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| <p class="admonition-title">Note</p> |
| <p>This tutorial can be used interactively with Google Colab! You can also click |
| <a class="reference internal" href="#sphx-glr-download-how-to-deploy-models-deploy-model-on-adreno-py"><span class="std std-ref">here</span></a> to run the Jupyter notebook locally.</p> |
| <a class="reference external image-reference" href="https://colab.research.google.com/github/apache/tvm-site/blob/asf-site/docs/_downloads/b9e7311d8c56eb6e6aca08f0be35ff03/deploy_model_on_adreno.ipynb"><img alt="https://raw.githubusercontent.com/tlc-pack/web-data/main/images/utilities/colab_button.svg" class="align-center" src="https://raw.githubusercontent.com/tlc-pack/web-data/main/images/utilities/colab_button.svg" width="300px" /></a> |
| </div> |
| <div class="sphx-glr-example-title section" id="deploy-the-pretrained-model-on-adreno"> |
| <span id="tutorial-deploy-model-on-adreno"></span><span id="sphx-glr-how-to-deploy-models-deploy-model-on-adreno-py"></span><h1>Deploy the Pretrained Model on Adreno™<a class="headerlink" href="#deploy-the-pretrained-model-on-adreno" title="Permalink to this headline">¶</a></h1> |
| <p><strong>Author</strong>: Daniil Barinov, Siva Rama Krishna</p> |
| <p>This article is a step-by-step tutorial to deploy pretrained Pytorch ResNet-18 model on Adreno (on different precisions).</p> |
| <p>For us to begin with, PyTorch must be installed. |
| TorchVision is also required since we will be using it as our model zoo.</p> |
| <p>A quick solution is to install it via pip:</p> |
| <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>torch |
| pip<span class="w"> </span>install<span class="w"> </span>torchvision |
| </pre></div> |
| </div> |
| <p>Besides that, you should have TVM builded for Android. |
| See the following instructions on how to build it.</p> |
| <p><a class="reference external" href="https://tvm.apache.org/docs/how_to/deploy/adreno.html">Deploy to Adreno GPU</a></p> |
| <p>After the build section there should be two files in <em>build</em> directory «libtvm_runtime.so» and «tvm_rpc». |
| Let’s push them to the device and run TVM RPC Server.</p> |
| <div class="section" id="tvm-rpc-server"> |
| <h2>TVM RPC Server<a class="headerlink" href="#tvm-rpc-server" title="Permalink to this headline">¶</a></h2> |
| <p>To get the hash of the device use:</p> |
| <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>adb<span class="w"> </span>devices |
| </pre></div> |
| </div> |
| <p>Set the android device to use, if you have several devices connected to your computer.</p> |
| <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">ANDROID_SERIAL</span><span class="o">=</span><device-hash> |
| </pre></div> |
| </div> |
| <p>Then to upload these two files to the device you should use:</p> |
| <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>adb<span class="w"> </span>push<span class="w"> </span><span class="o">{</span>libtvm_runtime.so,tvm_rpc<span class="o">}</span><span class="w"> </span>/data/local/tmp |
| </pre></div> |
| </div> |
| <p>At this moment you will have «libtvm_runtime.so» and «tvm_rpc» on path /data/local/tmp on your device. |
| Sometimes cmake can’t find «libc++_shared.so». Use:</p> |
| <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>find<span class="w"> </span><span class="si">${</span><span class="nv">ANDROID_NDK_HOME</span><span class="si">}</span><span class="w"> </span>-name<span class="w"> </span>libc++_shared.so |
| </pre></div> |
| </div> |
| <p>to find it and also push it with adb on the desired device:</p> |
| <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>adb<span class="w"> </span>push<span class="w"> </span>libc++_shared.so<span class="w"> </span>/data/local/tmp |
| </pre></div> |
| </div> |
| <p>We are now ready to run the TVM RPC Server. |
| Launch rpc_tracker with following line in 1st console:</p> |
| <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>-m<span class="w"> </span>tvm.exec.rpc_tracker<span class="w"> </span>--port<span class="w"> </span><span class="m">9190</span> |
| </pre></div> |
| </div> |
| <p>Then we need to run tvm_rpc server from under the desired device in 2nd console:</p> |
| <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>adb<span class="w"> </span>reverse<span class="w"> </span>tcp:9190<span class="w"> </span>tcp:9190 |
| adb<span class="w"> </span>forward<span class="w"> </span>tcp:5000<span class="w"> </span>tcp:5000 |
| adb<span class="w"> </span>forward<span class="w"> </span>tcp:5002<span class="w"> </span>tcp:5001 |
| adb<span class="w"> </span>forward<span class="w"> </span>tcp:5003<span class="w"> </span>tcp:5002 |
| adb<span class="w"> </span>forward<span class="w"> </span>tcp:5004<span class="w"> </span>tcp:5003 |
| adb<span class="w"> </span>shell<span class="w"> </span><span class="nv">LD_LIBRARY_PATH</span><span class="o">=</span>/data/local/tmp<span class="w"> </span>/data/local/tmp/tvm_rpc<span class="w"> </span>server<span class="w"> </span>--host<span class="o">=</span><span class="m">0</span>.0.0.0<span class="w"> </span>--port<span class="o">=</span><span class="m">5000</span><span class="w"> </span>--tracker<span class="o">=</span><span class="m">127</span>.0.0.1:9190<span class="w"> </span>--key<span class="o">=</span>android<span class="w"> </span>--port-end<span class="o">=</span><span class="m">5100</span> |
| </pre></div> |
| </div> |
| <p>Before proceeding to compile and infer model, specify TVM_TRACKER_HOST and TVM_TRACKER_PORT</p> |
| <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">TVM_TRACKER_HOST</span><span class="o">=</span><span class="m">0</span>.0.0.0 |
| <span class="nb">export</span><span class="w"> </span><span class="nv">TVM_TRACKER_PORT</span><span class="o">=</span><span class="m">9190</span> |
| </pre></div> |
| </div> |
| <p>check that the tracker is running and the device is available</p> |
| <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>-m<span class="w"> </span>tvm.exec.query_rpc_tracker<span class="w"> </span>--port<span class="w"> </span><span class="m">9190</span> |
| </pre></div> |
| </div> |
| <p>For example, if we have 1 Android device, |
| the output can be:</p> |
| <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>Queue<span class="w"> </span>Status |
| ---------------------------------- |
| key<span class="w"> </span>total<span class="w"> </span>free<span class="w"> </span>pending |
| ---------------------------------- |
| android<span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="m">0</span> |
| ---------------------------------- |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="configuration"> |
| <h2>Configuration<a class="headerlink" href="#configuration" title="Permalink to this headline">¶</a></h2> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span> |
| <span class="kn">import</span> <span class="nn">torch</span> |
| <span class="kn">import</span> <span class="nn">torchvision</span> |
| <span class="kn">import</span> <span class="nn">tvm</span> |
| <span class="kn">from</span> <span class="nn">tvm</span> <span class="kn">import</span> <span class="n">te</span> |
| <span class="kn">from</span> <span class="nn">tvm</span> <span class="kn">import</span> <span class="n">relay</span><span class="p">,</span> <span class="n">rpc</span> |
| <span class="kn">from</span> <span class="nn">tvm.contrib</span> <span class="kn">import</span> <span class="n">utils</span><span class="p">,</span> <span class="n">ndk</span> |
| <span class="kn">from</span> <span class="nn">tvm.contrib</span> <span class="kn">import</span> <span class="n">graph_executor</span> |
| <span class="kn">from</span> <span class="nn">tvm.relay.op.contrib</span> <span class="kn">import</span> <span class="n">clml</span> |
| <span class="kn">from</span> <span class="nn">tvm</span> <span class="kn">import</span> <span class="n">autotvm</span> |
| |
| <span class="c1"># Below are set of configuration that controls the behaviour of this script like</span> |
| <span class="c1"># local run or device run, target definitions, dtype setting and auto tuning enablement.</span> |
| <span class="c1"># Change these settings as needed if required.</span> |
| |
| <span class="c1"># Adreno devices are efficient with float16 compared to float32</span> |
| <span class="c1"># Given the expected output doesn't effect by lowering precision</span> |
| <span class="c1"># it's advisable to use lower precision.</span> |
| <span class="c1"># We have a helper API to make the precision conversion simple and</span> |
| <span class="c1"># it supports dtype with "float16" and "float16_acc32" modes.</span> |
| <span class="c1"># Let's choose "float16" for calculation and "float32" for accumulation.</span> |
| |
| <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">calculation_dtype</span></a> <span class="o">=</span> <span class="s2">"float16"</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">acc_dtype</span></a> <span class="o">=</span> <span class="s2">"float32"</span> |
| |
| <span class="c1"># Specify Adreno target before compiling to generate texture</span> |
| <span class="c1"># leveraging kernels and get all the benefits of textures</span> |
| <span class="c1"># Note: This generated example running on our x86 server for demonstration.</span> |
| <span class="c1"># If running it on the Android device, we need to</span> |
| <span class="c1"># specify its instruction set. Set :code:`local_demo` to False if you want</span> |
| <span class="c1"># to run this tutorial with a real device over rpc.</span> |
| <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">local_demo</span></a> <span class="o">=</span> <span class="kc">True</span> |
| |
| <span class="c1"># by default on CPU target will execute.</span> |
| <span class="c1"># select 'cpu', 'opencl' and 'opencl -device=adreno'</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_target</span></a> <span class="o">=</span> <span class="s2">"cpu"</span> |
| |
| <span class="c1"># Change target configuration.</span> |
| <span class="c1"># Run `adb shell cat /proc/cpuinfo` to find the arch.</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">arch</span></a> <span class="o">=</span> <span class="s2">"arm64"</span> |
| <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a> <span class="o">=</span> <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class"><span class="n">tvm</span><span class="o">.</span><span class="n">target</span><span class="o">.</span><span class="n">Target</span></a><span class="p">(</span><span class="s2">"llvm -mtriple=</span><span class="si">%s</span><span class="s2">-linux-android"</span> <span class="o">%</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">arch</span></a><span class="p">)</span> |
| |
| <span class="c1"># Auto tuning is compute intensive and time taking task,</span> |
| <span class="c1"># hence disabling for default run. Please enable it if required.</span> |
| <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">is_tuning</span></a> <span class="o">=</span> <span class="kc">False</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tune_log</span></a> <span class="o">=</span> <span class="s2">"adreno-resnet18.log"</span> |
| |
| <span class="c1"># To enable OpenCLML accelerated operator library.</span> |
| <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">enable_clml</span></a> <span class="o">=</span> <span class="kc">False</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="get-a-pytorch-model"> |
| <h2>Get a PyTorch Model<a class="headerlink" href="#get-a-pytorch-model" title="Permalink to this headline">¶</a></h2> |
| <p>Get resnet18 from torchvision models</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model_name</span></a> <span class="o">=</span> <span class="s2">"resnet18"</span> |
| <span class="n">model</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">torchvision</span><span class="o">.</span><span class="n">models</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model_name</span></a><span class="p">)(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span> |
| |
| <span class="c1"># We grab the TorchScripted model via tracing</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_shape</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">]</span> |
| <span class="n">input_data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_shape</span></a><span class="p">)</span> |
| <span class="n">scripted_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">input_data</span><span class="p">)</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead. |
| warnings.warn( |
| /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights. |
| warnings.warn(msg) |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="load-a-test-image"> |
| <h2>Load a test image<a class="headerlink" href="#load-a-test-image" title="Permalink to this headline">¶</a></h2> |
| <p>As an example we would use classical cat image from ImageNet</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span> |
| <span class="kn">from</span> <span class="nn">tvm.contrib.download</span> <span class="kn">import</span> <span class="n">download_testdata</span> |
| <span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span> |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> |
| |
| <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">img_url</span></a> <span class="o">=</span> <span class="s2">"https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">img_path</span></a> <span class="o">=</span> <span class="n">download_testdata</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">img_url</span></a><span class="p">,</span> <span class="s2">"cat.png"</span><span class="p">,</span> <span class="n">module</span><span class="o">=</span><span class="s2">"data"</span><span class="p">)</span> |
| <span class="n">img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">img_path</span></a><span class="p">)</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">))</span> |
| <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">img</span><span class="p">)</span> |
| <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> |
| |
| <span class="c1"># Preprocess the image and convert to tensor</span> |
| <span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">transforms</span> |
| |
| <span class="n">my_preprocess</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">(</span> |
| <span class="p">[</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">256</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="p">[</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">],</span> <span class="n">std</span><span class="o">=</span><span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">]),</span> |
| <span class="p">]</span> |
| <span class="p">)</span> |
| <span class="n">img</span> <span class="o">=</span> <span class="n">my_preprocess</span><span class="p">(</span><span class="n">img</span><span class="p">)</span> |
| <span class="n">img</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <img src="../../_images/sphx_glr_deploy_model_on_adreno_001.png" srcset="../../_images/sphx_glr_deploy_model_on_adreno_001.png" alt="deploy model on adreno" class = "sphx-glr-single-img"/></div> |
| <div class="section" id="convert-pytorch-model-to-relay-module"> |
| <h2>Convert PyTorch model to Relay module<a class="headerlink" href="#convert-pytorch-model-to-relay-module" title="Permalink to this headline">¶</a></h2> |
| <p>TVM has frontend api for various frameworks under relay.frontend and now |
| for pytorch model import we have relay.frontend.from_pytorch api. |
| Input name can be arbitrary</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_name</span></a> <span class="o">=</span> <span class="s2">"input0"</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">shape_list</span></a> <span class="o">=</span> <span class="p">[(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_name</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">img</span><span class="o">.</span><span class="n">shape</span></a><span class="p">)]</span> |
| |
| <span class="n">mod</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">params</span></a> <span class="o">=</span> <a href="../../reference/api/python/relay/frontend.html#tvm.relay.frontend.from_pytorch" title="tvm.relay.frontend.from_pytorch" class="sphx-glr-backref-module-tvm-relay-frontend sphx-glr-backref-type-py-function"><span class="n">relay</span><span class="o">.</span><span class="n">frontend</span><span class="o">.</span><span class="n">from_pytorch</span></a><span class="p">(</span><span class="n">scripted_model</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">shape_list</span></a><span class="p">)</span> |
| </pre></div> |
| </div> |
| <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/workspace/python/tvm/relay/frontend/pytorch_utils.py:47: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead. |
| return LooseVersion(torch_ver) > ver |
| /venv/apache-tvm-py3.8/lib/python3.8/site-packages/setuptools/_distutils/version.py:346: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead. |
| other = LooseVersion(other) |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="precisions"> |
| <h2>Precisions<a class="headerlink" href="#precisions" title="Permalink to this headline">¶</a></h2> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Adreno devices are efficient with float16 compared to float32</span> |
| <span class="c1"># Given the expected output doesn't effect by lowering precision</span> |
| <span class="c1"># it's advisable to use lower precision.</span> |
| |
| <span class="c1"># TVM support Mixed Precision through ToMixedPrecision transformation pass.</span> |
| <span class="c1"># We may need to register precision rules like precision type, accumultation</span> |
| <span class="c1"># datatype ...etc. for the required operators to override the default settings.</span> |
| <span class="c1"># The below helper api simplifies the precision conversions across the module.</span> |
| |
| <span class="c1"># Calculation dtype is set to "float16" and accumulation dtype is set to "float32"</span> |
| <span class="c1"># in configuration section above.</span> |
| |
| <span class="kn">from</span> <span class="nn">tvm.driver.tvmc.transform</span> <span class="kn">import</span> <span class="n">apply_graph_transforms</span> |
| |
| <span class="n">mod</span> <span class="o">=</span> <span class="n">apply_graph_transforms</span><span class="p">(</span> |
| <span class="n">mod</span><span class="p">,</span> |
| <span class="p">{</span> |
| <span class="s2">"mixed_precision"</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span> |
| <span class="s2">"mixed_precision_ops"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"nn.conv2d"</span><span class="p">,</span> <span class="s2">"nn.dense"</span><span class="p">],</span> |
| <span class="s2">"mixed_precision_calculation_type"</span><span class="p">:</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">calculation_dtype</span></a><span class="p">,</span> |
| <span class="s2">"mixed_precision_acc_type"</span><span class="p">:</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">acc_dtype</span></a><span class="p">,</span> |
| <span class="p">},</span> |
| <span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>As you can see in the IR, the architecture now contains cast operations, which are |
| needed to convert to FP16 precision. |
| You can also use “float16” or “float32” precisions as other dtype options.</p> |
| </div> |
| <div class="section" id="prepare-tvm-target"> |
| <h2>Prepare TVM Target<a class="headerlink" href="#prepare-tvm-target" title="Permalink to this headline">¶</a></h2> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># This generated example running on our x86 server for demonstration.</span> |
| |
| <span class="c1"># To deply and tun on real target over RPC please set :code:`local_demo` to False in above configuration sestion.</span> |
| <span class="c1"># Also, :code:`test_target` is set to :code:`llvm` as this example to make compatible for x86 demonstration.</span> |
| <span class="c1"># Please change it to :code:`opencl` or :code:`opencl -device=adreno` for RPC target in configuration above.</span> |
| |
| <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">local_demo</span></a><span class="p">:</span> |
| <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a> <span class="o">=</span> <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class"><span class="n">tvm</span><span class="o">.</span><span class="n">target</span><span class="o">.</span><span class="n">Target</span></a><span class="p">(</span><span class="s2">"llvm"</span><span class="p">)</span> |
| <span class="k">elif</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_target</span></a><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s2">"opencl"</span><span class="p">):</span> |
| <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a> <span class="o">=</span> <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class"><span class="n">tvm</span><span class="o">.</span><span class="n">target</span><span class="o">.</span><span class="n">Target</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_target</span></a><span class="p">,</span> <span class="n">host</span><span class="o">=</span><a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="autotuning"> |
| <h2>AutoTuning<a class="headerlink" href="#autotuning" title="Permalink to this headline">¶</a></h2> |
| <p>The below few instructions can auto tune the relay module with xgboost being the tuner algorithm.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Auto Tuning process involces stages of extracting the tasks, defining tuning congiguration and</span> |
| <span class="c1"># tuning each task for best performing kernel configuration.</span> |
| |
| <span class="c1"># Get RPC related settings.</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">rpc_tracker_host</span></a> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"TVM_TRACKER_HOST"</span><span class="p">,</span> <span class="s2">"127.0.0.1"</span><span class="p">)</span> |
| <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">rpc_tracker_port</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"TVM_TRACKER_PORT"</span><span class="p">,</span> <span class="mi">9190</span><span class="p">))</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">key</span></a> <span class="o">=</span> <span class="s2">"android"</span> |
| |
| <span class="c1"># Auto tuning is compute intensive and time taking task.</span> |
| <span class="c1"># It is set to False in above configuration as this script runs in x86 for demonstration.</span> |
| <span class="c1"># Please to set :code:`is_tuning` to True to enable auto tuning.</span> |
| |
| <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">is_tuning</span></a><span class="p">:</span> |
| <span class="c1"># Auto Tuning Stage 1: Extract tunable tasks</span> |
| <span class="n">tasks</span> <span class="o">=</span> <span class="n">autotvm</span><span class="o">.</span><span class="n">task</span><span class="o">.</span><span class="n">extract_from_program</span><span class="p">(</span> |
| <span class="n">mod</span><span class="p">,</span> <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_target</span></a><span class="p">,</span> <span class="n">target_host</span><span class="o">=</span><a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">params</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">params</span></a> |
| <span class="p">)</span> |
| |
| <span class="c1"># Auto Tuning Stage 2: Define tuning configuration</span> |
| <span class="n">tmp_log_file</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tune_log</span></a> <span class="o">+</span> <span class="s2">".tmp"</span> |
| <span class="n">measure_option</span> <span class="o">=</span> <span class="n">autotvm</span><span class="o">.</span><span class="n">measure_option</span><span class="p">(</span> |
| <span class="n">builder</span><span class="o">=</span><span class="n">autotvm</span><span class="o">.</span><span class="n">LocalBuilder</span><span class="p">(</span> |
| <span class="n">build_func</span><span class="o">=</span><a href="../../reference/api/python/contrib.html#tvm.contrib.ndk.create_shared" title="tvm.contrib.ndk.create_shared" class="sphx-glr-backref-module-tvm-contrib-ndk sphx-glr-backref-type-py-function"><span class="n">ndk</span><span class="o">.</span><span class="n">create_shared</span></a><span class="p">,</span> <span class="n">timeout</span><span class="o">=</span><span class="mi">15</span> |
| <span class="p">),</span> <span class="c1"># Build the test kernel locally</span> |
| <span class="n">runner</span><span class="o">=</span><span class="n">autotvm</span><span class="o">.</span><span class="n">RPCRunner</span><span class="p">(</span> <span class="c1"># The runner would be on a remote device.</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">key</span></a><span class="p">,</span> <span class="c1"># RPC Key</span> |
| <span class="n">host</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">rpc_tracker_host</span></a><span class="p">,</span> <span class="c1"># Tracker host</span> |
| <span class="n">port</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">rpc_tracker_port</span></a><span class="p">),</span> <span class="c1"># Tracker port</span> |
| <span class="n">number</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="c1"># Number of runs before averaging</span> |
| <span class="n">timeout</span><span class="o">=</span><span class="mi">600</span><span class="p">,</span> <span class="c1"># RPC Timeout</span> |
| <span class="p">),</span> |
| <span class="p">)</span> |
| <span class="n">n_trial</span> <span class="o">=</span> <span class="mi">1024</span> <span class="c1"># Number of iteration of training before choosing the best kernel config</span> |
| <span class="n">early_stopping</span> <span class="o">=</span> <span class="kc">False</span> <span class="c1"># Can be enabled to stop tuning while the loss is not minimizing.</span> |
| |
| <span class="c1"># Auto Tuning Stage 3: Iterate through the tasks and tune.</span> |
| <span class="kn">from</span> <span class="nn">tvm.autotvm.tuner</span> <span class="kn">import</span> <a href="../../reference/api/python/autotvm.html#tvm.autotvm.tuner.XGBTuner" title="tvm.autotvm.tuner.XGBTuner" class="sphx-glr-backref-module-tvm-autotvm-tuner sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">XGBTuner</span></a> |
| |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">tsk</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">reversed</span><span class="p">(</span><span class="n">tasks</span><span class="p">[:</span><span class="mi">3</span><span class="p">])):</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"Task:"</span><span class="p">,</span> <span class="n">tsk</span><span class="p">)</span> |
| <span class="n">prefix</span> <span class="o">=</span> <span class="s2">"[Task </span><span class="si">%2d</span><span class="s2">/</span><span class="si">%2d</span><span class="s2">] "</span> <span class="o">%</span> <span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">tasks</span><span class="p">))</span> |
| |
| <span class="c1"># choose tuner</span> |
| <span class="n">tuner</span> <span class="o">=</span> <span class="s2">"xgb"</span> |
| |
| <span class="c1"># create tuner</span> |
| <span class="k">if</span> <span class="n">tuner</span> <span class="o">==</span> <span class="s2">"xgb"</span><span class="p">:</span> |
| <span class="n">tuner_obj</span> <span class="o">=</span> <a href="../../reference/api/python/autotvm.html#tvm.autotvm.tuner.XGBTuner" title="tvm.autotvm.tuner.XGBTuner" class="sphx-glr-backref-module-tvm-autotvm-tuner sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">XGBTuner</span></a><span class="p">(</span><span class="n">tsk</span><span class="p">,</span> <span class="n">loss_type</span><span class="o">=</span><span class="s2">"reg"</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">tuner</span> <span class="o">==</span> <span class="s2">"xgb_knob"</span><span class="p">:</span> |
| <span class="n">tuner_obj</span> <span class="o">=</span> <a href="../../reference/api/python/autotvm.html#tvm.autotvm.tuner.XGBTuner" title="tvm.autotvm.tuner.XGBTuner" class="sphx-glr-backref-module-tvm-autotvm-tuner sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">XGBTuner</span></a><span class="p">(</span><span class="n">tsk</span><span class="p">,</span> <span class="n">loss_type</span><span class="o">=</span><span class="s2">"reg"</span><span class="p">,</span> <span class="n">feature_type</span><span class="o">=</span><span class="s2">"knob"</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">tuner</span> <span class="o">==</span> <span class="s2">"xgb_itervar"</span><span class="p">:</span> |
| <span class="n">tuner_obj</span> <span class="o">=</span> <a href="../../reference/api/python/autotvm.html#tvm.autotvm.tuner.XGBTuner" title="tvm.autotvm.tuner.XGBTuner" class="sphx-glr-backref-module-tvm-autotvm-tuner sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">XGBTuner</span></a><span class="p">(</span><span class="n">tsk</span><span class="p">,</span> <span class="n">loss_type</span><span class="o">=</span><span class="s2">"reg"</span><span class="p">,</span> <span class="n">feature_type</span><span class="o">=</span><span class="s2">"itervar"</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">tuner</span> <span class="o">==</span> <span class="s2">"xgb_curve"</span><span class="p">:</span> |
| <span class="n">tuner_obj</span> <span class="o">=</span> <a href="../../reference/api/python/autotvm.html#tvm.autotvm.tuner.XGBTuner" title="tvm.autotvm.tuner.XGBTuner" class="sphx-glr-backref-module-tvm-autotvm-tuner sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">XGBTuner</span></a><span class="p">(</span><span class="n">tsk</span><span class="p">,</span> <span class="n">loss_type</span><span class="o">=</span><span class="s2">"reg"</span><span class="p">,</span> <span class="n">feature_type</span><span class="o">=</span><span class="s2">"curve"</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">tuner</span> <span class="o">==</span> <span class="s2">"xgb_rank"</span><span class="p">:</span> |
| <span class="n">tuner_obj</span> <span class="o">=</span> <a href="../../reference/api/python/autotvm.html#tvm.autotvm.tuner.XGBTuner" title="tvm.autotvm.tuner.XGBTuner" class="sphx-glr-backref-module-tvm-autotvm-tuner sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">XGBTuner</span></a><span class="p">(</span><span class="n">tsk</span><span class="p">,</span> <span class="n">loss_type</span><span class="o">=</span><span class="s2">"rank"</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">tuner</span> <span class="o">==</span> <span class="s2">"xgb_rank_knob"</span><span class="p">:</span> |
| <span class="n">tuner_obj</span> <span class="o">=</span> <a href="../../reference/api/python/autotvm.html#tvm.autotvm.tuner.XGBTuner" title="tvm.autotvm.tuner.XGBTuner" class="sphx-glr-backref-module-tvm-autotvm-tuner sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">XGBTuner</span></a><span class="p">(</span><span class="n">tsk</span><span class="p">,</span> <span class="n">loss_type</span><span class="o">=</span><span class="s2">"rank"</span><span class="p">,</span> <span class="n">feature_type</span><span class="o">=</span><span class="s2">"knob"</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">tuner</span> <span class="o">==</span> <span class="s2">"xgb_rank_itervar"</span><span class="p">:</span> |
| <span class="n">tuner_obj</span> <span class="o">=</span> <a href="../../reference/api/python/autotvm.html#tvm.autotvm.tuner.XGBTuner" title="tvm.autotvm.tuner.XGBTuner" class="sphx-glr-backref-module-tvm-autotvm-tuner sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">XGBTuner</span></a><span class="p">(</span><span class="n">tsk</span><span class="p">,</span> <span class="n">loss_type</span><span class="o">=</span><span class="s2">"rank"</span><span class="p">,</span> <span class="n">feature_type</span><span class="o">=</span><span class="s2">"itervar"</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">tuner</span> <span class="o">==</span> <span class="s2">"xgb_rank_curve"</span><span class="p">:</span> |
| <span class="n">tuner_obj</span> <span class="o">=</span> <a href="../../reference/api/python/autotvm.html#tvm.autotvm.tuner.XGBTuner" title="tvm.autotvm.tuner.XGBTuner" class="sphx-glr-backref-module-tvm-autotvm-tuner sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">XGBTuner</span></a><span class="p">(</span><span class="n">tsk</span><span class="p">,</span> <span class="n">loss_type</span><span class="o">=</span><span class="s2">"rank"</span><span class="p">,</span> <span class="n">feature_type</span><span class="o">=</span><span class="s2">"curve"</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">tuner</span> <span class="o">==</span> <span class="s2">"xgb_rank_binary"</span><span class="p">:</span> |
| <span class="n">tuner_obj</span> <span class="o">=</span> <a href="../../reference/api/python/autotvm.html#tvm.autotvm.tuner.XGBTuner" title="tvm.autotvm.tuner.XGBTuner" class="sphx-glr-backref-module-tvm-autotvm-tuner sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">XGBTuner</span></a><span class="p">(</span><span class="n">tsk</span><span class="p">,</span> <span class="n">loss_type</span><span class="o">=</span><span class="s2">"rank-binary"</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">tuner</span> <span class="o">==</span> <span class="s2">"xgb_rank_binary_knob"</span><span class="p">:</span> |
| <span class="n">tuner_obj</span> <span class="o">=</span> <a href="../../reference/api/python/autotvm.html#tvm.autotvm.tuner.XGBTuner" title="tvm.autotvm.tuner.XGBTuner" class="sphx-glr-backref-module-tvm-autotvm-tuner sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">XGBTuner</span></a><span class="p">(</span><span class="n">tsk</span><span class="p">,</span> <span class="n">loss_type</span><span class="o">=</span><span class="s2">"rank-binary"</span><span class="p">,</span> <span class="n">feature_type</span><span class="o">=</span><span class="s2">"knob"</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">tuner</span> <span class="o">==</span> <span class="s2">"xgb_rank_binary_itervar"</span><span class="p">:</span> |
| <span class="n">tuner_obj</span> <span class="o">=</span> <a href="../../reference/api/python/autotvm.html#tvm.autotvm.tuner.XGBTuner" title="tvm.autotvm.tuner.XGBTuner" class="sphx-glr-backref-module-tvm-autotvm-tuner sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">XGBTuner</span></a><span class="p">(</span><span class="n">tsk</span><span class="p">,</span> <span class="n">loss_type</span><span class="o">=</span><span class="s2">"rank-binary"</span><span class="p">,</span> <span class="n">feature_type</span><span class="o">=</span><span class="s2">"itervar"</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">tuner</span> <span class="o">==</span> <span class="s2">"xgb_rank_binary_curve"</span><span class="p">:</span> |
| <span class="n">tuner_obj</span> <span class="o">=</span> <a href="../../reference/api/python/autotvm.html#tvm.autotvm.tuner.XGBTuner" title="tvm.autotvm.tuner.XGBTuner" class="sphx-glr-backref-module-tvm-autotvm-tuner sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">XGBTuner</span></a><span class="p">(</span><span class="n">tsk</span><span class="p">,</span> <span class="n">loss_type</span><span class="o">=</span><span class="s2">"rank-binary"</span><span class="p">,</span> <span class="n">feature_type</span><span class="o">=</span><span class="s2">"curve"</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">tuner</span> <span class="o">==</span> <span class="s2">"ga"</span><span class="p">:</span> |
| <span class="n">tuner_obj</span> <span class="o">=</span> <span class="n">GATuner</span><span class="p">(</span><span class="n">tsk</span><span class="p">,</span> <span class="n">pop_size</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">tuner</span> <span class="o">==</span> <span class="s2">"random"</span><span class="p">:</span> |
| <span class="n">tuner_obj</span> <span class="o">=</span> <span class="n">RandomTuner</span><span class="p">(</span><span class="n">tsk</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">tuner</span> <span class="o">==</span> <span class="s2">"gridsearch"</span><span class="p">:</span> |
| <span class="n">tuner_obj</span> <span class="o">=</span> <span class="n">GridSearchTuner</span><span class="p">(</span><span class="n">tsk</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Invalid tuner: "</span> <span class="o">+</span> <span class="n">tuner</span><span class="p">)</span> |
| |
| <span class="n">tsk_trial</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">n_trial</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">tsk</span><span class="o">.</span><span class="n">config_space</span><span class="p">))</span> |
| <span class="n">tuner_obj</span><span class="o">.</span><span class="n">tune</span><span class="p">(</span> |
| <span class="n">n_trial</span><span class="o">=</span><span class="n">tsk_trial</span><span class="p">,</span> |
| <span class="n">early_stopping</span><span class="o">=</span><span class="n">early_stopping</span><span class="p">,</span> |
| <span class="n">measure_option</span><span class="o">=</span><span class="n">measure_option</span><span class="p">,</span> |
| <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span> |
| <span class="n">autotvm</span><span class="o">.</span><span class="n">callback</span><span class="o">.</span><span class="n">progress_bar</span><span class="p">(</span><span class="n">tsk_trial</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="n">prefix</span><span class="p">),</span> |
| <span class="n">autotvm</span><span class="o">.</span><span class="n">callback</span><span class="o">.</span><span class="n">log_to_file</span><span class="p">(</span><span class="n">tmp_log_file</span><span class="p">),</span> |
| <span class="p">],</span> |
| <span class="p">)</span> |
| <span class="c1"># Auto Tuning Stage 4: Pick the best performing configurations from the overall log.</span> |
| <a href="../../reference/api/python/autotvm.html#tvm.autotvm.record.pick_best" title="tvm.autotvm.record.pick_best" class="sphx-glr-backref-module-tvm-autotvm-record sphx-glr-backref-type-py-function"><span class="n">autotvm</span><span class="o">.</span><span class="n">record</span><span class="o">.</span><span class="n">pick_best</span></a><span class="p">(</span><span class="n">tmp_log_file</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tune_log</span></a><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="enable-openclml-offloading"> |
| <h2>Enable OpenCLML Offloading<a class="headerlink" href="#enable-openclml-offloading" title="Permalink to this headline">¶</a></h2> |
| <p>OpenCLML offloading will try to accelerate supported operators |
| by using OpenCLML proprietory operator library.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># By default :code:`enable_clml` is set to False in above configuration section.</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">local_demo</span></a> <span class="ow">and</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">enable_clml</span></a><span class="p">:</span> |
| <span class="n">mod</span> <span class="o">=</span> <span class="n">clml</span><span class="o">.</span><span class="n">partition_for_clml</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">params</span></a><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="compilation"> |
| <h2>Compilation<a class="headerlink" href="#compilation" title="Permalink to this headline">¶</a></h2> |
| <p>Use tuning cache if exists.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <a href="https://docs.python.org/3/library/os.path.html#os.path.exists" title="os.path.exists" class="sphx-glr-backref-module-os-path sphx-glr-backref-type-py-function"><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tune_log</span></a><span class="p">):</span> |
| <span class="k">with</span> <a href="../../reference/api/python/autotvm.html#tvm.autotvm.apply_history_best" title="tvm.autotvm.apply_history_best" class="sphx-glr-backref-module-tvm-autotvm sphx-glr-backref-type-py-function"><span class="n">autotvm</span><span class="o">.</span><span class="n">apply_history_best</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tune_log</span></a><span class="p">):</span> |
| <span class="k">with</span> <a href="../../reference/api/python/ir.html#tvm.transform.PassContext" title="tvm.transform.PassContext" class="sphx-glr-backref-module-tvm-transform sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tvm</span><span class="o">.</span><span class="n">transform</span><span class="o">.</span><span class="n">PassContext</span></a><span class="p">(</span><span class="n">opt_level</span><span class="o">=</span><span class="mi">3</span><span class="p">):</span> |
| <span class="n">lib</span> <span class="o">=</span> <span class="n">relay</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a><span class="o">=</span><a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">params</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">params</span></a><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">with</span> <a href="../../reference/api/python/ir.html#tvm.transform.PassContext" title="tvm.transform.PassContext" class="sphx-glr-backref-module-tvm-transform sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tvm</span><span class="o">.</span><span class="n">transform</span><span class="o">.</span><span class="n">PassContext</span></a><span class="p">(</span><span class="n">opt_level</span><span class="o">=</span><span class="mi">3</span><span class="p">):</span> |
| <span class="n">lib</span> <span class="o">=</span> <span class="n">relay</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a><span class="o">=</span><a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">params</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">params</span></a><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="deploy-the-model-remotely-by-rpc"> |
| <h2>Deploy the Model Remotely by RPC<a class="headerlink" href="#deploy-the-model-remotely-by-rpc" title="Permalink to this headline">¶</a></h2> |
| <p>Using RPC you can deploy the model from host |
| machine to the remote Adreno device</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">local_demo</span></a><span class="p">:</span> |
| <a href="../../reference/api/python/rpc.html#tvm.rpc.LocalSession" title="tvm.rpc.LocalSession" class="sphx-glr-backref-module-tvm-rpc sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">remote</span></a> <span class="o">=</span> <a href="../../reference/api/python/rpc.html#tvm.rpc.LocalSession" title="tvm.rpc.LocalSession" class="sphx-glr-backref-module-tvm-rpc sphx-glr-backref-type-py-class"><span class="n">rpc</span><span class="o">.</span><span class="n">LocalSession</span></a><span class="p">()</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">tracker</span> <span class="o">=</span> <a href="../../reference/api/python/rpc.html#tvm.rpc.connect_tracker" title="tvm.rpc.connect_tracker" class="sphx-glr-backref-module-tvm-rpc sphx-glr-backref-type-py-function"><span class="n">rpc</span><span class="o">.</span><span class="n">connect_tracker</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">rpc_tracker_host</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">rpc_tracker_port</span></a><span class="p">)</span> |
| <span class="c1"># When running a heavy model, we should increase the `session_timeout`</span> |
| <a href="../../reference/api/python/rpc.html#tvm.rpc.LocalSession" title="tvm.rpc.LocalSession" class="sphx-glr-backref-module-tvm-rpc sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">remote</span></a> <span class="o">=</span> <span class="n">tracker</span><span class="o">.</span><span class="n">request</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">key</span></a><span class="p">,</span> <span class="n">priority</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">session_timeout</span><span class="o">=</span><span class="mi">60</span><span class="p">)</span> |
| |
| <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">local_demo</span></a><span class="p">:</span> |
| <span class="n">dev</span> <span class="o">=</span> <a href="../../reference/api/python/rpc.html#tvm.rpc.RPCSession.cpu" title="tvm.rpc.RPCSession.cpu" class="sphx-glr-backref-module-tvm-rpc sphx-glr-backref-type-py-method"><span class="n">remote</span><span class="o">.</span><span class="n">cpu</span></a><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| <span class="k">elif</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_target</span></a><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s2">"opencl"</span><span class="p">):</span> |
| <span class="n">dev</span> <span class="o">=</span> <a href="../../reference/api/python/rpc.html#tvm.rpc.RPCSession.cl" title="tvm.rpc.RPCSession.cl" class="sphx-glr-backref-module-tvm-rpc sphx-glr-backref-type-py-method"><span class="n">remote</span><span class="o">.</span><span class="n">cl</span></a><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">dev</span> <span class="o">=</span> <a href="../../reference/api/python/rpc.html#tvm.rpc.RPCSession.cpu" title="tvm.rpc.RPCSession.cpu" class="sphx-glr-backref-module-tvm-rpc sphx-glr-backref-type-py-method"><span class="n">remote</span><span class="o">.</span><span class="n">cpu</span></a><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| |
| <a href="../../reference/api/python/contrib.html#tvm.contrib.utils.TempDirectory" title="tvm.contrib.utils.TempDirectory" class="sphx-glr-backref-module-tvm-contrib-utils sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp</span></a> <span class="o">=</span> <a href="../../reference/api/python/contrib.html#tvm.contrib.utils.tempdir" title="tvm.contrib.utils.tempdir" class="sphx-glr-backref-module-tvm-contrib-utils sphx-glr-backref-type-py-function"><span class="n">utils</span><span class="o">.</span><span class="n">tempdir</span></a><span class="p">()</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dso_binary</span></a> <span class="o">=</span> <span class="s2">"dev_lib_cl.so"</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dso_binary_path</span></a> <span class="o">=</span> <a href="../../reference/api/python/contrib.html#tvm.contrib.utils.TempDirectory.relpath" title="tvm.contrib.utils.TempDirectory.relpath" class="sphx-glr-backref-module-tvm-contrib-utils sphx-glr-backref-type-py-method"><span class="n">temp</span><span class="o">.</span><span class="n">relpath</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dso_binary</span></a><span class="p">)</span> |
| <span class="n">fcompile</span> <span class="o">=</span> <a href="../../reference/api/python/contrib.html#tvm.contrib.ndk.create_shared" title="tvm.contrib.ndk.create_shared" class="sphx-glr-backref-module-tvm-contrib-ndk sphx-glr-backref-type-py-function"><span class="n">ndk</span><span class="o">.</span><span class="n">create_shared</span></a> <span class="k">if</span> <span class="ow">not</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">local_demo</span></a> <span class="k">else</span> <span class="kc">None</span> |
| <span class="n">lib</span><span class="o">.</span><span class="n">export_library</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dso_binary_path</span></a><span class="p">,</span> <span class="n">fcompile</span><span class="o">=</span><span class="n">fcompile</span><span class="p">)</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">remote_path</span></a> <span class="o">=</span> <span class="s2">"/data/local/tmp/"</span> <span class="o">+</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dso_binary</span></a> |
| <a href="../../reference/api/python/rpc.html#tvm.rpc.RPCSession.upload" title="tvm.rpc.RPCSession.upload" class="sphx-glr-backref-module-tvm-rpc sphx-glr-backref-type-py-method"><span class="n">remote</span><span class="o">.</span><span class="n">upload</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dso_binary_path</span></a><span class="p">)</span> |
| <a href="../../reference/api/python/runtime.html#tvm.runtime.Module" title="tvm.runtime.Module" class="sphx-glr-backref-module-tvm-runtime sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">rlib</span></a> <span class="o">=</span> <a href="../../reference/api/python/rpc.html#tvm.rpc.RPCSession.load_module" title="tvm.rpc.RPCSession.load_module" class="sphx-glr-backref-module-tvm-rpc sphx-glr-backref-type-py-method"><span class="n">remote</span><span class="o">.</span><span class="n">load_module</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dso_binary</span></a><span class="p">)</span> |
| <a href="../../reference/api/python/graph_executor.html#tvm.contrib.graph_executor.GraphModule" title="tvm.contrib.graph_executor.GraphModule" class="sphx-glr-backref-module-tvm-contrib-graph_executor sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">m</span></a> <span class="o">=</span> <a href="../../reference/api/python/graph_executor.html#tvm.contrib.graph_executor.GraphModule" title="tvm.contrib.graph_executor.GraphModule" class="sphx-glr-backref-module-tvm-contrib-graph_executor sphx-glr-backref-type-py-class"><span class="n">graph_executor</span><span class="o">.</span><span class="n">GraphModule</span></a><span class="p">(</span><a href="../../reference/api/python/runtime.html#tvm.runtime.Module" title="tvm.runtime.Module" class="sphx-glr-backref-module-tvm-runtime sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">rlib</span></a><span class="p">[</span><span class="s2">"default"</span><span class="p">](</span><span class="n">dev</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="run-inference"> |
| <h2>Run inference<a class="headerlink" href="#run-inference" title="Permalink to this headline">¶</a></h2> |
| <p>We now can set inputs, infer our model and get predictions as output</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../reference/api/python/graph_executor.html#tvm.contrib.graph_executor.GraphModule.set_input" title="tvm.contrib.graph_executor.GraphModule.set_input" class="sphx-glr-backref-module-tvm-contrib-graph_executor sphx-glr-backref-type-py-method"><span class="n">m</span><span class="o">.</span><span class="n">set_input</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_name</span></a><span class="p">,</span> <a href="../../reference/api/python/ndarray.html#tvm.nd.array" title="tvm.nd.array" class="sphx-glr-backref-module-tvm-nd sphx-glr-backref-type-py-function"><span class="n">tvm</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span></a><span class="p">(</span><span class="n">img</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"float32"</span><span class="p">)))</span> |
| <a href="../../reference/api/python/graph_executor.html#tvm.contrib.graph_executor.GraphModule.run" title="tvm.contrib.graph_executor.GraphModule.run" class="sphx-glr-backref-module-tvm-contrib-graph_executor sphx-glr-backref-type-py-method"><span class="n">m</span><span class="o">.</span><span class="n">run</span></a><span class="p">()</span> |
| <span class="n">tvm_output</span> <span class="o">=</span> <a href="../../reference/api/python/graph_executor.html#tvm.contrib.graph_executor.GraphModule.get_output" title="tvm.contrib.graph_executor.GraphModule.get_output" class="sphx-glr-backref-module-tvm-contrib-graph_executor sphx-glr-backref-type-py-method"><span class="n">m</span><span class="o">.</span><span class="n">get_output</span></a><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="get-predictions-and-performance-statistic"> |
| <h2>Get predictions and performance statistic<a class="headerlink" href="#get-predictions-and-performance-statistic" title="Permalink to this headline">¶</a></h2> |
| <p>This piece of code displays the top-1 and top-5 predictions, as |
| well as provides information about the model’s performance</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">os.path</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/os.path.html#os.path.join" title="os.path.join" class="sphx-glr-backref-module-os-path sphx-glr-backref-type-py-function"><span class="n">join</span></a><span class="p">,</span> <span class="n">isfile</span> |
| <span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span> |
| <span class="kn">from</span> <span class="nn">tvm.contrib</span> <span class="kn">import</span> <span class="n">download</span> |
| |
| <span class="c1"># Download ImageNet categories</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">categ_url</span></a> <span class="o">=</span> <span class="s2">"https://github.com/uwsampl/web-data/raw/main/vta/models/"</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">categ_fn</span></a> <span class="o">=</span> <span class="s2">"synset.txt"</span> |
| <span class="n">download</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><a href="https://docs.python.org/3/library/os.path.html#os.path.join" title="os.path.join" class="sphx-glr-backref-module-os-path sphx-glr-backref-type-py-function"><span class="n">join</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">categ_url</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">categ_fn</span></a><span class="p">),</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">categ_fn</span></a><span class="p">)</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synset</span></a> <span class="o">=</span> <span class="nb">eval</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">categ_fn</span></a><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">())</span> |
| |
| <span class="n">top_categories</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">tvm_output</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()[</span><span class="mi">0</span><span class="p">])</span> |
| <span class="n">top5</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="n">top_categories</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[:</span><span class="mi">5</span><span class="p">]</span> |
| |
| <span class="c1"># Report top-1 classification result</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"Top-1 id: </span><span class="si">{}</span><span class="s2">, class name: </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">top5</span><span class="p">[</span><span class="mi">1</span> <span class="o">-</span> <span class="mi">1</span><span class="p">],</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synset</span></a><span class="p">[</span><span class="n">top5</span><span class="p">[</span><span class="mi">1</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]]))</span> |
| |
| <span class="c1"># Report top-5 classification results</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Top5 predictions: </span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\t</span><span class="s2">#1:"</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synset</span></a><span class="p">[</span><span class="n">top5</span><span class="p">[</span><span class="mi">1</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]])</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\t</span><span class="s2">#2:"</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synset</span></a><span class="p">[</span><span class="n">top5</span><span class="p">[</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]])</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\t</span><span class="s2">#3:"</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synset</span></a><span class="p">[</span><span class="n">top5</span><span class="p">[</span><span class="mi">3</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]])</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\t</span><span class="s2">#4:"</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synset</span></a><span class="p">[</span><span class="n">top5</span><span class="p">[</span><span class="mi">4</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]])</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\t</span><span class="s2">#5:"</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synset</span></a><span class="p">[</span><span class="n">top5</span><span class="p">[</span><span class="mi">5</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]])</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\t</span><span class="s2">"</span><span class="p">,</span> <span class="n">top5</span><span class="p">)</span> |
| <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ImageNetClassifier</span></a> <span class="o">=</span> <span class="kc">False</span> |
| <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">top_categories</span><span class="p">[</span><span class="o">-</span><span class="mi">5</span><span class="p">:]:</span> |
| <span class="k">if</span> <span class="s2">"cat"</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synset</span></a><span class="p">[</span><span class="n">k</span><span class="p">]:</span> |
| <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ImageNetClassifier</span></a> <span class="o">=</span> <span class="kc">True</span> |
| <span class="k">assert</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ImageNetClassifier</span></a><span class="p">,</span> <span class="s2">"Failed ImageNet classifier validation check"</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"Evaluate inference time cost..."</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><a href="../../reference/api/python/graph_executor.html#tvm.contrib.graph_executor.GraphModule.benchmark" title="tvm.contrib.graph_executor.GraphModule.benchmark" class="sphx-glr-backref-module-tvm-contrib-graph_executor sphx-glr-backref-type-py-method"><span class="n">m</span><span class="o">.</span><span class="n">benchmark</span></a><span class="p">(</span><span class="n">dev</span><span class="p">,</span> <span class="n">number</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">repeat</span><span class="o">=</span><span class="mi">10</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/workspace/python/tvm/runtime/ndarray.py:217: DeprecationWarning: NDArray.asnumpy() will be deprecated in TVM v0.8 release. Please use NDArray.numpy() instead. |
| warnings.warn( |
| Top-1 id: 281, class name: tabby, tabby cat |
| |
| Top5 predictions: |
| |
| #1: tabby, tabby cat |
| #2: tiger cat |
| #3: lynx, catamount |
| #4: red fox, Vulpes vulpes |
| #5: Egyptian cat |
| [281 282 287 277 285] |
| Evaluate inference time cost... |
| Execution time summary: |
| mean (ms) median (ms) max (ms) min (ms) std (ms) |
| 4127.8531 4127.8278 4130.5083 4124.9284 1.6534 |
| </pre></div> |
| </div> |
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