.py
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"""
Hello, NAS!
===========
This is the 101 tutorial of Neural Architecture Search (NAS) on NNI.
In this tutorial, we will search for a neural architecture on MNIST dataset with the help of NAS framework of NNI, i.e., *Retiarii*.
We use multi-trial NAS as an example to show how to construct and explore a model space.
There are mainly three crucial components for a neural architecture search task, namely,
* Model search space that defines a set of models to explore.
* A proper strategy as the method to explore this model space.
* A model evaluator that reports the performance of every model in the space.
Currently, PyTorch is the only supported framework by Retiarii, and we have only tested **PyTorch 1.9 to 1.13**.
This tutorial assumes PyTorch context but it should also apply to other frameworks, which is in our future plan.
Define your Model Space
-----------------------
Model space is defined by users to express a set of models that users want to explore, which contains potentially good-performing models.
In this framework, a model space is defined with two parts: a base model and possible mutations on the base model.
"""
# %%
#
# Define Base Model
# ^^^^^^^^^^^^^^^^^
#
# Defining a base model is almost the same as defining a PyTorch (or TensorFlow) model.
#
# Below is a very simple example of defining a base model.
import torch
import torch.nn as nn
import torch.nn.functional as F
import nni
from nni.nas.nn.pytorch import LayerChoice, ModelSpace, MutableDropout, MutableLinear
class Net(ModelSpace): # should inherit ModelSpace rather than nn.Module
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout(0.25)
self.dropout2 = nn.Dropout(0.5)
self.fc1 = nn.Linear(9216, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(self.conv2(x), 2)
x = torch.flatten(self.dropout1(x), 1)
x = self.fc2(self.dropout2(F.relu(self.fc1(x))))
output = F.log_softmax(x, dim=1)
return output
# %%
#
# Define Model Variations
# ^^^^^^^^^^^^^^^^^^^^^^^
#
# A base model is only one concrete model not a model space. We provide :doc:`API and Primitives </nas/construct_space>`
# for users to express how the base model can be mutated. That is, to build a model space which includes many models.
#
# Based on the above base model, we can define a model space as below.
#
# .. code-block:: diff
#
# class Net(ModelSpace):
# def __init__(self):
# super().__init__()
# self.conv1 = nn.Conv2d(1, 32, 3, 1)
# - self.conv2 = nn.Conv2d(32, 64, 3, 1)
# + self.conv2 = LayerChoice([
# + nn.Conv2d(32, 64, 3, 1),
# + DepthwiseSeparableConv(32, 64)
# + ], label='conv2)
# - self.dropout1 = nn.Dropout(0.25)
# + self.dropout1 = MutableDropout(nni.choice('dropout', [0.25, 0.5, 0.75]))
# self.dropout2 = nn.Dropout(0.5)
# - self.fc1 = nn.Linear(9216, 128)
# - self.fc2 = nn.Linear(128, 10)
# + feature = nni.choice('feature', [64, 128, 256])
# + self.fc1 = MutableLinear(9216, feature)
# + self.fc2 = MutableLinear(feature, 10)
#
# def forward(self, x):
# x = F.relu(self.conv1(x))
# x = F.max_pool2d(self.conv2(x), 2)
# x = torch.flatten(self.dropout1(x), 1)
# x = self.fc2(self.dropout2(F.relu(self.fc1(x))))
# output = F.log_softmax(x, dim=1)
# return output
#
# This results in the following code:
class DepthwiseSeparableConv(nn.Module):
def __init__(self, in_ch, out_ch):
super().__init__()
self.depthwise = nn.Conv2d(in_ch, in_ch, kernel_size=3, groups=in_ch)
self.pointwise = nn.Conv2d(in_ch, out_ch, kernel_size=1)
def forward(self, x):
return self.pointwise(self.depthwise(x))
class MyModelSpace(ModelSpace):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
# LayerChoice is used to select a layer between Conv2d and DwConv.
self.conv2 = LayerChoice([
nn.Conv2d(32, 64, 3, 1),
DepthwiseSeparableConv(32, 64)
], label='conv2')
# nni.choice is used to select a dropout rate.
# The result can be used as parameters of `MutableXXX`.
self.dropout1 = MutableDropout(nni.choice('dropout', [0.25, 0.5, 0.75])) # choose dropout rate from 0.25, 0.5 and 0.75
self.dropout2 = nn.Dropout(0.5)
feature = nni.choice('feature', [64, 128, 256])
self.fc1 = MutableLinear(9216, feature)
self.fc2 = MutableLinear(feature, 10)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(self.conv2(x), 2)
x = torch.flatten(self.dropout1(x), 1)
x = self.fc2(self.dropout2(F.relu(self.fc1(x))))
output = F.log_softmax(x, dim=1)
return output
model_space = MyModelSpace()
model_space
# %%
# This example uses two mutation APIs,
# :class:`nn.LayerChoice <nni.nas.nn.pytorch.LayerChoice>` and
# :func:`nni.choice`.
# :class:`nn.LayerChoice <nni.nas.nn.pytorch.LayerChoice>`
# takes a list of candidate modules (two in this example), one will be chosen for each sampled model.
# It can be used like normal PyTorch module.
# :func:`nni.choice` is used as parameter of `MutableDropout`, which then takes the result as dropout rate.
#
# More detailed API description and usage can be found :doc:`here </nas/construct_space>`.
#
# .. note::
#
# We are actively enriching the mutation APIs, to facilitate easy construction of model space.
# If the currently supported mutation APIs cannot express your model space,
# please refer to :doc:`this doc </nas/mutator>` for customizing mutators.
#
# Explore the Defined Model Space
# -------------------------------
#
# There are basically two exploration approaches: (1) search by evaluating each sampled model independently,
# which is the search approach in :ref:`multi-trial NAS <multi-trial-nas>`
# and (2) one-shot weight-sharing based search, which is used in one-shot NAS.
# We demonstrate the first approach in this tutorial. Users can refer to :ref:`here <one-shot-nas>` for the second approach.
#
# First, users need to pick a proper exploration strategy to explore the defined model space.
# Second, users need to pick or customize a model evaluator to evaluate the performance of each explored model.
#
# Pick an exploration strategy
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# NNI NAS supports many :doc:`exploration strategies </nas/exploration_strategy>`.
#
# Simply choosing (i.e., instantiate) an exploration strategy as below.
import nni.nas.strategy as strategy
search_strategy = strategy.Random() # dedup=False if deduplication is not wanted
# %%
# Pick or customize a model evaluator
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# In the exploration process, the exploration strategy repeatedly generates new models. A model evaluator is for training
# and validating each generated model to obtain the model's performance.
# The performance is sent to the exploration strategy for the strategy to generate better models.
#
# NNI NAS has provided :doc:`built-in model evaluators </nas/evaluator>`, but to start with,
# it is recommended to use :class:`FunctionalEvaluator <nni.nas.evaluator.FunctionalEvaluator>`,
# that is, to wrap your own training and evaluation code with one single function.
# This function should receive one single model class and uses :func:`nni.report_final_result` to report the final score of this model.
#
# An example here creates a simple evaluator that runs on MNIST dataset, trains for 2 epochs, and reports its validation accuracy.
import nni
from torchvision import transforms
from torchvision.datasets import MNIST
from torch.utils.data import DataLoader
def train_epoch(model, device, train_loader, optimizer, epoch):
loss_fn = torch.nn.CrossEntropyLoss()
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = loss_fn(output, target)
loss.backward()
optimizer.step()
if batch_idx % 10 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
def test_epoch(model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
accuracy = 100. * correct / len(test_loader.dataset)
print('\nTest set: Accuracy: {}/{} ({:.0f}%)\n'.format(
correct, len(test_loader.dataset), accuracy))
return accuracy
def evaluate_model(model):
# By v3.0, the model will be instantiated by default.
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
transf = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
train_loader = DataLoader(MNIST('data/mnist', download=True, transform=transf), batch_size=64, shuffle=True)
test_loader = DataLoader(MNIST('data/mnist', download=True, train=False, transform=transf), batch_size=64)
for epoch in range(3):
# train the model for one epoch
train_epoch(model, device, train_loader, optimizer, epoch)
# test the model for one epoch
accuracy = test_epoch(model, device, test_loader)
# call report intermediate result. Result can be float or dict
nni.report_intermediate_result(accuracy)
# report final test result
nni.report_final_result(accuracy)
# %%
# Create the evaluator
from nni.nas.evaluator import FunctionalEvaluator
evaluator = FunctionalEvaluator(evaluate_model)
# %%
#
# The ``train_epoch`` and ``test_epoch`` here can be any customized function,
# where users can write their own training recipe.
#
# It is recommended that the ``evaluate_model`` here accepts no additional arguments other than ``model``.
# However, in the :doc:`advanced tutorial </nas/evaluator>`, we will show how to use additional arguments in case you actually need those.
# In future, we will support mutation on the arguments of evaluators, which is commonly called "Hyper-parameter tuning".
#
# Launch an Experiment
# --------------------
#
# After all the above are prepared, it is time to start an experiment to do the model search. An example is shown below.
from nni.nas.experiment import NasExperiment
exp = NasExperiment(model_space, evaluator, search_strategy)
# %%
# Different from HPO experiment, NAS experiment will generate an experiment config automatically.
# It should work for most cases. For example, when using multi-trial strategies,
# local training service with concurrency 1 will be used by default.
# Users can customize the config. For example,
exp.config.max_trial_number = 3 # spawn 3 trials at most
exp.config.trial_concurrency = 1 # will run 1 trial concurrently
exp.config.trial_gpu_number = 0 # will not use GPU
# %%
# Remember to set the following config if you want to GPU.
# ``use_active_gpu`` should be set true if you wish to use an occupied GPU (possibly running a GUI)::
#
# exp.config.trial_gpu_number = 1
# exp.config.training_service.use_active_gpu = True
#
# Launch the experiment. The experiment should take several minutes to finish on a workstation with 2 GPUs.
exp.run(port=8081)
# %%
# Users can also run NAS Experiment with :doc:`different training services </experiment/training_service/overview>`
# besides ``local`` training service.
#
# Visualize the Experiment
# ------------------------
#
# Users can visualize their experiment in the same way as visualizing a normal hyper-parameter tuning experiment.
# For example, open ``localhost:8081`` in your browser, 8081 is the port that you set in ``exp.run``.
# Please refer to :doc:`here </experiment/web_portal/web_portal>` for details.
#
# We support visualizing models with 3rd-party visualization engines (like `Netron <https://netron.app/>`__).
# This can be used by clicking ``Visualization`` in detail panel for each trial.
# Note that current visualization is based on `onnx <https://onnx.ai/>`__ ,
# thus visualization is not feasible if the model cannot be exported into onnx.
#
# Built-in evaluators (e.g., Classification) will automatically export the model into a file.
# For your own evaluator, you need to save your file into ``$NNI_OUTPUT_DIR/model.onnx`` to make this work.
# For instance,
import os
from pathlib import Path
def evaluate_model_with_visualization(model):
# dump the model into an onnx
if 'NNI_OUTPUT_DIR' in os.environ:
dummy_input = torch.zeros(1, 3, 32, 32)
torch.onnx.export(model, (dummy_input, ),
Path(os.environ['NNI_OUTPUT_DIR']) / 'model.onnx')
evaluate_model(model)
# %%
# Relaunch the experiment, and a button is shown on Web portal.
#
# .. image:: ../../img/netron_entrance_webui.png
#
# Export Top Models
# -----------------
#
# Users can export top models after the exploration is done using ``export_top_models``.
for model_dict in exp.export_top_models(formatter='dict'):
print(model_dict)