add dtk24.04 code

docs/source/tutorials/quantization_speedup.rst

+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "tutorials/quantization_speedup.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_tutorials_quantization_speedup.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials_quantization_speedup.py:
+
+
+SpeedUp Model with Calibration Config
+======================================
+
+
+Introduction
+------------
+
+Deep learning network has been computational intensive and memory intensive 
+which increases the difficulty of deploying deep neural network model. Quantization is a 
+fundamental technology which is widely used to reduce memory footprint and speedup inference 
+process. Many frameworks begin to support quantization, but few of them support mixed precision 
+quantization and get real speedup. Frameworks like `HAQ: Hardware-Aware Automated Quantization with Mixed Precision <https://arxiv.org/pdf/1811.08886.pdf>`__\, only support simulated mixed precision quantization which will 
+not speedup the inference process. To get real speedup of mixed precision quantization and 
+help people get the real feedback from hardware, we design a general framework with simple interface to allow NNI quantization algorithms to connect different 
+DL model optimization backends (e.g., TensorRT, NNFusion), which gives users an end-to-end experience that after quantizing their model 
+with quantization algorithms, the quantized model can be directly speeded up with the connected optimization backend. NNI connects 
+TensorRT at this stage, and will support more backends in the future.
+
+
+Design and Implementation
+-------------------------
+
+To support speeding up mixed precision quantization, we divide framework into two part, frontend and backend.  
+Frontend could be popular training frameworks such as PyTorch, TensorFlow etc. Backend could be inference 
+framework for different hardwares, such as TensorRT. At present, we support PyTorch as frontend and 
+TensorRT as backend. To convert PyTorch model to TensorRT engine, we leverage onnx as intermediate graph 
+representation. In this way, we convert PyTorch model to onnx model, then TensorRT parse onnx 
+model to generate inference engine. 
+
+
+Quantization aware training combines NNI quantization algorithm 'QAT' and NNI quantization speedup tool.
+Users should set config to train quantized model using QAT algorithm(please refer to :doc:`NNI Quantization Algorithms <../compression/quantizer>`  ).
+After quantization aware training, users can get new config with calibration parameters and model with quantized weight. By passing new config and model to quantization speedup tool, users can get real mixed precision speedup engine to do inference.
+
+
+After getting mixed precision engine, users can do inference with input data.
+
+
+Note
+
+
+* Recommend using "cpu"(host) as data device(for both inference data and calibration data) since data should be on host initially and it will be transposed to device before inference. If data type is not "cpu"(host), this tool will transpose it to "cpu" which may increases unnecessary overhead.
+* User can also do post-training quantization leveraging TensorRT directly(need to provide calibration dataset).
+* Not all op types are supported right now. At present, NNI supports Conv, Linear, Relu and MaxPool. More op types will be supported in the following release.
+
+
+Prerequisite
+------------
+CUDA version >= 11.0
+
+TensorRT version >= 7.2
+
+Note
+
+* If you haven't installed TensorRT before or use the old version, please refer to `TensorRT Installation Guide <https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html>`__\  
+
+Usage
+-----
+
+.. GENERATED FROM PYTHON SOURCE LINES 64-92
+
+.. code-block:: default
+
+    import torch
+    import torch.nn.functional as F
+    from torch.optim import SGD
+    from nni_assets.compression.mnist_model import TorchModel, device, trainer, evaluator, test_trt
+
+    config_list = [{
+        'quant_types': ['input', 'weight'],
+        'quant_bits': {'input': 8, 'weight': 8},
+        'op_types': ['Conv2d']
+    }, {
+        'quant_types': ['output'],
+        'quant_bits': {'output': 8},
+        'op_types': ['ReLU']
+    }, {
+        'quant_types': ['input', 'weight'],
+        'quant_bits': {'input': 8, 'weight': 8},
+        'op_names': ['fc1', 'fc2']
+    }]
+
+    model = TorchModel().to(device)
+    optimizer = SGD(model.parameters(), lr=0.01, momentum=0.5)
+    criterion = F.nll_loss
+    dummy_input = torch.rand(32, 1, 28, 28).to(device)
+
+    from nni.algorithms.compression.pytorch.quantization import QAT_Quantizer
+    quantizer = QAT_Quantizer(model, config_list, optimizer, dummy_input)
+    quantizer.compress()
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+
+    TorchModel(
+      (conv1): QuantizerModuleWrapper(
+        (module): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))
+      )
+      (conv2): QuantizerModuleWrapper(
+        (module): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
+      )
+      (fc1): QuantizerModuleWrapper(
+        (module): Linear(in_features=256, out_features=120, bias=True)
+      )
+      (fc2): QuantizerModuleWrapper(
+        (module): Linear(in_features=120, out_features=84, bias=True)
+      )
+      (fc3): Linear(in_features=84, out_features=10, bias=True)
+      (relu1): QuantizerModuleWrapper(
+        (module): ReLU()
+      )
+      (relu2): QuantizerModuleWrapper(
+        (module): ReLU()
+      )
+      (relu3): QuantizerModuleWrapper(
+        (module): ReLU()
+      )
+      (relu4): QuantizerModuleWrapper(
+        (module): ReLU()
+      )
+      (pool1): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
+      (pool2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
+    )
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 93-94
+
+finetuning the model by using QAT
+
+.. GENERATED FROM PYTHON SOURCE LINES 94-98
+
+.. code-block:: default
+
+    for epoch in range(3):
+        trainer(model, optimizer, criterion)
+        evaluator(model)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Average test loss: 0.6058, Accuracy: 8534/10000 (85%)
+    Average test loss: 0.1585, Accuracy: 9508/10000 (95%)
+    Average test loss: 0.0920, Accuracy: 9717/10000 (97%)
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 99-100
+
+export model and get calibration_config
+
+.. GENERATED FROM PYTHON SOURCE LINES 100-108
+
+.. code-block:: default
+
+    import os
+    os.makedirs('log', exist_ok=True)
+    model_path = "./log/mnist_model.pth"
+    calibration_path = "./log/mnist_calibration.pth"
+    calibration_config = quantizer.export_model(model_path, calibration_path)
+
+    print("calibration_config: ", calibration_config)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    calibration_config:  {'conv1': {'weight_bits': 8, 'weight_scale': tensor([0.0029], device='cuda:0'), 'weight_zero_point': tensor([97.], device='cuda:0'), 'input_bits': 8, 'tracked_min_input': -0.4242129623889923, 'tracked_max_input': 2.821486711502075}, 'conv2': {'weight_bits': 8, 'weight_scale': tensor([0.0017], device='cuda:0'), 'weight_zero_point': tensor([115.], device='cuda:0'), 'input_bits': 8, 'tracked_min_input': 0.0, 'tracked_max_input': 7.800363063812256}, 'fc1': {'weight_bits': 8, 'weight_scale': tensor([0.0010], device='cuda:0'), 'weight_zero_point': tensor([121.], device='cuda:0'), 'input_bits': 8, 'tracked_min_input': 0.0, 'tracked_max_input': 13.914573669433594}, 'fc2': {'weight_bits': 8, 'weight_scale': tensor([0.0012], device='cuda:0'), 'weight_zero_point': tensor([125.], device='cuda:0'), 'input_bits': 8, 'tracked_min_input': 0.0, 'tracked_max_input': 11.657418251037598}, 'relu1': {'output_bits': 8, 'tracked_min_output': 0.0, 'tracked_max_output': 7.897384166717529}, 'relu2': {'output_bits': 8, 'tracked_min_output': 0.0, 'tracked_max_output': 14.337020874023438}, 'relu3': {'output_bits': 8, 'tracked_min_output': 0.0, 'tracked_max_output': 11.884227752685547}, 'relu4': {'output_bits': 8, 'tracked_min_output': 0.0, 'tracked_max_output': 9.330422401428223}}
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 109-110
+
+build tensorRT engine to make a real speedup
+
+.. GENERATED FROM PYTHON SOURCE LINES 110-117
+
+.. code-block:: default
+
+
+    from nni.compression.pytorch.quantization_speedup import ModelSpeedupTensorRT
+    input_shape = (32, 1, 28, 28)
+    engine = ModelSpeedupTensorRT(model, input_shape, config=calibration_config, batchsize=32)
+    engine.compress()
+    test_trt(engine)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Loss: 0.09235906448364258  Accuracy: 97.19%
+    Inference elapsed_time (whole dataset): 0.03632998466491699s
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 118-169
+
+Note that NNI also supports post-training quantization directly, please refer to complete examples for detail.
+
+For complete examples please refer to :githublink:`the code <examples/model_compress/quantization/mixed_precision_speedup_mnist.py>`.
+
+For more parameters about the class 'TensorRTModelSpeedUp', you can refer to :doc:`Model Compression API Reference <../reference/compression/quantization_speedup>`.
+
+Mnist test
+^^^^^^^^^^
+
+on one GTX2080 GPU,
+input tensor: ``torch.randn(128, 1, 28, 28)``
+
+.. list-table::
+   :header-rows: 1
+   :widths: auto
+
+   * - quantization strategy
+     - Latency
+     - accuracy
+   * - all in 32bit
+     - 0.001199961
+     - 96%
+   * - mixed precision(average bit 20.4)
+     - 0.000753688
+     - 96%
+   * - all in 8bit
+     - 0.000229869
+     - 93.7%
+
+Cifar10 resnet18 test (train one epoch)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+on one GTX2080 GPU,
+input tensor: ``torch.randn(128, 3, 32, 32)``
+
+.. list-table::
+   :header-rows: 1
+   :widths: auto
+
+   * - quantization strategy
+     - Latency
+     - accuracy
+   * - all in 32bit
+     - 0.003286268
+     - 54.21%
+   * - mixed precision(average bit 11.55)
+     - 0.001358022
+     - 54.78%
+   * - all in 8bit
+     - 0.000859139
+     - 52.81%
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 1 minutes  13.658 seconds)
+
+
+.. _sphx_glr_download_tutorials_quantization_speedup.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-python
+
+     :download:`Download Python source code: quantization_speedup.py <quantization_speedup.py>`
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+     :download:`Download Jupyter notebook: quantization_speedup.ipynb <quantization_speedup.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_

docs/source/tutorials/sg_execution_times.rst

+
+:orphan:
+
+.. _sphx_glr_tutorials_sg_execution_times:
+
+Computation times
+=================
+**01:39.686** total execution time for **tutorials** files:
+
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_quantization_quick_start_mnist.py` (``quantization_quick_start_mnist.py``) | 01:39.686 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_darts.py` (``darts.py``)                                                   | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_hello_nas.py` (``hello_nas.py``)                                           | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_nasbench_as_dataset.py` (``nasbench_as_dataset.py``)                       | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_pruning_bert_glue.py` (``pruning_bert_glue.py``)                           | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_pruning_customize.py` (``pruning_customize.py``)                           | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_pruning_quick_start_mnist.py` (``pruning_quick_start_mnist.py``)           | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_pruning_speedup.py` (``pruning_speedup.py``)                               | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_quantization_customize.py` (``quantization_customize.py``)                 | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_quantization_speedup.py` (``quantization_speedup.py``)                     | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+

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+    bottom: 0;
+    width: 100%;
+}
+
+.codesnippet-card a:not(:hover) {
+    color: rgba(0, 0, 0, .68);
+}
+
+.codesnippet-card-title-container {
+    margin-top: 0.3rem;
+    position: relative;
+}
+
+.codesnippet-card-title-container h4 {
+    padding-left: 2.3rem;
+    line-height: 1.6rem;
+    height: 1.6rem;
+    margin-top: 0;
+}
+
+.codesnippet-card-icon {
+    position: absolute;
+    top: 0;
+    left: 0;
+}
+
+.codesnippet-card-icon img {
+    max-width: 100%;
+    max-height: 100%;
+    /* horizontal and vertical center */
+    /* https://stackoverflow.com/questions/7273338/how-to-vertically-align-an-image-inside-a-div */
+    text-align: center;
+    vertical-align: middle;
+    position: absolute;
+    left: 0;
+    right: 0;
+    top: 0;
+    bottom: 0;
+    margin: auto;
+}
+
+.codesnippet-card-icon {
+    width: 1.6rem;
+    height: 1.6rem;
+    padding: 0;
+}
+
+.codesnippet-card-link {
+    position: relative;
+}
+
+.codesnippet-card-link .material-icons {
+    position: absolute;
+    right: 0;
+}
+
+/* fixes reference overlapping issue */
+/* This is originally defined to be negative in application_fixes.css */
+/* They did that to ensure the header doesn't disappear in jump links */
+/* We did this by using scroll-margin-top instead */
+dt:target {
+    margin-top: 0.15rem !important;
+    padding-top: 0.5rem !important;
+}
+
+:target {
+    /* header height */
+    scroll-margin-top: 3.5rem;
+}
+
+/* fix code block style on mobile */
+@media only screen and (max-width: 44.9375em) {
+    .md-typeset pre {
+        margin: 1em -0.3em;
+    }
+}
+
+/* Responsive nav bar */
+.md-source__fact {
+    padding: 0 !important;
+}
+
+/* collapsible toctree */
+.md-nav--primary ul li {
+    padding-left: .8rem;
+}
+
+.md-nav__item {
+    position: relative;
+}
+
+.md-nav__expand > a > .md-nav__tocarrow {
+    transform: rotate(-90deg);
+    font-size: inherit;
+    transition: all 0.1s ease;
+    position: absolute;
+    left: .1rem;
+    top: .05rem;
+}
+
+.md-nav__expand .md-nav__list {
+    display: none;
+}
+
+.md-nav__expand--active > .md-nav__list {
+    display: block;
+}
+
+.md-nav__expand--active > a > .md-nav__tocarrow {
+    transform: rotate(0);
+}
+
+@media only screen and (max-width:76.1875em) {
+    .md-nav--primary .md-nav__link {
+        padding: .15rem .2rem .15rem .6rem;
+    }
+    .md-nav__expand > a > .md-nav__tocarrow {
+        left: 0;
+        top: .25rem;
+    }
+    .md-nav--primary span.md-nav__link.caption {
+        margin-top: 0.75em;
+    }
+    .md-nav--primary .md-nav__item .md-nav__list .md-nav__item {
+        padding-left: .3rem;
+    }
+    html .md-nav--primary .md-nav__title--site .md-nav__button {
+        height: auto;
+        font-size: inherit;
+        left: 0;
+    }
+    html .md-nav--primary .md-nav__title {
+        padding-top: 2rem;
+        padding-left: .6rem;
+        height: 4.6rem;
+    }
+    .md-nav--primary .md-nav__item, .md-nav--primary .md-nav__title {
+        font-size: .7rem;
+        line-height: 1.3;
+    }
+}
+
+/* Increase TOC padding */
+.md-nav--primary ul li ul li {
+    padding-left: 0.8rem;
+}
+
+/* Nav bar and heroes */
+@media only screen and (min-width:60em) {
+    .md-search__form, .md-search__input {
+        border-radius: .3rem;   /* even rounder */
+    }
+}
+
+.md-header-nav__source, .md-source {
+    padding-right: 0 !important;
+}
+
+.md-hero {
+    position: relative;
+}
+
+.md-hero__background {
+    max-width: 73rem;
+    position: absolute;
+    bottom: -46px;
+    left: 0;
+    right: 0;
+    margin-left: auto;
+    margin-right: auto;
+    width: 100%;
+    z-index: 0;
+}
+
+.md-hero__background img {
+    width: 100%;
+}
+
+@media only screen and (max-width:59.9375em) {
+    .md-hero__background {
+        display: none;
+    }
+}
+
+@media only screen and (max-width:76.1875em) {
+    .md-hero__background {
+        bottom: -5%;
+        top: auto;
+    }
+}
+
+.md-hero__inner {
+    z-index: 1;
+    position: relative;
+    padding-right: 35%;
+}
+
+@media only screen and (min-width:76.2em) {
+    .md-hero__inner {
+        padding-top: 2.4rem;
+        padding-bottom: 1.2rem;
+    }
+}
+
+/* make title look larger */
+.md-typeset h1 {
+    margin: 0 0 1.5rem;
+    color: rgba(0,0,0,.85);
+    font-size: 1.5625rem;
+    line-height: 1.3;
+}
+
+.md-typeset h1, .md-typeset h2, .md-typeset h3 {
+    font-weight: 400;
+    letter-spacing: 0;
+}
+
+/* Enlarge table */
+.md-typeset table:not([class]) {
+    font-size: 0.7rem;
+    box-shadow: 0 2px 2px 0 rgb(0 0 0 / 8%), 0 1px 5px 0 rgb(0 0 0 / 7%), 0 3px 1px -2px rgb(0 0 0 / 14%);
+}
+
+.md-typeset table:not([class]) th {
+    padding: .5rem .7rem;
+    background-color: #e6e7e8;
+    color: inherit;
+    font-weight: 500;
+}
+
+.md-typeset table:not([class]) td {
+    padding: .4rem .7rem;
+}
+
+/* On this page TOC */
+.md-sidebar--secondary .md-nav--secondary {
+    border-inline-start: 4px solid var(--custom-color-primary);
+}
+
+.md-nav__link {
+    margin-top: 0.45em;
+}
+
+/* Override style for copy button */
+button.copybtn {
+    opacity: 1;
+}
+
+.o-tooltip--left:after {
+    transform: translateX(-5%) translateY(-125%);
+    padding: .4em;
+    font-size: .5rem;
+    font-weight: 600;
+    background: #5f6368;
+}
+
+.o-tooltip--left:hover:after {
+    transform: translateX(-5%) translateY(-120%);
+}
+
+/* Sphinx tabs */
+/* Copied from https://github.com/executablebooks/sphinx-tabs/blob/master/sphinx_tabs/static/tabs.css with modifications */
+.sphinx-tabs.container {
+    margin-bottom: 1rem;
+    border: 1px solid rgb(232, 234, 237);
+    border-radius: 8px;
+}
+
+[role="tablist"] {
+    padding: .3rem 0 0 0;
+    border-bottom: 1px solid #a0b3bf;
+}
+
+.sphinx-tabs-tab {
+    position: relative;
+    line-height: 2rem;
+    font-weight: 600;
+    padding: 0 1rem;
+    color: #80868b;
+}
+
+.sphinx-tabs-tab[aria-selected="true"] {
+    color: #3f51b5;                     /* primary color */
+    border-bottom: 2px solid #3f51b5;
+}
+
+.sphinx-tabs-tab:focus {
+    z-index: 1;
+    outline-offset: 1px;
+}
+
+.sphinx-tabs-panel {
+    position: relative;
+    padding: 1rem;
+}
+
+.sphinx-tabs-panel.code-tab {
+    padding: 0;
+}
+
+.sphinx-tabs-panel.code-tab .highlight {
+    margin: 0;
+    padding: .5rem;
+}
+
+.sphinx-tab img {
+    margin-bottom: 2rem;
+}

docs/static/css/material_dropdown.css

+/* https://codepen.io/mildrenben/pen/RPwQEY */
+
+@media only screen and (max-width:44.9375em) {
+    .drop {
+        display: none;
+    }
+}
+
+.drop {
+    width: 5.3rem;
+    vertical-align: middle;
+}
+
+@media only screen and (min-width:60em) and (max-width:70em) {
+    .drop {
+        width: 4.7rem;
+    }
+    /* also narrow nav source width */
+    .md-header-nav__source {
+        width: 10rem;
+    }
+}
+
+.drop button {
+    color: inherit;
+    font-weight: 700;
+    font-size: .65rem;
+    cursor: pointer;
+}
+
+.drop button:after {
+    content: "\e5c5";
+    padding: 0 0 0.3rem 0.2rem;
+    font-size: 0.5rem;
+    font-family: "Material Icons";
+    display: inline-block;
+    transition: all 0.2s ease;
+    transform: rotate(0);
+    transform-origin: 65% 30%;
+}
+
+.drop button.active:after {
+    transform: rotate(180deg);
+}
+
+.drop ul {
+    position: absolute;
+    left: 0;
+    top: 6em;
+    transition: all 0.1s ease;
+    padding: .625em 0;
+    margin-top: -0.9rem;
+    transform: scale(0);
+    transform-origin: 0 0;
+    background: #fcfcfc;
+    box-shadow: 0 2px 4px 0 rgba(0, 0, 0, 0.16), 0 2px 8px 0 rgba(0, 0, 0, 0.12);
+}
+
+.drop ul.active {
+    transform: scale(1);
+}
+
+.drop ul li {
+    display: block;
+    width: 100%;
+}
+
+.drop ul li a.md-nav__link {
+    width: 100%;
+    padding: .4rem 1em;
+    display: inline-block;
+    white-space: pre;
+    box-sizing: border-box;
+    color: rgba(0, 0, 0, .87);
+    margin-top: 0;
+    font-size: .75rem;
+}

docs/static/css/material_theme.css

+/* Theme related customization */
+/* https://github.com/bashtage/sphinx-material/pull/122 */
+
+/* first part */
+button[data-md-color-primary=custom] {
+    background-color: var(--custom-color-primary)
+}
+
+[data-md-color-primary=custom] .md-typeset a {
+    color: var(--custom-color-primary)
+}
+
+[data-md-color-primary=custom] .md-header,
+[data-md-color-primary=custom] .md-hero {
+    background-color: var(--custom-color-primary)
+}
+
+[data-md-color-primary=custom] .md-nav__link--active,
+[data-md-color-primary=custom] .md-nav__link:active {
+    color: var(--custom-color-primary)
+}
+
+[data-md-color-primary=custom] .md-nav__item--nested>.md-nav__link {
+    color: inherit
+}
+
+[data-md-color-primary=custom] .md-nav__extra_link:active {
+    color: var(--custom-color-primary)
+}
+
+[data-md-color-primary=custom] .md-nav__item--nested > .md-nav__extra_link {
+    color: inherit
+}
+
+/* second part */
+button[data-md-color-accent=custom] {
+    background-color: var(--custom-color-accent)
+}
+
+[data-md-color-accent=custom] .md-typeset a:active,
+[data-md-color-accent=custom] .md-typeset a:hover {
+    color: var(--custom-color-accent)
+}
+
+[data-md-color-accent=custom] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,
+[data-md-color-accent=custom] .md-typeset pre code::-webkit-scrollbar-thumb:hover {
+    background-color: var(--custom-color-accent)
+}
+
+[data-md-color-accent=custom] .md-nav__link:focus,
+[data-md-color-accent=custom] .md-nav__link:hover,
+[data-md-color-accent=custom] .md-typeset .footnote li:hover .footnote-backref:hover,
+[data-md-color-accent=custom] .md-typeset .footnote li:target .footnote-backref,
+[data-md-color-accent=custom] .md-typeset .md-clipboard:active:before,
+[data-md-color-accent=custom] .md-typeset .md-clipboard:hover:before,
+[data-md-color-accent=custom] .md-typeset [id] .headerlink:focus,
+[data-md-color-accent=custom] .md-typeset [id]:hover .headerlink:hover,
+[data-md-color-accent=custom] .md-typeset [id]:target .headerlink {
+    color: var(--custom-color-accent)
+}
+
+[data-md-color-accent=custom] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover {
+    background-color: var(--custom-color-accent)
+}
+
+[data-md-color-accent=custom] .md-search-result__link:hover,
+[data-md-color-accent=custom] .md-search-result__link[data-md-state=active] {
+    background-color: rgba(83, 109, 254, .1)
+}
+
+[data-md-color-accent=custom] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover {
+    background-color: var(--custom-color-accent)
+}
+
+[data-md-color-accent=custom] .md-source-file:hover:before {
+    background-color: var(--custom-color-accent)
+}
+
+/* third part */
+@media only screen and (max-width:59.9375em) {
+    [data-md-color-primary=custom] .md-nav__source {
+        background-color: var(--custom-color-primary);
+        opacity: 0.9675;
+    }
+}
+
+@media only screen and (max-width:76.1875em) {
+    html [data-md-color-primary=custom] .md-nav--primary .md-nav__title--site {
+        background-color: var(--custom-color-primary)
+    }
+}
+
+@media only screen and (min-width:76.25em) {
+    [data-md-color-primary=custom] .md-tabs {
+        background-color: var(--custom-color-primary)
+    }
+}

docs/static/css/sphinx_gallery.css

+/* Customization to sphinx-gallery generated notebooks */
+
+p.sphx-glr-script-out {
+    margin: 0;
+    padding: 0;
+    font-size: .7rem;
+}
+
+.sphx-glr-script-out .highlight {
+    margin: 0 !important;
+}
+
+.sphx-glr-script-out .highlight pre {
+    background-color: #f0f6fa !important;
+    padding: .6rem !important;
+    margin-bottom: 1.5rem;
+}
+
+div.sphx-glr-footer {
+    display: none;
+}
+
+.notebook-action-link {
+    padding-right: 1rem;
+}
+
+.sphx-glr-download-link-note {
+    margin-bottom: 1.5rem;
+    line-height: 2.5rem;
+}
+
+.notebook-action-div {
+    display: inline;
+    border-bottom: 1px solid #f3f4f7;
+    padding-right: 2rem;
+    padding-bottom: 0.625rem;
+}
+
+.notebook-action-link:hover .notebook-action-div {
+    /* match theme */
+    border-bottom-color: var(--custom-color-accent);
+}
+
+.notebook-action-link img {
+    width: 1.1rem;
+    height: 1.1rem;
+    margin-right: 10px;
+    vertical-align: middle;
+}
+
+.notebook-action-link div {
+    display: inline;
+}
+
+.notebook-action-link:not(:hover) div {
+    color: rgba(0, 0, 0, .68);
+}
+
+.card-link.admonition {
+    border-left: 0;
+    padding: 0.9rem;
+}
+
+.card-link .card-link-body {
+    min-height: 4rem;
+    position: relative;
+}
+
+.card-link-text {
+    padding-left: 4.9rem;
+}
+
+.card-link a:not(:hover) .card-link-text {
+    color: rgba(0, 0, 0, .68);
+}
+
+.card-link-text .card-link-title-container h4 {
+    margin: 0;
+}
+
+.card-link-text .card-link-summary {
+    margin-top: 0.3rem;
+    margin-bottom: 0.5rem;
+    font-size: 0.75rem;
+}
+
+/* hide link */
+.card-link-anchor {
+    display: none;
+}
+
+.card-link-tag {
+    margin-right: 0.4rem;
+    background: #eeeff2;
+    border: 1px solid #e0dcda;
+    color: rgba(0, 0, 0, 0.7);
+    border-radius: 18px;
+    cursor: pointer;
+    display: inline-block;
+    position: relative;
+    padding: 0.1rem 0.6rem;
+    font-size: 0.7rem;
+}
+
+.card-link-icon {
+    position: absolute;
+    top: 0;
+    left: 0;
+}
+
+.card-link-icon img {
+    max-width: 75%;
+    max-height: 75%;
+    /* horizontal and vertical center */
+    /* https://stackoverflow.com/questions/7273338/how-to-vertically-align-an-image-inside-a-div */
+    text-align: center;
+    vertical-align: middle;
+    position: absolute;
+    left: 0;
+    right: 0;
+    top: 0;
+    bottom: 0;
+    margin: auto;
+}
+
+/* link icon background color */
+.card-link-icon.circle {
+    background-color: #E8DCEE;
+    border-radius: 50%;
+    width: 4rem;
+    height: 4rem;
+    padding: 0;
+}
+
+/* pallette */
+.card-link-icon.cyan {
+    background-color: #DDEFF2;
+}
+
+.card-link-icon.purple {
+    background-color: #E8DCEE;
+}
+
+.card-link-icon.blue {
+    background-color: #DBE8FC;
+}
+
+.card-link-icon.indigo {
+    background-color: #E1E4F3;
+}

docs/static/img/README.md

+This folder is deprecated. Please do not put files here in future!

docs/static/img/gallery-colab.svg

+<?xml version="1.0" encoding="utf-8"?>
+<!-- Generator: Adobe Illustrator 23.0.1, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 181.13 105" style="enable-background:new 0 0 181.13 105;" xml:space="preserve">
+<style type="text/css">
+	.st0{fill:#FFA800;}
+	.st1{fill:#FFDD3D;}
+</style>
+<g>
+	<path class="st0" d="M26.84,52.6c0-1.78,0.18-3.51,0.52-5.18c0.34-1.67,0.85-3.29,1.5-4.83c0.65-1.54,1.45-3,2.37-4.37
+		c0.92-1.37,1.98-2.64,3.14-3.81L16.92,16.96C7.8,26.08,2.16,38.68,2.16,52.6S7.8,79.12,16.92,88.24l17.45-17.45
+		C29.72,66.13,26.84,59.7,26.84,52.6z"/>
+	<path class="st1" d="M52.56,26.87c6.84,0,13.03,2.68,17.64,7.03l14.23-20.35C75.74,6.45,64.65,2.19,52.56,2.19
+		c-13.92,0-26.52,5.64-35.64,14.76l17.45,17.45C39.03,29.75,45.46,26.87,52.56,26.87z"/>
+	<path class="st1" d="M70.2,71.29c-4.61,4.35-10.8,7.03-17.64,7.03c-7.1,0-13.53-2.88-18.19-7.53L16.92,88.24
+		C26.04,97.36,38.64,103,52.56,103c12.04,0,23.09-4.23,31.76-11.27L70.2,71.29z"/>
+	<path class="st0" d="M164.44,16.96l-17.45,17.45c4.66,4.66,7.53,11.09,7.53,18.19c0,14.21-11.52,25.72-25.72,25.72
+		c-7.1,0-13.53-2.88-18.19-7.53L93.16,88.24c9.12,9.12,21.72,14.76,35.64,14.76c27.84,0,50.4-22.57,50.4-50.4
+		C179.2,38.68,173.56,26.08,164.44,16.96z"/>
+	<path class="st1" d="M103.07,52.6c0-14.21,11.52-25.72,25.72-25.72c7.1,0,13.53,2.88,18.19,7.53l17.45-17.45
+		c-9.12-9.12-21.72-14.76-35.64-14.76c-27.84,0-50.4,22.57-50.4,50.4c0,13.92,5.64,26.52,14.76,35.64l17.45-17.45
+		C105.95,66.13,103.07,59.7,103.07,52.6z"/>
+</g>
+</svg>

docs/static/img/gallery-download.svg

+<?xml version="1.0" encoding="utf-8"?>
+<!-- Generator: Adobe Illustrator 23.0.1, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 105 105" style="enable-background:new 0 0 105 105;" xml:space="preserve">
+<style type="text/css">
+	.st0{fill-rule:evenodd;clip-rule:evenodd;fill:#333333;}
+</style>
+<polygon class="st0" points="91.74,42.58 59.53,74.8 59.52,74.84 59.52,74.8 59.52,3 44.8,3 44.8,74.8 12.58,42.58 2.16,53
+	52.16,103 102.15,53 "/>
+</svg>

docs/static/img/gallery-github.svg

+<?xml version="1.0" encoding="utf-8"?>
+<!-- Generator: Adobe Illustrator 23.0.1, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 105 105" style="enable-background:new 0 0 105 105;" xml:space="preserve">
+<style type="text/css">
+	.st0{fill-rule:evenodd;clip-rule:evenodd;fill:#333333;}
+</style>
+<path class="st0" d="M52.54,2.44c-27.61,0-50,22.39-50,50c0,22.09,14.33,40.83,34.2,47.44c2.5,0.46,3.41-1.09,3.41-2.41
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+</linearGradient>
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+<stop stop-color="#EDE8FF"/>
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+</linearGradient>
+</defs>
+</svg>

docs/static/js/github.js

+function add_descriptive_text(texts) {
+    const d = $('*[data-md-source="nni"]');
+    // clear first
+    d.find("ul").remove();
+
+    let s = "";
+    for (const text of texts) {
+        s += '<li class="md-source__fact">' + text + '</li>';
+    }
+    d.find(".md-source__repository").append('<ul class="md-source__facts">' + s + '</ul>');
+}
+
+function kFormatter(num) {
+    // https://stackoverflow.com/questions/9461621/format-a-number-as-2-5k-if-a-thousand-or-more-otherwise-900
+    return Math.abs(num) > 999 ? Math.sign(num)*((Math.abs(num)/1000).toFixed(1)) + 'k' : Math.sign(num)*Math.abs(num);
+}
+
+$(document).ready(function() {
+    add_descriptive_text(["View on GitHub"]);
+    $.getJSON("https://api.github.com/repos/microsoft/nni", function (data) {
+        add_descriptive_text([
+            kFormatter(data["stargazers_count"]) + " stars",
+            kFormatter(data["forks"]) + " forks",
+        ]);
+    });
+});

docs/static/js/misc.js

+// Fix the hero text effect on large screens
+function resetHeroHidden() {
+    const scrollAmount = window.scrollY;
+    if (window.matchMedia("only screen and (min-width: 76.25em)").matches) {
+        // only enable this on large screens
+        if (scrollAmount == 0) {
+            $(".md-hero").attr("data-md-state", "");
+        } else {
+            $(".md-hero").attr("data-md-state", "hidden");
+        }
+    }
+}
+
+// https://github.com/bashtage/sphinx-material/blob/6e0ef822e58df57d6a9de5a58dc40c17fc34f557/sphinx_material/sphinx_material/static/javascripts/application.js#L1384
+$(window).on("scroll", resetHeroHidden);
+$(window).on("resize", resetHeroHidden);
+$(window).on("orientationchange", resetHeroHidden);
+
+
+// Sidebar header
+$(document).ready(function() {
+    let language = "en";  // default english
+    try {
+        language = READTHEDOCS_DATA["language"];
+    } catch (e) {}
+
+    const title = $(".md-sidebar--secondary .md-nav--secondary label.md-nav__title");
+    if (language == "en") {
+        title.text("On this page");
+    } else if (language == "zh") {
+        title.text("本页内容");
+    }
+});
+
+
+// Hide navigation bar when it's too short
+// Hide TOC header when it coincides with page title
+function hide_nav() {
+    const d = $('nav.md-tabs[data-md-component="tabs"]');
+    if (d.find('li').length <= 1) {
+        d.addClass('hidden');
+    }
+}
+
+// Expand link
+function expand_link() {
+    // on load, collapse all links without active on the inside
+    $(".md-nav__expand").filter(function (index) {
+        return $(".md-nav__link--active", this).length >= 1;
+    }).addClass("md-nav__expand--active");
+
+    function toggleExpand(event) {
+        event.preventDefault();
+
+        $(event.target)
+            .closest(".md-nav__expand")
+            .toggleClass("md-nav__expand--active");
+
+        return false;
+    }
+
+    // bind click events
+    $(".md-nav__expand > a").click(toggleExpand);
+    $(".md-nav__expand > a > .md-nav__tocarrow").click(toggleExpand);
+}
+
+// Propagate card link from another element
+function propagate_card_link() {
+    $(".card-link-clickable").each(function() {
+        $(this).attr("href", $(this).next("a.reference").attr("href"));
+    });
+}
+
+$(document).ready(function() {
+    hide_nav();
+    expand_link();
+    propagate_card_link();
+});

docs/static/js/sphinx_gallery.js

+$(document).ready(function() {
+    const downloadNote = $(".sphx-glr-download-link-note.admonition.note");
+    if (downloadNote.length > 0) {
+        const githubLink = "https://github.com/microsoft/nni/blob/" + GIT_COMMIT_ID + "/examples/" + PAGENAME + ".py";
+        const notebookLink = $(".sphx-glr-download-jupyter .reference.download").attr("href");
+
+        // link to generated notebook file
+        const colabLink = "https://colab.research.google.com/github/microsoft/nni/blob/" + GIT_COMMIT_ID +
+            "/docs/source/" + PAGENAME + ".ipynb";
+
+        downloadNote.removeClass("admonition");
+        // the image links are stored in layout.html
+        // to leverage jinja engine
+        downloadNote.html(`
+            <a class="notebook-action-link" href="${notebookLink}">
+                <div class="notebook-action-div">
+                    <img src="${GALLERY_LINKS.notebook}"/>
+                    <div>Download Notebook</div>
+                </div>
+            </a>
+            <a class="notebook-action-link" href="${colabLink}">
+                <div class="notebook-action-div">
+                    <img src="${GALLERY_LINKS.colab}"/>
+                    <div>Run in Google Colab</div>
+                </div>
+            </a>
+            <a class="notebook-action-link" href="${githubLink}">
+                <div class="notebook-action-div">
+                    <img src="${GALLERY_LINKS.github}"/>
+                    <div>View on GitHub</div>
+                </div>
+            </a>
+        `);
+    }
+});

docs/static/js/version.js

+try {
+    READTHEDOCS_DATA;
+} catch (e) {
+    console.log('READTHEDOCS_DATA is undefined. In debug mode.');
+
+    // mock info
+    READTHEDOCS_DATA = {
+        "ad_free": false,
+        "api_host": "https://readthedocs.org",
+        "build_date": "2022-01-25T06:27:55Z",
+        "builder": "sphinx",
+        "canonical_url": null,
+        "commit": "ca66e346",
+        "docroot": "/docs/en_US/",
+        "features": { "docsearch_disabled": false },
+        "global_analytics_code": "UA-17997319-1",
+        "language": "en",
+        "page": "Tutorial",
+        "programming_language": "words",
+        "project": "nni",
+        "proxied_api_host": "/_",
+        "source_suffix": ".rst",
+        "subprojects": { "nni-zh": "https://nni.readthedocs.io/zh/stable/" },
+        "theme": "sphinx_material",
+        "user_analytics_code": "UA-136029994-1",
+        "version": "latest"
+    };
+    
+    READTHEDOCS_VERSIONS = [
+        ["latest", "/en/latest/"],
+        ["stable", "/en/stable/"],
+        ["v2.6", "/en/v2.6/"],
+        ["v2.5", "/en/v2.5/"],
+        ["v2.4", "/en/v2.4/"],
+        ["v2.3", "/en/v2.3/"],
+        ["test-version", "/en/test-version"]
+    ];
+    // The above code is injected by readthedocs in production.
+}
+
+function create_dropdown(selector, button_text, items) {
+    const dropdown = $(selector);
+    const button = document.createElement("button");
+    button.innerHTML = button_text;
+    const content = document.createElement("ul");
+    // content.className = "dropdown-content md-hero";
+    dropdown.append(button);
+    dropdown.append(content);
+
+    for (const key in items) {
+        if (items.hasOwnProperty(key)) {
+            const li = document.createElement("li");
+            const a = document.createElement("a");
+            a.className = "md-nav__link"
+            a.innerHTML = key;
+            a.title = key;
+            a.href = items[key];
+            li.appendChild(a);
+            content.appendChild(li);
+        }
+    }
+
+    $(button).click(function (e) {
+        // first close all others.
+        $(".drop").find(".active").removeClass("active");
+        dropdown.find("ul").addClass("active");
+        dropdown.find("button").addClass("active");
+        e.stopPropagation();
+    })
+    $(document).click(function () {
+        $(".drop").find(".active").removeClass("active");
+    })
+    return dropdown;
+}
+
+function add_version_dropdown() {
+    const prev_versions = Object.assign(
+        {},
+        ...READTHEDOCS_VERSIONS
+            .filter(([k, v]) => (k === 'stable' || k == 'latest' || k.startsWith('v')))
+            .map(([k, v]) => ({ [k]: v }))
+    );
+
+    const current_version = 'v: ' + READTHEDOCS_DATA["version"];
+    create_dropdown(".drop.version", current_version, prev_versions);
+}
+
+function add_language_dropdown() {
+    const language_dropdown = {
+        'en': 'English',
+        'zh': '简体中文'
+    };
+    let current_language = 'en';
+    const pathname_prefix = window.location.pathname.split('/');
+    if (pathname_prefix.length > 1 && language_dropdown.hasOwnProperty(pathname_prefix[1])) {
+        current_language = pathname_prefix[1];
+    }
+
+    function get_dropdown_href(lang) {
+        let pathname = window.location.pathname.split('/');
+        if (pathname.length > 1) {
+            pathname[1] = lang;
+        }
+        return pathname.join('/');
+    }
+
+    create_dropdown(".drop.language", language_dropdown[current_language], {
+        [language_dropdown['en']]: get_dropdown_href('en'),
+        [language_dropdown['zh']]: get_dropdown_href('zh')
+    });
+}
+
+$(document).ready(function () {
+    add_language_dropdown();
+    add_version_dropdown();
+});

docs/templates/autosummary/module.rst

+.. Modified from https://raw.githubusercontent.com/sphinx-doc/sphinx/4.x/sphinx/ext/autosummary/templates/autosummary/module.rst
+
+{% if fullname == 'nni' %}
+Python API Reference
+====================
+{% else %}
+{{ fullname | escape | underline }}
+{% endif %}
+
+.. automodule:: {{ fullname }}
+   :noindex:
+
+   {% block attributes %}
+   {% if attributes %}
+   .. rubric:: {{ _('Module Attributes') }}
+
+   .. autosummary::
+   {% for item in attributes %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block functions %}
+   {% if functions %}
+   .. rubric:: {{ _('Functions') }}
+
+   .. autosummary::
+   {% for item in functions %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block classes %}
+   {% if classes %}
+   .. rubric:: {{ _('Classes') }}
+
+   .. autosummary::
+   {% for item in classes %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block exceptions %}
+   {% if exceptions %}
+   .. rubric:: {{ _('Exceptions') }}
+
+   .. autosummary::
+   {% for item in exceptions %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+{% block modules %}
+{% if modules %}
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :recursive:
+{% for item in modules %}
+   {{ item }}
+{%- endfor %}
+{% endif %}
+{% endblock %}