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<h1>LLM inference</h1>
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#attention-layer">Attention layer</a></li>
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#putting-it-all-together">Putting it all together</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#converting-the-weights">Converting the weights</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#weight-loading-and-benchmarking">Weight loading and benchmarking</a></li>
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<article class="bd-article" role="main">
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<section id="llm-inference">
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<h1>LLM inference<a class="headerlink" href="#llm-inference" title="Permalink to this heading"></a></h1>
|
||||
<h1>LLM inference<a class="headerlink" href="#llm-inference" title="Permalink to this heading">#</a></h1>
|
||||
<p>MLX enables efficient inference of large-ish transformers on Apple silicon
|
||||
without compromising on ease of use. In this example we will create an
|
||||
inference script for the Llama family of transformer models in which the model
|
||||
is defined in less than 200 lines of python.</p>
|
||||
<section id="implementing-the-model">
|
||||
<h2>Implementing the model<a class="headerlink" href="#implementing-the-model" title="Permalink to this heading"></a></h2>
|
||||
<h2>Implementing the model<a class="headerlink" href="#implementing-the-model" title="Permalink to this heading">#</a></h2>
|
||||
<p>We will use the neural network building blocks defined in the <code class="xref py py-mod docutils literal notranslate"><span class="pre">mlx.nn</span></code>
|
||||
module to concisely define the model architecture.</p>
|
||||
<section id="attention-layer">
|
||||
<h3>Attention layer<a class="headerlink" href="#attention-layer" title="Permalink to this heading"></a></h3>
|
||||
<h3>Attention layer<a class="headerlink" href="#attention-layer" title="Permalink to this heading">#</a></h3>
|
||||
<p>We will start with the llama attention layer which notably uses the RoPE
|
||||
positional encoding. <a class="footnote-reference brackets" href="#id5" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> In addition, our attention layer will optionally use a
|
||||
key/value cache that will be concatenated with the provided keys and values to
|
||||
@@ -190,7 +630,7 @@ support efficient inference.</p>
|
||||
</div>
|
||||
</section>
|
||||
<section id="encoder-layer">
|
||||
<h3>Encoder layer<a class="headerlink" href="#encoder-layer" title="Permalink to this heading"></a></h3>
|
||||
<h3>Encoder layer<a class="headerlink" href="#encoder-layer" title="Permalink to this heading">#</a></h3>
|
||||
<p>The other component of the Llama model is the encoder layer which uses RMS
|
||||
normalization <a class="footnote-reference brackets" href="#id6" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a> and SwiGLU. <a class="footnote-reference brackets" href="#id7" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a> For RMS normalization we will use
|
||||
<a class="reference internal" href="../python/_autosummary/mlx.nn.RMSNorm.html#mlx.nn.RMSNorm" title="mlx.nn.RMSNorm"><code class="xref py py-class docutils literal notranslate"><span class="pre">mlx.nn.RMSNorm</span></code></a> that is already provided in <code class="xref py py-mod docutils literal notranslate"><span class="pre">mlx.nn</span></code>.</p>
|
||||
@@ -224,7 +664,7 @@ normalization <a class="footnote-reference brackets" href="#id6" id="id2" role="
|
||||
</div>
|
||||
</section>
|
||||
<section id="full-model">
|
||||
<h3>Full model<a class="headerlink" href="#full-model" title="Permalink to this heading"></a></h3>
|
||||
<h3>Full model<a class="headerlink" href="#full-model" title="Permalink to this heading">#</a></h3>
|
||||
<p>To implement any Llama model we simply have to combine <code class="docutils literal notranslate"><span class="pre">LlamaEncoderLayer</span></code>
|
||||
instances with an <a class="reference internal" href="../python/_autosummary/mlx.nn.Embedding.html#mlx.nn.Embedding" title="mlx.nn.Embedding"><code class="xref py py-class docutils literal notranslate"><span class="pre">mlx.nn.Embedding</span></code></a> to embed the input tokens.</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Llama</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
|
||||
@@ -255,7 +695,7 @@ instances with an <a class="reference internal" href="../python/_autosummary/mlx
|
||||
layers but using <code class="docutils literal notranslate"><span class="pre">model.parameters()</span></code> will still consider these layers.</p>
|
||||
</section>
|
||||
<section id="generation">
|
||||
<h3>Generation<a class="headerlink" href="#generation" title="Permalink to this heading"></a></h3>
|
||||
<h3>Generation<a class="headerlink" href="#generation" title="Permalink to this heading">#</a></h3>
|
||||
<p>Our <code class="docutils literal notranslate"><span class="pre">Llama</span></code> module can be used for training but not inference as the
|
||||
<code class="docutils literal notranslate"><span class="pre">__call__</span></code> method above processes one input, completely ignores the cache and
|
||||
performs no sampling whatsoever. In the rest of this subsection, we will
|
||||
@@ -313,7 +753,7 @@ prompt and then autoregressively yields tokens one at a time.</p>
|
||||
</div>
|
||||
</section>
|
||||
<section id="putting-it-all-together">
|
||||
<h3>Putting it all together<a class="headerlink" href="#putting-it-all-together" title="Permalink to this heading"></a></h3>
|
||||
<h3>Putting it all together<a class="headerlink" href="#putting-it-all-together" title="Permalink to this heading">#</a></h3>
|
||||
<p>We now have everything we need to create a Llama model and sample tokens from
|
||||
it. In the following code, we randomly initialize a small Llama model, process
|
||||
6 tokens of prompt and generate 10 tokens.</p>
|
||||
@@ -343,7 +783,7 @@ it. In the following code, we randomly initialize a small Llama model, process
|
||||
</section>
|
||||
</section>
|
||||
<section id="converting-the-weights">
|
||||
<h2>Converting the weights<a class="headerlink" href="#converting-the-weights" title="Permalink to this heading"></a></h2>
|
||||
<h2>Converting the weights<a class="headerlink" href="#converting-the-weights" title="Permalink to this heading">#</a></h2>
|
||||
<p>This section assumes that you have access to the original Llama weights and the
|
||||
SentencePiece model that comes with them. We will write a small script to
|
||||
convert the PyTorch weights to MLX compatible ones and write them in a NPZ file
|
||||
@@ -397,7 +837,7 @@ that can be loaded directly by MLX.</p>
|
||||
</div>
|
||||
</section>
|
||||
<section id="weight-loading-and-benchmarking">
|
||||
<h2>Weight loading and benchmarking<a class="headerlink" href="#weight-loading-and-benchmarking" title="Permalink to this heading"></a></h2>
|
||||
<h2>Weight loading and benchmarking<a class="headerlink" href="#weight-loading-and-benchmarking" title="Permalink to this heading">#</a></h2>
|
||||
<p>After converting the weights to be compatible to our implementation, all that is
|
||||
left is to load them from disk and we can finally use the LLM to generate text.
|
||||
We can load numpy format files using the <a class="reference internal" href="../python/_autosummary/mlx.core.load.html#mlx.core.load" title="mlx.core.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">mlx.core.load()</span></code></a> operation.</p>
|
||||
@@ -456,7 +896,7 @@ take<span class="w"> </span>his<span class="w"> </span>eyes<span class="w"> </sp
|
||||
</div>
|
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</section>
|
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<section id="scripts">
|
||||
<h2>Scripts<a class="headerlink" href="#scripts" title="Permalink to this heading"></a></h2>
|
||||
<h2>Scripts<a class="headerlink" href="#scripts" title="Permalink to this heading">#</a></h2>
|
||||
<div class="admonition-download-the-code admonition">
|
||||
<p class="admonition-title">Download the code</p>
|
||||
<p>The full example code is available in <a class="reference external" href="code">mlx-examples</a>.</p>
|
||||
@@ -481,34 +921,112 @@ arXiv:2002.05202.</p>
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