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| <div class="sphx-glr-download-link-note admonition note"> |
| <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-tune-with-autoscheduler-tune-network-x86-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/ad2a7f55d615d188ad664d56696815a6/tune_network_x86.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="auto-scheduling-a-neural-network-for-x86-cpu"> |
| <span id="sphx-glr-how-to-tune-with-autoscheduler-tune-network-x86-py"></span><h1>Auto-scheduling a Neural Network for x86 CPU<a class="headerlink" href="#auto-scheduling-a-neural-network-for-x86-cpu" title="Permalink to this headline">¶</a></h1> |
| <p><strong>Author</strong>: <a class="reference external" href="https://github.com/merrymercy">Lianmin Zheng</a>, <a class="reference external" href="https://github.com/jcf94/">Chengfan Jia</a></p> |
| <p>Auto-tuning for specific devices and workloads is critical for getting the |
| best performance. This is a tutorial on how to tune a whole neural |
| network for x86 CPU with the auto-scheduler.</p> |
| <p>To auto-tune a neural network, we partition the network into small subgraphs and |
| tune them independently. Each subgraph is treated as one search task. |
| A task scheduler slices the time and dynamically allocates time resources to |
| these tasks. The task scheduler predicts the impact of each task on the end-to-end |
| execution time and prioritizes the one that can reduce the execution time the most.</p> |
| <p>For each subgraph, we use the compute declaration in <code class="code docutils literal notranslate"><span class="pre">tvm/python/topi</span></code> to |
| get the computational DAG in the tensor expression form. |
| We then use the auto-scheduler to construct a search space of this DAG and search |
| for good schedules (low-level optimizations).</p> |
| <p>Different from the template-based <a class="reference internal" href="../tune_with_autotvm/index.html#tutorials-autotvm-sec"><span class="std std-ref">autotvm</span></a> which relies on |
| manual templates to define the search space, the auto-scheduler does not require any |
| schedule templates. In other words, the auto-scheduler only uses the compute declarations |
| in <code class="code docutils literal notranslate"><span class="pre">tvm/python/topi</span></code> and does not use existing schedule templates.</p> |
| <p>Note that this tutorial will not run on Windows or recent versions of macOS. To |
| get it to run, you will need to wrap the body of this tutorial in a <code class="code docutils literal notranslate"><span class="pre">if</span> |
| <span class="pre">__name__</span> <span class="pre">==</span> <span class="pre">"__main__":</span></code> block.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span> |
| |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</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">relay</span><span class="p">,</span> <span class="n">auto_scheduler</span> |
| <span class="kn">from</span> <span class="nn">tvm.relay</span> <span class="kn">import</span> <span class="n">data_dep_optimization</span> <span class="k">as</span> <span class="n">ddo</span> |
| <span class="kn">import</span> <span class="nn">tvm.relay.testing</span> |
| <span class="kn">from</span> <span class="nn">tvm.contrib</span> <span class="kn">import</span> <span class="n">graph_executor</span> |
| </pre></div> |
| </div> |
| <div class="section" id="define-a-network"> |
| <h2>Define a Network<a class="headerlink" href="#define-a-network" title="Permalink to this headline">¶</a></h2> |
| <p>First, we need to define the network with relay frontend API. |
| We can load some pre-defined network from <code class="code docutils literal notranslate"><span class="pre">tvm.relay.testing</span></code>. |
| We can also load models from MXNet, ONNX, PyTorch, and TensorFlow |
| (see <a class="reference internal" href="../compile_models/index.html#tutorial-frontend"><span class="std std-ref">front end tutorials</span></a>).</p> |
| <p>For convolutional neural networks, although auto-scheduler can work correctly |
| with any layout, we found the best performance is typically achieved with NHWC layout. |
| We also implemented more optimizations for NHWC layout with the auto-scheduler. |
| So it is recommended to convert your models to NHWC layout to use the auto-scheduler. |
| You can use <a class="reference internal" href="../../arch/convert_layout.html#convert-layout-usage"><span class="std std-ref">ConvertLayout</span></a> pass to do the layout conversion in TVM.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">get_network</span><span class="p">(</span><span class="n">name</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">batch_size</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">layout</span></a><span class="o">=</span><span class="s2">"NHWC"</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">dtype</span></a><span class="o">=</span><span class="s2">"float32"</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">use_sparse</span></a><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Get the symbol definition and random weight of a network"""</span> |
| |
| <span class="c1"># auto-scheduler prefers NHWC layout</span> |
| <span class="k">if</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">layout</span></a> <span class="o">==</span> <span class="s2">"NHWC"</span><span class="p">:</span> |
| <span class="n">image_shape</span> <span class="o">=</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="mi">3</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">layout</span></a> <span class="o">==</span> <span class="s2">"NCHW"</span><span class="p">:</span> |
| <span class="n">image_shape</span> <span class="o">=</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="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 layout: "</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">layout</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">input_shape</span></a> <span class="o">=</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">batch_size</span></a><span class="p">,)</span> <span class="o">+</span> <span class="n">image_shape</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">output_shape</span></a> <span class="o">=</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">batch_size</span></a><span class="p">,</span> <span class="mi">1000</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="n">name</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">"resnet-"</span><span class="p">):</span> |
| <span class="n">n_layer</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">"-"</span><span class="p">)[</span><span class="mi">1</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="o">=</span> <a href="../../reference/api/python/relay/testing.html#tvm.relay.testing.resnet.get_workload" title="tvm.relay.testing.resnet.get_workload" class="sphx-glr-backref-module-tvm-relay-testing-resnet sphx-glr-backref-type-py-function"><span class="n">relay</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">resnet</span><span class="o">.</span><span class="n">get_workload</span></a><span class="p">(</span> |
| <span class="n">num_layers</span><span class="o">=</span><span class="n">n_layer</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">batch_size</span></a><span class="o">=</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">batch_size</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">layout</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">layout</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">dtype</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">dtype</span></a><span class="p">,</span> |
| <span class="n">image_shape</span><span class="o">=</span><span class="n">image_shape</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="k">elif</span> <span class="n">name</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">"resnet3d-"</span><span class="p">):</span> |
| <span class="n">n_layer</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">"-"</span><span class="p">)[</span><span class="mi">1</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="o">=</span> <a href="../../reference/api/python/relay/testing.html#tvm.relay.testing.resnet.get_workload" title="tvm.relay.testing.resnet.get_workload" class="sphx-glr-backref-module-tvm-relay-testing-resnet sphx-glr-backref-type-py-function"><span class="n">relay</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">resnet</span><span class="o">.</span><span class="n">get_workload</span></a><span class="p">(</span> |
| <span class="n">num_layers</span><span class="o">=</span><span class="n">n_layer</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">batch_size</span></a><span class="o">=</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">batch_size</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">layout</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">layout</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">dtype</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">dtype</span></a><span class="p">,</span> |
| <span class="n">image_shape</span><span class="o">=</span><span class="n">image_shape</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="k">elif</span> <span class="n">name</span> <span class="o">==</span> <span class="s2">"mobilenet"</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="o">=</span> <a href="../../reference/api/python/relay/testing.html#tvm.relay.testing.mobilenet.get_workload" title="tvm.relay.testing.mobilenet.get_workload" class="sphx-glr-backref-module-tvm-relay-testing-mobilenet sphx-glr-backref-type-py-function"><span class="n">relay</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">mobilenet</span><span class="o">.</span><span class="n">get_workload</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">batch_size</span></a><span class="o">=</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">batch_size</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">layout</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">layout</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">dtype</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">dtype</span></a><span class="p">,</span> <span class="n">image_shape</span><span class="o">=</span><span class="n">image_shape</span> |
| <span class="p">)</span> |
| <span class="k">elif</span> <span class="n">name</span> <span class="o">==</span> <span class="s2">"squeezenet_v1.1"</span><span class="p">:</span> |
| <span class="k">assert</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">layout</span></a> <span class="o">==</span> <span class="s2">"NCHW"</span><span class="p">,</span> <span class="s2">"squeezenet_v1.1 only supports NCHW layout"</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/testing.html#tvm.relay.testing.squeezenet.get_workload" title="tvm.relay.testing.squeezenet.get_workload" class="sphx-glr-backref-module-tvm-relay-testing-squeezenet sphx-glr-backref-type-py-function"><span class="n">relay</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">squeezenet</span><span class="o">.</span><span class="n">get_workload</span></a><span class="p">(</span> |
| <span class="n">version</span><span class="o">=</span><span class="s2">"1.1"</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">batch_size</span></a><span class="o">=</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">batch_size</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">dtype</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">dtype</span></a><span class="p">,</span> |
| <span class="n">image_shape</span><span class="o">=</span><span class="n">image_shape</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="k">elif</span> <span class="n">name</span> <span class="o">==</span> <span class="s2">"inception_v3"</span><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">input_shape</span></a> <span class="o">=</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">batch_size</span></a><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">299</span><span class="p">,</span> <span class="mi">299</span><span class="p">)</span> <span class="k">if</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">layout</span></a> <span class="o">==</span> <span class="s2">"NCHW"</span> <span class="k">else</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">batch_size</span></a><span class="p">,</span> <span class="mi">299</span><span class="p">,</span> <span class="mi">299</span><span class="p">,</span> <span class="mi">3</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="o">=</span> <a href="../../reference/api/python/relay/testing.html#tvm.relay.testing.inception_v3.get_workload" title="tvm.relay.testing.inception_v3.get_workload" class="sphx-glr-backref-module-tvm-relay-testing-inception_v3 sphx-glr-backref-type-py-function"><span class="n">relay</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">inception_v3</span><span class="o">.</span><span class="n">get_workload</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">batch_size</span></a><span class="o">=</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">batch_size</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">dtype</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">dtype</span></a><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">name</span> <span class="o">==</span> <span class="s2">"mlp"</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="o">=</span> <a href="../../reference/api/python/relay/testing.html#tvm.relay.testing.mlp.get_workload" title="tvm.relay.testing.mlp.get_workload" class="sphx-glr-backref-module-tvm-relay-testing-mlp sphx-glr-backref-type-py-function"><span class="n">relay</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">mlp</span><span class="o">.</span><span class="n">get_workload</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">batch_size</span></a><span class="o">=</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">batch_size</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">dtype</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">dtype</span></a><span class="p">,</span> <span class="n">image_shape</span><span class="o">=</span><span class="n">image_shape</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="mi">1000</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">"Network not found."</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">use_sparse</span></a><span class="p">:</span> |
| <span class="kn">from</span> <span class="nn">tvm.topi.sparse.utils</span> <span class="kn">import</span> <span class="n">convert_model_dense_to_sparse</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> <span class="n">convert_model_dense_to_sparse</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> <span class="n">bs_r</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">random_params</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| |
| <span class="k">return</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> <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">input_shape</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">output_shape</span></a> |
| |
| |
| <span class="c1"># Define the neural network and compilation target.</span> |
| <span class="c1"># If the target machine supports avx512 instructions, replace the</span> |
| <span class="c1"># "llvm -mcpu=core-avx2" with "llvm -mcpu=skylake-avx512"</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">network</span></a> <span class="o">=</span> <span class="s2">"resnet-50"</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">use_sparse</span></a> <span class="o">=</span> <span class="kc">False</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">batch_size</span></a> <span class="o">=</span> <span class="mi">1</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">layout</span></a> <span class="o">=</span> <span class="s2">"NHWC"</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 -mcpu=core-avx2"</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">dtype</span></a> <span class="o">=</span> <span class="s2">"float32"</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">log_file</span></a> <span class="o">=</span> <span class="s2">"</span><span class="si">%s</span><span class="s2">-</span><span class="si">%s</span><span class="s2">-B</span><span class="si">%d</span><span class="s2">-</span><span class="si">%s</span><span class="s2">.json"</span> <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">network</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">layout</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">batch_size</span></a><span class="p">,</span> <a href="../../reference/api/python/runtime.html#tvm.runtime.String" title="tvm.runtime.String" class="sphx-glr-backref-module-tvm-runtime sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span><span class="o">.</span><span class="n">kind</span><span class="o">.</span><span class="n">name</span></a><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="extract-search-tasks"> |
| <h2>Extract Search Tasks<a class="headerlink" href="#extract-search-tasks" title="Permalink to this headline">¶</a></h2> |
| <p>Next, we extract the search tasks and their weights from a network. |
| The weight of a task is the number of appearances of the task’s subgraph |
| in the whole network. |
| By using the weight, we can approximate the end-to-end latency of the network |
| as <code class="code docutils literal notranslate"><span class="pre">sum(latency[t]</span> <span class="pre">*</span> <span class="pre">weight[t])</span></code>, where <code class="code docutils literal notranslate"><span class="pre">latency[t]</span></code> is the |
| latency of a task and <code class="code docutils literal notranslate"><span class="pre">weight[t]</span></code> is the weight of the task. |
| The task scheduler will just optimize this objective.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Extract tasks from the network</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"Get model..."</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> <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">input_shape</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">output_shape</span></a> <span class="o">=</span> <span class="n">get_network</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">network</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">batch_size</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">layout</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">dtype</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">dtype</span></a><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">use_sparse</span></a><span class="o">=</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">use_sparse</span></a><span class="p">,</span> |
| <span class="p">)</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"Extract tasks..."</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">tasks</span></a><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">task_weights</span></a> <span class="o">=</span> <a href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.extract_tasks" title="tvm.auto_scheduler.extract_tasks" class="sphx-glr-backref-module-tvm-auto_scheduler sphx-glr-backref-type-py-function"><span class="n">auto_scheduler</span><span class="o">.</span><span class="n">extract_tasks</span></a><span class="p">(</span><span class="n">mod</span><span class="p">[</span><span class="s2">"main"</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> <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> |
| |
| <span class="k">for</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">idx</span></a><span class="p">,</span> <a href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.SearchTask" title="tvm.auto_scheduler.SearchTask" class="sphx-glr-backref-module-tvm-auto_scheduler sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">task</span></a> <span class="ow">in</span> <span class="nb">enumerate</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">tasks</span></a><span class="p">):</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"========== Task </span><span class="si">%d</span><span class="s2"> (workload key: </span><span class="si">%s</span><span class="s2">) =========="</span> <span class="o">%</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">idx</span></a><span class="p">,</span> <a href="../../reference/api/python/runtime.html#tvm.runtime.String" title="tvm.runtime.String" class="sphx-glr-backref-module-tvm-runtime sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">task</span><span class="o">.</span><span class="n">workload_key</span></a><span class="p">))</span> |
| <span class="nb">print</span><span class="p">(</span><a href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.ComputeDAG" title="tvm.auto_scheduler.ComputeDAG" class="sphx-glr-backref-module-tvm-auto_scheduler sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">task</span><span class="o">.</span><span class="n">compute_dag</span></a><span class="p">)</span> |
| </pre></div> |
| </div> |
| <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Get model... |
| Extract tasks... |
| ========== Task 0 (workload key: ["6d628209072e3e3dd8f49359935acea6", [1, 28, 28, 512], [1, 1, 512, 128], [1, 1, 1, 128], [1, 28, 28, 128]]) ========== |
| p0 = PLACEHOLDER [1, 28, 28, 512] |
| pad_temp(i0, i1, i2, i3) = p0[i0, i1, i2, i3] |
| p1 = PLACEHOLDER [1, 1, 512, 128] |
| conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, (yy + ry), (xx + rx), rc]*p1[ry, rx, rc, ff]) |
| p2 = PLACEHOLDER [1, 1, 1, 128] |
| T_add(ax0, ax1, ax2, ax3) = (conv2d_nhwc[ax0, ax1, ax2, ax3] + p2[ax0, 0, 0, ax3]) |
| T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f) |
| |
| ========== Task 1 (workload key: ["3060808fc5c74e18b1276729071fbae0", [1, 56, 56, 64], [1, 1, 64, 256], [1, 56, 56, 256], [1, 56, 56, 256]]) ========== |
| p0 = PLACEHOLDER [1, 56, 56, 64] |
| pad_temp(i0, i1, i2, i3) = p0[i0, i1, i2, i3] |
| p1 = PLACEHOLDER [1, 1, 64, 256] |
| conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, (yy + ry), (xx + rx), rc]*p1[ry, rx, rc, ff]) |
| p2 = PLACEHOLDER [1, 56, 56, 256] |
| T_add(ax0, ax1, ax2, ax3) = (conv2d_nhwc[ax0, ax1, ax2, ax3] + p2[ax0, ax1, ax2, ax3]) |
| |
| ========== Task 2 (workload key: ["2d10de6646307f0e3e5cf4b31c20e69b", [1, 56, 56, 64], [1, 1, 64, 256], [1, 56, 56, 256]]) ========== |
| p0 = PLACEHOLDER [1, 56, 56, 64] |
| pad_temp(i0, i1, i2, i3) = p0[i0, i1, i2, i3] |
| p1 = PLACEHOLDER [1, 1, 64, 256] |
| conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, (yy + ry), (xx + rx), rc]*p1[ry, rx, rc, ff]) |
| |
| ========== Task 3 (workload key: ["6d628209072e3e3dd8f49359935acea6", [1, 56, 56, 64], [1, 1, 64, 64], [1, 1, 1, 64], [1, 56, 56, 64]]) ========== |
| p0 = PLACEHOLDER [1, 56, 56, 64] |
| pad_temp(i0, i1, i2, i3) = p0[i0, i1, i2, i3] |
| p1 = PLACEHOLDER [1, 1, 64, 64] |
| conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, (yy + ry), (xx + rx), rc]*p1[ry, rx, rc, ff]) |
| p2 = PLACEHOLDER [1, 1, 1, 64] |
| T_add(ax0, ax1, ax2, ax3) = (conv2d_nhwc[ax0, ax1, ax2, ax3] + p2[ax0, 0, 0, ax3]) |
| T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f) |
| |
| ========== Task 4 (workload key: ["08f7449d79e570b7274174709e5e5e01", [1, 2048], [1000, 2048], [1, 1000], [1, 1000]]) ========== |
| p0 = PLACEHOLDER [1, 2048] |
| p1 = PLACEHOLDER [1000, 2048] |
| T_matmul_NT(i0, i1) += (p0[i0, k]*p1[i1, k]) |
| p2 = PLACEHOLDER [1, 1000] |
| T_add(ax0, ax1) = (T_matmul_NT[ax0, ax1] + p2[ax0, ax1]) |
| |
| ========== Task 5 (workload key: ["76afb7bf408a1ffa0b8b7bc09d077dc3", [1, 56, 56, 64], [1, 1, 64, 256], [1, 56, 56, 256], [1, 1, 1, 256], [1, 56, 56, 256]]) ========== |
| p0 = PLACEHOLDER [1, 56, 56, 64] |
| pad_temp(i0, i1, i2, i3) = p0[i0, i1, i2, i3] |
| p1 = PLACEHOLDER [1, 1, 64, 256] |
| conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, (yy + ry), (xx + rx), rc]*p1[ry, rx, rc, ff]) |
| p2 = PLACEHOLDER [1, 56, 56, 256] |
| T_add(ax0, ax1, ax2, ax3) = (conv2d_nhwc[ax0, ax1, ax2, ax3] + p2[ax0, ax1, ax2, ax3]) |
| p3 = PLACEHOLDER [1, 1, 1, 256] |
| T_add(ax0, ax1, ax2, ax3) = (T_add[ax0, ax1, ax2, ax3] + p3[ax0, 0, 0, ax3]) |
| T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f) |
| |
| ========== Task 6 (workload key: ["8c53ca2904398da2889aa7508082d7bb", [1, 7, 7, 2048], [1, 1, 1, 2048]]) ========== |
| p0 = PLACEHOLDER [1, 7, 7, 2048] |
| adaptive_pool_sum(ax0, ax1, ax2, ax3) += p0[ax0, ((ax1*7) + rv0), ((ax2*7) + rv1), ax3] |
| adaptive_pool_avg(ax0, ax1, ax2, ax3) = (adaptive_pool_sum[ax0, ax1, ax2, ax3]/(float32((select((bool)1, ((ax1 + 1)*7), (((ax1 + 1)*7) + 1)) - (ax1*7)))*float32((select((bool)1, ((ax2 + 1)*7), (((ax2 + 1)*7) + 1)) - (ax2*7))))) |
| |
| ========== Task 7 (workload key: ["2beb39e9afe4c74822fffbcbb8533595", [1, 14, 14, 1024], [1, 1, 1024, 512], [1, 1, 1, 512], [1, 7, 7, 512]]) ========== |
| p0 = PLACEHOLDER [1, 14, 14, 1024] |
| pad_temp(i0, i1, i2, i3) = p0[i0, i1, i2, i3] |
| p1 = PLACEHOLDER [1, 1, 1024, 512] |
| conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*p1[ry, rx, rc, ff]) |
| p2 = PLACEHOLDER [1, 1, 1, 512] |
| T_add(ax0, ax1, ax2, ax3) = (conv2d_nhwc[ax0, ax1, ax2, ax3] + p2[ax0, 0, 0, ax3]) |
| T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f) |
| |
| ========== Task 8 (workload key: ["0fad1b42d0d33418e0a8d15d3bbad3c9", [1, 14, 14, 1024], [1, 1, 1024, 2048], [1, 7, 7, 2048]]) ========== |
| p0 = PLACEHOLDER [1, 14, 14, 1024] |
| pad_temp(i0, i1, i2, i3) = p0[i0, i1, i2, i3] |
| p1 = PLACEHOLDER [1, 1, 1024, 2048] |
| conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*p1[ry, rx, rc, ff]) |
| |
| ========== Task 9 (workload key: ["3060808fc5c74e18b1276729071fbae0", [1, 7, 7, 512], [1, 1, 512, 2048], [1, 7, 7, 2048], [1, 7, 7, 2048]]) ========== |
| p0 = PLACEHOLDER [1, 7, 7, 512] |
| pad_temp(i0, i1, i2, i3) = p0[i0, i1, i2, i3] |
| p1 = PLACEHOLDER [1, 1, 512, 2048] |
| conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, (yy + ry), (xx + rx), rc]*p1[ry, rx, rc, ff]) |
| p2 = PLACEHOLDER [1, 7, 7, 2048] |
| T_add(ax0, ax1, ax2, ax3) = (conv2d_nhwc[ax0, ax1, ax2, ax3] + p2[ax0, ax1, ax2, ax3]) |
| |
| ========== Task 10 (workload key: ["2beb39e9afe4c74822fffbcbb8533595", [1, 56, 56, 256], [1, 1, 256, 128], [1, 1, 1, 128], [1, 28, 28, 128]]) ========== |
| p0 = PLACEHOLDER [1, 56, 56, 256] |
| pad_temp(i0, i1, i2, i3) = p0[i0, i1, i2, i3] |
| p1 = PLACEHOLDER [1, 1, 256, 128] |
| conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*p1[ry, rx, rc, ff]) |
| p2 = PLACEHOLDER [1, 1, 1, 128] |
| T_add(ax0, ax1, ax2, ax3) = (conv2d_nhwc[ax0, ax1, ax2, ax3] + p2[ax0, 0, 0, ax3]) |
| T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f) |
| |
| ========== Task 11 (workload key: ["76afb7bf408a1ffa0b8b7bc09d077dc3", [1, 28, 28, 128], [1, 1, 128, 512], [1, 28, 28, 512], [1, 1, 1, 512], [1, 28, 28, 512]]) ========== |
| p0 = PLACEHOLDER [1, 28, 28, 128] |
| pad_temp(i0, i1, i2, i3) = p0[i0, i1, i2, i3] |
| p1 = PLACEHOLDER [1, 1, 128, 512] |
| conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, (yy + ry), (xx + rx), rc]*p1[ry, rx, rc, ff]) |
| p2 = PLACEHOLDER [1, 28, 28, 512] |
| T_add(ax0, ax1, ax2, ax3) = (conv2d_nhwc[ax0, ax1, ax2, ax3] + p2[ax0, ax1, ax2, ax3]) |
| p3 = PLACEHOLDER [1, 1, 1, 512] |
| T_add(ax0, ax1, ax2, ax3) = (T_add[ax0, ax1, ax2, ax3] + p3[ax0, 0, 0, ax3]) |
| T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f) |
| |
| ========== Task 12 (workload key: ["0fad1b42d0d33418e0a8d15d3bbad3c9", [1, 56, 56, 256], [1, 1, 256, 512], [1, 28, 28, 512]]) ========== |
| p0 = PLACEHOLDER [1, 56, 56, 256] |
| pad_temp(i0, i1, i2, i3) = p0[i0, i1, i2, i3] |
| p1 = PLACEHOLDER [1, 1, 256, 512] |
| conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*p1[ry, rx, rc, ff]) |
| |
| ========== Task 13 (workload key: ["7d79c516e212fe1d73f5dbb90eaca2cf", [1, 1000], [1, 1000]]) ========== |
| p0 = PLACEHOLDER [1, 1000] |
| T_softmax_maxelem(i0) max= p0[i0, k] |
| T_softmax_exp(i0, i1) = tir.exp((p0[i0, i1] - T_softmax_maxelem[i0])) |
| T_softmax_expsum(i0) += T_softmax_exp[i0, k] |
| T_softmax_norm(i0, i1) = (T_softmax_exp[i0, i1]/T_softmax_expsum[i0]) |
| |
| ========== Task 14 (workload key: ["3060808fc5c74e18b1276729071fbae0", [1, 14, 14, 256], [1, 1, 256, 1024], [1, 14, 14, 1024], [1, 14, 14, 1024]]) ========== |
| p0 = PLACEHOLDER [1, 14, 14, 256] |
| pad_temp(i0, i1, i2, i3) = p0[i0, i1, i2, i3] |
| p1 = PLACEHOLDER [1, 1, 256, 1024] |
| conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, (yy + ry), (xx + rx), rc]*p1[ry, rx, rc, ff]) |
| p2 = PLACEHOLDER [1, 14, 14, 1024] |
| T_add(ax0, ax1, ax2, ax3) = (conv2d_nhwc[ax0, ax1, ax2, ax3] + p2[ax0, ax1, ax2, ax3]) |
| |
| ========== Task 15 (workload key: ["07f9fcad27bdd3233f86fe35a5185d33", [1, 224, 224, 3], [7, 7, 3, 64], [1, 1, 1, 64], [1, 112, 112, 64]]) ========== |
| p0 = PLACEHOLDER [1, 224, 224, 3] |
| pad_temp(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 3) && (i1 < 227)) && (i2 >= 3)) && (i2 < 227)), p0[i0, (i1 - 3), (i2 - 3), i3], 0f) |
| p1 = PLACEHOLDER [7, 7, 3, 64] |
| conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*p1[ry, rx, rc, ff]) |
| p2 = PLACEHOLDER [1, 1, 1, 64] |
| T_add(ax0, ax1, ax2, ax3) = (conv2d_nhwc[ax0, ax1, ax2, ax3] + p2[ax0, 0, 0, ax3]) |
| T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f) |
| |
| ========== Task 16 (workload key: ["6d012ba18a086c11ee2b85c7324e16f2", [1, 112, 112, 64], [1, 1, 1, 64], [1, 56, 56, 64]]) ========== |
| p0 = PLACEHOLDER [1, 112, 112, 64] |
| pad_temp(ax0, ax1, ax2, ax3) = tir.if_then_else(((((ax1 >= 1) && (ax1 < 113)) && (ax2 >= 1)) && (ax2 < 113)), p0[ax0, (ax1 - 1), (ax2 - 1), ax3], -3.40282e+38f) |
| pool_max(ax0, ax1, ax2, ax3) max= pad_temp[ax0, ((ax1*2) + rv0), ((ax2*2) + rv1), ax3] |
| p1 = PLACEHOLDER [1, 1, 1, 64] |
| T_add(ax0, ax1, ax2, ax3) = (pool_max[ax0, ax1, ax2, ax3] + p1[ax0, 0, 0, ax3]) |
| T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f) |
| |
| ========== Task 17 (workload key: ["6d628209072e3e3dd8f49359935acea6", [1, 7, 7, 2048], [1, 1, 2048, 512], [1, 1, 1, 512], [1, 7, 7, 512]]) ========== |
| p0 = PLACEHOLDER [1, 7, 7, 2048] |
| pad_temp(i0, i1, i2, i3) = p0[i0, i1, i2, i3] |
| p1 = PLACEHOLDER [1, 1, 2048, 512] |
| conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, (yy + ry), (xx + rx), rc]*p1[ry, rx, rc, ff]) |
| p2 = PLACEHOLDER [1, 1, 1, 512] |
| T_add(ax0, ax1, ax2, ax3) = (conv2d_nhwc[ax0, ax1, ax2, ax3] + p2[ax0, 0, 0, ax3]) |
| T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f) |
| |
| ========== Task 18 (workload key: ["6d628209072e3e3dd8f49359935acea6", [1, 14, 14, 1024], [1, 1, 1024, 256], [1, 1, 1, 256], [1, 14, 14, 256]]) ========== |
| p0 = PLACEHOLDER [1, 14, 14, 1024] |
| pad_temp(i0, i1, i2, i3) = p0[i0, i1, i2, i3] |
| p1 = PLACEHOLDER [1, 1, 1024, 256] |
| conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, (yy + ry), (xx + rx), rc]*p1[ry, rx, rc, ff]) |
| p2 = PLACEHOLDER [1, 1, 1, 256] |
| T_add(ax0, ax1, ax2, ax3) = (conv2d_nhwc[ax0, ax1, ax2, ax3] + p2[ax0, 0, 0, ax3]) |
| T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f) |
| |
| ========== Task 19 (workload key: ["38552500208b25b4035682b0e93cbce3", [1, 14, 14, 256], [6, 6, 256, 256], [1, 1, 1, 256], [1, 14, 14, 256]]) ========== |
| p0 = PLACEHOLDER [1, 14, 14, 256] |
| data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 15)) && (i2 >= 1)) && (i2 < 15)), p0[i0, (i1 - 1), (i2 - 1), i3], 0f) |
| input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 16), ((floormod(floordiv(p, 4), 4)*4) + eps), ((floormod(p, 4)*4) + nu), ci] |
| B(i, j) = select(((floormod(i, 6) == 5) && (floormod(j, 6) == 5)), 1f, select(((floormod(i, 6) == 5) && (floormod(j, 6) == 4)), ..(OMITTED).. (floormod(j, 6) == 1)), 0f, select(((floormod(i, 6) == 0) && (floormod(j, 6) == 0)), 1f, 0f)))))))))))))))))))))))))))))))))))) |
| data_pack(eps, nu, p, ci) += ((input_tile[r_a, r_b, p, ci]*B[r_a, eps])*B[r_b, nu]) |
| p1 = PLACEHOLDER [6, 6, 256, 256] |
| bgemm(eps, nu, p, co) += (data_pack[eps, nu, p, ci]*p1[eps, nu, co, ci]) |
| A(i, j) = select(((floormod(i, 6) == 5) && (floormod(j, 4) == 3)), 1f, select(((floormod(i, 6) == 5) && (floormod(j, 4) == 2)), ..(OMITTED).. 6) == 0) && (floormod(j, 4) == 1)), 0f, select(((floormod(i, 6) == 0) && (floormod(j, 4) == 0)), 1f, 0f)))))))))))))))))))))))) |
| inverse(vh, vw, p, co) += ((bgemm[r_a, r_b, p, co]*A[r_a, vh])*A[r_b, vw]) |
| conv2d_winograd(n, h, w, co) = inverse[floormod(h, 4), floormod(w, 4), ((((n*4)*4) + (floordiv(h, 4)*4)) + floordiv(w, 4)), co] |
| p2 = PLACEHOLDER [1, 1, 1, 256] |
| T_add(ax0, ax1, ax2, ax3) = (conv2d_winograd[ax0, ax1, ax2, ax3] + p2[ax0, 0, 0, ax3]) |
| T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f) |
| |
| ========== Task 20 (workload key: ["6d628209072e3e3dd8f49359935acea6", [1, 56, 56, 256], [1, 1, 256, 64], [1, 1, 1, 64], [1, 56, 56, 64]]) ========== |
| p0 = PLACEHOLDER [1, 56, 56, 256] |
| pad_temp(i0, i1, i2, i3) = p0[i0, i1, i2, i3] |
| p1 = PLACEHOLDER [1, 1, 256, 64] |
| conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, (yy + ry), (xx + rx), rc]*p1[ry, rx, rc, ff]) |
| p2 = PLACEHOLDER [1, 1, 1, 64] |
| T_add(ax0, ax1, ax2, ax3) = (conv2d_nhwc[ax0, ax1, ax2, ax3] + p2[ax0, 0, 0, ax3]) |
| T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f) |
| |
| ========== Task 21 (workload key: ["f07e228ef5f642b386d23a62df615e7b", [1, 7, 7, 512], [1, 1, 512, 2048], [1, 7, 7, 2048], [1, 1, 1, 2048], [1, 1, 1, 2048], [1, 7, 7, 2048]]) ========== |
| p0 = PLACEHOLDER [1, 7, 7, 512] |
| pad_temp(i0, i1, i2, i3) = p0[i0, i1, i2, i3] |
| p1 = PLACEHOLDER [1, 1, 512, 2048] |
| conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, (yy + ry), (xx + rx), rc]*p1[ry, rx, rc, ff]) |
| p2 = PLACEHOLDER [1, 7, 7, 2048] |
| T_add(ax0, ax1, ax2, ax3) = (conv2d_nhwc[ax0, ax1, ax2, ax3] + p2[ax0, ax1, ax2, ax3]) |
| p3 = PLACEHOLDER [1, 1, 1, 2048] |
| T_multiply(ax0, ax1, ax2, ax3) = (T_add[ax0, ax1, ax2, ax3]*p3[ax0, 0, 0, ax3]) |
| p4 = PLACEHOLDER [1, 1, 1, 2048] |
| T_add(ax0, ax1, ax2, ax3) = (T_multiply[ax0, ax1, ax2, ax3] + p4[ax0, 0, 0, ax3]) |
| T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f) |
| |
| ========== Task 22 (workload key: ["3060808fc5c74e18b1276729071fbae0", [1, 28, 28, 128], [1, 1, 128, 512], [1, 28, 28, 512], [1, 28, 28, 512]]) ========== |
| p0 = PLACEHOLDER [1, 28, 28, 128] |
| pad_temp(i0, i1, i2, i3) = p0[i0, i1, i2, i3] |
| p1 = PLACEHOLDER [1, 1, 128, 512] |
| conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, (yy + ry), (xx + rx), rc]*p1[ry, rx, rc, ff]) |
| p2 = PLACEHOLDER [1, 28, 28, 512] |
| T_add(ax0, ax1, ax2, ax3) = (conv2d_nhwc[ax0, ax1, ax2, ax3] + p2[ax0, ax1, ax2, ax3]) |
| |
| ========== Task 23 (workload key: ["0fad1b42d0d33418e0a8d15d3bbad3c9", [1, 28, 28, 512], [1, 1, 512, 1024], [1, 14, 14, 1024]]) ========== |
| p0 = PLACEHOLDER [1, 28, 28, 512] |
| pad_temp(i0, i1, i2, i3) = p0[i0, i1, i2, i3] |
| p1 = PLACEHOLDER [1, 1, 512, 1024] |
| conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*p1[ry, rx, rc, ff]) |
| |
| ========== Task 24 (workload key: ["2beb39e9afe4c74822fffbcbb8533595", [1, 28, 28, 512], [1, 1, 512, 256], [1, 1, 1, 256], [1, 14, 14, 256]]) ========== |
| p0 = PLACEHOLDER [1, 28, 28, 512] |
| pad_temp(i0, i1, i2, i3) = p0[i0, i1, i2, i3] |
| p1 = PLACEHOLDER [1, 1, 512, 256] |
| conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*p1[ry, rx, rc, ff]) |
| p2 = PLACEHOLDER [1, 1, 1, 256] |
| T_add(ax0, ax1, ax2, ax3) = (conv2d_nhwc[ax0, ax1, ax2, ax3] + p2[ax0, 0, 0, ax3]) |
| T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f) |
| |
| ========== Task 25 (workload key: ["76afb7bf408a1ffa0b8b7bc09d077dc3", [1, 14, 14, 256], [1, 1, 256, 1024], [1, 14, 14, 1024], [1, 1, 1, 1024], [1, 14, 14, 1024]]) ========== |
| p0 = PLACEHOLDER [1, 14, 14, 256] |
| pad_temp(i0, i1, i2, i3) = p0[i0, i1, i2, i3] |
| p1 = PLACEHOLDER [1, 1, 256, 1024] |
| conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, (yy + ry), (xx + rx), rc]*p1[ry, rx, rc, ff]) |
| p2 = PLACEHOLDER [1, 14, 14, 1024] |
| T_add(ax0, ax1, ax2, ax3) = (conv2d_nhwc[ax0, ax1, ax2, ax3] + p2[ax0, ax1, ax2, ax3]) |
| p3 = PLACEHOLDER [1, 1, 1, 1024] |
| T_add(ax0, ax1, ax2, ax3) = (T_add[ax0, ax1, ax2, ax3] + p3[ax0, 0, 0, ax3]) |
| T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f) |
| |
| ========== Task 26 (workload key: ["d37380659057397544e056461ea3bad3", [1, 56, 56, 64], [3, 3, 64, 64], [1, 1, 1, 64], [1, 56, 56, 64]]) ========== |
| p0 = PLACEHOLDER [1, 56, 56, 64] |
| pad_temp(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 57)) && (i2 >= 1)) && (i2 < 57)), p0[i0, (i1 - 1), (i2 - 1), i3], 0f) |
| p1 = PLACEHOLDER [3, 3, 64, 64] |
| conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, (yy + ry), (xx + rx), rc]*p1[ry, rx, rc, ff]) |
| p2 = PLACEHOLDER [1, 1, 1, 64] |
| T_add(ax0, ax1, ax2, ax3) = (conv2d_nhwc[ax0, ax1, ax2, ax3] + p2[ax0, 0, 0, ax3]) |
| T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f) |
| |
| ========== Task 27 (workload key: ["cfd09cf1ca9e943f0ee12a18813a5c75", [1, 28, 28, 128], [6, 6, 128, 128], [1, 1, 1, 128], [1, 28, 28, 128]]) ========== |
| p0 = PLACEHOLDER [1, 28, 28, 128] |
| data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 29)) && (i2 >= 1)) && (i2 < 29)), p0[i0, (i1 - 1), (i2 - 1), i3], 0f) |
| input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 49), ((floormod(floordiv(p, 7), 7)*4) + eps), ((floormod(p, 7)*4) + nu), ci] |
| B(i, j) = select(((floormod(i, 6) == 5) && (floormod(j, 6) == 5)), 1f, select(((floormod(i, 6) == 5) && (floormod(j, 6) == 4)), ..(OMITTED).. (floormod(j, 6) == 1)), 0f, select(((floormod(i, 6) == 0) && (floormod(j, 6) == 0)), 1f, 0f)))))))))))))))))))))))))))))))))))) |
| data_pack(eps, nu, p, ci) += ((input_tile[r_a, r_b, p, ci]*B[r_a, eps])*B[r_b, nu]) |
| p1 = PLACEHOLDER [6, 6, 128, 128] |
| bgemm(eps, nu, p, co) += (data_pack[eps, nu, p, ci]*p1[eps, nu, co, ci]) |
| A(i, j) = select(((floormod(i, 6) == 5) && (floormod(j, 4) == 3)), 1f, select(((floormod(i, 6) == 5) && (floormod(j, 4) == 2)), ..(OMITTED).. 6) == 0) && (floormod(j, 4) == 1)), 0f, select(((floormod(i, 6) == 0) && (floormod(j, 4) == 0)), 1f, 0f)))))))))))))))))))))))) |
| inverse(vh, vw, p, co) += ((bgemm[r_a, r_b, p, co]*A[r_a, vh])*A[r_b, vw]) |
| conv2d_winograd(n, h, w, co) = inverse[floormod(h, 4), floormod(w, 4), ((((n*7)*7) + (floordiv(h, 4)*7)) + floordiv(w, 4)), co] |
| p2 = PLACEHOLDER [1, 1, 1, 128] |
| T_add(ax0, ax1, ax2, ax3) = (conv2d_winograd[ax0, ax1, ax2, ax3] + p2[ax0, 0, 0, ax3]) |
| T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f) |
| |
| ========== Task 28 (workload key: ["d37380659057397544e056461ea3bad3", [1, 7, 7, 512], [3, 3, 512, 512], [1, 1, 1, 512], [1, 7, 7, 512]]) ========== |
| p0 = PLACEHOLDER [1, 7, 7, 512] |
| pad_temp(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 8)) && (i2 >= 1)) && (i2 < 8)), p0[i0, (i1 - 1), (i2 - 1), i3], 0f) |
| p1 = PLACEHOLDER [3, 3, 512, 512] |
| conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, (yy + ry), (xx + rx), rc]*p1[ry, rx, rc, ff]) |
| p2 = PLACEHOLDER [1, 1, 1, 512] |
| T_add(ax0, ax1, ax2, ax3) = (conv2d_nhwc[ax0, ax1, ax2, ax3] + p2[ax0, 0, 0, ax3]) |
| T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f) |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="begin-tuning"> |
| <h2>Begin Tuning<a class="headerlink" href="#begin-tuning" title="Permalink to this headline">¶</a></h2> |
| <p>Now, we set some options for tuning and launch the search tasks</p> |
| <ul class="simple"> |
| <li><p><code class="code docutils literal notranslate"><span class="pre">num_measure_trials</span></code> is the number of measurement trials we can use during the tuning. |
| You can set it to a small number (e.g., 200) for a fast demonstrative run. |
| In practice, we recommend setting it around <code class="code docutils literal notranslate"><span class="pre">800</span> <span class="pre">*</span> <span class="pre">len(tasks)</span></code>, |
| which is typically enough for the search to converge. |
| For example, there are 29 tasks in resnet-50, so we can set it as 20000. |
| You can adjust this parameter according to your time budget.</p></li> |
| <li><p>In addition, we use <code class="code docutils literal notranslate"><span class="pre">RecordToFile</span></code> to dump measurement records into a log file, |
| The measurement records can be used to query the history best, resume the search, |
| and do more analyses later.</p></li> |
| <li><p>see <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.TuningOptions" title="tvm.auto_scheduler.TuningOptions"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.TuningOptions</span></code></a>, |
| <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.LocalRunner" title="tvm.auto_scheduler.LocalRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.LocalRunner</span></code></a> for more parameters.</p></li> |
| </ul> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">run_tuning</span><span class="p">():</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"Begin tuning..."</span><span class="p">)</span> |
| <span class="n">tuner</span> <span class="o">=</span> <a href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.TaskScheduler" title="tvm.auto_scheduler.TaskScheduler" class="sphx-glr-backref-module-tvm-auto_scheduler sphx-glr-backref-type-py-class"><span class="n">auto_scheduler</span><span class="o">.</span><span class="n">TaskScheduler</span></a><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">tasks</span></a><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">task_weights</span></a><span class="p">)</span> |
| <span class="n">tune_option</span> <span class="o">=</span> <a href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.TuningOptions" title="tvm.auto_scheduler.TuningOptions" class="sphx-glr-backref-module-tvm-auto_scheduler sphx-glr-backref-type-py-class"><span class="n">auto_scheduler</span><span class="o">.</span><span class="n">TuningOptions</span></a><span class="p">(</span> |
| <span class="n">num_measure_trials</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="c1"># change this to 20000 to achieve the best performance</span> |
| <span class="n">runner</span><span class="o">=</span><a href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.LocalRunner" title="tvm.auto_scheduler.LocalRunner" class="sphx-glr-backref-module-tvm-auto_scheduler sphx-glr-backref-type-py-class"><span class="n">auto_scheduler</span><span class="o">.</span><span class="n">LocalRunner</span></a><span class="p">(</span><span class="n">repeat</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">enable_cpu_cache_flush</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span> |
| <span class="n">measure_callbacks</span><span class="o">=</span><span class="p">[</span><a href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RecordToFile" title="tvm.auto_scheduler.RecordToFile" class="sphx-glr-backref-module-tvm-auto_scheduler sphx-glr-backref-type-py-class"><span class="n">auto_scheduler</span><span class="o">.</span><span class="n">RecordToFile</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">log_file</span></a><span class="p">)],</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">use_sparse</span></a><span class="p">:</span> |
| <span class="kn">from</span> <span class="nn">tvm.topi.sparse.utils</span> <span class="kn">import</span> <span class="n">sparse_sketch_rules</span> |
| |
| <span class="n">search_policy</span> <span class="o">=</span> <span class="p">[</span> |
| <a href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.SketchPolicy" title="tvm.auto_scheduler.SketchPolicy" class="sphx-glr-backref-module-tvm-auto_scheduler sphx-glr-backref-type-py-class"><span class="n">auto_scheduler</span><span class="o">.</span><span class="n">SketchPolicy</span></a><span class="p">(</span> |
| <a href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.SearchTask" title="tvm.auto_scheduler.SearchTask" class="sphx-glr-backref-module-tvm-auto_scheduler sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">task</span></a><span class="p">,</span> |
| <span class="n">program_cost_model</span><span class="o">=</span><a href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.XGBModel" title="tvm.auto_scheduler.XGBModel" class="sphx-glr-backref-module-tvm-auto_scheduler sphx-glr-backref-type-py-class"><span class="n">auto_scheduler</span><span class="o">.</span><span class="n">XGBModel</span></a><span class="p">(),</span> |
| <span class="n">init_search_callbacks</span><span class="o">=</span><span class="n">sparse_sketch_rules</span><span class="p">(),</span> |
| <span class="p">)</span> |
| <span class="k">for</span> <a href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.SearchTask" title="tvm.auto_scheduler.SearchTask" class="sphx-glr-backref-module-tvm-auto_scheduler sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">task</span></a> <span class="ow">in</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">tasks</span></a> |
| <span class="p">]</span> |
| |
| <span class="n">tuner</span><span class="o">.</span><span class="n">tune</span><span class="p">(</span><span class="n">tune_option</span><span class="p">,</span> <span class="n">search_policy</span><span class="o">=</span><span class="n">search_policy</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">tuner</span><span class="o">.</span><span class="n">tune</span><span class="p">(</span><span class="n">tune_option</span><span class="p">)</span> |
| |
| |
| <span class="c1"># We do not run the tuning in our webpage server since it takes too long.</span> |
| <span class="c1"># Uncomment the following line to run it by yourself.</span> |
| |
| <span class="c1"># run_tuning()</span> |
| </pre></div> |
| </div> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>Explain the printed information during tuning</p> |
| <p>During the tuning, a lot of information will be printed on the console. |
| They are used for debugging purposes. The most important info is the output |
| of the task scheduler. The following table is a sample output.</p> |
| <div class="highlight-c notranslate"><div class="highlight"><pre><span></span><span class="o">----------------------------------------------------------------------</span> |
| <span class="o">------------------------------</span><span class="w"> </span><span class="p">[</span><span class="w"> </span><span class="n">Task</span><span class="w"> </span><span class="n">Scheduler</span><span class="w"> </span><span class="p">]</span> |
| <span class="o">----------------------------------------------------------------------</span> |
| <span class="o">|</span><span class="w"> </span><span class="n">ID</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="n">Latency</span><span class="w"> </span><span class="p">(</span><span class="n">ms</span><span class="p">)</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="n">Speed</span><span class="w"> </span><span class="p">(</span><span class="n">GFLOPS</span><span class="p">)</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="n">Trials</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">-------------------------------------------------</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">0</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.010</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.40</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">64</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">1</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.087</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">47.19</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">64</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">2</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.008</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">-0.00</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">64</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">3</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.177</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">582.07</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">64</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">4</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.268</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">862.37</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">256</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">5</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.166</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">621.13</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">128</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">6</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.170</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">605.10</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">128</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">7</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.128</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">403.20</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">64</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">8</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.189</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">545.71</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">64</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">9</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.231</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">1001.01</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">448</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">10</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.155</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">664.80</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">256</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">11</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.155</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">662.86</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">256</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">12</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.119</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">434.08</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">64</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">13</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.199</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">522.13</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">64</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">14</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.235</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">986.56</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">320</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">15</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.149</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">689.13</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">128</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">16</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.155</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">664.80</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">192</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">17</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.151</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">340.64</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">64</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">18</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.176</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">597.55</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">128</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">19</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.220</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">1054.37</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">192</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">20</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.150</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">686.01</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">128</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">21</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.159</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">650.88</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">128</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">22</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.073</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">358.19</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">64</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">23</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.031</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">70.63</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">64</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">24</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.251</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">947.73</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">128</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">25</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.157</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">652.47</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">128</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">26</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.215</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">954.84</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">128</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">27</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.237</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">868.92</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">128</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">|</span><span class="w"> </span><span class="mi">28</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">0.266</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mf">774.06</span><span class="w"> </span><span class="o">|</span><span class="w"> </span><span class="mi">128</span><span class="w"> </span><span class="o">|</span> |
| <span class="o">-------------------------------------------------</span> |
| <span class="n">Estimated</span><span class="w"> </span><span class="n">total</span><span class="w"> </span><span class="n">latency</span><span class="o">:</span><span class="w"> </span><span class="mf">10.016</span><span class="w"> </span><span class="n">ms</span><span class="w"> </span><span class="n">Trials</span><span class="o">:</span><span class="w"> </span><span class="mi">3992</span><span class="w"> </span><span class="n">Used</span><span class="w"> </span><span class="n">time</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="mi">1131</span><span class="w"> </span><span class="n">s</span><span class="w"> </span><span class="n">Next</span><span class="w"> </span><span class="n">ID</span><span class="o">:</span><span class="w"> </span><span class="mi">15</span> |
| </pre></div> |
| </div> |
| <p>This table lists the latency and (estimated) speed of all tasks. |
| It also lists the allocation of measurement trials for all tasks. |
| The last line prints the total weighted latency of these tasks, |
| which can be a rough estimation of the end-to-end execution time |
| of the network. |
| The last line also prints the total number of measurement trials, |
| total time spent on auto-tuning and the id of the next task to tune.</p> |
| <p>There will also be some “tvm::Error”s errors, because the |
| auto-scheduler will try some invalid schedules. |
| You can safely ignore them if the tuning can continue, because these |
| errors are isolated from the main process.</p> |
| </div> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>Terminate the tuning earlier</p> |
| <p>You can terminate the tuning earlier by forcibly killing this process. |
| As long as you get at least one valid schedule for each task in the log file, |
| you should be able to do the compilation (the secion below).</p> |
| </div> |
| </div> |
| <div class="section" id="compile-and-evaluate"> |
| <h2>Compile and Evaluate<a class="headerlink" href="#compile-and-evaluate" title="Permalink to this headline">¶</a></h2> |
| <p>After auto-tuning, we can compile the network with the best schedules we found. |
| All measurement records are dumped into the log file during auto-tuning, |
| so we can read the log file and load the best schedules.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Compile with the history best</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"Compile..."</span><span class="p">)</span> |
| <span class="k">with</span> <a href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.ApplyHistoryBest" title="tvm.auto_scheduler.ApplyHistoryBest" class="sphx-glr-backref-module-tvm-auto_scheduler sphx-glr-backref-type-py-class"><span class="n">auto_scheduler</span><span class="o">.</span><span class="n">ApplyHistoryBest</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">log_file</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">config</span><span class="o">=</span><span class="p">{</span><span class="s2">"relay.backend.use_auto_scheduler"</span><span class="p">:</span> <span class="kc">True</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="c1"># Create graph executor</span> |
| <span class="n">dev</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="nb">str</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="p">),</span> <span class="mi">0</span><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">module</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><span class="n">lib</span><span class="p">[</span><span class="s2">"default"</span><span class="p">](</span><span class="n">dev</span><span class="p">))</span> |
| <span class="n">data_tvm</span> <span class="o">=</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">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</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">input_shape</span></a><span class="p">))</span><span class="o">.</span><span class="n">astype</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">dtype</span></a><span class="p">))</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">module</span><span class="o">.</span><span class="n">set_input</span></a><span class="p">(</span><span class="s2">"data"</span><span class="p">,</span> <span class="n">data_tvm</span><span class="p">)</span> |
| |
| <span class="c1"># Evaluate</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">module</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">repeat</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">min_repeat_ms</span><span class="o">=</span><span class="mi">500</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Compile... |
| Evaluate inference time cost... |
| Execution time summary: |
| mean (ms) median (ms) max (ms) min (ms) std (ms) |
| 760.8692 760.1869 764.7312 757.6894 2.9150 |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="other-tips"> |
| <h2>Other Tips<a class="headerlink" href="#other-tips" title="Permalink to this headline">¶</a></h2> |
| <ol class="arabic simple"> |
| <li><p>During the tuning, the auto-scheduler needs to compile many programs and |
| extract feature from them. This part is CPU-intensive, |
| so a high-performance CPU with many cores is recommended for faster search.</p></li> |
| <li><p>You can use <code class="code docutils literal notranslate"><span class="pre">python3</span> <span class="pre">-m</span> <span class="pre">tvm.auto_scheduler.measure_record</span> <span class="pre">--mode</span> <span class="pre">distill</span> <span class="pre">-i</span> <span class="pre">log.json</span></code> |
| to distill the large log file and only save the best useful records.</p></li> |
| <li><p>You can resume a search from the previous log file. You just need to |
| add a new argument <code class="code docutils literal notranslate"><span class="pre">load_log_file</span></code> when creating the task scheduler |
| in function <code class="code docutils literal notranslate"><span class="pre">run_tuning</span></code>. Say, |
| <code class="code docutils literal notranslate"><span class="pre">tuner</span> <span class="pre">=</span> <span class="pre">auto_scheduler.TaskScheduler(tasks,</span> <span class="pre">task_weights,</span> <span class="pre">load_log_file=log_file)</span></code></p></li> |
| <li><p>If you have multiple target CPUs, you can use all of them for measurements to |
| parallelize the measurements. Check this <a class="reference internal" href="../tune_with_autotvm/tune_relay_cuda.html#tutorials-autotvm-scale-up-rpc-tracker"><span class="std std-ref">section</span></a> |
| to learn how to use the RPC Tracker and RPC Server. |
| To use the RPC Tracker in auto-scheduler, replace the runner in <code class="code docutils literal notranslate"><span class="pre">TuningOptions</span></code> |
| with <a class="reference internal" href="../../reference/api/python/auto_scheduler.html#tvm.auto_scheduler.RPCRunner" title="tvm.auto_scheduler.RPCRunner"><code class="xref any py py-class docutils literal notranslate"><span class="pre">auto_scheduler.RPCRunner</span></code></a>.</p></li> |
| </ol> |
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