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Runtime
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<span class="md-nav__icon md-icon"></span>
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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>Creation</h1>
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<h2 id="creation-basic">Creation (basic)<a class="headerlink" href="#creation-basic" title="Permanent link">¤</a></h2>
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<div class="doc doc-object doc-function">
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<h3 id="tinygrad.Tensor.empty" 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">empty</span>
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<span class="doc doc-labels">
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<small class="doc doc-label doc-label-classmethod"><code>classmethod</code></small>
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<a href="#tinygrad.Tensor.empty" 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">empty</span><span class="p">(</span>
|
|
<span class="o">*</span><span class="n">shape</span><span class="p">,</span>
|
|
<span class="n">device</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="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#str">str</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="n">dtype</span><span class="p">:</span> <span class="n"><span title="tinygrad.dtype.DTypeLike">DTypeLike</span></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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<p>Creates an empty tensor with the given shape.</p>
|
|
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.</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">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="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>
|
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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">2</span><span class="p">,</span> <span class="mi">3</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/creation.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">22</span>
|
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<span class="normal">23</span>
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<span class="normal">24</span>
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<span class="normal">25</span>
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<span class="normal">26</span>
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<span class="normal">27</span>
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<span class="normal">28</span>
|
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<span class="normal">29</span>
|
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<span class="normal">30</span>
|
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<span class="normal">31</span>
|
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<span class="normal">32</span>
|
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<span class="normal">33</span>
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<span class="normal">34</span>
|
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<span class="normal">35</span>
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<span class="normal">36</span>
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<span class="normal">37</span>
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<span class="normal">38</span>
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<span class="normal">39</span>
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<span class="normal">40</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@classmethod</span>
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">empty</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">device</span><span class="p">:</span><span class="nb">str</span><span class="o">|</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">str</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="n">dtype</span><span class="p">:</span><span class="n">DTypeLike</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"> Creates an empty tensor with the given shape.</span>
|
|
|
|
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> t = Tensor.empty(2, 3)</span>
|
|
<span class="sd"> print(t.shape)</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="kn">from</span><span class="w"> </span><span class="nn">tinygrad.uop.ops</span><span class="w"> </span><span class="kn">import</span> <span class="n">UOp</span><span class="p">,</span> <span class="n">to_max_shape</span>
|
|
<span class="kn">from</span><span class="w"> </span><span class="nn">tinygrad.device</span><span class="w"> </span><span class="kn">import</span> <span class="n">canonicalize_device</span>
|
|
<span class="n">dt</span> <span class="o">=</span> <span class="n">to_dtype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span> <span class="k">if</span> <span class="n">dtype</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">default_float</span>
|
|
<span class="n">new_shape</span> <span class="o">=</span> <span class="n">argfix</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">)</span>
|
|
<span class="n">max_shape</span> <span class="o">=</span> <span class="n">to_max_shape</span><span class="p">(</span><span class="n">new_shape</span><span class="p">)</span>
|
|
<span class="n">u</span> <span class="o">=</span> <span class="n">UOp</span><span class="o">.</span><span class="n">new_buffer</span><span class="p">(</span><span class="n">canonicalize_device</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">prod</span><span class="p">(</span><span class="n">max_shape</span><span class="p">),</span> <span class="n">dt</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">max_shape</span><span class="p">)</span><span class="o">.</span><span class="n">shrink_to</span><span class="p">(</span><span class="n">new_shape</span><span class="p">)</span>
|
|
<span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">_wrap_uop</span><span class="p">(</span><span class="n">u</span><span class="p">)</span>
|
|
</code></pre></div></td></tr></table></div>
|
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</details>
|
|
</div>
|
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|
</div>
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|
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<div class="doc doc-object doc-function">
|
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|
|
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|
<h3 id="tinygrad.Tensor.zeros" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">zeros</span>
|
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|
|
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<span class="doc doc-labels">
|
|
<small class="doc doc-label doc-label-classmethod"><code>classmethod</code></small>
|
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</span>
|
|
|
|
<a href="#tinygrad.Tensor.zeros" 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">zeros</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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>
|
|
|
|
<div class="doc doc-contents first">
|
|
|
|
<p>Creates a tensor with the given shape, filled with zeros.</p>
|
|
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
|
|
Additionally, all other keyword arguments are passed to the constructor of the tensor.</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">zeros</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="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</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">zeros</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">dtypes</span><span class="o">.</span><span class="n">int32</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">0</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">0</span> <span class="mi">0</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/creation.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">104</span>
|
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<span class="normal">105</span>
|
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<span class="normal">106</span>
|
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<span class="normal">107</span>
|
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<span class="normal">108</span>
|
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<span class="normal">109</span>
|
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<span class="normal">110</span>
|
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<span class="normal">111</span>
|
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<span class="normal">112</span>
|
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<span class="normal">113</span>
|
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<span class="normal">114</span>
|
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<span class="normal">115</span>
|
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<span class="normal">116</span>
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<span class="normal">117</span>
|
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<span class="normal">118</span>
|
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<span class="normal">119</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@classmethod</span>
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">zeros</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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"> Creates a tensor with the given shape, filled with zeros.</span>
|
|
|
|
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
|
|
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(Tensor.zeros(2, 3).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(Tensor.zeros(2, 3, dtype=dtypes.int32).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">argfix</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">),</span> <span class="mf">0.0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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.ones" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">ones</span>
|
|
|
|
|
|
<span class="doc doc-labels">
|
|
<small class="doc doc-label doc-label-classmethod"><code>classmethod</code></small>
|
|
</span>
|
|
|
|
<a href="#tinygrad.Tensor.ones" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">ones</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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>Creates a tensor with the given shape, filled with ones.</p>
|
|
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
|
|
Additionally, all other keyword arguments are passed to the constructor of the tensor.</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">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="mf">1.</span> <span class="mf">1.</span> <span class="mf">1.</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mf">1.</span> <span class="mf">1.</span> <span class="mf">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">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">dtypes</span><span class="o">.</span><span class="n">int32</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="mi">1</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">1</span> <span class="mi">1</span> <span class="mi">1</span><span class="p">]]</span>
|
|
</code></pre></div></p>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/creation.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">134</span>
|
|
<span class="normal">135</span>
|
|
<span class="normal">136</span>
|
|
<span class="normal">137</span>
|
|
<span class="normal">138</span>
|
|
<span class="normal">139</span>
|
|
<span class="normal">140</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></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@classmethod</span>
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">ones</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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"> Creates a tensor with the given shape, filled with ones.</span>
|
|
|
|
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
|
|
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(Tensor.ones(2, 3).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(Tensor.ones(2, 3, dtype=dtypes.int32).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">argfix</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">),</span> <span class="mf">1.0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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.full" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">full</span>
|
|
|
|
|
|
<span class="doc doc-labels">
|
|
<small class="doc doc-label doc-label-classmethod"><code>classmethod</code></small>
|
|
</span>
|
|
|
|
<a href="#tinygrad.Tensor.full" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">full</span><span class="p">(</span>
|
|
<span class="n">shape</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="o">...</span><span class="p">],</span>
|
|
<span class="n">fill_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="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">UOp</span> (<code>tinygrad.uop.ops.UOp</code>)" href="../../developer/uop/#tinygrad.uop.ops.UOp">UOp</a></span><span class="p">,</span>
|
|
<span class="n">dtype</span><span class="p">:</span> <span class="n"><span title="tinygrad.dtype.DTypeLike">DTypeLike</span></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="n">device</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="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#str">str</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="n">buffer</span><span class="o">=</span><span class="kc">True</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>Creates a tensor with the given shape, filled with the given value.</p>
|
|
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
|
|
Pass <code class="language-python highlight"><span class="n">buffer</span><span class="o">=</span><span class="kc">False</span></code> to get a broadcast const value instead of a materialized buffer.</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">full</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">42</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">42</span> <span class="mi">42</span> <span class="mi">42</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">42</span> <span class="mi">42</span> <span class="mi">42</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">full</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="kc">False</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="kc">False</span> <span class="kc">False</span> <span class="kc">False</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="kc">False</span> <span class="kc">False</span> <span class="kc">False</span><span class="p">]]</span>
|
|
</code></pre></div></p>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/creation.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">60</span>
|
|
<span class="normal">61</span>
|
|
<span class="normal">62</span>
|
|
<span class="normal">63</span>
|
|
<span class="normal">64</span>
|
|
<span class="normal">65</span>
|
|
<span class="normal">66</span>
|
|
<span class="normal">67</span>
|
|
<span class="normal">68</span>
|
|
<span class="normal">69</span>
|
|
<span class="normal">70</span>
|
|
<span class="normal">71</span>
|
|
<span class="normal">72</span>
|
|
<span class="normal">73</span>
|
|
<span class="normal">74</span>
|
|
<span class="normal">75</span>
|
|
<span class="normal">76</span>
|
|
<span class="normal">77</span>
|
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<span class="normal">78</span>
|
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<span class="normal">79</span>
|
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<span class="normal">80</span>
|
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<span class="normal">81</span>
|
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<span class="normal">82</span>
|
|
<span class="normal">83</span>
|
|
<span class="normal">84</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@classmethod</span>
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">full</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">shape</span><span class="p">:</span><span class="s1">'tuple[sint, ...]'</span><span class="p">,</span> <span class="n">fill_value</span><span class="p">:</span><span class="s1">'ConstType|UOp'</span><span class="p">,</span> <span class="n">dtype</span><span class="p">:</span><span class="n">DTypeLike</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="n">device</span><span class="p">:</span><span class="nb">str</span><span class="o">|</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">str</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="n">buffer</span><span class="o">=</span><span class="kc">True</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"> Creates a tensor with the given shape, filled with the given value.</span>
|
|
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<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
|
|
<span class="sd"> Pass `buffer=False` to get a broadcast const value instead of a materialized buffer.</span>
|
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|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(Tensor.full((2, 3), 42).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(Tensor.full((2, 3), False).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="c1"># TODO: enable this check</span>
|
|
<span class="c1"># if not buffer: assert device is None, "buffer=False does not support device specification"</span>
|
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<span class="kn">from</span><span class="w"> </span><span class="nn">tinygrad.uop.ops</span><span class="w"> </span><span class="kn">import</span> <span class="n">UOp</span>
|
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<span class="n">new_shape</span> <span class="o">=</span> <span class="n">argfix</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>
|
|
<span class="n">dt</span> <span class="o">=</span> <span class="n">to_dtype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span> <span class="k">if</span> <span class="n">dtype</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">fill_value</span><span class="o">.</span><span class="n">dtype</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">fill_value</span><span class="p">,</span> <span class="n">UOp</span><span class="p">)</span> <span class="k">else</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">from_py</span><span class="p">(</span><span class="n">fill_value</span><span class="p">)</span>
|
|
<span class="n">val</span> <span class="o">=</span> <span class="bp">cls</span><span class="o">.</span><span class="n">const</span><span class="p">(</span><span class="n">fill_value</span><span class="p">,</span> <span class="n">dt</span><span class="p">)</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">new_shape</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="ow">not</span> <span class="n">buffer</span><span class="p">:</span> <span class="k">return</span> <span class="n">val</span>
|
|
<span class="n">ret</span> <span class="o">=</span> <span class="n">val</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="kc">None</span> <span class="k">if</span> <span class="n">dt</span> <span class="ow">in</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">weaks</span> <span class="k">else</span> <span class="n">dt</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
|
|
<span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">_wrap_uop</span><span class="p">(</span><span class="n">ret</span><span class="o">.</span><span class="n">_uop</span><span class="o">.</span><span class="n">after</span><span class="p">(</span><span class="n">ret</span><span class="o">.</span><span class="n">_uop</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">val</span><span class="o">.</span><span class="n">_uop</span><span class="p">)))</span>
|
|
</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.arange" 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">arange</span>
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<span class="doc doc-labels">
|
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<small class="doc doc-label doc-label-classmethod"><code>classmethod</code></small>
|
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</span>
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<a href="#tinygrad.Tensor.arange" 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">arange</span><span class="p">(</span>
|
|
<span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="p">:</span> <span class="n"><span title="tinygrad.dtype.DTypeLike">DTypeLike</span></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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<p>Returns a 1-D tensor of size <code class="language-python highlight"><span class="n">ceil</span><span class="p">((</span><span class="n">stop</span> <span class="o">-</span> <span class="n">start</span><span class="p">)</span> <span class="o">/</span> <span class="n">step</span><span class="p">)</span></code> with values from <code class="language-python highlight"><span class="p">[</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">)</span></code>, with spacing between values given by <code class="language-python highlight"><span class="n">step</span></code>.</p>
|
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<p>If <code class="language-python highlight"><span class="n">stop</span></code> is not specified, values are generated from <code class="language-python highlight"><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">start</span><span class="p">)</span></code> with the given <code class="language-python highlight"><span class="n">step</span></code>.</p>
|
|
<p>If <code class="language-python highlight"><span class="n">stop</span></code> is specified, values are generated from <code class="language-python highlight"><span class="p">[</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">)</span></code> with the given <code class="language-python highlight"><span class="n">step</span></code>.</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">arange</span><span class="p">(</span><span class="mi">5</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">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">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">10</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">5</span> <span class="mi">6</span> <span class="mi">7</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="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">10</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">5</span> <span class="mi">7</span> <span class="mi">9</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">arange</span><span class="p">(</span><span class="mf">5.5</span><span class="p">,</span> <span class="mi">10</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="mf">5.5</span> <span class="mf">7.5</span> <span class="mf">9.5</span><span class="p">]</span>
|
|
</code></pre></div></p>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/op.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">163</span>
|
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<span class="normal">164</span>
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<span class="normal">166</span>
|
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|
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|
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|
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|
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|
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|
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|
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|
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<span class="normal">182</span>
|
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|
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|
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|
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|
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|
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|
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|
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|
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<span class="normal">191</span>
|
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<span class="normal">192</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@classmethod</span>
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">arange</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="p">:</span><span class="n">DTypeLike</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 1-D tensor of size `ceil((stop - start) / step)` with values from `[start, stop)`, with spacing between values given by `step`.</span>
|
|
|
|
<span class="sd"> If `stop` is not specified, values are generated from `[0, start)` with the given `step`.</span>
|
|
|
|
<span class="sd"> If `stop` is specified, values are generated from `[start, stop)` with the given `step`.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(Tensor.arange(5).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(Tensor.arange(5, 10).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(Tensor.arange(5, 10, 2).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(Tensor.arange(5.5, 10, 2).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">if</span> <span class="n">stop</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="n">stop</span><span class="p">,</span> <span class="n">start</span> <span class="o">=</span> <span class="n">start</span><span class="p">,</span> <span class="mi">0</span>
|
|
<span class="n">lo</span><span class="p">,</span> <span class="n">hi</span> <span class="o">=</span> <span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="o">-</span><span class="n">step</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="k">else</span> <span class="p">(</span><span class="n">stop</span><span class="o">-</span><span class="n">step</span><span class="p">,</span> <span class="n">start</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="n">dtype</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
|
<span class="n">dtype</span> <span class="o">=</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">default_float</span> <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="nb">float</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">,</span> <span class="n">step</span><span class="p">))</span> <span class="k">else</span> <span class="n">commit_int</span><span class="p">(</span><span class="n">lo</span><span class="p">,</span> <span class="n">hi</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="n">lo</span> <span class="o"><</span> <span class="p">(</span><span class="n">dt</span><span class="o">:=</span><span class="n">to_dtype</span><span class="p">(</span><span class="n">dtype</span><span class="p">))</span><span class="o">.</span><span class="n">min</span> <span class="ow">or</span> <span class="n">dt</span><span class="o">.</span><span class="n">max</span> <span class="o"><</span> <span class="n">hi</span><span class="p">:</span> <span class="k">raise</span> <span class="ne">OverflowError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"arange [</span><span class="si">{</span><span class="n">start</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">stop</span><span class="si">}</span><span class="s2">) is not representable in dtype </span><span class="si">{</span><span class="n">dtype</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
|
<span class="c1"># NOTE: this matches numpy, torch raises RuntimeError if stop-start and step have different signs</span>
|
|
<span class="k">if</span> <span class="p">(</span><span class="n">output_len</span><span class="o">:=</span><span class="n">ceildiv</span><span class="p">(</span><span class="n">stop</span><span class="o">-</span><span class="n">start</span><span class="p">,</span> <span class="n">step</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="bp">cls</span><span class="o">.</span><span class="n">full</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="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">buffer</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
|
<span class="k">return</span> <span class="p">(</span><span class="bp">cls</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="n">output_len</span><span class="p">,),</span> <span class="n">step</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">buffer</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">_cumalu</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">Ops</span><span class="o">.</span><span class="n">ADD</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="n">start</span> <span class="o">-</span> <span class="n">step</span><span class="p">))</span><span class="o">.</span><span class="n">cast</span><span class="p">(</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.linspace" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">linspace</span>
|
|
|
|
|
|
<span class="doc doc-labels">
|
|
<small class="doc doc-label doc-label-classmethod"><code>classmethod</code></small>
|
|
</span>
|
|
|
|
<a href="#tinygrad.Tensor.linspace" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">linspace</span><span class="p">(</span>
|
|
<span class="n">start</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/functions.html#float">float</a></span><span class="p">,</span>
|
|
<span class="n">stop</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/functions.html#float">float</a></span><span class="p">,</span>
|
|
<span class="n">steps</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">dtype</span><span class="p">:</span> <span class="n"><span title="tinygrad.dtype.DTypeLike">DTypeLike</span></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>Returns a 1-D tensor of <code class="language-python highlight"><span class="n">steps</span></code> evenly spaced values from <code class="language-python highlight"><span class="n">start</span></code> to <code class="language-python highlight"><span class="n">stop</span></code>, inclusive.</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">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">5</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="mf">0.</span> <span class="mf">2.5</span> <span class="mf">5.</span> <span class="mf">7.5</span> <span class="mf">10.</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">linspace</span><span class="p">(</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="mi">5</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="mf">1.</span> <span class="o">-</span><span class="mf">0.5</span> <span class="mf">0.</span> <span class="mf">0.5</span> <span class="mf">1.</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/op.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">194</span>
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<span class="normal">195</span>
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<span class="normal">196</span>
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<span class="normal">197</span>
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<span class="normal">198</span>
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<span class="normal">199</span>
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<span class="normal">200</span>
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<span class="normal">201</span>
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<span class="normal">202</span>
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<span class="normal">203</span>
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<span class="normal">204</span>
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<span class="normal">205</span>
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<span class="normal">206</span>
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<span class="normal">207</span>
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<span class="normal">208</span>
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<span class="normal">209</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@classmethod</span>
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">linspace</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">start</span><span class="p">:</span><span class="nb">int</span><span class="o">|</span><span class="nb">float</span><span class="p">,</span> <span class="n">stop</span><span class="p">:</span><span class="nb">int</span><span class="o">|</span><span class="nb">float</span><span class="p">,</span> <span class="n">steps</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">dtype</span><span class="p">:</span><span class="n">DTypeLike</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 1-D tensor of `steps` evenly spaced values from `start` to `stop`, inclusive.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(Tensor.linspace(0, 10, 5).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(Tensor.linspace(-1, 1, 5).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">if</span> <span class="n">steps</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">"number of steps must be non-negative"</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="p">(</span><span class="n">dtype</span> <span class="o">:=</span> <span class="n">to_dtype</span><span class="p">(</span><span class="n">dtype</span> <span class="ow">or</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">default_float</span><span class="p">))</span> <span class="o">==</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">bool</span><span class="p">:</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"linspace with bool dtype is not supported"</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="n">steps</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="mi">1</span><span class="p">,),</span> <span class="n">start</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">buffer</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
|
<span class="k">return</span> <span class="p">(</span><span class="n">start</span> <span class="o">+</span> <span class="bp">cls</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">steps</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtypes</span><span class="o">.</span><span class="n">default_float</span><span class="p">)</span> <span class="o">*</span> <span class="p">((</span><span class="n">stop</span> <span class="o">-</span> <span class="n">start</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">steps</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)))</span><span class="o">.</span><span class="n">cast</span><span class="p">(</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.eye" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">eye</span>
|
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|
|
|
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<span class="doc doc-labels">
|
|
<small class="doc doc-label doc-label-classmethod"><code>classmethod</code></small>
|
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</span>
|
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|
|
<a href="#tinygrad.Tensor.eye" 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">eye</span><span class="p">(</span>
|
|
<span class="n">n</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">m</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="n">dtype</span><span class="p">:</span> <span class="n"><span title="tinygrad.dtype.DTypeLike">DTypeLike</span></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>Returns a 2-D tensor with <code class="language-python highlight"><span class="n">n</span></code> rows and <code class="language-python highlight"><span class="n">m</span></code> columns, with ones on the diagonal and zeros elsewhere.</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">eye</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="mf">1.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mf">0.</span> <span class="mf">1.</span> <span class="mf">0.</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">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">eye</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</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="mf">1.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mf">0.</span> <span class="mf">1.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/op.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></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>
|
|
<span class="normal">224</span>
|
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<span class="normal">225</span>
|
|
<span class="normal">226</span>
|
|
<span class="normal">227</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@classmethod</span>
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">eye</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">n</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">m</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="n">dtype</span><span class="p">:</span><span class="n">DTypeLike</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 2-D tensor with `n` rows and `m` columns, with ones on the diagonal and zeros elsewhere.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(Tensor.eye(3).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> print(Tensor.eye(2, 4).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="n">m_</span> <span class="o">=</span> <span class="n">n</span> <span class="k">if</span> <span class="n">m</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">m</span>
|
|
<span class="k">if</span> <span class="n">n</span> <span class="o"><</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">m_</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="sa">f</span><span class="s2">"cannot have negative </span><span class="si">{</span><span class="n">n</span><span class="si">=}</span><span class="s2">, </span><span class="si">{</span><span class="n">m_</span><span class="si">=}</span><span class="s2">"</span><span class="p">)</span>
|
|
<span class="n">out_dtype</span> <span class="o">=</span> <span class="n">to_dtype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span> <span class="k">if</span> <span class="n">dtype</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">default_float</span>
|
|
<span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n</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">eq</span><span class="p">(</span><span class="bp">cls</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">m_</span><span class="p">))</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">out_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.full_like" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">full_like</span>
|
|
|
|
|
|
<a href="#tinygrad.Tensor.full_like" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">full_like</span><span class="p">(</span>
|
|
<span class="n">fill_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="p">,</span>
|
|
<span class="n">dtype</span><span class="p">:</span> <span class="n"><span title="tinygrad.dtype.DTypeLike">DTypeLike</span></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="n">device</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="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#str">str</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="n">buffer</span><span class="o">=</span><span class="kc">True</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>Creates a tensor with the same shape as <code class="language-python highlight"><span class="bp">self</span></code>, filled with the given value.
|
|
If <code class="language-python highlight"><span class="n">dtype</span></code> is not specified, the dtype of <code class="language-python highlight"><span class="bp">self</span></code> is used.</p>
|
|
<p>You can pass in the <code class="language-python highlight"><span class="n">device</span></code> keyword argument to control device of the tensor.
|
|
Pass <code class="language-python highlight"><span class="n">buffer</span><span class="o">=</span><span class="kc">False</span></code> to get a broadcast const value instead of a materialized buffer.</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">ones</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">Tensor</span><span class="o">.</span><span class="n">full_like</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="mi">42</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="mf">42.</span> <span class="mf">42.</span> <span class="mf">42.</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mf">42.</span> <span class="mf">42.</span> <span class="mf">42.</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/creation.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 86</span>
|
|
<span class="normal"> 87</span>
|
|
<span class="normal"> 88</span>
|
|
<span class="normal"> 89</span>
|
|
<span class="normal"> 90</span>
|
|
<span class="normal"> 91</span>
|
|
<span class="normal"> 92</span>
|
|
<span class="normal"> 93</span>
|
|
<span class="normal"> 94</span>
|
|
<span class="normal"> 95</span>
|
|
<span class="normal"> 96</span>
|
|
<span class="normal"> 97</span>
|
|
<span class="normal"> 98</span>
|
|
<span class="normal"> 99</span>
|
|
<span class="normal">100</span>
|
|
<span class="normal">101</span>
|
|
<span class="normal">102</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">full_like</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fill_value</span><span class="p">:</span><span class="n">ConstType</span><span class="p">,</span> <span class="n">dtype</span><span class="p">:</span><span class="n">DTypeLike</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="n">device</span><span class="p">:</span><span class="nb">str</span><span class="o">|</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">str</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="n">buffer</span><span class="o">=</span><span class="kc">True</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"> Creates a tensor with the same shape as `self`, filled with the given value.</span>
|
|
<span class="sd"> If `dtype` is not specified, the dtype of `self` is used.</span>
|
|
|
|
<span class="sd"> You can pass in the `device` keyword argument to control device of the tensor.</span>
|
|
<span class="sd"> Pass `buffer=False` to get a broadcast const value instead of a materialized buffer.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> t = Tensor.ones(2, 3)</span>
|
|
<span class="sd"> print(Tensor.full_like(t, 42).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
|
|
<span class="k">if</span> <span class="n">device</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"cannot specify `device` on `*_like` of a multi device tensor"</span><span class="p">)</span>
|
|
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_multi_like</span><span class="p">(</span><span class="k">lambda</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dev</span><span class="p">:</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">fill_value</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">dev</span><span class="p">,</span> <span class="n">buffer</span><span class="o">=</span><span class="n">buffer</span><span class="p">))</span>
|
|
<span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">full</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">fill_value</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</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="k">if</span> <span class="n">device</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">device</span><span class="p">,</span> <span class="n">buffer</span><span class="o">=</span><span class="n">buffer</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.zeros_like" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">zeros_like</span>
|
|
|
|
|
|
<a href="#tinygrad.Tensor.zeros_like" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">zeros_like</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</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>Creates a tensor with the same shape as <code class="language-python highlight"><span class="bp">self</span></code>, filled with zeros.</p>
|
|
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the 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="o">.</span><span class="n">ones</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">Tensor</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">t</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="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/creation.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">121</span>
|
|
<span class="normal">122</span>
|
|
<span class="normal">123</span>
|
|
<span class="normal">124</span>
|
|
<span class="normal">125</span>
|
|
<span class="normal">126</span>
|
|
<span class="normal">127</span>
|
|
<span class="normal">128</span>
|
|
<span class="normal">129</span>
|
|
<span class="normal">130</span>
|
|
<span class="normal">131</span>
|
|
<span class="normal">132</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">zeros_like</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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"> Creates a tensor with the same shape as `self`, filled with zeros.</span>
|
|
|
|
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> t = Tensor.ones(2, 3)</span>
|
|
<span class="sd"> print(Tensor.zeros_like(t).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">full_like</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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.ones_like" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">ones_like</span>
|
|
|
|
|
|
<a href="#tinygrad.Tensor.ones_like" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">ones_like</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</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>Creates a tensor with the same shape as <code class="language-python highlight"><span class="bp">self</span></code>, filled with ones.</p>
|
|
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the 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="o">.</span><span class="n">zeros</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">Tensor</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">t</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="mf">1.</span> <span class="mf">1.</span> <span class="mf">1.</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mf">1.</span> <span class="mf">1.</span> <span class="mf">1.</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/creation.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">151</span>
|
|
<span class="normal">152</span>
|
|
<span class="normal">153</span>
|
|
<span class="normal">154</span>
|
|
<span class="normal">155</span>
|
|
<span class="normal">156</span>
|
|
<span class="normal">157</span>
|
|
<span class="normal">158</span>
|
|
<span class="normal">159</span>
|
|
<span class="normal">160</span>
|
|
<span class="normal">161</span>
|
|
<span class="normal">162</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">ones_like</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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"> Creates a tensor with the same shape as `self`, filled with ones.</span>
|
|
|
|
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> t = Tensor.zeros(2, 3)</span>
|
|
<span class="sd"> print(Tensor.ones_like(t).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">full_like</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
|
</code></pre></div></td></tr></table></div>
|
|
</details>
|
|
</div>
|
|
|
|
</div><h2 id="creation-external">Creation (external)<a class="headerlink" href="#creation-external" title="Permanent link">¤</a></h2>
|
|
|
|
|
|
<div class="doc doc-object doc-function">
|
|
|
|
|
|
<h3 id="tinygrad.Tensor.from_blob" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">from_blob</span>
|
|
|
|
|
|
<span class="doc doc-labels">
|
|
<small class="doc doc-label doc-label-staticmethod"><code>staticmethod</code></small>
|
|
</span>
|
|
|
|
<a href="#tinygrad.Tensor.from_blob" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">from_blob</span><span class="p">(</span>
|
|
<span class="n">ptr</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">shape</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">kwargs</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>Exposes the pointer as a Tensor without taking ownership of the original data.
|
|
The pointer must remain valid for the entire lifetime of the created Tensor.</p>
|
|
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
|
|
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">589</span>
|
|
<span class="normal">590</span>
|
|
<span class="normal">591</span>
|
|
<span class="normal">592</span>
|
|
<span class="normal">593</span>
|
|
<span class="normal">594</span>
|
|
<span class="normal">595</span>
|
|
<span class="normal">596</span>
|
|
<span class="normal">597</span>
|
|
<span class="normal">598</span>
|
|
<span class="normal">599</span>
|
|
<span class="normal">600</span>
|
|
<span class="normal">601</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@staticmethod</span>
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">from_blob</span><span class="p">(</span><span class="n">ptr</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">shape</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">kwargs</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"> Exposes the pointer as a Tensor without taking ownership of the original data.</span>
|
|
<span class="sd"> The pointer must remain valid for the entire lifetime of the created Tensor.</span>
|
|
|
|
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
|
|
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
|
|
<span class="sd"> """</span>
|
|
<span class="n">r</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="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
|
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">r</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
|
|
<span class="n">cast</span><span class="p">(</span><span class="n">Buffer</span><span class="p">,</span> <span class="n">r</span><span class="o">.</span><span class="n">uop</span><span class="o">.</span><span class="n">buffer</span><span class="p">)</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="n">external_ptr</span><span class="o">=</span><span class="n">ptr</span><span class="p">)</span>
|
|
<span class="k">return</span> <span class="n">r</span>
|
|
</code></pre></div></td></tr></table></div>
|
|
</details>
|
|
</div>
|
|
|
|
</div>
|
|
|
|
<div class="doc doc-object doc-function">
|
|
|
|
|
|
<h3 id="tinygrad.Tensor.from_url" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">from_url</span>
|
|
|
|
|
|
<span class="doc doc-labels">
|
|
<small class="doc doc-label doc-label-staticmethod"><code>staticmethod</code></small>
|
|
</span>
|
|
|
|
<a href="#tinygrad.Tensor.from_url" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">from_url</span><span class="p">(</span>
|
|
<span class="n">url</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="n">gunzip</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#bool">bool</a></span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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>Creates a Tensor from a URL.</p>
|
|
<p>This is the preferred way to access Internet resources.
|
|
It currently returns a DISK Tensor, but in the future it may return an HTTP Tensor.
|
|
This also will soon become lazy (when possible) and not print progress without DEBUG.</p>
|
|
<p>The <code class="language-python highlight"><span class="n">gunzip</span></code> flag will gzip extract the resource and return an extracted Tensor.</p>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">603</span>
|
|
<span class="normal">604</span>
|
|
<span class="normal">605</span>
|
|
<span class="normal">606</span>
|
|
<span class="normal">607</span>
|
|
<span class="normal">608</span>
|
|
<span class="normal">609</span>
|
|
<span class="normal">610</span>
|
|
<span class="normal">611</span>
|
|
<span class="normal">612</span>
|
|
<span class="normal">613</span>
|
|
<span class="normal">614</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@staticmethod</span>
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">from_url</span><span class="p">(</span><span class="n">url</span><span class="p">:</span><span class="nb">str</span><span class="p">,</span> <span class="n">gunzip</span><span class="p">:</span><span class="nb">bool</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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"> Creates a Tensor from a URL.</span>
|
|
|
|
<span class="sd"> This is the preferred way to access Internet resources.</span>
|
|
<span class="sd"> It currently returns a DISK Tensor, but in the future it may return an HTTP Tensor.</span>
|
|
<span class="sd"> This also will soon become lazy (when possible) and not print progress without DEBUG.</span>
|
|
|
|
<span class="sd"> The `gunzip` flag will gzip extract the resource and return an extracted Tensor.</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">return</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">fetch</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">gunzip</span><span class="o">=</span><span class="n">gunzip</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
|
</code></pre></div></td></tr></table></div>
|
|
</details>
|
|
</div>
|
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</div><h2 id="creation-random">Creation (random)<a class="headerlink" href="#creation-random" title="Permanent link">¤</a></h2>
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<div class="doc doc-object doc-function">
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<h3 id="tinygrad.Tensor.manual_seed" 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">manual_seed</span>
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<span class="doc doc-labels">
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<small class="doc doc-label doc-label-staticmethod"><code>staticmethod</code></small>
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</span>
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<a href="#tinygrad.Tensor.manual_seed" 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">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span>
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</code></pre></div>
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<div class="doc doc-contents first">
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<p>Sets the seed for random operations.</p>
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<p><div class="language-python highlight"><pre><span></span><code><span class="n">Tensor</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</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">rand</span><span class="p">(</span><span class="mi">5</span><span class="p">)</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">Tensor</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">5</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="mf">0.381</span> <span class="mf">0.0098</span> <span class="mf">0.1128</span> <span class="mf">0.1177</span> <span class="mf">0.5054</span><span class="p">]</span>
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<span class="p">[</span><span class="mf">0.8984</span> <span class="mf">0.9686</span> <span class="mf">0.5969</span> <span class="mf">0.9117</span> <span class="mf">0.9869</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">Tensor</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span> <span class="c1"># reset to the same seed</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">5</span><span class="p">)</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">Tensor</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">5</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="mf">0.381</span> <span class="mf">0.0098</span> <span class="mf">0.1128</span> <span class="mf">0.1177</span> <span class="mf">0.5054</span><span class="p">]</span>
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<span class="p">[</span><span class="mf">0.8984</span> <span class="mf">0.9686</span> <span class="mf">0.5969</span> <span class="mf">0.9117</span> <span class="mf">0.9869</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/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">619</span>
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<span class="normal">635</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@staticmethod</span>
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
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<span class="w"> </span><span class="sd">"""</span>
|
|
<span class="sd"> Sets the seed for random operations.</span>
|
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|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> Tensor.manual_seed(42)</span>
|
|
<span class="sd"> print(Tensor.rand(5).numpy())</span>
|
|
<span class="sd"> print(Tensor.rand(5).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> Tensor.manual_seed(42) # reset to the same seed</span>
|
|
<span class="sd"> print(Tensor.rand(5).numpy())</span>
|
|
<span class="sd"> print(Tensor.rand(5).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="n">Tensor</span><span class="o">.</span><span class="n">_seed</span><span class="p">,</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">_device_seeds</span><span class="p">,</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">_device_rng_counters</span> <span class="o">=</span> <span class="n">seed</span><span class="p">,</span> <span class="p">{},</span> <span class="p">{}</span>
|
|
</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.rand" 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">rand</span>
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<span class="doc doc-labels">
|
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<small class="doc doc-label doc-label-classmethod"><code>classmethod</code></small>
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</span>
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<a href="#tinygrad.Tensor.rand" 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">rand</span><span class="p">(</span>
|
|
<span class="o">*</span><span class="n">shape</span><span class="p">,</span>
|
|
<span class="n">device</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="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
|
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<span class="n">dtype</span><span class="p">:</span> <span class="n"><span title="tinygrad.dtype.DTypeLike">DTypeLike</span></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="n">contiguous</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#bool">bool</a></span> <span class="o">=</span> <span class="kc">True</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">
|
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|
|
<p>Creates a tensor with the given shape, filled with random values from a uniform distribution over the interval <code class="language-python highlight"><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span></code>.</p>
|
|
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.</p>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="n">Tensor</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
|
|
<span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">rand</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="mf">0.381</span> <span class="mf">0.0098</span> <span class="mf">0.1128</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mf">0.1177</span> <span class="mf">0.5054</span> <span class="mf">0.3721</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/rand.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">49</span>
|
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|
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|
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|
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<span class="normal">67</span>
|
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<span class="normal">68</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@classmethod</span>
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">rand</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">device</span><span class="p">:</span><span class="nb">str</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="n">dtype</span><span class="p">:</span><span class="n">DTypeLike</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="n">contiguous</span><span class="p">:</span><span class="nb">bool</span><span class="o">=</span><span class="kc">True</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"> Creates a tensor with the given shape, filled with random values from a uniform distribution over the interval `[0, 1)`.</span>
|
|
|
|
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> Tensor.manual_seed(42)</span>
|
|
<span class="sd"> t = Tensor.rand(2, 3)</span>
|
|
<span class="sd"> print(t.numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="n">dt</span> <span class="o">=</span> <span class="n">to_dtype</span><span class="p">(</span><span class="n">dtype</span> <span class="ow">or</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">default_float</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="ow">not</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">is_float</span><span class="p">(</span><span class="n">dt</span><span class="p">)</span> <span class="ow">or</span> <span class="n">dt</span> <span class="ow">in</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">weaks</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">"rand only supports concrete float dtypes, got </span><span class="si">{</span><span class="n">dt</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="ow">not</span> <span class="n">all_int</span><span class="p">(</span><span class="n">shape</span><span class="o">:=</span><span class="n">argfix</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">))</span> <span class="ow">or</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span><span class="n">s</span> <span class="o">>=</span> <span class="mi">0</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">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">"invalid input </span><span class="si">{</span><span class="n">shape</span><span class="si">=}</span><span class="s2">"</span><span class="p">)</span>
|
|
<span class="k">if</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="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="nb">str</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">"rand only supports single device, got </span><span class="si">{</span><span class="n">device</span><span class="si">=}</span><span class="s2">"</span><span class="p">)</span>
|
|
<span class="n">device</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">canonicalize_device</span><span class="p">(</span><span class="n">device</span><span class="p">))</span>
|
|
<span class="n">key</span><span class="p">,</span> <span class="n">counter</span> <span class="o">=</span> <span class="bp">cls</span><span class="o">.</span><span class="n">_next_counter</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="n">ceildiv</span><span class="p">(</span><span class="n">prod</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> <span class="o">*</span> <span class="n">dt</span><span class="o">.</span><span class="n">itemsize</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
|
|
<span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">_rand</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">counter</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">contiguous</span><span class="o">=</span><span class="n">contiguous</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.rand_like" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">rand_like</span>
|
|
|
|
|
|
<a href="#tinygrad.Tensor.rand_like" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">rand_like</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</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>Creates a tensor with the same shape and sharding as <code class="language-python highlight"><span class="bp">self</span></code>, filled with random values from a uniform distribution over the interval <code class="language-python highlight"><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span></code>.</p>
|
|
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
|
|
Additionally, all other keyword arguments are passed to the constructor of the 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="o">.</span><span class="n">ones</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">Tensor</span><span class="o">.</span><span class="n">rand_like</span><span class="p">(</span><span class="n">t</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="mf">0.2103</span> <span class="mf">0.611</span> <span class="mf">0.1345</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mf">0.0131</span> <span class="mf">0.368</span> <span class="mf">0.9245</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/rand.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">70</span>
|
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<span class="normal">71</span>
|
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<span class="normal">72</span>
|
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<span class="normal">73</span>
|
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<span class="normal">74</span>
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<span class="normal">77</span>
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<span class="normal">85</span>
|
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<span class="normal">86</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">rand_like</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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"> Creates a tensor with the same shape and sharding as `self`, filled with random values from a uniform distribution over the interval `[0, 1)`.</span>
|
|
|
|
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
|
|
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> t = Tensor.ones(2, 3)</span>
|
|
<span class="sd"> print(Tensor.rand_like(t).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
|
|
<span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">"device"</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"cannot specify `device` on `*_like` of a multi device tensor"</span><span class="p">)</span>
|
|
<span class="n">dtype</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">"dtype"</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
|
|
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_multi_like</span><span class="p">(</span><span class="k">lambda</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dev</span><span class="p">:</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">dev</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
|
|
<span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">rand</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">device</span><span class="o">=</span><span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">"device"</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">"dtype"</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</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.randn" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">randn</span>
|
|
|
|
|
|
<span class="doc doc-labels">
|
|
<small class="doc doc-label doc-label-classmethod"><code>classmethod</code></small>
|
|
</span>
|
|
|
|
<a href="#tinygrad.Tensor.randn" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">randn</span><span class="p">(</span>
|
|
<span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">:</span> <span class="n"><span title="tinygrad.dtype.DTypeLike">DTypeLike</span></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">kwargs</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>Creates a tensor with the given shape, filled with random values from a normal distribution with mean <code class="language-python highlight"><span class="mi">0</span></code> and standard deviation <code class="language-python highlight"><span class="mi">1</span></code>.
|
|
If <code class="language-python highlight"><span class="n">dtype</span></code> is not specified, the default type is used.</p>
|
|
<p>You can pass in the <code class="language-python highlight"><span class="n">device</span></code> keyword argument to control device of the tensor.
|
|
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="n">Tensor</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">randn</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="mf">1.9576</span> <span class="o">-</span><span class="mf">0.1859</span> <span class="mf">1.6404</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="o">-</span><span class="mf">0.7647</span> <span class="o">-</span><span class="mf">0.8695</span> <span class="o">-</span><span class="mf">0.4379</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/rand.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">105</span>
|
|
<span class="normal">106</span>
|
|
<span class="normal">107</span>
|
|
<span class="normal">108</span>
|
|
<span class="normal">109</span>
|
|
<span class="normal">110</span>
|
|
<span class="normal">111</span>
|
|
<span class="normal">112</span>
|
|
<span class="normal">113</span>
|
|
<span class="normal">114</span>
|
|
<span class="normal">115</span>
|
|
<span class="normal">116</span>
|
|
<span class="normal">117</span>
|
|
<span class="normal">118</span>
|
|
<span class="normal">119</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@classmethod</span>
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">randn</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">:</span><span class="n">DTypeLike</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">kwargs</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"> Creates a tensor with the given shape, filled with random values from a normal distribution with mean `0` and standard deviation `1`.</span>
|
|
<span class="sd"> If `dtype` is not specified, the default type is used.</span>
|
|
|
|
<span class="sd"> You can pass in the `device` keyword argument to control device of the tensor.</span>
|
|
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> Tensor.manual_seed(42)</span>
|
|
<span class="sd"> print(Tensor.randn(2, 3).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span><span class="o">.</span><span class="n">randn_like</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> <span class="c1"># type: ignore[attr-defined]</span>
|
|
</code></pre></div></td></tr></table></div>
|
|
</details>
|
|
</div>
|
|
|
|
</div>
|
|
|
|
<div class="doc doc-object doc-function">
|
|
|
|
|
|
<h3 id="tinygrad.Tensor.randn_like" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">randn_like</span>
|
|
|
|
|
|
<a href="#tinygrad.Tensor.randn_like" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">randn_like</span><span class="p">(</span>
|
|
<span class="n">dtype</span><span class="p">:</span> <span class="n"><span title="tinygrad.dtype.DTypeLike">DTypeLike</span></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">kwargs</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>Creates a tensor with the same shape and sharding as <code class="language-python highlight"><span class="bp">self</span></code>, filled with random values from a normal distribution with mean 0 and variance 1.</p>
|
|
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
|
|
Additionally, all other keyword arguments are passed to the constructor of the 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="o">.</span><span class="n">ones</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">Tensor</span><span class="o">.</span><span class="n">randn_like</span><span class="p">(</span><span class="n">t</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="o">-</span><span class="mf">0.7382</span> <span class="mf">1.5164</span> <span class="o">-</span><span class="mf">0.3065</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="o">-</span><span class="mf">0.7862</span> <span class="mf">0.5411</span> <span class="o">-</span><span class="mf">0.4394</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/rand.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 88</span>
|
|
<span class="normal"> 89</span>
|
|
<span class="normal"> 90</span>
|
|
<span class="normal"> 91</span>
|
|
<span class="normal"> 92</span>
|
|
<span class="normal"> 93</span>
|
|
<span class="normal"> 94</span>
|
|
<span class="normal"> 95</span>
|
|
<span class="normal"> 96</span>
|
|
<span class="normal"> 97</span>
|
|
<span class="normal"> 98</span>
|
|
<span class="normal"> 99</span>
|
|
<span class="normal">100</span>
|
|
<span class="normal">101</span>
|
|
<span class="normal">102</span>
|
|
<span class="normal">103</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">randn_like</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dtype</span><span class="p">:</span><span class="n">DTypeLike</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">kwargs</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"> Creates a tensor with the same shape and sharding as `self`, filled with random values from a normal distribution with mean 0 and variance 1.</span>
|
|
|
|
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
|
|
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> t = Tensor.ones(2, 3)</span>
|
|
<span class="sd"> print(Tensor.randn_like(t).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">if</span> <span class="p">(</span><span class="n">dt</span><span class="o">:=</span><span class="n">to_dtype</span><span class="p">(</span><span class="n">dtype</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">))</span> <span class="ow">in</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">weaks</span> <span class="ow">and</span> <span class="n">dtype</span> <span class="ow">is</span> <span class="kc">None</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">"randn_like requires an explicit dtype for </span><span class="si">{</span><span class="n">dt</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
|
<span class="n">src</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">rand_like</span><span class="p">(</span><span class="o">**</span><span class="p">{</span><span class="o">**</span><span class="n">kwargs</span><span class="p">,</span> <span class="s2">"dtype"</span><span class="p">:</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">float32</span><span class="p">})</span>
|
|
<span class="c1"># https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform</span>
|
|
<span class="k">return</span> <span class="n">src</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span><span class="o">.</span><span class="n">cos</span><span class="p">()</span><span class="o">.</span><span class="n">mul</span><span class="p">((</span><span class="mi">1</span> <span class="o">-</span> <span class="n">src</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">log</span><span class="p">()</span><span class="o">.</span><span class="n">mul</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">sqrt</span><span class="p">())</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">dt</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.randint" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">randint</span>
|
|
|
|
|
|
<span class="doc doc-labels">
|
|
<small class="doc doc-label doc-label-classmethod"><code>classmethod</code></small>
|
|
</span>
|
|
|
|
<a href="#tinygrad.Tensor.randint" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">randint</span><span class="p">(</span>
|
|
<span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">low</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n"><span title="tinygrad.dtype.dtypes.int32">int32</span></span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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>Creates a tensor with the given shape, filled with random integer values generated uniformly from the interval <code class="language-python highlight"><span class="p">[</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">)</span></code>.
|
|
Requires <code class="language-python highlight"><span class="n">low</span> <span class="o"><</span> <span class="n">high</span></code>. If <code class="language-python highlight"><span class="n">dtype</span></code> is not specified, the default type is used.</p>
|
|
<p>You can pass in the <code class="language-python highlight"><span class="n">device</span></code> keyword argument to control device of the tensor.
|
|
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="n">Tensor</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">randint</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">low</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">10</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">6</span> <span class="mi">5</span> <span class="mi">5</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mi">5</span> <span class="mi">7</span> <span class="mi">6</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/rand.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">121</span>
|
|
<span class="normal">122</span>
|
|
<span class="normal">123</span>
|
|
<span class="normal">124</span>
|
|
<span class="normal">125</span>
|
|
<span class="normal">126</span>
|
|
<span class="normal">127</span>
|
|
<span class="normal">128</span>
|
|
<span class="normal">129</span>
|
|
<span class="normal">130</span>
|
|
<span class="normal">131</span>
|
|
<span class="normal">132</span>
|
|
<span class="normal">133</span>
|
|
<span class="normal">134</span>
|
|
<span class="normal">135</span>
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|
<span class="normal">136</span>
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<span class="normal">137</span>
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|
<span class="normal">138</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@classmethod</span>
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">randint</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">low</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtypes</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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"> Creates a tensor with the given shape, filled with random integer values generated uniformly from the interval `[low, high)`.</span>
|
|
<span class="sd"> Requires `low < high`. If `dtype` is not specified, the default type is used.</span>
|
|
|
|
<span class="sd"> You can pass in the `device` keyword argument to control device of the tensor.</span>
|
|
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> Tensor.manual_seed(42)</span>
|
|
<span class="sd"> print(Tensor.randint(2, 3, low=5, high=10).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">if</span> <span class="ow">not</span> <span class="n">all_int</span><span class="p">([</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">]):</span> <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">low</span><span class="si">=}</span><span class="s2"> and </span><span class="si">{</span><span class="n">high</span><span class="si">=}</span><span class="s2"> must be integers"</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="ow">not</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">is_int</span><span class="p">(</span><span class="n">dtype</span> <span class="o">:=</span> <span class="n">to_dtype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)):</span> <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">dtype</span><span class="si">=}</span><span class="s2"> must be int"</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="n">low</span> <span class="o">>=</span> <span class="n">high</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">"Tensor.randint requires low < high, got </span><span class="si">{</span><span class="n">low</span><span class="si">=}</span><span class="s2">, </span><span class="si">{</span><span class="n">high</span><span class="si">=}</span><span class="s2">"</span><span class="p">)</span>
|
|
<span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">low</span><span class="o">=</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="n">high</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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.randperm" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">randperm</span>
|
|
|
|
|
|
<span class="doc doc-labels">
|
|
<small class="doc doc-label doc-label-classmethod"><code>classmethod</code></small>
|
|
</span>
|
|
|
|
<a href="#tinygrad.Tensor.randperm" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">randperm</span><span class="p">(</span>
|
|
<span class="n">n</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">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n"><span title="tinygrad.dtype.dtypes.int32">int32</span></span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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 random permutation of integers from <code class="language-python highlight"><span class="mi">0</span></code> to <code class="language-python highlight"><span class="n">n</span><span class="o">-</span><span class="mi">1</span></code>.</p>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="n">Tensor</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">randperm</span><span class="p">(</span><span class="mi">6</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">5</span> <span class="mi">0</span> <span class="mi">4</span><span class="p">]</span>
|
|
</code></pre></div>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/rand.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">239</span>
|
|
<span class="normal">240</span>
|
|
<span class="normal">241</span>
|
|
<span class="normal">242</span>
|
|
<span class="normal">243</span>
|
|
<span class="normal">244</span>
|
|
<span class="normal">245</span>
|
|
<span class="normal">246</span>
|
|
<span class="normal">247</span>
|
|
<span class="normal">248</span>
|
|
<span class="normal">249</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@classmethod</span>
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">randperm</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">n</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtypes</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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 random permutation of integers from `0` to `n-1`.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> Tensor.manual_seed(42)</span>
|
|
<span class="sd"> print(Tensor.randperm(6).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span><span class="o">.</span><span class="n">argsort</span><span class="p">()</span><span class="o">.</span><span class="n">cast</span><span class="p">(</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.normal" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">normal</span>
|
|
|
|
|
|
<span class="doc doc-labels">
|
|
<small class="doc doc-label doc-label-classmethod"><code>classmethod</code></small>
|
|
</span>
|
|
|
|
<a href="#tinygrad.Tensor.normal" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">normal</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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>Creates a tensor with the given shape, filled with random values from a normal distribution with the given <code class="language-python highlight"><span class="n">mean</span></code> and standard deviation <code class="language-python highlight"><span class="n">std</span></code>.
|
|
Requires <code class="language-python highlight"><span class="n">std</span> <span class="o">>=</span> <span class="mi">0</span></code>.</p>
|
|
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
|
|
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="n">Tensor</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">normal</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">mean</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">std</span><span class="o">=</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="mf">13.9153</span> <span class="mf">9.6281</span> <span class="mf">13.2808</span><span class="p">]</span>
|
|
<span class="p">[</span> <span class="mf">8.4707</span> <span class="mf">8.261</span> <span class="mf">9.1242</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/rand.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">140</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>
|
|
<span class="normal">155</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@classmethod</span>
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">normal</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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"> Creates a tensor with the given shape, filled with random values from a normal distribution with the given `mean` and standard deviation `std`.</span>
|
|
<span class="sd"> Requires `std >= 0`.</span>
|
|
|
|
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
|
|
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> Tensor.manual_seed(42)</span>
|
|
<span class="sd"> print(Tensor.normal(2, 3, mean=10, std=2).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">if</span> <span class="n">std</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="sa">f</span><span class="s2">"Tensor.normal requires std >= 0, got </span><span class="si">{</span><span class="n">std</span><span class="si">=}</span><span class="s2">"</span><span class="p">)</span>
|
|
<span class="k">return</span> <span class="n">std</span> <span class="o">*</span> <span class="bp">cls</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">+</span> <span class="n">mean</span>
|
|
</code></pre></div></td></tr></table></div>
|
|
</details>
|
|
</div>
|
|
|
|
</div>
|
|
|
|
<div class="doc doc-object doc-function">
|
|
|
|
|
|
<h3 id="tinygrad.Tensor.uniform" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">uniform</span>
|
|
|
|
|
|
<span class="doc doc-labels">
|
|
<small class="doc doc-label doc-label-classmethod"><code>classmethod</code></small>
|
|
</span>
|
|
|
|
<a href="#tinygrad.Tensor.uniform" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">uniform</span><span class="p">(</span>
|
|
<span class="o">*</span><span class="n">shape</span><span class="p">,</span>
|
|
<span class="n">low</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
|
|
<span class="n">high</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
|
|
<span class="n">dtype</span><span class="p">:</span> <span class="n"><span title="tinygrad.dtype.DTypeLike">DTypeLike</span></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">kwargs</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>Creates a tensor with the given shape, filled with random values from a uniform distribution over the interval <code class="language-python highlight"><span class="p">[</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">)</span></code>.
|
|
Requires <code class="language-python highlight"><span class="n">low</span> <span class="o"><</span> <span class="n">high</span></code>.</p>
|
|
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
|
|
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="n">Tensor</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">uniform</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">low</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">10</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="mf">5.0483</span> <span class="mf">2.0782</span> <span class="mf">2.9024</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="mf">2.9416</span> <span class="mf">6.0429</span> <span class="mf">4.9769</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/rand.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">157</span>
|
|
<span class="normal">158</span>
|
|
<span class="normal">159</span>
|
|
<span class="normal">160</span>
|
|
<span class="normal">161</span>
|
|
<span class="normal">162</span>
|
|
<span class="normal">163</span>
|
|
<span class="normal">164</span>
|
|
<span class="normal">165</span>
|
|
<span class="normal">166</span>
|
|
<span class="normal">167</span>
|
|
<span class="normal">168</span>
|
|
<span class="normal">169</span>
|
|
<span class="normal">170</span>
|
|
<span class="normal">171</span>
|
|
<span class="normal">172</span>
|
|
<span class="normal">173</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@classmethod</span>
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">uniform</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">low</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">dtype</span><span class="p">:</span><span class="n">DTypeLike</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">kwargs</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"> Creates a tensor with the given shape, filled with random values from a uniform distribution over the interval `[low, high)`.</span>
|
|
<span class="sd"> Requires `low < high`.</span>
|
|
|
|
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
|
|
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> Tensor.manual_seed(42)</span>
|
|
<span class="sd"> print(Tensor.uniform(2, 3, low=2, high=10).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">if</span> <span class="ow">not</span> <span class="n">all_int</span><span class="p">(</span><span class="n">shape</span><span class="o">:=</span><span class="n">argfix</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">))</span> <span class="ow">or</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span><span class="n">s</span> <span class="o">>=</span> <span class="mi">0</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">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">"invalid input </span><span class="si">{</span><span class="n">shape</span><span class="si">=}</span><span class="s2">"</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="n">low</span> <span class="o">>=</span> <span class="n">high</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">"Tensor.uniform requires low < high, got </span><span class="si">{</span><span class="n">low</span><span class="si">=}</span><span class="s2">, </span><span class="si">{</span><span class="n">high</span><span class="si">=}</span><span class="s2">"</span><span class="p">)</span>
|
|
<span class="k">return</span> <span class="p">((</span><span class="n">high</span><span class="o">-</span><span class="n">low</span><span class="p">)</span> <span class="o">*</span> <span class="bp">cls</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">dtype</span> <span class="ow">or</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">default_float</span><span class="p">)</span> <span class="o">+</span> <span class="n">low</span>
|
|
</code></pre></div></td></tr></table></div>
|
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</details>
|
|
</div>
|
|
|
|
</div>
|
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|
|
<div class="doc doc-object doc-function">
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|
|
|
|
<h3 id="tinygrad.Tensor.scaled_uniform" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">scaled_uniform</span>
|
|
|
|
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<span class="doc doc-labels">
|
|
<small class="doc doc-label doc-label-classmethod"><code>classmethod</code></small>
|
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</span>
|
|
|
|
<a href="#tinygrad.Tensor.scaled_uniform" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">scaled_uniform</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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>Creates a tensor with the given shape, filled with random values from a uniform distribution
|
|
over the interval <code class="language-python highlight"><span class="p">[</span><span class="o">-</span><span class="n">prod</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span><span class="o">**-</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">prod</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span><span class="o">**-</span><span class="mf">0.5</span><span class="p">)</span></code>.</p>
|
|
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
|
|
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="n">Tensor</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">scaled_uniform</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="o">-</span><span class="mf">0.0971</span> <span class="o">-</span><span class="mf">0.4003</span> <span class="o">-</span><span class="mf">0.3161</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="o">-</span><span class="mf">0.3121</span> <span class="mf">0.0044</span> <span class="o">-</span><span class="mf">0.1044</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/rand.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">175</span>
|
|
<span class="normal">176</span>
|
|
<span class="normal">177</span>
|
|
<span class="normal">178</span>
|
|
<span class="normal">179</span>
|
|
<span class="normal">180</span>
|
|
<span class="normal">181</span>
|
|
<span class="normal">182</span>
|
|
<span class="normal">183</span>
|
|
<span class="normal">184</span>
|
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<span class="normal">185</span>
|
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<span class="normal">186</span>
|
|
<span class="normal">187</span>
|
|
<span class="normal">188</span>
|
|
<span class="normal">189</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@classmethod</span>
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">scaled_uniform</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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"> Creates a tensor with the given shape, filled with random values from a uniform distribution</span>
|
|
<span class="sd"> over the interval `[-prod(shape)**-0.5, prod(shape)**-0.5)`.</span>
|
|
|
|
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
|
|
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> Tensor.manual_seed(42)</span>
|
|
<span class="sd"> print(Tensor.scaled_uniform(2, 3).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">low</span><span class="o">=-</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="n">prod</span><span class="p">(</span><span class="n">argfix</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">))</span><span class="o">**-</span><span class="mf">0.5</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.glorot_uniform" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">glorot_uniform</span>
|
|
|
|
|
|
<span class="doc doc-labels">
|
|
<small class="doc doc-label doc-label-classmethod"><code>classmethod</code></small>
|
|
</span>
|
|
|
|
<a href="#tinygrad.Tensor.glorot_uniform" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">glorot_uniform</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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><a href="https://www.tensorflow.org/api_docs/python/tf/keras/initializers/GlorotUniform">https://www.tensorflow.org/api_docs/python/tf/keras/initializers/GlorotUniform</a></p>
|
|
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
|
|
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="n">Tensor</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">glorot_uniform</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="o">-</span><span class="mf">0.2606</span> <span class="o">-</span><span class="mf">1.074</span> <span class="o">-</span><span class="mf">0.8483</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="o">-</span><span class="mf">0.8376</span> <span class="mf">0.0117</span> <span class="o">-</span><span class="mf">0.2802</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/rand.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></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>
|
|
<span class="normal">205</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@classmethod</span>
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">glorot_uniform</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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"> <https://www.tensorflow.org/api_docs/python/tf/keras/initializers/GlorotUniform></span>
|
|
|
|
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
|
|
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> Tensor.manual_seed(42)</span>
|
|
<span class="sd"> print(Tensor.glorot_uniform(2, 3).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="n">bound</span> <span class="o">=</span> <span class="p">(</span><span class="mi">6</span> <span class="o">/</span> <span class="p">(</span><span class="n">argfix</span><span class="p">(</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">prod</span><span class="p">(</span><span class="n">argfix</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">)[</span><span class="mi">1</span><span class="p">:])))</span> <span class="o">**</span> <span class="mf">0.5</span>
|
|
<span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">low</span><span class="o">=-</span><span class="n">bound</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="n">bound</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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.kaiming_uniform" class="doc doc-heading">
|
|
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">kaiming_uniform</span>
|
|
|
|
|
|
<span class="doc doc-labels">
|
|
<small class="doc doc-label doc-label-classmethod"><code>classmethod</code></small>
|
|
</span>
|
|
|
|
<a href="#tinygrad.Tensor.kaiming_uniform" class="headerlink" title="Permanent link">¤</a></h3>
|
|
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">kaiming_uniform</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">a</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#float">float</a></span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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><a href="https://pytorch.org/docs/stable/_modules/torch/nn/init.html#kaiming_uniform_">https://pytorch.org/docs/stable/_modules/torch/nn/init.html#kaiming_uniform_</a></p>
|
|
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
|
|
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="n">Tensor</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">kaiming_uniform</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="o">-</span><span class="mf">0.3364</span> <span class="o">-</span><span class="mf">1.3865</span> <span class="o">-</span><span class="mf">1.0951</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="o">-</span><span class="mf">1.0813</span> <span class="mf">0.0152</span> <span class="o">-</span><span class="mf">0.3617</span><span class="p">]]</span>
|
|
</code></pre></div>
|
|
|
|
|
|
<details class="mkdocstrings-source">
|
|
<summary>Source code in <code>tinygrad/mixin/rand.py</code></summary>
|
|
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></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>
|
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<span class="normal">218</span>
|
|
<span class="normal">219</span>
|
|
<span class="normal">220</span>
|
|
<span class="normal">221</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@classmethod</span>
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">kaiming_uniform</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">a</span><span class="p">:</span><span class="nb">float</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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"> <https://pytorch.org/docs/stable/_modules/torch/nn/init.html#kaiming_uniform_></span>
|
|
|
|
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
|
|
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> Tensor.manual_seed(42)</span>
|
|
<span class="sd"> print(Tensor.kaiming_uniform(2, 3).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="n">bound</span> <span class="o">=</span> <span class="p">(</span><span class="mi">6</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">a</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="n">prod</span><span class="p">(</span><span class="n">argfix</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">)[</span><span class="mi">1</span><span class="p">:]))</span> <span class="o">**</span> <span class="mf">0.5</span>
|
|
<span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">low</span><span class="o">=-</span><span class="n">bound</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="n">bound</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
|
</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.kaiming_normal" 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">kaiming_normal</span>
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<span class="doc doc-labels">
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<small class="doc doc-label doc-label-classmethod"><code>classmethod</code></small>
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</span>
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<a href="#tinygrad.Tensor.kaiming_normal" 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">kaiming_normal</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">a</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#float">float</a></span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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">
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<p><a href="https://pytorch.org/docs/stable/_modules/torch/nn/init.html#kaiming_normal_">https://pytorch.org/docs/stable/_modules/torch/nn/init.html#kaiming_normal_</a></p>
|
|
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
|
|
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
|
|
<div class="language-python highlight"><pre><span></span><code><span class="n">Tensor</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">kaiming_normal</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="mf">1.5983</span> <span class="o">-</span><span class="mf">0.1518</span> <span class="mf">1.3393</span><span class="p">]</span>
|
|
<span class="p">[</span><span class="o">-</span><span class="mf">0.6243</span> <span class="o">-</span><span class="mf">0.7099</span> <span class="o">-</span><span class="mf">0.3575</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/rand.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">223</span>
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<span class="normal">224</span>
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<span class="normal">225</span>
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<span class="normal">226</span>
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<span class="normal">227</span>
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<span class="normal">228</span>
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<span class="normal">229</span>
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<span class="normal">230</span>
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<span class="normal">231</span>
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<span class="normal">232</span>
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<span class="normal">233</span>
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<span class="normal">234</span>
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<span class="normal">235</span>
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<span class="normal">236</span>
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<span class="normal">237</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@classmethod</span>
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">kaiming_normal</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">a</span><span class="p">:</span><span class="nb">float</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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"> <https://pytorch.org/docs/stable/_modules/torch/nn/init.html#kaiming_normal_></span>
|
|
|
|
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
|
|
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
|
|
|
|
<span class="sd"> ```python exec="true" source="above" session="tensor" result="python"</span>
|
|
<span class="sd"> Tensor.manual_seed(42)</span>
|
|
<span class="sd"> print(Tensor.kaiming_normal(2, 3).numpy())</span>
|
|
<span class="sd"> ```</span>
|
|
<span class="sd"> """</span>
|
|
<span class="n">std</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">a</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="n">prod</span><span class="p">(</span><span class="n">argfix</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">)[</span><span class="mi">1</span><span class="p">:]))</span> <span class="o">**</span> <span class="mf">0.5</span>
|
|
<span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="n">std</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
|
</code></pre></div></td></tr></table></div>
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