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< span id = "id1" > < / span > < h1 > Optimizers< a class = "headerlink" href = "#optimizers" title = "Link to this heading" > #< / a > < / h1 >
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< p > The optimizers in MLX can be used both with < code class = "xref py py-mod docutils literal notranslate" > < span class = "pre" > mlx.nn< / span > < / code > but also with pure
< code class = "xref py py-mod docutils literal notranslate" > < span class = "pre" > mlx.core< / span > < / code > functions. A typical example involves calling
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< a class = "reference internal" href = "optimizers/_autosummary/mlx.optimizers.Optimizer.update.html#mlx.optimizers.Optimizer.update" title = "mlx.optimizers.Optimizer.update" > < code class = "xref py py-meth docutils literal notranslate" > < span class = "pre" > Optimizer.update()< / span > < / code > < / a > to update a model’
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gradients and subsequently calling < a class = "reference internal" href = "_autosummary/mlx.core.eval.html#mlx.core.eval" title = "mlx.core.eval" > < code class = "xref py py-func docutils literal notranslate" > < span class = "pre" > mlx.core.eval()< / span > < / code > < / a > to evaluate both the
model’ < strong > optimizer state< / strong > .< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "c1" > # Create a model< / span >
< span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > MLP< / span > < span class = "p" > (< / span > < span class = "n" > num_layers< / span > < span class = "p" > ,< / span > < span class = "n" > train_images< / span > < span class = "o" > .< / span > < span class = "n" > shape< / span > < span class = "p" > [< / span > < span class = "o" > -< / span > < span class = "mi" > 1< / span > < span class = "p" > ],< / span > < span class = "n" > hidden_dim< / span > < span class = "p" > ,< / span > < span class = "n" > num_classes< / span > < span class = "p" > )< / span >
< span class = "n" > mx< / span > < span class = "o" > .< / span > < span class = "n" > eval< / span > < span class = "p" > (< / span > < span class = "n" > model< / span > < span class = "o" > .< / span > < span class = "n" > parameters< / span > < span class = "p" > ())< / span >
< span class = "c1" > # Create the gradient function and the optimizer< / span >
< span class = "n" > loss_and_grad_fn< / span > < span class = "o" > =< / span > < span class = "n" > nn< / span > < span class = "o" > .< / span > < span class = "n" > value_and_grad< / span > < span class = "p" > (< / span > < span class = "n" > model< / span > < span class = "p" > ,< / span > < span class = "n" > loss_fn< / span > < span class = "p" > )< / span >
< span class = "n" > optimizer< / span > < span class = "o" > =< / span > < span class = "n" > optim< / span > < span class = "o" > .< / span > < span class = "n" > SGD< / span > < span class = "p" > (< / span > < span class = "n" > learning_rate< / span > < span class = "o" > =< / span > < span class = "n" > learning_rate< / span > < span class = "p" > )< / span >
< span class = "k" > for< / span > < span class = "n" > e< / span > < span class = "ow" > in< / span > < span class = "nb" > range< / span > < span class = "p" > (< / span > < span class = "n" > num_epochs< / span > < span class = "p" > ):< / span >
< span class = "k" > for< / span > < span class = "n" > X< / span > < span class = "p" > ,< / span > < span class = "n" > y< / span > < span class = "ow" > in< / span > < span class = "n" > batch_iterate< / span > < span class = "p" > (< / span > < span class = "n" > batch_size< / span > < span class = "p" > ,< / span > < span class = "n" > train_images< / span > < span class = "p" > ,< / span > < span class = "n" > train_labels< / span > < span class = "p" > ):< / span >
< span class = "n" > loss< / span > < span class = "p" > ,< / span > < span class = "n" > grads< / span > < span class = "o" > =< / span > < span class = "n" > loss_and_grad_fn< / span > < span class = "p" > (< / span > < span class = "n" > model< / span > < span class = "p" > ,< / span > < span class = "n" > X< / span > < span class = "p" > ,< / span > < span class = "n" > y< / span > < span class = "p" > )< / span >
< span class = "c1" > # Update the model with the gradients. So far no computation has happened.< / span >
< span class = "n" > optimizer< / span > < span class = "o" > .< / span > < span class = "n" > update< / span > < span class = "p" > (< / span > < span class = "n" > model< / span > < span class = "p" > ,< / span > < span class = "n" > grads< / span > < span class = "p" > )< / span >
< span class = "c1" > # Compute the new parameters but also the optimizer state.< / span >
< span class = "n" > mx< / span > < span class = "o" > .< / span > < span class = "n" > eval< / span > < span class = "p" > (< / span > < span class = "n" > model< / span > < span class = "o" > .< / span > < span class = "n" > parameters< / span > < span class = "p" > (),< / span > < span class = "n" > optimizer< / span > < span class = "o" > .< / span > < span class = "n" > state< / span > < span class = "p" > )< / span >
< / pre > < / div >
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< h2 > Saving and Loading< a class = "headerlink" href = "#saving-and-loading" title = "Link to this heading" > #< / a > < / h2 >
< p > To serialize an optimizer, save its state. To load an optimizer, load and set
the saved state. Here’ < / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > mlx.core< / span > < span class = "k" > as< / span > < span class = "nn" > mx< / span >
< span class = "kn" > from< / span > < span class = "nn" > mlx.utils< / span > < span class = "kn" > import< / span > < span class = "n" > tree_flatten< / span > < span class = "p" > ,< / span > < span class = "n" > tree_unflatten< / span >
< span class = "kn" > import< / span > < span class = "nn" > mlx.optimizers< / span > < span class = "k" > as< / span > < span class = "nn" > optim< / span >
< span class = "n" > optimizer< / span > < span class = "o" > =< / span > < span class = "n" > optim< / span > < span class = "o" > .< / span > < span class = "n" > Adam< / span > < span class = "p" > (< / span > < span class = "n" > learning_rate< / span > < span class = "o" > =< / span > < span class = "mf" > 1e-2< / span > < span class = "p" > )< / span >
< span class = "c1" > # Perform some updates with the optimizer< / span >
< span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "p" > {< / span > < span class = "s2" > " w" < / span > < span class = "p" > :< / span > < span class = "n" > mx< / span > < span class = "o" > .< / span > < span class = "n" > zeros< / span > < span class = "p" > ((< / span > < span class = "mi" > 5< / span > < span class = "p" > ,< / span > < span class = "mi" > 5< / span > < span class = "p" > ))}< / span >
< span class = "n" > grads< / span > < span class = "o" > =< / span > < span class = "p" > {< / span > < span class = "s2" > " w" < / span > < span class = "p" > :< / span > < span class = "n" > mx< / span > < span class = "o" > .< / span > < span class = "n" > ones< / span > < span class = "p" > ((< / span > < span class = "mi" > 5< / span > < span class = "p" > ,< / span > < span class = "mi" > 5< / span > < span class = "p" > ))}< / span >
< span class = "n" > optimizer< / span > < span class = "o" > .< / span > < span class = "n" > update< / span > < span class = "p" > (< / span > < span class = "n" > model< / span > < span class = "p" > ,< / span > < span class = "n" > grads< / span > < span class = "p" > )< / span >
< span class = "c1" > # Save the state< / span >
< span class = "n" > state< / span > < span class = "o" > =< / span > < span class = "n" > tree_flatten< / span > < span class = "p" > (< / span > < span class = "n" > optimizer< / span > < span class = "o" > .< / span > < span class = "n" > state< / span > < span class = "p" > )< / span >
< span class = "n" > mx< / span > < span class = "o" > .< / span > < span class = "n" > save_safetensors< / span > < span class = "p" > (< / span > < span class = "s2" > " optimizer.safetensors" < / span > < span class = "p" > ,< / span > < span class = "nb" > dict< / span > < span class = "p" > (< / span > < span class = "n" > state< / span > < span class = "p" > ))< / span >
< span class = "c1" > # Later on, for example when loading from a checkpoint,< / span >
< span class = "c1" > # recreate the optimizer and load the state< / span >
< span class = "n" > optimizer< / span > < span class = "o" > =< / span > < span class = "n" > optim< / span > < span class = "o" > .< / span > < span class = "n" > Adam< / span > < span class = "p" > (< / span > < span class = "n" > learning_rate< / span > < span class = "o" > =< / span > < span class = "mf" > 1e-2< / span > < span class = "p" > )< / span >
< span class = "n" > state< / span > < span class = "o" > =< / span > < span class = "n" > tree_unflatten< / span > < span class = "p" > (< / span > < span class = "nb" > list< / span > < span class = "p" > (< / span > < span class = "n" > mx< / span > < span class = "o" > .< / span > < span class = "n" > load< / span > < span class = "p" > (< / span > < span class = "s2" > " optimizer.safetensors" < / span > < span class = "p" > )< / span > < span class = "o" > .< / span > < span class = "n" > items< / span > < span class = "p" > ()))< / span >
< span class = "n" > optimizer< / span > < span class = "o" > .< / span > < span class = "n" > state< / span > < span class = "o" > =< / span > < span class = "n" > state< / span >
< / pre > < / div >
< / div >
< p > Note, not every optimizer configuation parameter is saved in the state. For
example, for Adam the learning rate is saved but the < code class = "docutils literal notranslate" > < span class = "pre" > betas< / span > < / code > and < code class = "docutils literal notranslate" > < span class = "pre" > eps< / span > < / code >
parameters are not. A good rule of thumb is if the parameter can be scheduled
then it will be included in the optimizer state.< / p >
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< p class = "prev-next-subtitle" > next< / p >
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< div class = "bd-sidebar-secondary bd-toc" > < div class = "sidebar-secondary-items sidebar-secondary__inner" >
< div class = "sidebar-secondary-item" >
< div class = "page-toc tocsection onthispage" >
< i class = "fa-solid fa-list" > < / i > Contents
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< nav class = "bd-toc-nav page-toc" >
< ul class = "visible nav section-nav flex-column" >
< li class = "toc-h2 nav-item toc-entry" > < a class = "reference internal nav-link" href = "#saving-and-loading" > Saving and Loading< / a > < ul class = "visible nav section-nav flex-column" >
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< p class = "component-author" >
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< p class = "copyright" >
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< footer class = "bd-footer" >
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