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Runtime
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<span class="md-nav__icon md-icon"></span>
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Runtime
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M10 20H6V4h7v5h5v3.1l2-2V8l-6-6H6c-1.1 0-2 .9-2 2v16c0 1.1.9 2 2 2h4zm10.2-7c.1 0 .3.1.4.2l1.3 1.3c.2.2.2.6 0 .8l-1 1-2.1-2.1 1-1c.1-.1.2-.2.4-.2m0 3.9L14.1 23H12v-2.1l6.1-6.1z"/></svg>
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M17 18c.56 0 1 .44 1 1s-.44 1-1 1-1-.44-1-1 .44-1 1-1m0-3c-2.73 0-5.06 1.66-6 4 .94 2.34 3.27 4 6 4s5.06-1.66 6-4c-.94-2.34-3.27-4-6-4m0 6.5a2.5 2.5 0 0 1-2.5-2.5 2.5 2.5 0 0 1 2.5-2.5 2.5 2.5 0 0 1 2.5 2.5 2.5 2.5 0 0 1-2.5 2.5M9.27 20H6V4h7v5h5v4.07c.7.08 1.36.25 2 .49V8l-6-6H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h4.5a8.2 8.2 0 0 1-1.23-2"/></svg>
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<h1>Movement</h1>
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<h2 id="movement-low-level">Movement (low level)<a class="headerlink" href="#movement-low-level" title="Permanent link">¤</a></h2>
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<div class="doc doc-object doc-function">
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<h3 id="tinygrad.Tensor.view" class="doc doc-heading">
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<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">view</span>
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<a href="#tinygrad.Tensor.view" class="headerlink" title="Permanent link">¤</a></h3>
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<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">view</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span>
|
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</code></pre></div>
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<div class="doc doc-contents first">
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<p><code class="language-python highlight"><span class="o">.</span><span class="n">view</span></code> is an alias for <code class="language-python highlight"><span class="o">.</span><span class="n">reshape</span></code>.</p>
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<details class="mkdocstrings-source">
|
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<summary>Source code in <code>tinygrad/mixin/movement.py</code></summary>
|
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<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">256</span>
|
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<span class="normal">257</span>
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<span class="normal">258</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">view</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> <span class="o">-></span> <span class="n">Self</span><span class="p">:</span>
|
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<span class="w"> </span><span class="sd">"""`.view` is an alias for `.reshape`."""</span>
|
|
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span>
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</code></pre></div></td></tr></table></div>
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<div class="doc doc-object doc-function">
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<h3 id="tinygrad.Tensor.reshape" class="doc doc-heading">
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<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">reshape</span>
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<a href="#tinygrad.Tensor.reshape" class="headerlink" title="Permanent link">¤</a></h3>
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<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">reshape</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span>
|
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</code></pre></div>
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<div class="doc doc-contents first">
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<p>Returns a tensor with the same data as the original tensor but with a different shape.
|
|
<code class="language-python highlight"><span class="n">shape</span></code> can be passed as a tuple or as separate arguments.</p>
|
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<div class="language-python highlight"><pre><span></span><code><span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">6</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">0</span> <span class="mi">1</span> <span class="mi">2</span><span class="p">]</span>
|
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<span class="p">[</span><span class="mi">3</span> <span class="mi">4</span> <span class="mi">5</span><span class="p">]]</span>
|
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</code></pre></div>
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<details class="mkdocstrings-source">
|
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<summary>Source code in <code>tinygrad/mixin/movement.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">156</span>
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<span class="normal">157</span>
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<span class="normal">158</span>
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<span class="normal">159</span>
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<span class="normal">160</span>
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<span class="normal">161</span>
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<span class="normal">162</span>
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<span class="normal">163</span>
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<span class="normal">164</span>
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<span class="normal">165</span>
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<span class="normal">166</span>
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<span class="normal">167</span>
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<span class="normal">168</span>
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<span class="normal">169</span>
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<span class="normal">170</span>
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<span class="normal">171</span>
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<span class="normal">172</span>
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<span class="normal">173</span>
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<span class="normal">174</span>
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<span class="normal">175</span>
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<span class="normal">176</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">reshape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> <span class="o">-></span> <span class="n">Self</span><span class="p">:</span>
|
|
<span class="w"> </span><span class="sd">"""</span>
|
|
<span class="sd"> Returns a tensor with the same data as the original tensor but with a different shape.</span>
|
|
<span class="sd"> `shape` can be passed as a tuple or as separate arguments.</span>
|
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|
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<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> t = Tensor.arange(6)</span>
|
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<span class="sd"> print(t.reshape(2, 3).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
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<span class="c1"># resolve None and args</span>
|
|
<span class="n">new_shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">([</span><span class="n">s</span> <span class="k">if</span> <span class="n">s</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">argfix</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">))])</span>
|
|
<span class="c1"># resolve -1</span>
|
|
<span class="k">if</span> <span class="p">(</span><span class="n">c</span> <span class="o">:=</span> <span class="n">new_shape</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
|
|
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"only one dimension can be inferred using -1, getting </span><span class="si">{</span><span class="n">new_shape</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="n">c</span><span class="p">:</span>
|
|
<span class="n">new_shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">([</span><span class="o">-</span><span class="n">prod</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">//</span> <span class="n">prod</span><span class="p">(</span><span class="n">new_shape</span><span class="p">)</span> <span class="k">if</span> <span class="n">s</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span> <span class="k">else</span> <span class="n">s</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">new_shape</span><span class="p">])</span>
|
|
<span class="k">if</span> <span class="n">prod</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">!=</span> <span class="n">prod</span><span class="p">(</span><span class="n">new_shape</span><span class="p">):</span>
|
|
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"size mismatch, can't reshape (</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">) -> (</span><span class="si">{</span><span class="n">new_shape</span><span class="si">}</span><span class="s2">)"</span><span class="p">)</span>
|
|
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mop</span><span class="p">(</span><span class="n">Ops</span><span class="o">.</span><span class="n">RESHAPE</span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="n">new_shape</span><span class="p">)</span>
|
|
<span class="k">return</span> <span class="bp">self</span> <span class="k">if</span> <span class="n">ret</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="k">else</span> <span class="n">ret</span>
|
|
</code></pre></div></td></tr></table></div>
|
|
</details>
|
|
</div>
|
|
|
|
</div>
|
|
|
|
<div class="doc doc-object doc-function">
|
|
|
|
|
|
<h3 id="tinygrad.Tensor.expand" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">expand</span>
|
|
|
|
|
|
<a href="#tinygrad.Tensor.expand" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">expand</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span>
|
|
</code></pre></div>
|
|
|
|
<div class="doc doc-contents first">
|
|
|
|
<p>Returns a tensor that is expanded to the shape that is specified.
|
|
Expand can also increase the number of dimensions that a tensor has.</p>
|
|
<p>Passing a <code class="language-python highlight"><span class="o">-</span><span class="mi">1</span></code> or <code class="language-python highlight"><span class="kc">None</span></code> to a dimension means that its size will not be changed.</p>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</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="n">t</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/movement.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">141</span>
|
|
<span class="normal">142</span>
|
|
<span class="normal">143</span>
|
|
<span class="normal">144</span>
|
|
<span class="normal">145</span>
|
|
<span class="normal">146</span>
|
|
<span class="normal">147</span>
|
|
<span class="normal">148</span>
|
|
<span class="normal">149</span>
|
|
<span class="normal">150</span>
|
|
<span class="normal">151</span>
|
|
<span class="normal">152</span>
|
|
<span class="normal">153</span>
|
|
<span class="normal">154</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">expand</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> <span class="o">-></span> <span class="n">Self</span><span class="p">:</span>
|
|
<span class="w"> </span><span class="sd">"""</span>
|
|
<span class="sd"> Returns a tensor that is expanded to the shape that is specified.</span>
|
|
<span class="sd"> Expand can also increase the number of dimensions that a tensor has.</span>
|
|
|
|
<span class="sd"> Passing a `-1` or `None` to a dimension means that its size will not be changed.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> t = Tensor([1, 2, 3])</span>
|
|
<span class="sd"> print(t.expand(4, -1).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="n">new_shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">from_</span> <span class="k">if</span> <span class="n">to</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span> <span class="ow">or</span> <span class="n">to</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">to</span> <span class="k">for</span> <span class="n">from_</span><span class="p">,</span> <span class="n">to</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="p">(</span><span class="n">_align_left</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">argfix</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)))))</span>
|
|
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_broadcast_to</span><span class="p">(</span><span class="n">new_shape</span><span class="p">)</span>
|
|
</code></pre></div></td></tr></table></div>
|
|
</details>
|
|
</div>
|
|
|
|
</div>
|
|
|
|
<div class="doc doc-object doc-function">
|
|
|
|
|
|
<h3 id="tinygrad.Tensor.permute" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">permute</span>
|
|
|
|
|
|
<a href="#tinygrad.Tensor.permute" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">permute</span><span class="p">(</span><span class="n">order</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span>
|
|
</code></pre></div>
|
|
|
|
<div class="doc doc-contents first">
|
|
|
|
<p>Returns a tensor that is a permutation of the original tensor.
|
|
The new tensor has the same data as the original tensor but with the dimensions permuted according to the order specified.
|
|
<code class="language-python highlight"><span class="n">order</span></code> can be passed as a tuple or as separate arguments.</p>
|
|
<p><div class="language-python highlight"><pre><span></span><code><span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
|
|
</code></pre></div></p>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/movement.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">206</span>
|
|
<span class="normal">207</span>
|
|
<span class="normal">208</span>
|
|
<span class="normal">209</span>
|
|
<span class="normal">210</span>
|
|
<span class="normal">211</span>
|
|
<span class="normal">212</span>
|
|
<span class="normal">213</span>
|
|
<span class="normal">214</span>
|
|
<span class="normal">215</span>
|
|
<span class="normal">216</span>
|
|
<span class="normal">217</span>
|
|
<span class="normal">218</span>
|
|
<span class="normal">219</span>
|
|
<span class="normal">220</span>
|
|
<span class="normal">221</span>
|
|
<span class="normal">222</span>
|
|
<span class="normal">223</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">permute</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> <span class="o">-></span> <span class="n">Self</span><span class="p">:</span>
|
|
<span class="w"> </span><span class="sd">"""</span>
|
|
<span class="sd"> Returns a tensor that is a permutation of the original tensor.</span>
|
|
<span class="sd"> The new tensor has the same data as the original tensor but with the dimensions permuted according to the order specified.</span>
|
|
<span class="sd"> `order` can be passed as a tuple or as separate arguments.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> t = Tensor.empty(2, 3, 5)</span>
|
|
<span class="sd"> print(t.shape)</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(t.permute(2, 0, 1).shape)</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="n">order_arg</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_resolve_dim</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">argfix</span><span class="p">(</span><span class="n">order</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">))</span>
|
|
<span class="k">if</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">order_arg</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="p">)):</span>
|
|
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"order is not a valid permutation, getting </span><span class="si">{</span><span class="n">order_arg</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
|
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mop</span><span class="p">(</span><span class="n">Ops</span><span class="o">.</span><span class="n">PERMUTE</span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="n">order_arg</span><span class="p">)</span> <span class="k">if</span> <span class="n">order_arg</span> <span class="o">!=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="p">))</span> <span class="k">else</span> <span class="bp">self</span>
|
|
</code></pre></div></td></tr></table></div>
|
|
</details>
|
|
</div>
|
|
|
|
</div>
|
|
|
|
<div class="doc doc-object doc-function">
|
|
|
|
|
|
<h3 id="tinygrad.Tensor.flip" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">flip</span>
|
|
|
|
|
|
<a href="#tinygrad.Tensor.flip" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">flip</span><span class="p">(</span><span class="n">axis</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span>
|
|
</code></pre></div>
|
|
|
|
<div class="doc doc-contents first">
|
|
|
|
<p>Returns a tensor that reverses the order of the original tensor along given <code class="language-python highlight"><span class="n">axis</span></code>.
|
|
<code class="language-python highlight"><span class="n">axis</span></code> can be passed as a tuple or as separate arguments.</p>
|
|
<p><div class="language-python highlight"><pre><span></span><code><span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">6</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">2</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="n">t</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">0</span> <span class="mi">1</span> <span class="mi">2</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">3</span> <span class="mi">4</span> <span class="mi">5</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">3</span> <span class="mi">4</span> <span class="mi">5</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">0</span> <span class="mi">1</span> <span class="mi">2</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">flip</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">5</span> <span class="mi">4</span> <span class="mi">3</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">2</span> <span class="mi">1</span> <span class="mi">0</span><span class="p">]]</span>
|
|
</code></pre></div></p>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/movement.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">225</span>
|
|
<span class="normal">226</span>
|
|
<span class="normal">227</span>
|
|
<span class="normal">228</span>
|
|
<span class="normal">229</span>
|
|
<span class="normal">230</span>
|
|
<span class="normal">231</span>
|
|
<span class="normal">232</span>
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<span class="normal">242</span>
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<span class="normal">243</span>
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<span class="normal">244</span>
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<span class="normal">245</span>
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<span class="normal">246</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">flip</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">axis</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> <span class="o">-></span> <span class="n">Self</span><span class="p">:</span>
|
|
<span class="w"> </span><span class="sd">"""</span>
|
|
<span class="sd"> Returns a tensor that reverses the order of the original tensor along given `axis`.</span>
|
|
<span class="sd"> `axis` can be passed as a tuple or as separate arguments.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> t = Tensor.arange(6).reshape(2, 3)</span>
|
|
<span class="sd"> print(t.numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(t.flip(0).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(t.flip((0, 1)).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="n">axis_arg</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_resolve_dim</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">argfix</span><span class="p">(</span><span class="n">axis</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">))</span>
|
|
<span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="nb">bool</span><span class="p">)</span> <span class="ow">and</span> <span class="n">x</span> <span class="o">>=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">x</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">axis_arg</span><span class="p">),</span> <span class="sa">f</span><span class="s2">"flip args must be axis ints </span><span class="si">{</span><span class="n">axis_arg</span><span class="si">}</span><span class="s2">"</span>
|
|
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">axis_arg</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">dedup</span><span class="p">(</span><span class="n">axis_arg</span><span class="p">)):</span>
|
|
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"dim can appear at most once, getting </span><span class="si">{</span><span class="n">axis_arg</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
|
<span class="n">flip_arg</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">([</span><span class="n">i</span> <span class="ow">in</span> <span class="n">axis_arg</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">))])</span>
|
|
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mop</span><span class="p">(</span><span class="n">Ops</span><span class="o">.</span><span class="n">FLIP</span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="n">flip_arg</span><span class="p">)</span> <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">flip_arg</span><span class="p">)</span> <span class="k">else</span> <span class="bp">self</span>
|
|
</code></pre></div></td></tr></table></div>
|
|
</details>
|
|
</div>
|
|
|
|
</div>
|
|
|
|
<div class="doc doc-object doc-function">
|
|
|
|
|
|
<h3 id="tinygrad.Tensor.shrink" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">shrink</span>
|
|
|
|
|
|
<a href="#tinygrad.Tensor.shrink" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">shrink</span><span class="p">(</span><span class="n">arg</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#tuple">tuple</a></span><span class="p">[</span><span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#tuple">tuple</a></span><span class="p">[</span><span class="n"><span title="tinygrad.uop.ops.sint">sint</span></span><span class="p">,</span> <span class="n"><span title="tinygrad.uop.ops.sint">sint</span></span><span class="p">]</span> <span class="o">|</span> <span class="kc">None</span><span class="p">,</span> <span class="o">...</span><span class="p">])</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span>
|
|
</code></pre></div>
|
|
|
|
<div class="doc doc-contents first">
|
|
|
|
<p>Returns a tensor that shrinks the each axis based on input arg.
|
|
<code class="language-python highlight"><span class="n">arg</span></code> must have the same length as <code class="language-python highlight"><span class="bp">self</span><span class="o">.</span><span class="n">ndim</span></code>.
|
|
For each axis, it can be <code class="language-python highlight"><span class="kc">None</span></code>, which means no shrink, or a tuple <code class="language-python highlight"><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">)</span></code> that works the same as Python slice.</p>
|
|
<p><div class="language-python highlight"><pre><span></span><code><span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">9</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">3</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="n">t</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">0</span> <span class="mi">1</span> <span class="mi">2</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">3</span> <span class="mi">4</span> <span class="mi">5</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">6</span> <span class="mi">7</span> <span class="mi">8</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">shrink</span><span class="p">(((</span><span class="kc">None</span><span class="p">,</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="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">1</span> <span class="mi">2</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">4</span> <span class="mi">5</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">7</span> <span class="mi">8</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">shrink</span><span class="p">((((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">))))</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">0</span> <span class="mi">1</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">3</span> <span class="mi">4</span><span class="p">]]</span>
|
|
</code></pre></div></p>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/movement.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">184</span>
|
|
<span class="normal">185</span>
|
|
<span class="normal">186</span>
|
|
<span class="normal">187</span>
|
|
<span class="normal">188</span>
|
|
<span class="normal">189</span>
|
|
<span class="normal">190</span>
|
|
<span class="normal">191</span>
|
|
<span class="normal">192</span>
|
|
<span class="normal">193</span>
|
|
<span class="normal">194</span>
|
|
<span class="normal">195</span>
|
|
<span class="normal">196</span>
|
|
<span class="normal">197</span>
|
|
<span class="normal">198</span>
|
|
<span class="normal">199</span>
|
|
<span class="normal">200</span>
|
|
<span class="normal">201</span>
|
|
<span class="normal">202</span>
|
|
<span class="normal">203</span>
|
|
<span class="normal">204</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">shrink</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">arg</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="n">sint</span><span class="p">,</span> <span class="n">sint</span><span class="p">]</span> <span class="o">|</span> <span class="kc">None</span><span class="p">,</span> <span class="o">...</span><span class="p">])</span> <span class="o">-></span> <span class="n">Self</span><span class="p">:</span>
|
|
<span class="w"> </span><span class="sd">"""</span>
|
|
<span class="sd"> Returns a tensor that shrinks the each axis based on input arg.</span>
|
|
<span class="sd"> `arg` must have the same length as `self.ndim`.</span>
|
|
<span class="sd"> For each axis, it can be `None`, which means no shrink, or a tuple `(start, end)` that works the same as Python slice.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> t = Tensor.arange(9).reshape(3, 3)</span>
|
|
<span class="sd"> print(t.numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(t.shrink(((None, (1, 3)))).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(t.shrink((((0, 2), (0, 2)))).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">arg</span><span class="p">):</span>
|
|
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="si">=}</span><span class="s2"> != </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">arg</span><span class="p">)</span><span class="si">=}</span><span class="s2">"</span><span class="p">)</span>
|
|
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mop</span><span class="p">(</span><span class="n">Ops</span><span class="o">.</span><span class="n">SHRINK</span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="p">[(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="k">if</span> <span class="n">x</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">arg</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)])</span>
|
|
<span class="k">return</span> <span class="bp">self</span> <span class="k">if</span> <span class="n">ret</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="k">else</span> <span class="n">ret</span>
|
|
</code></pre></div></td></tr></table></div>
|
|
</details>
|
|
</div>
|
|
|
|
</div>
|
|
|
|
<div class="doc doc-object doc-function">
|
|
|
|
|
|
<h3 id="tinygrad.Tensor.pad" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">pad</span>
|
|
|
|
|
|
<a href="#tinygrad.Tensor.pad" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">pad</span><span class="p">(</span>
|
|
<span class="n">padding</span><span class="p">:</span> <span class="p">(</span>
|
|
<span class="n"><a class="autorefs autorefs-external" title="<code>typing.Sequence</code>" href="https://docs.python.org/3/library/typing.html#typing.Sequence">Sequence</a></span><span class="p">[</span><span class="n"><span title="tinygrad.uop.ops.sint">sint</span></span><span class="p">]</span> <span class="o">|</span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Sequence</code>" href="https://docs.python.org/3/library/typing.html#typing.Sequence">Sequence</a></span><span class="p">[</span><span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#tuple">tuple</a></span><span class="p">[</span><span class="n"><span title="tinygrad.uop.ops.sint">sint</span></span><span class="p">,</span> <span class="n"><span title="tinygrad.uop.ops.sint">sint</span></span><span class="p">]</span> <span class="o">|</span> <span class="kc">None</span><span class="p">]</span>
|
|
<span class="p">),</span>
|
|
<span class="n">mode</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#str">str</a></span> <span class="o">=</span> <span class="s2">"constant"</span><span class="p">,</span>
|
|
<span class="n">value</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-internal" title="<code class="doc-symbol doc-symbol-heading doc-symbol-attribute"></code> <span class="doc doc-object-name doc-attribute-name">ConstType</span>
|
|
|
|
|
|
<span class="doc doc-labels">
|
|
<small class="doc doc-label doc-label-module-attribute"><code>module-attribute</code></small>
|
|
</span> (<code>tinygrad.dtype.ConstType</code>)" href="../../dtypes/#tinygrad.dtype.ConstType">ConstType</a></span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span>
|
|
<span class="p">)</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span>
|
|
</code></pre></div>
|
|
|
|
<div class="doc doc-contents first">
|
|
|
|
<p>Returns a tensor with padding applied based on the input <code class="language-python highlight"><span class="n">padding</span></code>.</p>
|
|
<p><code class="language-python highlight"><span class="n">padding</span></code> supports two padding structures:</p>
|
|
<ol>
|
|
<li>
|
|
<p>Flat padding: <code class="language-python highlight"><span class="p">(</span><span class="n">padding_left</span><span class="p">,</span> <span class="n">padding_right</span><span class="p">,</span> <span class="n">padding_top</span><span class="p">,</span> <span class="n">padding_bottom</span><span class="p">,</span> <span class="o">...</span><span class="p">)</span></code></p>
|
|
<ul>
|
|
<li>This structure matches PyTorch's pad.</li>
|
|
<li><code class="language-python highlight"><span class="n">padding</span></code> length must be even.</li>
|
|
</ul>
|
|
</li>
|
|
<li>
|
|
<p>Group padding: <code class="language-python highlight"><span class="p">(</span><span class="o">...</span><span class="p">,</span> <span class="p">(</span><span class="n">padding_top</span><span class="p">,</span> <span class="n">padding_bottom</span><span class="p">),</span> <span class="p">(</span><span class="n">padding_left</span><span class="p">,</span> <span class="n">padding_right</span><span class="p">))</span></code></p>
|
|
<ul>
|
|
<li>This structure matches pad for JAX, NumPy, TensorFlow, and others.</li>
|
|
<li>For each axis, padding can be <code class="language-python highlight"><span class="kc">None</span></code>, meaning no padding, or a tuple <code class="language-python highlight"><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">)</span></code>.</li>
|
|
<li><code class="language-python highlight"><span class="n">padding</span></code> must have the same length as <code class="language-python highlight"><span class="bp">self</span><span class="o">.</span><span class="n">ndim</span></code>.</li>
|
|
</ul>
|
|
</li>
|
|
</ol>
|
|
<p>Padding values can be negative, resulting in dimension shrinks that work similarly to Python negative slices.
|
|
Padding modes is selected with <code class="language-python highlight"><span class="n">mode</span></code> which supports <code class="language-python highlight"><span class="n">constant</span></code>, <code class="language-python highlight"><span class="n">reflect</span></code> and <code class="language-python highlight"><span class="n">replicate</span></code>.</p>
|
|
<p><div class="language-python highlight"><pre><span></span><code><span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">9</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</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">3</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
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</code></pre></div>
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<div class="language-python highlight"><pre><span></span><code><span class="p">[[[[</span><span class="mi">0</span> <span class="mi">1</span> <span class="mi">2</span><span class="p">]</span>
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<span class="p">[</span><span class="mi">3</span> <span class="mi">4</span> <span class="mi">5</span><span class="p">]</span>
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<span class="p">[</span><span class="mi">6</span> <span class="mi">7</span> <span class="mi">8</span><span class="p">]]]]</span>
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</code></pre></div>
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<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">pad</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
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</code></pre></div>
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<div class="language-python highlight"><pre><span></span><code><span class="p">[[[[</span><span class="mi">0</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">0</span> <span class="mi">0</span><span class="p">]</span>
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<span class="p">[</span><span class="mi">0</span> <span class="mi">3</span> <span class="mi">4</span> <span class="mi">5</span> <span class="mi">0</span> <span class="mi">0</span><span class="p">]]]]</span>
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</code></pre></div>
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<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">pad</span><span class="p">(((</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">))))</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
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</code></pre></div>
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<div class="language-python highlight"><pre><span></span><code><span class="p">[[[[</span><span class="mi">0</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">0</span> <span class="mi">0</span><span class="p">]</span>
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<span class="p">[</span><span class="mi">0</span> <span class="mi">3</span> <span class="mi">4</span> <span class="mi">5</span> <span class="mi">0</span> <span class="mi">0</span><span class="p">]]]]</span>
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</code></pre></div>
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<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">pad</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">value</span><span class="o">=-</span><span class="nb">float</span><span class="p">(</span><span class="s1">'inf'</span><span class="p">))</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
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</code></pre></div>
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<div class="language-python highlight"><pre><span></span><code><span class="p">[[[[</span><span class="o">-</span><span class="n">inf</span> <span class="mf">0.</span> <span class="mf">1.</span> <span class="mf">2.</span> <span class="o">-</span><span class="n">inf</span> <span class="o">-</span><span class="n">inf</span><span class="p">]</span>
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<span class="p">[</span><span class="o">-</span><span class="n">inf</span> <span class="mf">3.</span> <span class="mf">4.</span> <span class="mf">5.</span> <span class="o">-</span><span class="n">inf</span> <span class="o">-</span><span class="n">inf</span><span class="p">]]]]</span>
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</code></pre></div></p>
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<details class="mkdocstrings-source">
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<summary>Source code in <code>tinygrad/mixin/__init__.py</code></summary>
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<span class="normal">319</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">pad</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">padding</span><span class="p">:</span><span class="n">Sequence</span><span class="p">[</span><span class="n">sint</span><span class="p">]</span><span class="o">|</span><span class="n">Sequence</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="n">sint</span><span class="p">,</span> <span class="n">sint</span><span class="p">]</span><span class="o">|</span><span class="kc">None</span><span class="p">],</span> <span class="n">mode</span><span class="p">:</span><span class="nb">str</span><span class="o">=</span><span class="s2">"constant"</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span><span class="n">ConstType</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span> <span class="o">-></span> <span class="n">Self</span><span class="p">:</span>
|
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<span class="w"> </span><span class="sd">"""</span>
|
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<span class="sd"> Returns a tensor with padding applied based on the input `padding`.</span>
|
|
|
|
<span class="sd"> `padding` supports two padding structures:</span>
|
|
|
|
<span class="sd"> 1. Flat padding: `(padding_left, padding_right, padding_top, padding_bottom, ...)`</span>
|
|
<span class="sd"> - This structure matches PyTorch's pad.</span>
|
|
<span class="sd"> - `padding` length must be even.</span>
|
|
|
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<span class="sd"> 2. Group padding: `(..., (padding_top, padding_bottom), (padding_left, padding_right))`</span>
|
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<span class="sd"> - This structure matches pad for JAX, NumPy, TensorFlow, and others.</span>
|
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<span class="sd"> - For each axis, padding can be `None`, meaning no padding, or a tuple `(start, end)`.</span>
|
|
<span class="sd"> - `padding` must have the same length as `self.ndim`.</span>
|
|
|
|
<span class="sd"> Padding values can be negative, resulting in dimension shrinks that work similarly to Python negative slices.</span>
|
|
<span class="sd"> Padding modes is selected with `mode` which supports `constant`, `reflect` and `replicate`.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> t = Tensor.arange(9).reshape(1, 1, 3, 3)</span>
|
|
<span class="sd"> print(t.numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(t.pad((1, 2, 0, -1)).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(t.pad(((None, None, (0, -1), (1, 2)))).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(t.pad((1, 2, 0, -1), value=-float('inf')).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="c1"># normalize to grouped format</span>
|
|
<span class="n">pX</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="n">sint</span><span class="p">,</span> <span class="n">sint</span><span class="p">],</span> <span class="o">...</span><span class="p">]</span>
|
|
<span class="k">if</span> <span class="ow">not</span> <span class="nb">any</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="kc">None</span><span class="p">)))</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">padding</span><span class="p">):</span>
|
|
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">padding</span><span class="p">)</span><span class="o">%</span><span class="mi">2</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Flat padding must have even number of pads"</span><span class="p">)</span>
|
|
<span class="n">pX</span> <span class="o">=</span> <span class="p">((</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">),)</span><span class="o">*</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">padding</span><span class="p">)</span><span class="o">//</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="n">flat_to_grouped</span><span class="p">(</span><span class="n">padding</span><span class="p">)</span> <span class="c1"># type: ignore[arg-type]</span>
|
|
<span class="k">else</span><span class="p">:</span> <span class="n">pX</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span> <span class="k">if</span> <span class="n">p</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">p</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">padding</span><span class="p">)</span> <span class="c1"># type: ignore[misc]</span>
|
|
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">pX</span><span class="p">)</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="p">:</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"padding length is improper, </span><span class="si">{</span><span class="n">padding</span><span class="si">=}</span><span class="s2"> </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="si">=}</span><span class="s2">"</span><span class="p">)</span>
|
|
<span class="c1"># dispatch</span>
|
|
<span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s2">"constant"</span><span class="p">:</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_constant</span><span class="p">(</span><span class="n">pX</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
|
|
<span class="k">assert</span> <span class="n">all_int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="sa">f</span><span class="s2">"does not support symbolic shape </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">"</span>
|
|
<span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s2">"circular"</span><span class="p">:</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_circular</span><span class="p">(</span><span class="n">pX</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="n">mode</span> <span class="ow">in</span> <span class="p">{</span><span class="s2">"reflect"</span><span class="p">,</span> <span class="s2">"replicate"</span><span class="p">}:</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_reflect_replicate</span><span class="p">(</span><span class="n">pX</span><span class="p">,</span> <span class="n">mode</span><span class="p">)</span>
|
|
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">mode</span><span class="si">=}</span><span class="s2"> is not supported"</span><span class="p">)</span>
|
|
</code></pre></div></td></tr></table></div>
|
|
</details>
|
|
</div>
|
|
|
|
</div><h2 id="movement-high-level">Movement (high level)<a class="headerlink" href="#movement-high-level" title="Permanent link">¤</a></h2>
|
|
|
|
|
|
<div class="doc doc-object doc-function">
|
|
|
|
|
|
<h3 id="tinygrad.Tensor.__getitem__" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">__getitem__</span>
|
|
|
|
|
|
<a href="#tinygrad.Tensor.__getitem__" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">__getitem__</span><span class="p">(</span><span class="n">indices</span><span class="p">)</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-internal" title="<code class="doc-symbol doc-symbol-heading doc-symbol-class"></code> <span class="doc doc-object-name doc-class-name">Tensor</span> (<code>tinygrad.tensor.Tensor</code>)" href="../#tinygrad.Tensor">Tensor</a></span>
|
|
</code></pre></div>
|
|
|
|
<div class="doc doc-contents first">
|
|
|
|
<p>Retrieves a sub-tensor using indexing.</p>
|
|
<p>Supported Index Types: <code class="language-python highlight"><span class="nb">int</span> <span class="o">|</span> <span class="nb">slice</span> <span class="o">|</span> <span class="n">Tensor</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">|</span> <span class="nb">list</span> <span class="o">|</span> <span class="nb">tuple</span> <span class="o">|</span> <span class="bp">Ellipsis</span></code></p>
|
|
<p>Examples:
|
|
<div class="language-python highlight"><pre><span></span><code><span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">12</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span><span class="p">]</span>
|
|
<span class="p">[</span> <span class="mi">4</span> <span class="mi">5</span> <span class="mi">6</span> <span class="mi">7</span><span class="p">]</span>
|
|
<span class="p">[</span> <span class="mi">8</span> <span class="mi">9</span> <span class="mi">10</span> <span class="mi">11</span><span class="p">]]</span>
|
|
</code></pre></div></p>
|
|
<ul>
|
|
<li>
|
|
<p>Int Indexing: Select an element or sub-tensor using integers for each dimension.
|
|
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="mi">6</span>
|
|
</code></pre></div></p>
|
|
</li>
|
|
<li>
|
|
<p>Slice Indexing: Select a range of elements using slice notation (<code class="language-python highlight"><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">:</span><span class="n">stride</span></code>).
|
|
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">,</span> <span class="p">::</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">0</span> <span class="mi">2</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">4</span> <span class="mi">6</span><span class="p">]]</span>
|
|
</code></pre></div></p>
|
|
</li>
|
|
<li>
|
|
<p>Tensor Indexing: Use another tensor as indices for advanced indexing. Using <code class="language-python highlight"><span class="nb">tuple</span></code> or <code class="language-python highlight"><span class="nb">list</span></code> here also works.
|
|
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="p">[</span><span class="n">Tensor</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span> <span class="n">Tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])]</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[</span><span class="mi">9</span> <span class="mi">2</span> <span class="mi">7</span><span class="p">]</span>
|
|
</code></pre></div></p>
|
|
</li>
|
|
<li>
|
|
<p><code class="language-python highlight"><span class="kc">None</span></code> Indexing: Add a new dimension to the tensor.
|
|
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
|
|
</code></pre></div></p>
|
|
</li>
|
|
</ul>
|
|
<div class="admonition note">
|
|
<p class="admonition-title">Note</p>
|
|
<p>Out-of-bounds indexing results in a value of <code class="language-python highlight"><span class="mi">0</span></code>.
|
|
<div class="language-python highlight"><pre><span></span><code><span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</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="n">t</span><span class="p">[</span><span class="n">Tensor</span><span class="p">([</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">])]</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[</span><span class="mi">0</span> <span class="mi">0</span> <span class="mi">3</span><span class="p">]</span>
|
|
</code></pre></div></p>
|
|
</div>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
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<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">955</span>
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<span class="normal">956</span>
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<span class="normal">957</span>
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<span class="normal">958</span>
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<span class="normal">959</span>
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<span class="normal">960</span>
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<span class="normal">961</span>
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<span class="normal">962</span>
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<span class="normal">963</span>
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<span class="normal">964</span>
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<span class="normal">965</span>
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<span class="normal">966</span>
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<span class="normal">967</span>
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<span class="normal">968</span>
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<span class="normal">969</span>
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<span class="normal">970</span>
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<span class="normal">971</span>
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<span class="normal">972</span>
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<span class="normal">973</span>
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<span class="normal">974</span>
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<span class="normal">975</span>
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<span class="normal">976</span>
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<span class="normal">977</span>
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<span class="normal">978</span>
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<span class="normal">979</span>
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<span class="normal">980</span>
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<span class="normal">981</span>
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<span class="normal">982</span>
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<span class="normal">983</span>
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<span class="normal">984</span>
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<span class="normal">986</span>
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<span class="normal">987</span>
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<span class="normal">988</span>
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<span class="normal">989</span>
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<span class="normal">990</span>
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<span class="normal">991</span>
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<span class="normal">992</span>
|
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<span class="normal">993</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indices</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tensor</span><span class="p">:</span>
|
|
<span class="w"> </span><span class="sd">"""</span>
|
|
<span class="sd"> Retrieves a sub-tensor using indexing.</span>
|
|
|
|
<span class="sd"> Supported Index Types: `int | slice | Tensor | None | list | tuple | Ellipsis`</span>
|
|
|
|
<span class="sd"> Examples:</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> t = Tensor.arange(12).reshape(3, 4)</span>
|
|
<span class="sd"> print(t.numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
|
|
<span class="sd"> - Int Indexing: Select an element or sub-tensor using integers for each dimension.</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(t[1, 2].numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
|
|
<span class="sd"> - Slice Indexing: Select a range of elements using slice notation (`start:end:stride`).</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(t[0:2, ::2].numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
|
|
<span class="sd"> - Tensor Indexing: Use another tensor as indices for advanced indexing. Using `tuple` or `list` here also works.</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(t[Tensor([2, 0, 1]), Tensor([1, 2, 3])].numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
|
|
<span class="sd"> - `None` Indexing: Add a new dimension to the tensor.</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(t[:, None].shape)</span>
|
|
<span class="sd"> ```</span>
|
|
|
|
<span class="sd"> NOTE: Out-of-bounds indexing results in a value of `0`.</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> t = Tensor([1, 2, 3])</span>
|
|
<span class="sd"> print(t[Tensor([4, 3, 2])].numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_getitem</span><span class="p">(</span><span class="n">indices</span><span class="p">)</span>
|
|
</code></pre></div></td></tr></table></div>
|
|
</details>
|
|
</div>
|
|
|
|
</div>
|
|
|
|
<div class="doc doc-object doc-function">
|
|
|
|
|
|
<h3 id="tinygrad.Tensor.gather" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">gather</span>
|
|
|
|
|
|
<a href="#tinygrad.Tensor.gather" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">gather</span><span class="p">(</span><span class="n">dim</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span><span class="p">,</span> <span class="n">index</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span><span class="p">)</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span>
|
|
</code></pre></div>
|
|
|
|
<div class="doc doc-contents first">
|
|
|
|
<p>Gathers values along an axis specified by <code class="language-python highlight"><span class="n">dim</span></code>.</p>
|
|
<p><div class="language-python highlight"><pre><span></span><code><span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">1</span> <span class="mi">2</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">3</span> <span class="mi">4</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">gather</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">Tensor</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]))</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">1</span> <span class="mi">1</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">4</span> <span class="mi">3</span><span class="p">]]</span>
|
|
</code></pre></div></p>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/__init__.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">933</span>
|
|
<span class="normal">934</span>
|
|
<span class="normal">935</span>
|
|
<span class="normal">936</span>
|
|
<span class="normal">937</span>
|
|
<span class="normal">938</span>
|
|
<span class="normal">939</span>
|
|
<span class="normal">940</span>
|
|
<span class="normal">941</span>
|
|
<span class="normal">942</span>
|
|
<span class="normal">943</span>
|
|
<span class="normal">944</span>
|
|
<span class="normal">945</span>
|
|
<span class="normal">946</span>
|
|
<span class="normal">947</span>
|
|
<span class="normal">948</span>
|
|
<span class="normal">949</span>
|
|
<span class="normal">950</span>
|
|
<span class="normal">951</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">gather</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dim</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">index</span><span class="p">:</span><span class="n">Self</span><span class="p">)</span> <span class="o">-></span> <span class="n">Self</span><span class="p">:</span>
|
|
<span class="w"> </span><span class="sd">"""</span>
|
|
<span class="sd"> Gathers values along an axis specified by `dim`.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> t = Tensor([[1, 2], [3, 4]])</span>
|
|
<span class="sd"> print(t.numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(t.gather(1, Tensor([[0, 0], [1, 0]])).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">if</span> <span class="n">index</span><span class="o">.</span><span class="n">device</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">index</span><span class="o">.</span><span class="n">device</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">:</span>
|
|
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"expected index and self on the same device, </span><span class="si">{</span><span class="n">index</span><span class="o">.</span><span class="n">device</span><span class="si">=}</span><span class="s2">, </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="si">=}</span><span class="s2">"</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="n">index</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="p">:</span> <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"self.ndim must equal index.ndim, </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="si">=}</span><span class="s2">, </span><span class="si">{</span><span class="n">index</span><span class="o">.</span><span class="n">ndim</span><span class="si">=}</span><span class="s2">"</span><span class="p">)</span>
|
|
<span class="n">dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_resolve_dim</span><span class="p">(</span><span class="n">dim</span><span class="p">)</span>
|
|
<span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="n">s</span> <span class="o">>=</span> <span class="n">i</span> <span class="k">for</span> <span class="n">d</span><span class="p">,(</span><span class="n">s</span><span class="p">,</span><span class="n">i</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">index</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span> <span class="k">if</span> <span class="n">d</span> <span class="o">!=</span> <span class="n">dim</span><span class="p">),</span> <span class="s2">"requires self.shape[d] >= index.shape[d] for all d != dim"</span>
|
|
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shrink_to</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">i</span> <span class="k">if</span> <span class="n">d</span> <span class="o">!=</span> <span class="n">dim</span> <span class="k">else</span> <span class="kc">None</span> <span class="k">for</span> <span class="n">d</span><span class="p">,</span><span class="n">i</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">index</span><span class="o">.</span><span class="n">shape</span><span class="p">)))</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">dim</span><span class="p">)</span>
|
|
<span class="k">return</span> <span class="p">(</span><span class="n">index</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">_one_hot_along_dim</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">dim</span><span class="p">])</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
|
|
</code></pre></div></td></tr></table></div>
|
|
</details>
|
|
</div>
|
|
|
|
</div>
|
|
|
|
<div class="doc doc-object doc-function">
|
|
|
|
|
|
<h3 id="tinygrad.Tensor.cat" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">cat</span>
|
|
|
|
|
|
<a href="#tinygrad.Tensor.cat" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">cat</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span><span class="p">,</span> <span class="n">dim</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span>
|
|
</code></pre></div>
|
|
|
|
<div class="doc doc-contents first">
|
|
|
|
<p>Concatenates self with other tensors in <code class="language-python highlight"><span class="n">args</span></code> along an axis specified by <code class="language-python highlight"><span class="n">dim</span></code>.
|
|
All tensors must have the same shape except in the concatenating dimension.</p>
|
|
<p><div class="language-python highlight"><pre><span></span><code><span class="n">t0</span><span class="p">,</span> <span class="n">t1</span><span class="p">,</span> <span class="n">t2</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]]),</span> <span class="n">Tensor</span><span class="p">([[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]]),</span> <span class="n">Tensor</span><span class="p">([[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]])</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">t0</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">t2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">1</span> <span class="mi">2</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">3</span> <span class="mi">4</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">5</span> <span class="mi">6</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">t0</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">t2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">4</span> <span class="mi">5</span> <span class="mi">6</span><span class="p">]]</span>
|
|
</code></pre></div></p>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/__init__.py</code></summary>
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<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">622</span>
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<span class="normal">623</span>
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<span class="normal">624</span>
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<span class="normal">625</span>
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<span class="normal">626</span>
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<span class="normal">627</span>
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<span class="normal">628</span>
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<span class="normal">629</span>
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<span class="normal">630</span>
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<span class="normal">631</span>
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<span class="normal">632</span>
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<span class="normal">633</span>
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<span class="normal">634</span>
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<span class="normal">635</span>
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<span class="normal">636</span>
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<span class="normal">637</span>
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<span class="normal">638</span>
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<span class="normal">639</span>
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<span class="normal">640</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">cat</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span><span class="n">Self</span><span class="p">,</span> <span class="n">dim</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">-></span> <span class="n">Self</span><span class="p">:</span>
|
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<span class="w"> </span><span class="sd">"""</span>
|
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<span class="sd"> Concatenates self with other tensors in `args` along an axis specified by `dim`.</span>
|
|
<span class="sd"> All tensors must have the same shape except in the concatenating dimension.</span>
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<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
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<span class="sd"> t0, t1, t2 = Tensor([[1, 2]]), Tensor([[3, 4]]), Tensor([[5, 6]])</span>
|
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<span class="sd"> print(t0.cat(t1, t2, dim=0).numpy())</span>
|
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<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
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<span class="sd"> print(t0.cat(t1, t2, dim=1).numpy())</span>
|
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<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
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<span class="n">dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_resolve_dim</span><span class="p">(</span><span class="n">dim</span><span class="p">)</span>
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<span class="k">for</span> <span class="n">arg</span> <span class="ow">in</span> <span class="n">args</span><span class="p">:</span> <span class="k">assert</span> <span class="n">arg</span><span class="o">.</span><span class="n">ndim</span><span class="o">==</span><span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="ow">and</span> <span class="nb">all</span><span class="p">(</span><span class="n">ti</span><span class="o">==</span><span class="n">ai</span> <span class="k">for</span> <span class="n">i</span><span class="p">,(</span><span class="n">ti</span><span class="p">,</span><span class="n">ai</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">arg</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span> <span class="k">if</span> <span class="n">i</span><span class="o">!=</span><span class="n">dim</span><span class="p">)</span>
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<span class="n">tensors</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">]</span>
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<span class="n">dim_cumsum</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">accumulate</span><span class="p">([</span><span class="n">t</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">dim</span><span class="p">]</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tensors</span><span class="p">],</span> <span class="n">initial</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
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<span class="n">padded</span> <span class="o">=</span> <span class="p">[</span><span class="n">t</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="nb">tuple</span><span class="p">((</span><span class="n">dim_cumsum</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">dim_cumsum</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">dim_cumsum</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="k">if</span> <span class="n">j</span><span class="o">==</span><span class="n">dim</span> <span class="k">else</span> <span class="kc">None</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">ndim</span><span class="p">)))</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">t</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tensors</span><span class="p">)]</span>
|
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<span class="k">return</span> <span class="n">padded</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">usum</span><span class="p">(</span><span class="o">*</span><span class="n">padded</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>
|
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</code></pre></div></td></tr></table></div>
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</details>
|
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</div>
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</div>
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<div class="doc doc-object doc-function">
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<h3 id="tinygrad.Tensor.stack" class="doc doc-heading">
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<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">stack</span>
|
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<a href="#tinygrad.Tensor.stack" class="headerlink" title="Permanent link">¤</a></h3>
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<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">stack</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span><span class="p">,</span> <span class="n">dim</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span>
|
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</code></pre></div>
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<div class="doc doc-contents first">
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<p>Concatenates self with other tensors in <code class="language-python highlight"><span class="n">args</span></code> along a new dimension specified by <code class="language-python highlight"><span class="n">dim</span></code>.</p>
|
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<p><div class="language-python highlight"><pre><span></span><code><span class="n">t0</span><span class="p">,</span> <span class="n">t1</span><span class="p">,</span> <span class="n">t2</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]),</span> <span class="n">Tensor</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]),</span> <span class="n">Tensor</span><span class="p">([</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">])</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">t0</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">t2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
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</code></pre></div>
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<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">1</span> <span class="mi">2</span><span class="p">]</span>
|
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<span class="p">[</span><span class="mi">3</span> <span class="mi">4</span><span class="p">]</span>
|
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<span class="p">[</span><span class="mi">5</span> <span class="mi">6</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">t0</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">t2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
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</code></pre></div>
|
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<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">1</span> <span class="mi">3</span> <span class="mi">5</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">2</span> <span class="mi">4</span> <span class="mi">6</span><span class="p">]]</span>
|
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</code></pre></div></p>
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|
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<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/__init__.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">642</span>
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<span class="normal">643</span>
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<span class="normal">644</span>
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<span class="normal">645</span>
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<span class="normal">646</span>
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<span class="normal">647</span>
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<span class="normal">648</span>
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<span class="normal">649</span>
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<span class="normal">650</span>
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<span class="normal">651</span>
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<span class="normal">652</span>
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<span class="normal">653</span>
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<span class="normal">654</span>
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<span class="normal">655</span>
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<span class="normal">656</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">stack</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span><span class="n">Self</span><span class="p">,</span> <span class="n">dim</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">-></span> <span class="n">Self</span><span class="p">:</span>
|
|
<span class="w"> </span><span class="sd">"""</span>
|
|
<span class="sd"> Concatenates self with other tensors in `args` along a new dimension specified by `dim`.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> t0, t1, t2 = Tensor([1, 2]), Tensor([3, 4]), Tensor([5, 6])</span>
|
|
<span class="sd"> print(t0.stack(t1, t2, dim=0).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(t0.stack(t1, t2, dim=1).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="c1"># checks for shapes and number of dimensions delegated to cat</span>
|
|
<span class="n">unsqueezed</span> <span class="o">=</span> <span class="p">[</span><span class="n">t</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="n">dim</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">argfix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)]</span>
|
|
<span class="k">return</span> <span class="n">unsqueezed</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="o">*</span><span class="n">unsqueezed</span><span class="p">[</span><span class="mi">1</span><span class="p">:],</span> <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">)</span>
|
|
</code></pre></div></td></tr></table></div>
|
|
</details>
|
|
</div>
|
|
|
|
</div>
|
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<div class="doc doc-object doc-function">
|
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|
|
|
|
<h3 id="tinygrad.Tensor.repeat" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">repeat</span>
|
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|
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|
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<a href="#tinygrad.Tensor.repeat" class="headerlink" title="Permanent link">¤</a></h3>
|
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<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">repeat</span><span class="p">(</span><span class="n">repeats</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span>
|
|
</code></pre></div>
|
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|
|
<div class="doc doc-contents first">
|
|
|
|
<p>Repeats tensor number of times along each dimension specified by <code class="language-python highlight"><span class="n">repeats</span></code>.
|
|
<code class="language-python highlight"><span class="n">repeats</span></code> can be passed as a tuple or as separate arguments.</p>
|
|
<p><div class="language-python highlight"><pre><span></span><code><span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</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="n">t</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
|
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</code></pre></div></p>
|
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|
|
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<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/movement.py</code></summary>
|
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<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">547</span>
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<span class="normal">548</span>
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<span class="normal">549</span>
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<span class="normal">550</span>
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<span class="normal">551</span>
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<span class="normal">552</span>
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<span class="normal">553</span>
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<span class="normal">554</span>
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<span class="normal">555</span>
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<span class="normal">556</span>
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<span class="normal">557</span>
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<span class="normal">558</span>
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<span class="normal">559</span>
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<span class="normal">560</span>
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<span class="normal">561</span>
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<span class="normal">562</span>
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<span class="normal">563</span>
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<span class="normal">564</span>
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<span class="normal">565</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">repeat</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">repeats</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> <span class="o">-></span> <span class="n">Self</span><span class="p">:</span>
|
|
<span class="w"> </span><span class="sd">"""</span>
|
|
<span class="sd"> Repeats tensor number of times along each dimension specified by `repeats`.</span>
|
|
<span class="sd"> `repeats` can be passed as a tuple or as separate arguments.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> t = Tensor([1, 2, 3])</span>
|
|
<span class="sd"> print(t.repeat(4, 2).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(t.repeat(4, 2, 1).shape)</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="n">repeats</span> <span class="o">=</span> <span class="n">argfix</span><span class="p">(</span><span class="n">repeats</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span>
|
|
<span class="n">base_shape</span> <span class="o">=</span> <span class="n">_align_left</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">repeats</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
|
|
<span class="n">unsqueezed_shape</span> <span class="o">=</span> <span class="n">flatten</span><span class="p">([[</span><span class="n">s</span><span class="p">]</span> <span class="k">if</span> <span class="n">r</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">s</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span><span class="p">,</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">repeats</span><span class="p">,</span> <span class="n">base_shape</span><span class="p">)])</span>
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|
<span class="n">expanded_shape</span> <span class="o">=</span> <span class="n">flatten</span><span class="p">([[</span><span class="n">s</span><span class="p">]</span> <span class="k">if</span> <span class="n">r</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="p">[</span><span class="n">r</span><span class="p">,</span> <span class="n">s</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span><span class="p">,</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">repeats</span><span class="p">,</span> <span class="n">base_shape</span><span class="p">)])</span>
|
|
<span class="n">final_shape</span> <span class="o">=</span> <span class="p">[</span><span class="n">r</span> <span class="o">*</span> <span class="n">s</span> <span class="k">for</span> <span class="n">r</span><span class="p">,</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">repeats</span><span class="p">,</span> <span class="n">base_shape</span><span class="p">)]</span>
|
|
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">unsqueezed_shape</span><span class="p">)</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">expanded_shape</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">final_shape</span><span class="p">)</span>
|
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</code></pre></div></td></tr></table></div>
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</details>
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</div>
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</div>
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<div class="doc doc-object doc-function">
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<h3 id="tinygrad.Tensor.repeat_interleave" class="doc doc-heading">
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<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">repeat_interleave</span>
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<a href="#tinygrad.Tensor.repeat_interleave" class="headerlink" title="Permanent link">¤</a></h3>
|
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<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">repeat_interleave</span><span class="p">(</span>
|
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<span class="n">repeats</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span><span class="p">,</span> <span class="n">dim</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span>
|
|
<span class="p">)</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span>
|
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</code></pre></div>
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<div class="doc doc-contents first">
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|
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<p>Repeats elements of a tensor.</p>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</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="n">t</span><span class="o">.</span><span class="n">repeat_interleave</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
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</code></pre></div>
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<div class="language-python highlight"><pre><span></span><code><span class="p">[</span><span class="mi">1</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">3</span><span class="p">]</span>
|
|
</code></pre></div>
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<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/movement.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">531</span>
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<span class="normal">532</span>
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<span class="normal">533</span>
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<span class="normal">534</span>
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<span class="normal">535</span>
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<span class="normal">536</span>
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<span class="normal">537</span>
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<span class="normal">538</span>
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<span class="normal">539</span>
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<span class="normal">540</span>
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<span class="normal">541</span>
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<span class="normal">542</span>
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<span class="normal">543</span>
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<span class="normal">544</span>
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<span class="normal">545</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">repeat_interleave</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">repeats</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">dim</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-></span> <span class="n">Self</span><span class="p">:</span>
|
|
<span class="w"> </span><span class="sd">"""</span>
|
|
<span class="sd"> Repeats elements of a tensor.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> t = Tensor([1, 2, 3])</span>
|
|
<span class="sd"> print(t.repeat_interleave(2).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="n">x</span><span class="p">,</span> <span class="n">dim</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="mi">0</span><span class="p">)</span> <span class="k">if</span> <span class="n">dim</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_resolve_dim</span><span class="p">(</span><span class="n">dim</span><span class="p">))</span>
|
|
<span class="n">shp</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span>
|
|
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">*</span><span class="n">shp</span><span class="p">[:</span> <span class="n">dim</span> <span class="o">+</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="o">*</span><span class="n">shp</span><span class="p">[</span><span class="n">dim</span> <span class="o">+</span> <span class="mi">1</span> <span class="p">:])</span>
|
|
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="o">*</span><span class="n">shp</span><span class="p">[:</span> <span class="n">dim</span> <span class="o">+</span> <span class="mi">1</span><span class="p">],</span> <span class="n">repeats</span><span class="p">,</span> <span class="o">*</span><span class="n">shp</span><span class="p">[</span><span class="n">dim</span> <span class="o">+</span> <span class="mi">1</span> <span class="p">:])</span>
|
|
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">*</span><span class="n">shp</span><span class="p">[:</span><span class="n">dim</span><span class="p">],</span> <span class="n">shp</span><span class="p">[</span><span class="n">dim</span><span class="p">]</span> <span class="o">*</span> <span class="n">repeats</span><span class="p">,</span> <span class="o">*</span><span class="n">shp</span><span class="p">[</span><span class="n">dim</span> <span class="o">+</span> <span class="mi">1</span> <span class="p">:])</span>
|
|
<span class="k">return</span> <span class="n">x</span>
|
|
</code></pre></div></td></tr></table></div>
|
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</details>
|
|
</div>
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|
</div>
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<div class="doc doc-object doc-function">
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<h3 id="tinygrad.Tensor.split" class="doc doc-heading">
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<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">split</span>
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|
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<a href="#tinygrad.Tensor.split" class="headerlink" title="Permanent link">¤</a></h3>
|
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<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">split</span><span class="p">(</span>
|
|
<span class="n">sizes</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">|</span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Sequence</code>" href="https://docs.python.org/3/library/typing.html#typing.Sequence">Sequence</a></span><span class="p">[</span><span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span><span class="p">],</span> <span class="n">dim</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">=</span> <span class="mi">0</span>
|
|
<span class="p">)</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#tuple">tuple</a></span><span class="p">[</span><span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span><span class="p">,</span> <span class="o">...</span><span class="p">]</span>
|
|
</code></pre></div>
|
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|
|
<div class="doc doc-contents first">
|
|
|
|
<p>Splits the tensor into chunks along the dimension specified by <code class="language-python highlight"><span class="n">dim</span></code>.
|
|
If <code class="language-python highlight"><span class="n">sizes</span></code> is an integer, it splits into equally sized chunks if possible, otherwise the last chunk will be smaller.
|
|
If <code class="language-python highlight"><span class="n">sizes</span></code> is a list, it splits into <code class="language-python highlight"><span class="nb">len</span><span class="p">(</span><span class="n">sizes</span><span class="p">)</span></code> chunks with size in <code class="language-python highlight"><span class="n">dim</span></code> according to <code class="language-python highlight"><span class="n">size</span></code>.</p>
|
|
<p><div class="language-python highlight"><pre><span></span><code><span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">0</span> <span class="mi">1</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">2</span> <span class="mi">3</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">4</span> <span class="mi">5</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">6</span> <span class="mi">7</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">8</span> <span class="mi">9</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="n">split</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">repr</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">split</span><span class="p">]))</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
|
|
<span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
|
<span class="n">array</span><span class="p">([[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
|
|
<span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
|
<span class="n">array</span><span class="p">([[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="n">split</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">split</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">repr</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">split</span><span class="p">]))</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
|
<span class="n">array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
|
|
<span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
|
|
<span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span>
|
|
<span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
|
</code></pre></div></p>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/movement.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">396</span>
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<span class="normal">397</span>
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<span class="normal">398</span>
|
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<span class="normal">399</span>
|
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<span class="normal">400</span>
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<span class="normal">401</span>
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<span class="normal">402</span>
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<span class="normal">403</span>
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<span class="normal">404</span>
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<span class="normal">405</span>
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<span class="normal">406</span>
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<span class="normal">407</span>
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<span class="normal">408</span>
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<span class="normal">409</span>
|
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<span class="normal">410</span>
|
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<span class="normal">411</span>
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<span class="normal">412</span>
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<span class="normal">413</span>
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<span class="normal">414</span>
|
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<span class="normal">415</span>
|
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<span class="normal">416</span>
|
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<span class="normal">417</span>
|
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<span class="normal">418</span>
|
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<span class="normal">419</span>
|
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<span class="normal">420</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">split</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sizes</span><span class="p">:</span><span class="nb">int</span><span class="o">|</span><span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span> <span class="n">dim</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">-></span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Self</span><span class="p">,</span> <span class="o">...</span><span class="p">]:</span>
|
|
<span class="w"> </span><span class="sd">"""</span>
|
|
<span class="sd"> Splits the tensor into chunks along the dimension specified by `dim`.</span>
|
|
<span class="sd"> If `sizes` is an integer, it splits into equally sized chunks if possible, otherwise the last chunk will be smaller.</span>
|
|
<span class="sd"> If `sizes` is a list, it splits into `len(sizes)` chunks with size in `dim` according to `size`.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> t = Tensor.arange(10).reshape(5, 2)</span>
|
|
<span class="sd"> print(t.numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> split = t.split(2)</span>
|
|
<span class="sd"> print("\\n".join([repr(x.numpy()) for x in split]))</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> split = t.split([1, 4])</span>
|
|
<span class="sd"> print("\\n".join([repr(x.numpy()) for x in split]))</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="n">dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_resolve_dim</span><span class="p">(</span><span class="n">dim</span><span class="p">)</span>
|
|
<span class="n">dim_sz</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">dim</span><span class="p">]</span>
|
|
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dim_sz</span><span class="p">,</span> <span class="nb">int</span><span class="p">),</span> <span class="sa">f</span><span class="s2">"does not support symbolic shape in split dimension </span><span class="si">{</span><span class="n">dim</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">"</span>
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<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sizes</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span> <span class="n">sizes</span> <span class="o">=</span> <span class="p">[</span><span class="nb">min</span><span class="p">(</span><span class="n">sizes</span><span class="p">,</span> <span class="n">dim_sz</span><span class="o">-</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">dim_sz</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">sizes</span><span class="p">))]</span>
|
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<span class="k">assert</span> <span class="nb">sum</span><span class="p">(</span><span class="n">sizes</span><span class="p">)</span> <span class="o">==</span> <span class="n">dim_sz</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"expect sizes to sum exactly to </span><span class="si">{</span><span class="n">dim_sz</span><span class="si">}</span><span class="s2">, but got </span><span class="si">{</span><span class="nb">sum</span><span class="p">(</span><span class="n">sizes</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span>
|
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<span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shrink</span><span class="p">(</span><span class="nb">tuple</span><span class="p">((</span><span class="nb">sum</span><span class="p">(</span><span class="n">sizes</span><span class="p">[:</span><span class="n">i</span><span class="p">]),</span> <span class="nb">sum</span><span class="p">(</span><span class="n">sizes</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="k">if</span> <span class="n">j</span> <span class="o">==</span> <span class="n">dim</span> <span class="k">else</span> <span class="kc">None</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="p">)))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">sizes</span><span class="p">)))</span>
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</code></pre></div></td></tr></table></div>
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</details>
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</div>
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</div>
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<div class="doc doc-object doc-function">
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<h3 id="tinygrad.Tensor.chunk" class="doc doc-heading">
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<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">chunk</span>
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<a href="#tinygrad.Tensor.chunk" class="headerlink" title="Permanent link">¤</a></h3>
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<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">chunk</span><span class="p">(</span><span class="n">chunks</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span><span class="p">,</span> <span class="n">dim</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#list">list</a></span><span class="p">[</span><span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span><span class="p">]</span>
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</code></pre></div>
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<div class="doc doc-contents first">
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<p>Splits the tensor into <code class="language-python highlight"><span class="n">chunks</span></code> number of chunks along the dimension <code class="language-python highlight"><span class="n">dim</span></code>.
|
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If the tensor size along <code class="language-python highlight"><span class="n">dim</span></code> is not divisible by <code class="language-python highlight"><span class="n">chunks</span></code>, all returned chunks will be the same size except the last one.
|
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The function may return fewer than the specified number of chunks.</p>
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<p><div class="language-python highlight"><pre><span></span><code><span class="n">chunked</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">11</span><span class="p">)</span><span class="o">.</span><span class="n">chunk</span><span class="p">(</span><span class="mi">6</span><span class="p">)</span>
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<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">repr</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">chunked</span><span class="p">]))</span>
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</code></pre></div>
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<div class="language-python highlight"><pre><span></span><code><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
|
<span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
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<span class="n">array</span><span class="p">([</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
|
<span class="n">array</span><span class="p">([</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
|
<span class="n">array</span><span class="p">([</span><span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
|
<span class="n">array</span><span class="p">([</span><span class="mi">10</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
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</code></pre></div>
|
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<div class="language-python highlight"><pre><span></span><code><span class="n">chunked</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">12</span><span class="p">)</span><span class="o">.</span><span class="n">chunk</span><span class="p">(</span><span class="mi">6</span><span class="p">)</span>
|
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<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">repr</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">chunked</span><span class="p">]))</span>
|
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</code></pre></div>
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<div class="language-python highlight"><pre><span></span><code><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
|
<span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
|
<span class="n">array</span><span class="p">([</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
|
<span class="n">array</span><span class="p">([</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
|
<span class="n">array</span><span class="p">([</span><span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
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<span class="n">array</span><span class="p">([</span><span class="mi">10</span><span class="p">,</span> <span class="mi">11</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
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</code></pre></div>
|
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<div class="language-python highlight"><pre><span></span><code><span class="n">chunked</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">13</span><span class="p">)</span><span class="o">.</span><span class="n">chunk</span><span class="p">(</span><span class="mi">6</span><span class="p">)</span>
|
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<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">repr</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">chunked</span><span class="p">]))</span>
|
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</code></pre></div>
|
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<div class="language-python highlight"><pre><span></span><code><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
|
<span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
|
<span class="n">array</span><span class="p">([</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
|
<span class="n">array</span><span class="p">([</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">11</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
|
<span class="n">array</span><span class="p">([</span><span class="mi">12</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
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</code></pre></div></p>
|
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<details class="mkdocstrings-source">
|
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<summary>Source code in <code>tinygrad/mixin/movement.py</code></summary>
|
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<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">422</span>
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<span class="normal">423</span>
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<span class="normal">424</span>
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<span class="normal">425</span>
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<span class="normal">426</span>
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<span class="normal">427</span>
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<span class="normal">428</span>
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<span class="normal">429</span>
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<span class="normal">430</span>
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<span class="normal">431</span>
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<span class="normal">432</span>
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<span class="normal">433</span>
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<span class="normal">434</span>
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<span class="normal">435</span>
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<span class="normal">436</span>
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<span class="normal">437</span>
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<span class="normal">438</span>
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<span class="normal">439</span>
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<span class="normal">440</span>
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<span class="normal">441</span>
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<span class="normal">442</span>
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<span class="normal">443</span>
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<span class="normal">444</span>
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<span class="normal">445</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">chunk</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">chunks</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">dim</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">-></span> <span class="nb">list</span><span class="p">[</span><span class="n">Self</span><span class="p">]:</span>
|
|
<span class="w"> </span><span class="sd">"""</span>
|
|
<span class="sd"> Splits the tensor into `chunks` number of chunks along the dimension `dim`.</span>
|
|
<span class="sd"> If the tensor size along `dim` is not divisible by `chunks`, all returned chunks will be the same size except the last one.</span>
|
|
<span class="sd"> The function may return fewer than the specified number of chunks.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> chunked = Tensor.arange(11).chunk(6)</span>
|
|
<span class="sd"> print("\\n".join([repr(x.numpy()) for x in chunked]))</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> chunked = Tensor.arange(12).chunk(6)</span>
|
|
<span class="sd"> print("\\n".join([repr(x.numpy()) for x in chunked]))</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> chunked = Tensor.arange(13).chunk(6)</span>
|
|
<span class="sd"> print("\\n".join([repr(x.numpy()) for x in chunked]))</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="n">dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_resolve_dim</span><span class="p">(</span><span class="n">dim</span><span class="p">)</span>
|
|
<span class="n">dim_sz</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">dim</span><span class="p">]</span>
|
|
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dim_sz</span><span class="p">,</span> <span class="nb">int</span><span class="p">),</span> <span class="sa">f</span><span class="s2">"does not support symbolic shape in split dimension </span><span class="si">{</span><span class="n">dim</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">"</span>
|
|
<span class="k">assert</span> <span class="n">chunks</span> <span class="o">></span> <span class="mi">0</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"expect chunks to be greater than 0, got: </span><span class="si">{</span><span class="n">chunks</span><span class="si">}</span><span class="s2">"</span>
|
|
<span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">ceildiv</span><span class="p">(</span><span class="n">dim_sz</span><span class="p">,</span> <span class="n">chunks</span><span class="p">)</span> <span class="k">if</span> <span class="n">dim_sz</span> <span class="k">else</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">chunks</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">))</span>
|
|
</code></pre></div></td></tr></table></div>
|
|
</details>
|
|
</div>
|
|
|
|
</div>
|
|
|
|
<div class="doc doc-object doc-function">
|
|
|
|
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|
<h3 id="tinygrad.Tensor.unfold" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">unfold</span>
|
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|
|
|
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<a href="#tinygrad.Tensor.unfold" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">unfold</span><span class="p">(</span><span class="n">dim</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">step</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span><span class="p">)</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span>
|
|
</code></pre></div>
|
|
|
|
<div class="doc doc-contents first">
|
|
|
|
<p>Unfolds the tensor along dimension <code class="language-python highlight"><span class="n">dim</span></code> into overlapping windows.</p>
|
|
<p>Each window has length <code class="language-python highlight"><span class="n">size</span></code> and begins every <code class="language-python highlight"><span class="n">step</span></code> elements of <code class="language-python highlight"><span class="bp">self</span></code>.
|
|
Returns the input tensor with dimension <code class="language-python highlight"><span class="n">dim</span></code> replaced by dims <code class="language-python highlight"><span class="p">(</span><span class="n">n_windows</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span></code>
|
|
where <code class="language-python highlight"><span class="n">n_windows</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">dim</span><span class="p">]</span> <span class="o">-</span> <span class="n">size</span><span class="p">)</span> <span class="o">//</span> <span class="n">step</span> <span class="o">+</span> <span class="mi">1</span></code>.</p>
|
|
<p><div class="language-python highlight"><pre><span></span><code><span class="n">unfolded</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span><span class="o">.</span><span class="n">unfold</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">repr</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">unfolded</span><span class="p">]))</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
|
<span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
|
<span class="n">array</span><span class="p">([</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
|
<span class="n">array</span><span class="p">([</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
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</code></pre></div>
|
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<div class="language-python highlight"><pre><span></span><code><span class="n">unfolded</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">27</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">unfold</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
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<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">repr</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">unfolded</span><span class="p">]))</span>
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</code></pre></div>
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<div class="language-python highlight"><pre><span></span><code><span class="n">array</span><span class="p">([[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]],</span>
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<span class="p">[[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]],</span>
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<span class="p">[[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">]]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
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<span class="n">array</span><span class="p">([[[</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">]],</span>
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<span class="p">[[</span><span class="mi">12</span><span class="p">,</span> <span class="mi">13</span><span class="p">]],</span>
|
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<span class="p">[[</span><span class="mi">15</span><span class="p">,</span> <span class="mi">16</span><span class="p">]]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
|
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<span class="n">array</span><span class="p">([[[</span><span class="mi">18</span><span class="p">,</span> <span class="mi">19</span><span class="p">]],</span>
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<span class="p">[[</span><span class="mi">21</span><span class="p">,</span> <span class="mi">22</span><span class="p">]],</span>
|
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<span class="p">[[</span><span class="mi">24</span><span class="p">,</span> <span class="mi">25</span><span class="p">]]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">int32</span><span class="p">)</span>
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</code></pre></div></p>
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<details class="mkdocstrings-source">
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<summary>Source code in <code>tinygrad/mixin/movement.py</code></summary>
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<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">589</span>
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<span class="normal">590</span>
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<span class="normal">591</span>
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<span class="normal">592</span>
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<span class="normal">593</span>
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<span class="normal">594</span>
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<span class="normal">595</span>
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<span class="normal">596</span>
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<span class="normal">597</span>
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<span class="normal">598</span>
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<span class="normal">599</span>
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<span class="normal">600</span>
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<span class="normal">601</span>
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<span class="normal">602</span>
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<span class="normal">603</span>
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<span class="normal">604</span>
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<span class="normal">605</span>
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<span class="normal">606</span>
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<span class="normal">607</span>
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<span class="normal">608</span>
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<span class="normal">609</span>
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<span class="normal">610</span>
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<span class="normal">611</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">unfold</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dim</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">step</span><span class="p">:</span><span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="n">Self</span><span class="p">:</span>
|
|
<span class="w"> </span><span class="sd">"""</span>
|
|
<span class="sd"> Unfolds the tensor along dimension `dim` into overlapping windows.</span>
|
|
|
|
<span class="sd"> Each window has length `size` and begins every `step` elements of `self`.</span>
|
|
<span class="sd"> Returns the input tensor with dimension `dim` replaced by dims `(n_windows, size)`</span>
|
|
<span class="sd"> where `n_windows = (self.shape[dim] - size) // step + 1`.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> unfolded = Tensor.arange(8).unfold(0,2,2)</span>
|
|
<span class="sd"> print("\\n".join([repr(x.numpy()) for x in unfolded]))</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> unfolded = Tensor.arange(27).reshape(3,3,3).unfold(-1,2,3)</span>
|
|
<span class="sd"> print("\\n".join([repr(x.numpy()) for x in unfolded]))</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">if</span> <span class="n">size</span> <span class="o"><</span> <span class="mi">0</span><span class="p">:</span> <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'size must be >= 0 but got </span><span class="si">{</span><span class="n">size</span><span class="si">=}</span><span class="s1">'</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="n">step</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">:</span> <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'step must be > 0 but got </span><span class="si">{</span><span class="n">step</span><span class="si">=}</span><span class="s1">'</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="n">size</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">dim</span><span class="p">]:</span> <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'maximum size for tensor at dimension </span><span class="si">{</span><span class="n">dim</span><span class="si">}</span><span class="s1"> is </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">dim</span><span class="p">]</span><span class="si">}</span><span class="s1"> but size is </span><span class="si">{</span><span class="n">size</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
|
<span class="n">dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_resolve_dim</span><span class="p">(</span><span class="n">dim</span><span class="p">)</span>
|
|
<span class="n">perm_to_last</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="p">)</span> <span class="k">if</span> <span class="n">i</span> <span class="o">!=</span> <span class="n">dim</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="n">dim</span><span class="p">,)</span>
|
|
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="n">perm_to_last</span><span class="p">)</span><span class="o">.</span><span class="n">_pool</span><span class="p">((</span><span class="n">size</span><span class="p">,),</span> <span class="n">step</span><span class="p">)</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="n">argsort</span><span class="p">(</span><span class="n">perm_to_last</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="p">,))</span>
|
|
</code></pre></div></td></tr></table></div>
|
|
</details>
|
|
</div>
|
|
|
|
</div>
|
|
|
|
<div class="doc doc-object doc-function">
|
|
|
|
|
|
<h3 id="tinygrad.Tensor.meshgrid" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">meshgrid</span>
|
|
|
|
|
|
<a href="#tinygrad.Tensor.meshgrid" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">meshgrid</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="n">indexing</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#str">str</a></span> <span class="o">=</span> <span class="s1">'ij'</span><span class="p">)</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#tuple">tuple</a></span><span class="p">[</span><span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span><span class="p">,</span> <span class="o">...</span><span class="p">]</span>
|
|
</code></pre></div>
|
|
|
|
<div class="doc doc-contents first">
|
|
|
|
<p>Generates coordinate matrices from coordinate vectors.
|
|
Input tensors can be scalars or 1D tensors.</p>
|
|
<p><code class="language-python highlight"><span class="n">indexing</span></code> determines how the output grids are aligned.
|
|
<code class="language-python highlight"><span class="n">ij</span></code> indexing follows matrix-style indexing and <code class="language-python highlight"><span class="n">xy</span></code> indexing follows Cartesian-style indexing.</p>
|
|
<p><div class="language-python highlight"><pre><span></span><code><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]),</span> <span class="n">Tensor</span><span class="p">([</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">])</span>
|
|
<span class="n">grid_x</span><span class="p">,</span> <span class="n">grid_y</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">grid_x</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">grid_y</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">1</span> <span class="mi">1</span> <span class="mi">1</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">2</span> <span class="mi">2</span> <span class="mi">2</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">3</span> <span class="mi">3</span> <span class="mi">3</span><span class="p">]]</span>
|
|
<span class="p">[[</span><span class="mi">4</span> <span class="mi">5</span> <span class="mi">6</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">4</span> <span class="mi">5</span> <span class="mi">6</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">4</span> <span class="mi">5</span> <span class="mi">6</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="n">grid_x</span><span class="p">,</span> <span class="n">grid_y</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">indexing</span><span class="o">=</span><span class="s2">"xy"</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">grid_x</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">grid_y</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span><span class="p">]]</span>
|
|
<span class="p">[[</span><span class="mi">4</span> <span class="mi">4</span> <span class="mi">4</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">5</span> <span class="mi">5</span> <span class="mi">5</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">6</span> <span class="mi">6</span> <span class="mi">6</span><span class="p">]]</span>
|
|
</code></pre></div></p>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/movement.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">447</span>
|
|
<span class="normal">448</span>
|
|
<span class="normal">449</span>
|
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<span class="normal">450</span>
|
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<span class="normal">451</span>
|
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<span class="normal">452</span>
|
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<span class="normal">453</span>
|
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<span class="normal">454</span>
|
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<span class="normal">455</span>
|
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<span class="normal">456</span>
|
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<span class="normal">457</span>
|
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<span class="normal">458</span>
|
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<span class="normal">459</span>
|
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<span class="normal">460</span>
|
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<span class="normal">461</span>
|
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<span class="normal">462</span>
|
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<span class="normal">463</span>
|
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<span class="normal">464</span>
|
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<span class="normal">465</span>
|
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<span class="normal">466</span>
|
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<span class="normal">467</span>
|
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<span class="normal">468</span>
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<span class="normal">469</span>
|
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<span class="normal">470</span>
|
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<span class="normal">471</span>
|
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<span class="normal">472</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">meshgrid</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="n">indexing</span><span class="p">:</span><span class="nb">str</span><span class="o">=</span><span class="s2">"ij"</span><span class="p">)</span> <span class="o">-></span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Self</span><span class="p">,</span> <span class="o">...</span><span class="p">]:</span>
|
|
<span class="w"> </span><span class="sd">"""</span>
|
|
<span class="sd"> Generates coordinate matrices from coordinate vectors.</span>
|
|
<span class="sd"> Input tensors can be scalars or 1D tensors.</span>
|
|
|
|
<span class="sd"> `indexing` determines how the output grids are aligned.</span>
|
|
<span class="sd"> `ij` indexing follows matrix-style indexing and `xy` indexing follows Cartesian-style indexing.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> x, y = Tensor([1, 2, 3]), Tensor([4, 5, 6])</span>
|
|
<span class="sd"> grid_x, grid_y = x.meshgrid(y)</span>
|
|
<span class="sd"> print(grid_x.numpy())</span>
|
|
<span class="sd"> print(grid_y.numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> grid_x, grid_y = x.meshgrid(y, indexing="xy")</span>
|
|
<span class="sd"> print(grid_x.numpy())</span>
|
|
<span class="sd"> print(grid_y.numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">if</span> <span class="n">indexing</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">"ij"</span><span class="p">,</span> <span class="s2">"xy"</span><span class="p">):</span> <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'indexing must be in ("ij", "xy"), got </span><span class="si">{</span><span class="n">indexing</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tensors</span><span class="o">:=</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">))</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> <span class="k">return</span> <span class="n">tensors</span>
|
|
<span class="n">basis</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">tensors</span><span class="p">)))</span> <span class="k">if</span> <span class="n">indexing</span> <span class="o">==</span> <span class="s2">"ij"</span> <span class="k">else</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="o">+</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">tensors</span><span class="p">)))</span>
|
|
<span class="n">tensors</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,)</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span><span class="o">*</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">-</span> <span class="n">i</span><span class="p">))</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">t</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">basis</span><span class="p">,</span> <span class="n">tensors</span><span class="p">))</span>
|
|
<span class="n">output_shape</span> <span class="o">=</span> <span class="n">_broadcast_shape</span><span class="p">(</span><span class="o">*</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">shape</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tensors</span><span class="p">))</span>
|
|
<span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">_broadcast_to</span><span class="p">(</span><span class="n">output_shape</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tensors</span><span class="p">)</span>
|
|
</code></pre></div></td></tr></table></div>
|
|
</details>
|
|
</div>
|
|
|
|
</div>
|
|
|
|
<div class="doc doc-object doc-function">
|
|
|
|
|
|
<h3 id="tinygrad.Tensor.squeeze" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">squeeze</span>
|
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|
|
|
|
<a href="#tinygrad.Tensor.squeeze" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">squeeze</span><span class="p">(</span><span class="n">dim</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span>
|
|
</code></pre></div>
|
|
|
|
<div class="doc doc-contents first">
|
|
|
|
<p>Returns a tensor with specified dimensions of input of size 1 removed.
|
|
If <code class="language-python highlight"><span class="n">dim</span></code> is not specified, all dimensions with size 1 are removed.</p>
|
|
<p><div class="language-python highlight"><pre><span></span><code><span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
|
|
</code></pre></div></p>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/movement.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">260</span>
|
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<span class="normal">261</span>
|
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<span class="normal">262</span>
|
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<span class="normal">263</span>
|
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<span class="normal">264</span>
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<span class="normal">265</span>
|
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<span class="normal">266</span>
|
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<span class="normal">267</span>
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<span class="normal">268</span>
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<span class="normal">269</span>
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<span class="normal">270</span>
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<span class="normal">271</span>
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<span class="normal">272</span>
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<span class="normal">273</span>
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<span class="normal">274</span>
|
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<span class="normal">275</span>
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<span class="normal">276</span>
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<span class="normal">277</span>
|
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<span class="normal">278</span>
|
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<span class="normal">279</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">squeeze</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dim</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-></span> <span class="n">Self</span><span class="p">:</span>
|
|
<span class="w"> </span><span class="sd">"""</span>
|
|
<span class="sd"> Returns a tensor with specified dimensions of input of size 1 removed.</span>
|
|
<span class="sd"> If `dim` is not specified, all dimensions with size 1 are removed.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> t = Tensor.zeros(2, 1, 2, 1, 2)</span>
|
|
<span class="sd"> print(t.squeeze().shape)</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(t.squeeze(0).shape)</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(t.squeeze(1).shape)</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">if</span> <span class="n">dim</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
|
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">dim</span> <span class="k">for</span> <span class="n">dim</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="k">if</span> <span class="n">dim</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">))</span>
|
|
<span class="n">dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_resolve_dim</span><span class="p">(</span><span class="n">dim</span><span class="p">)</span>
|
|
<span class="k">return</span> <span class="bp">self</span> <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">dim</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">1</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="n">dim</span><span class="p">]</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">dim</span> <span class="o">+</span> <span class="mi">1</span> <span class="p">:])</span>
|
|
</code></pre></div></td></tr></table></div>
|
|
</details>
|
|
</div>
|
|
|
|
</div>
|
|
|
|
<div class="doc doc-object doc-function">
|
|
|
|
|
|
<h3 id="tinygrad.Tensor.unsqueeze" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">unsqueeze</span>
|
|
|
|
|
|
<a href="#tinygrad.Tensor.unsqueeze" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">unsqueeze</span><span class="p">(</span><span class="n">dim</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span><span class="p">)</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span>
|
|
</code></pre></div>
|
|
|
|
<div class="doc doc-contents first">
|
|
|
|
<p>Returns a tensor with a new dimension of size 1 inserted at the specified <code class="language-python highlight"><span class="n">dim</span></code>.</p>
|
|
<p><div class="language-python highlight"><pre><span></span><code><span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">4</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">1</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">2</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">3</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">4</span><span class="p">]]</span>
|
|
</code></pre></div></p>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/movement.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">281</span>
|
|
<span class="normal">282</span>
|
|
<span class="normal">283</span>
|
|
<span class="normal">284</span>
|
|
<span class="normal">285</span>
|
|
<span class="normal">286</span>
|
|
<span class="normal">287</span>
|
|
<span class="normal">288</span>
|
|
<span class="normal">289</span>
|
|
<span class="normal">290</span>
|
|
<span class="normal">291</span>
|
|
<span class="normal">292</span>
|
|
<span class="normal">293</span>
|
|
<span class="normal">294</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">unsqueeze</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="n">Self</span><span class="p">:</span>
|
|
<span class="w"> </span><span class="sd">"""</span>
|
|
<span class="sd"> Returns a tensor with a new dimension of size 1 inserted at the specified `dim`.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> t = Tensor([1, 2, 3, 4])</span>
|
|
<span class="sd"> print(t.unsqueeze(0).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(t.unsqueeze(1).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="n">dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_resolve_dim</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="n">extra</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
|
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="n">dim</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">dim</span><span class="p">:])</span>
|
|
</code></pre></div></td></tr></table></div>
|
|
</details>
|
|
</div>
|
|
|
|
</div>
|
|
|
|
<div class="doc doc-object doc-attribute">
|
|
|
|
|
|
|
|
<h3 id="tinygrad.Tensor.T" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-attribute"></code> <span class="doc doc-object-name doc-attribute-name">T</span>
|
|
|
|
|
|
<span class="doc doc-labels">
|
|
<small class="doc doc-label doc-label-property"><code>property</code></small>
|
|
</span>
|
|
|
|
<a href="#tinygrad.Tensor.T" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="n">T</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span>
|
|
</code></pre></div>
|
|
|
|
<div class="doc doc-contents first">
|
|
|
|
<p><code class="language-python highlight"><span class="o">.</span><span class="n">T</span></code> is an alias for <code class="language-python highlight"><span class="o">.</span><span class="n">transpose</span><span class="p">()</span></code>.</p>
|
|
|
|
</div>
|
|
|
|
</div>
|
|
|
|
<div class="doc doc-object doc-function">
|
|
|
|
|
|
<h3 id="tinygrad.Tensor.transpose" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">transpose</span>
|
|
|
|
|
|
<a href="#tinygrad.Tensor.transpose" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">transpose</span><span class="p">(</span><span class="n">dim0</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">dim1</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span>
|
|
</code></pre></div>
|
|
|
|
<div class="doc doc-contents first">
|
|
|
|
<p>Returns a tensor that is a transposed version of the original tensor.
|
|
The given dimensions <code class="language-python highlight"><span class="n">dim0</span></code> and <code class="language-python highlight"><span class="n">dim1</span></code> are swapped.</p>
|
|
<p><div class="language-python highlight"><pre><span></span><code><span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">6</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">2</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="n">t</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">0</span> <span class="mi">1</span> <span class="mi">2</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">3</span> <span class="mi">4</span> <span class="mi">5</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">0</span> <span class="mi">3</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">1</span> <span class="mi">4</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">2</span> <span class="mi">5</span><span class="p">]]</span>
|
|
</code></pre></div></p>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/movement.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">301</span>
|
|
<span class="normal">302</span>
|
|
<span class="normal">303</span>
|
|
<span class="normal">304</span>
|
|
<span class="normal">305</span>
|
|
<span class="normal">306</span>
|
|
<span class="normal">307</span>
|
|
<span class="normal">308</span>
|
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<span class="normal">309</span>
|
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<span class="normal">310</span>
|
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<span class="normal">311</span>
|
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<span class="normal">312</span>
|
|
<span class="normal">313</span>
|
|
<span class="normal">314</span>
|
|
<span class="normal">315</span>
|
|
<span class="normal">316</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">transpose</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dim0</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">dim1</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">-></span> <span class="n">Self</span><span class="p">:</span>
|
|
<span class="w"> </span><span class="sd">"""</span>
|
|
<span class="sd"> Returns a tensor that is a transposed version of the original tensor.</span>
|
|
<span class="sd"> The given dimensions `dim0` and `dim1` are swapped.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> t = Tensor.arange(6).reshape(2, 3)</span>
|
|
<span class="sd"> print(t.numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(t.transpose(0, 1).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="n">order</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="p">))</span>
|
|
<span class="n">order</span><span class="p">[</span><span class="n">dim0</span><span class="p">],</span> <span class="n">order</span><span class="p">[</span><span class="n">dim1</span><span class="p">]</span> <span class="o">=</span> <span class="n">order</span><span class="p">[</span><span class="n">dim1</span><span class="p">],</span> <span class="n">order</span><span class="p">[</span><span class="n">dim0</span><span class="p">]</span>
|
|
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="n">order</span><span class="p">)</span>
|
|
</code></pre></div></td></tr></table></div>
|
|
</details>
|
|
</div>
|
|
|
|
</div>
|
|
|
|
<div class="doc doc-object doc-function">
|
|
|
|
|
|
<h3 id="tinygrad.Tensor.flatten" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">flatten</span>
|
|
|
|
|
|
<a href="#tinygrad.Tensor.flatten" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">flatten</span><span class="p">(</span><span class="n">start_dim</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">end_dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span>
|
|
</code></pre></div>
|
|
|
|
<div class="doc doc-contents first">
|
|
|
|
<p>Flattens the tensor by reshaping it into a one-dimensional tensor.
|
|
If <code class="language-python highlight"><span class="n">start_dim</span></code> or <code class="language-python highlight"><span class="n">end_dim</span></code> are passed, only dimensions starting with <code class="language-python highlight"><span class="n">start_dim</span></code> and ending with <code class="language-python highlight"><span class="n">end_dim</span></code> are flattened.</p>
|
|
<p><div class="language-python highlight"><pre><span></span><code><span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">8</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[</span><span class="mi">0</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">4</span> <span class="mi">5</span> <span class="mi">6</span> <span class="mi">7</span><span class="p">]</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">start_dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">0</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">4</span> <span class="mi">5</span> <span class="mi">6</span> <span class="mi">7</span><span class="p">]]</span>
|
|
</code></pre></div></p>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/movement.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">318</span>
|
|
<span class="normal">319</span>
|
|
<span class="normal">320</span>
|
|
<span class="normal">321</span>
|
|
<span class="normal">322</span>
|
|
<span class="normal">323</span>
|
|
<span class="normal">324</span>
|
|
<span class="normal">325</span>
|
|
<span class="normal">326</span>
|
|
<span class="normal">327</span>
|
|
<span class="normal">328</span>
|
|
<span class="normal">329</span>
|
|
<span class="normal">330</span>
|
|
<span class="normal">331</span>
|
|
<span class="normal">332</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">flatten</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">start_dim</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">end_dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> <span class="o">-></span> <span class="n">Self</span><span class="p">:</span>
|
|
<span class="w"> </span><span class="sd">"""</span>
|
|
<span class="sd"> Flattens the tensor by reshaping it into a one-dimensional tensor.</span>
|
|
<span class="sd"> If `start_dim` or `end_dim` are passed, only dimensions starting with `start_dim` and ending with `end_dim` are flattened.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> t = Tensor.arange(8).reshape(2, 2, 2)</span>
|
|
<span class="sd"> print(t.flatten().numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(t.flatten(start_dim=1).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="n">start_dim</span><span class="p">,</span> <span class="n">end_dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_resolve_dim</span><span class="p">(</span><span class="n">start_dim</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">_resolve_dim</span><span class="p">(</span><span class="n">end_dim</span><span class="p">)</span>
|
|
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="n">start_dim</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="n">prod</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">start_dim</span> <span class="p">:</span> <span class="n">end_dim</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]),)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">end_dim</span> <span class="o">+</span> <span class="mi">1</span> <span class="p">:])</span>
|
|
</code></pre></div></td></tr></table></div>
|
|
</details>
|
|
</div>
|
|
|
|
</div>
|
|
|
|
<div class="doc doc-object doc-function">
|
|
|
|
|
|
<h3 id="tinygrad.Tensor.unflatten" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">unflatten</span>
|
|
|
|
|
|
<a href="#tinygrad.Tensor.unflatten" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">unflatten</span><span class="p">(</span><span class="n">dim</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span><span class="p">,</span> <span class="n">sizes</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#tuple">tuple</a></span><span class="p">[</span><span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span><span class="p">,</span> <span class="o">...</span><span class="p">])</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span>
|
|
</code></pre></div>
|
|
|
|
<div class="doc doc-contents first">
|
|
|
|
<p>Unflattens dimension <code class="language-python highlight"><span class="n">dim</span></code> of the tensor into multiple dimensions specified by <code class="language-python highlight"><span class="n">sizes</span></code>. <code class="language-python highlight"><span class="n">Tensor</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span></code> is the inverse of this function.</p>
|
|
<p><div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unflatten</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unflatten</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">unflatten</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
|
|
</code></pre></div></p>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/movement.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">334</span>
|
|
<span class="normal">335</span>
|
|
<span class="normal">336</span>
|
|
<span class="normal">337</span>
|
|
<span class="normal">338</span>
|
|
<span class="normal">339</span>
|
|
<span class="normal">340</span>
|
|
<span class="normal">341</span>
|
|
<span class="normal">342</span>
|
|
<span class="normal">343</span>
|
|
<span class="normal">344</span>
|
|
<span class="normal">345</span>
|
|
<span class="normal">346</span>
|
|
<span class="normal">347</span>
|
|
<span class="normal">348</span>
|
|
<span class="normal">349</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">unflatten</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">sizes</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">])</span> <span class="o">-></span> <span class="n">Self</span><span class="p">:</span>
|
|
<span class="w"> </span><span class="sd">"""</span>
|
|
<span class="sd"> Unflattens dimension `dim` of the tensor into multiple dimensions specified by `sizes`. `Tensor.flatten()` is the inverse of this function.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(Tensor.ones(3, 4, 1).unflatten(1, (2, 2)).shape)</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(Tensor.ones(3, 4, 1).unflatten(1, (-1, 2)).shape)</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(Tensor.ones(5, 12, 3).unflatten(-2, (2, 2, 3, 1, 1)).shape)</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="n">dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_resolve_dim</span><span class="p">(</span><span class="n">dim</span><span class="p">)</span>
|
|
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="n">dim</span><span class="p">]</span> <span class="o">+</span> <span class="n">sizes</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">dim</span> <span class="o">+</span> <span class="mi">1</span> <span class="p">:])</span>
|
|
</code></pre></div></td></tr></table></div>
|
|
</details>
|
|
</div>
|
|
|
|
</div>
|
|
|
|
<div class="doc doc-object doc-function">
|
|
|
|
|
|
<h3 id="tinygrad.Tensor.diag" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">diag</span>
|
|
|
|
|
|
<a href="#tinygrad.Tensor.diag" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">diag</span><span class="p">()</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span>
|
|
</code></pre></div>
|
|
|
|
<div class="doc doc-contents first">
|
|
|
|
<p>Returns a 2-D square tensor with the elements of input as the main diagonal.</p>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span><span class="o">.</span><span class="n">diag</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">1</span> <span class="mi">0</span> <span class="mi">0</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">0</span> <span class="mi">2</span> <span class="mi">0</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">0</span> <span class="mi">0</span> <span class="mi">3</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/movement.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">474</span>
|
|
<span class="normal">475</span>
|
|
<span class="normal">476</span>
|
|
<span class="normal">477</span>
|
|
<span class="normal">478</span>
|
|
<span class="normal">479</span>
|
|
<span class="normal">480</span>
|
|
<span class="normal">481</span>
|
|
<span class="normal">482</span>
|
|
<span class="normal">483</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">diag</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">Self</span><span class="p">:</span>
|
|
<span class="w"> </span><span class="sd">"""</span>
|
|
<span class="sd"> Returns a 2-D square tensor with the elements of input as the main diagonal.</span>
|
|
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<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
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<span class="sd"> print(Tensor([1, 2, 3]).diag().numpy())</span>
|
|
<span class="sd"> ```</span>
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<span class="sd"> """</span>
|
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"expect input to be 1-D, getting </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="si">}</span><span class="s2">-D"</span><span class="p">)</span>
|
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<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">pad_to</span><span class="p">((</span><span class="kc">None</span><span class="p">,</span> <span class="mi">1</span><span class="o">+</span><span class="p">(</span><span class="n">n</span><span class="o">:=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])))</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">shrink_to</span><span class="p">((</span><span class="n">n</span><span class="o">*</span><span class="n">n</span><span class="p">,))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">n</span><span class="p">,</span><span class="n">n</span><span class="p">)</span>
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</code></pre></div></td></tr></table></div>
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</details>
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</div>
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</div>
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<div class="doc doc-object doc-function">
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<h3 id="tinygrad.Tensor.diagonal" class="doc doc-heading">
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<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">diagonal</span>
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<a href="#tinygrad.Tensor.diagonal" class="headerlink" title="Permanent link">¤</a></h3>
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<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">diagonal</span><span class="p">(</span>
|
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<span class="n">offset</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">dim1</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">dim2</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">=</span> <span class="mi">1</span>
|
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<span class="p">)</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span>
|
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</code></pre></div>
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<div class="doc doc-contents first">
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<p>Returns a view of the diagonal elements with respect to <code class="language-python highlight"><span class="n">dim1</span></code> and <code class="language-python highlight"><span class="n">dim2</span></code>.
|
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<code class="language-python highlight"><span class="n">offset</span></code> controls which diagonal: 0 is main, positive is above, negative is below.</p>
|
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<p><div class="language-python highlight"><pre><span></span><code><span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">9</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">3</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="n">t</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
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</code></pre></div>
|
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<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">0</span> <span class="mi">1</span> <span class="mi">2</span><span class="p">]</span>
|
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<span class="p">[</span><span class="mi">3</span> <span class="mi">4</span> <span class="mi">5</span><span class="p">]</span>
|
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<span class="p">[</span><span class="mi">6</span> <span class="mi">7</span> <span class="mi">8</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">diagonal</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
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</code></pre></div>
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<div class="language-python highlight"><pre><span></span><code><span class="p">[</span><span class="mi">0</span> <span class="mi">4</span> <span class="mi">8</span><span class="p">]</span>
|
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</code></pre></div>
|
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<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">diagonal</span><span class="p">(</span><span class="n">offset</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
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</code></pre></div>
|
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<div class="language-python highlight"><pre><span></span><code><span class="p">[</span><span class="mi">1</span> <span class="mi">5</span><span class="p">]</span>
|
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</code></pre></div></p>
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<details class="mkdocstrings-source">
|
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<summary>Source code in <code>tinygrad/mixin/movement.py</code></summary>
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<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">485</span>
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<span class="normal">486</span>
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<span class="normal">487</span>
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<span class="normal">488</span>
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<span class="normal">489</span>
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<span class="normal">490</span>
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<span class="normal">491</span>
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<span class="normal">492</span>
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<span class="normal">493</span>
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<span class="normal">494</span>
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<span class="normal">495</span>
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<span class="normal">496</span>
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<span class="normal">497</span>
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<span class="normal">498</span>
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<span class="normal">499</span>
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<span class="normal">500</span>
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<span class="normal">501</span>
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<span class="normal">502</span>
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<span class="normal">503</span>
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<span class="normal">504</span>
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<span class="normal">505</span>
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<span class="normal">506</span>
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<span class="normal">507</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">diagonal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">offset</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dim1</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dim2</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">-></span> <span class="n">Self</span><span class="p">:</span>
|
|
<span class="w"> </span><span class="sd">"""</span>
|
|
<span class="sd"> Returns a view of the diagonal elements with respect to `dim1` and `dim2`.</span>
|
|
<span class="sd"> `offset` controls which diagonal: 0 is main, positive is above, negative is below.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> t = Tensor.arange(9).reshape(3, 3)</span>
|
|
<span class="sd"> print(t.numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(t.diagonal().numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(t.diagonal(offset=1).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">if</span> <span class="p">(</span><span class="n">dim1</span><span class="o">:=</span><span class="bp">self</span><span class="o">.</span><span class="n">_resolve_dim</span><span class="p">(</span><span class="n">dim1</span><span class="p">))</span> <span class="o">==</span> <span class="p">(</span><span class="n">dim2</span><span class="o">:=</span><span class="bp">self</span><span class="o">.</span><span class="n">_resolve_dim</span><span class="p">(</span><span class="n">dim2</span><span class="p">)):</span> <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"dim1 and dim2 cannot be the same dimension"</span><span class="p">)</span>
|
|
<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="p">)</span> <span class="k">if</span> <span class="n">i</span> <span class="o">!=</span> <span class="n">dim1</span> <span class="ow">and</span> <span class="n">i</span> <span class="o">!=</span> <span class="n">dim2</span><span class="p">],</span> <span class="n">dim1</span><span class="p">,</span> <span class="n">dim2</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="n">offset</span> <span class="o">>=</span> <span class="mi">0</span><span class="p">:</span> <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">shrink</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="kc">None</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="o">+</span> <span class="p">((</span><span class="n">offset</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),))</span>
|
|
<span class="k">else</span><span class="p">:</span> <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">shrink</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="kc">None</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span> <span class="o">+</span> <span class="p">((</span><span class="o">-</span><span class="n">offset</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]),</span> <span class="kc">None</span><span class="p">))</span>
|
|
<span class="k">if</span> <span class="p">(</span><span class="n">d</span> <span class="o">:=</span> <span class="nb">min</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]),</span> <span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])))</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">:</span> <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">*</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">2</span><span class="p">],</span> <span class="mi">0</span><span class="p">)</span>
|
|
<span class="n">nones</span><span class="p">,</span> <span class="n">x</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="kc">None</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">2</span><span class="p">]),</span> <span class="n">x</span><span class="o">.</span><span class="n">shrink_to</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="kc">None</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span> <span class="o">+</span> <span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">d</span><span class="p">))</span>
|
|
<span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">pad_to</span><span class="p">(</span><span class="n">nones</span><span class="o">+</span><span class="p">(</span><span class="n">d</span><span class="o">*</span><span class="p">(</span><span class="n">d</span><span class="o">+</span><span class="mi">1</span><span class="p">),))</span><span class="o">.</span><span class="n">unflatten</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">d</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">shrink_to</span><span class="p">(</span><span class="n">nones</span><span class="o">+</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
|
|
</code></pre></div></td></tr></table></div>
|
|
</details>
|
|
</div>
|
|
|
|
</div>
|
|
|
|
<div class="doc doc-object doc-function">
|
|
|
|
|
|
<h3 id="tinygrad.Tensor.roll" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">roll</span>
|
|
|
|
|
|
<a href="#tinygrad.Tensor.roll" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">roll</span><span class="p">(</span>
|
|
<span class="n">shifts</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">|</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#tuple">tuple</a></span><span class="p">[</span><span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span><span class="p">,</span> <span class="o">...</span><span class="p">],</span>
|
|
<span class="n">dims</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">|</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#tuple">tuple</a></span><span class="p">[</span><span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
|
|
<span class="p">)</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span>
|
|
</code></pre></div>
|
|
|
|
<div class="doc doc-contents first">
|
|
|
|
<p>Rolls the tensor along specified dimension(s).
|
|
The rolling operation is circular, meaning that elements that go beyond the edge are wrapped around to the beginning of the dimension.</p>
|
|
<p><div class="language-python highlight"><pre><span></span><code><span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">roll</span><span class="p">(</span><span class="n">shifts</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">dims</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[</span><span class="mi">3</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">2</span><span class="p">]</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">roll</span><span class="p">(</span><span class="n">shifts</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">dims</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
|
|
</code></pre></div>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="p">[</span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">0</span><span class="p">]</span>
|
|
</code></pre></div></p>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/movement.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">509</span>
|
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<span class="normal">510</span>
|
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<span class="normal">511</span>
|
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<span class="normal">512</span>
|
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<span class="normal">513</span>
|
|
<span class="normal">514</span>
|
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<span class="normal">515</span>
|
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<span class="normal">516</span>
|
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<span class="normal">517</span>
|
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<span class="normal">518</span>
|
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<span class="normal">519</span>
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<span class="normal">520</span>
|
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<span class="normal">521</span>
|
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<span class="normal">522</span>
|
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<span class="normal">523</span>
|
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<span class="normal">524</span>
|
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<span class="normal">525</span>
|
|
<span class="normal">526</span>
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<span class="normal">527</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">roll</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">shifts</span><span class="p">:</span><span class="nb">int</span><span class="o">|</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">dims</span><span class="p">:</span><span class="nb">int</span><span class="o">|</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span><span class="o">|</span><span class="kc">None</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-></span> <span class="n">Self</span><span class="p">:</span>
|
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<span class="w"> </span><span class="sd">"""</span>
|
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<span class="sd"> Rolls the tensor along specified dimension(s).</span>
|
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<span class="sd"> The rolling operation is circular, meaning that elements that go beyond the edge are wrapped around to the beginning of the dimension.</span>
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<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
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<span class="sd"> t = Tensor.arange(4)</span>
|
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<span class="sd"> print(t.roll(shifts=1, dims=0).numpy())</span>
|
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<span class="sd"> ```</span>
|
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<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
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<span class="sd"> print(t.roll(shifts=-1, dims=0).numpy())</span>
|
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<span class="sd"> ```</span>
|
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<span class="sd"> """</span>
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<span class="k">if</span> <span class="n">dims</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">roll</span><span class="p">(</span><span class="n">shifts</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<span class="n">dims</span><span class="p">,</span> <span class="n">shifts</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_resolve_dim</span><span class="p">(</span><span class="n">d</span><span class="p">)</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">make_tuple</span><span class="p">(</span><span class="n">dims</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">make_tuple</span><span class="p">(</span><span class="n">shifts</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
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<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">dims</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">shifts</span><span class="p">):</span> <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">dims</span><span class="p">)</span><span class="si">=}</span><span class="s2"> != </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">shifts</span><span class="p">)</span><span class="si">=}</span><span class="s2">"</span><span class="p">)</span>
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<span class="n">shrink_arg</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="n">sint</span><span class="p">,</span> <span class="n">sint</span><span class="p">]</span><span class="o">|</span><span class="kc">None</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndim</span>
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<span class="k">for</span> <span class="n">d</span><span class="p">,</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">dims</span><span class="p">,</span> <span class="n">shifts</span><span class="p">):</span> <span class="n">shrink_arg</span><span class="p">[</span><span class="n">d</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">delta</span><span class="o">:=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">d</span><span class="p">]</span><span class="o">-</span><span class="n">s</span><span class="o">%</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">d</span><span class="p">],</span> <span class="n">delta</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">d</span><span class="p">])</span>
|
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<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="o">*</span><span class="nb">tuple</span><span class="p">(</span><span class="mi">2</span> <span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">dims</span> <span class="k">else</span> <span class="mi">1</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="p">)))</span><span class="o">.</span><span class="n">shrink</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">shrink_arg</span><span class="p">))</span>
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</code></pre></div></td></tr></table></div>
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</details>
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</div>
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</div>
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<div class="doc doc-object doc-function">
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<h3 id="tinygrad.Tensor.rearrange" class="doc doc-heading">
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<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">rearrange</span>
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<a href="#tinygrad.Tensor.rearrange" class="headerlink" title="Permanent link">¤</a></h3>
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<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">rearrange</span><span class="p">(</span><span class="n">formula</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#str">str</a></span><span class="p">,</span> <span class="o">**</span><span class="n">sizes</span><span class="p">)</span> <span class="o">-></span> <span class="n"><a class="autorefs autorefs-external" title="<code>typing.Self</code>" href="https://docs.python.org/3/library/typing.html#typing.Self">Self</a></span>
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</code></pre></div>
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<div class="doc doc-contents first">
|
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<p>Rearranges input according to formula</p>
|
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<p>See: <a href="https://einops.rocks/api/rearrange/">https://einops.rocks/api/rearrange/</a></p>
|
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<div class="language-python highlight"><pre><span></span><code><span class="n">x</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
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<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">rearrange</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s2">"batch channel -> (batch channel)"</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
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</code></pre></div>
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<div class="language-python highlight"><pre><span></span><code><span class="p">[</span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">4</span><span class="p">]</span>
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</code></pre></div>
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<details class="mkdocstrings-source">
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<summary>Source code in <code>tinygrad/mixin/movement.py</code></summary>
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<span class="normal">394</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">rearrange</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">formula</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="o">**</span><span class="n">sizes</span><span class="p">)</span> <span class="o">-></span> <span class="n">Self</span><span class="p">:</span>
|
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<span class="w"> </span><span class="sd">"""</span>
|
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<span class="sd"> Rearranges input according to formula</span>
|
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|
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<span class="sd"> See: https://einops.rocks/api/rearrange/</span>
|
|
|
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<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
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<span class="sd"> x = Tensor([[1, 2], [3, 4]])</span>
|
|
<span class="sd"> print(Tensor.rearrange(x, "batch channel -> (batch channel)").numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">parse_side</span><span class="p">(</span><span class="n">s</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]]]:</span>
|
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<span class="w"> </span><span class="sd">"""Parse one side of formula into (axis_names, dims) where dims are (start, end) index pairs for parens."""</span>
|
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<span class="n">tokens</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">" </span><span class="si">{</span><span class="n">s</span><span class="si">}</span><span class="s2"> "</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"…"</span><span class="p">,</span> <span class="s2">"..."</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"("</span><span class="p">,</span> <span class="s2">" ( "</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">")"</span><span class="p">,</span> <span class="s2">" ) "</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">" "</span><span class="p">,</span> <span class="s2">" "</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">" 1 "</span><span class="p">,</span> <span class="s2">" ( ) "</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
|
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<span class="n">lparens</span><span class="p">,</span> <span class="n">rparens</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">tok</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tokens</span><span class="p">)</span> <span class="k">if</span> <span class="n">tok</span> <span class="o">==</span> <span class="s2">"("</span><span class="p">],</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">tok</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tokens</span><span class="p">)</span> <span class="k">if</span> <span class="n">tok</span> <span class="o">==</span> <span class="s2">")"</span><span class="p">]</span>
|
|
<span class="n">pairs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">lparens</span><span class="p">,</span> <span class="n">rparens</span><span class="p">))</span>
|
|
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">lparens</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">rparens</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">flatten</span><span class="p">(</span><span class="n">pairs</span><span class="p">))</span> <span class="o">==</span> <span class="n">flatten</span><span class="p">(</span><span class="n">pairs</span><span class="p">),</span> <span class="s2">"bracket mismatch"</span>
|
|
<span class="k">return</span> <span class="p">[</span><span class="n">tok</span> <span class="k">for</span> <span class="n">tok</span> <span class="ow">in</span> <span class="n">tokens</span> <span class="k">if</span> <span class="n">tok</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">"("</span><span class="p">,</span> <span class="s2">")"</span><span class="p">)],</span> <span class="p">[(</span><span class="n">lp</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">i</span><span class="p">,</span> <span class="n">rp</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">lp</span><span class="p">,</span> <span class="n">rp</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">pairs</span><span class="p">)]</span>
|
|
|
|
<span class="k">assert</span> <span class="n">formula</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="s2">"->"</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="s1">'need exactly one "->" in formula'</span>
|
|
<span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">unflatten_dims</span><span class="p">),</span> <span class="p">(</span><span class="n">rhs</span><span class="p">,</span> <span class="n">flatten_dims</span><span class="p">)</span> <span class="o">=</span> <span class="nb">map</span><span class="p">(</span><span class="n">parse_side</span><span class="p">,</span> <span class="n">formula</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">"->"</span><span class="p">))</span>
|
|
|
|
<span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">sizes</span><span class="p">:</span> <span class="k">assert</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">lhs</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"axis </span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2"> is not used in transform"</span>
|
|
<span class="k">assert</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">lhs</span><span class="p">)</span> <span class="o">==</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">rhs</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">lhs</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">lhs</span><span class="p">)),</span> <span class="sa">f</span><span class="s2">"name mismatch in </span><span class="si">{</span><span class="n">formula</span><span class="si">}</span><span class="s2">"</span>
|
|
<span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">lhs</span><span class="o">+</span><span class="n">rhs</span><span class="p">:</span> <span class="k">assert</span> <span class="n">name</span> <span class="o">==</span> <span class="s2">"..."</span> <span class="ow">or</span> <span class="p">(</span><span class="n">name</span><span class="o">.</span><span class="n">isidentifier</span><span class="p">()</span> <span class="ow">and</span> <span class="s2">"_"</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="n">name</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">name</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])),</span> <span class="sa">f</span><span class="s2">"invalid axis name </span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">"</span>
|
|
<span class="k">assert</span> <span class="s2">"..."</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">flatten</span><span class="p">([</span><span class="n">lhs</span><span class="p">[</span><span class="n">s</span><span class="p">:</span><span class="n">e</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span><span class="p">,</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">unflatten_dims</span><span class="p">]),</span> <span class="sa">f</span><span class="s2">"cannot have collapsed ellipsis (...) in lhs of </span><span class="si">{</span><span class="n">formula</span><span class="si">}</span><span class="s2">"</span>
|
|
<span class="k">assert</span> <span class="n">lhs</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="s2">"..."</span><span class="p">)</span> <span class="o"><=</span> <span class="mi">1</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"too many ellipses in </span><span class="si">{</span><span class="n">formula</span><span class="si">}</span><span class="s2">"</span>
|
|
|
|
<span class="c1"># resolve ellipsis</span>
|
|
<span class="k">if</span> <span class="s2">"..."</span> <span class="ow">in</span> <span class="n">lhs</span><span class="p">:</span>
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<span class="n">ell_len</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">lhs</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">+</span> <span class="nb">sum</span><span class="p">(</span><span class="n">e</span> <span class="o">-</span> <span class="n">s</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">for</span> <span class="n">s</span><span class="p">,</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">unflatten_dims</span><span class="p">)</span>
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<span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span> <span class="o">=</span> <span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">l</span><span class="p">:</span> <span class="n">l</span><span class="p">[:(</span><span class="n">i</span> <span class="o">:=</span> <span class="n">l</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="s2">"..."</span><span class="p">))]</span> <span class="o">+</span> <span class="p">[</span><span class="sa">f</span><span class="s2">"...</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2">"</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ell_len</span><span class="p">)]</span> <span class="o">+</span> <span class="n">l</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="k">if</span> <span class="s2">"..."</span> <span class="ow">in</span> <span class="n">l</span> <span class="k">else</span> <span class="n">l</span><span class="p">,</span> <span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">))</span>
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<span class="k">def</span><span class="w"> </span><span class="nf">newdims</span><span class="p">(</span><span class="n">side</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">e</span><span class="p">):</span> <span class="k">return</span> <span class="p">(</span><span class="n">s</span> <span class="o">+</span> <span class="p">(</span><span class="n">ell_len</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="s2">"...0"</span> <span class="ow">in</span> <span class="n">side</span><span class="p">[:</span><span class="n">s</span><span class="p">]</span> <span class="k">else</span> <span class="mi">0</span><span class="p">),</span> <span class="n">e</span> <span class="o">+</span> <span class="p">(</span><span class="n">ell_len</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="s2">"...0"</span> <span class="ow">in</span> <span class="n">side</span><span class="p">[:</span><span class="n">e</span><span class="p">]</span> <span class="k">else</span> <span class="mi">0</span><span class="p">))</span>
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<span class="n">unflatten_dims</span><span class="p">,</span> <span class="n">flatten_dims</span> <span class="o">=</span> <span class="p">[</span><span class="n">newdims</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">e</span><span class="p">)</span> <span class="k">for</span> <span class="n">s</span><span class="p">,</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">unflatten_dims</span><span class="p">],</span> <span class="p">[</span><span class="n">newdims</span><span class="p">(</span><span class="n">rhs</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">e</span><span class="p">)</span> <span class="k">for</span> <span class="n">s</span><span class="p">,</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">flatten_dims</span><span class="p">]</span>
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<span class="c1"># unflatten -> permute -> flatten</span>
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<span class="n">t</span> <span class="o">=</span> <span class="bp">self</span>
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<span class="k">for</span> <span class="n">start</span><span class="p">,</span> <span class="n">end</span> <span class="ow">in</span> <span class="n">unflatten_dims</span><span class="p">:</span> <span class="n">t</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">unflatten</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">sizes</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">lhs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">)))</span>
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<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">lhs</span><span class="p">):</span>
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<span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">sizes</span><span class="p">:</span> <span class="k">assert</span> <span class="n">sizes</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">==</span> <span class="n">t</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="sa">f</span><span class="s2">"size provided for dimension </span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2"> incorrect"</span>
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<span class="n">t</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">permute</span><span class="p">([</span><span class="n">lhs</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">name</span><span class="p">)</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">rhs</span><span class="p">])</span>
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<span class="k">for</span> <span class="n">start</span><span class="p">,</span> <span class="n">end</span> <span class="ow">in</span> <span class="nb">reversed</span><span class="p">(</span><span class="n">flatten_dims</span><span class="p">):</span> <span class="n">t</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">end</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="k">if</span> <span class="n">start</span> <span class="o"><</span> <span class="n">end</span> <span class="k">else</span> <span class="n">t</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="n">start</span><span class="p">)</span>
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|
<span class="k">return</span> <span class="n">t</span>
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</code></pre></div></td></tr></table></div>
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