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< span id = "lazy-eval" > < / span > < h1 > Lazy Evaluation< a class = "headerlink" href = "#lazy-evaluation" title = "Link to this heading" > #< / a > < / h1 >
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< section id = "why-lazy-evaluation" >
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< h2 > Why Lazy Evaluation< a class = "headerlink" href = "#why-lazy-evaluation" title = "Link to this heading" > #< / a > < / h2 >
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< p > When you perform operations in MLX, no computation actually happens. Instead a
compute graph is recorded. The actual computation only happens if an
< a class = "reference internal" href = "../python/_autosummary/mlx.core.eval.html#mlx.core.eval" title = "mlx.core.eval" > < code class = "xref py py-func docutils literal notranslate" > < span class = "pre" > eval()< / span > < / code > < / a > is performed.< / p >
< p > MLX uses lazy evaluation because it has some nice features, some of which we
describe below.< / p >
< section id = "transforming-compute-graphs" >
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< h3 > Transforming Compute Graphs< a class = "headerlink" href = "#transforming-compute-graphs" title = "Link to this heading" > #< / a > < / h3 >
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< p > Lazy evaluation lets us record a compute graph without actually doing any
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computations. This is useful for function transformations like < a class = "reference internal" href = "../python/_autosummary/mlx.core.grad.html#mlx.core.grad" title = "mlx.core.grad" > < code class = "xref py py-func docutils literal notranslate" > < span class = "pre" > grad()< / span > < / code > < / a > and
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< a class = "reference internal" href = "../python/_autosummary/mlx.core.vmap.html#mlx.core.vmap" title = "mlx.core.vmap" > < code class = "xref py py-func docutils literal notranslate" > < span class = "pre" > vmap()< / span > < / code > < / a > and graph optimizations.< / p >
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< p > Currently, MLX does not compile and rerun compute graphs. They are all
generated dynamically. However, lazy evaluation makes it much easier to
integrate compilation for future performance enhancements.< / p >
< / section >
< section id = "only-compute-what-you-use" >
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< h3 > Only Compute What You Use< a class = "headerlink" href = "#only-compute-what-you-use" title = "Link to this heading" > #< / a > < / h3 >
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< p > In MLX you do not need to worry as much about computing outputs that are never
used. For example:< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "k" > def< / span > < span class = "nf" > fun< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > ):< / span >
< span class = "n" > a< / span > < span class = "o" > =< / span > < span class = "n" > fun1< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > )< / span >
< span class = "n" > b< / span > < span class = "o" > =< / span > < span class = "n" > expensive_fun< / span > < span class = "p" > (< / span > < span class = "n" > a< / span > < span class = "p" > )< / span >
< span class = "k" > return< / span > < span class = "n" > a< / span > < span class = "p" > ,< / span > < span class = "n" > b< / span >
< span class = "n" > y< / span > < span class = "p" > ,< / span > < span class = "n" > _< / span > < span class = "o" > =< / span > < span class = "n" > fun< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p > Here, we never actually compute the output of < code class = "docutils literal notranslate" > < span class = "pre" > expensive_fun< / span > < / code > . Use this
pattern with care though, as the graph of < code class = "docutils literal notranslate" > < span class = "pre" > expensive_fun< / span > < / code > is still built, and
that has some cost associated to it.< / p >
< p > Similarly, lazy evaluation can be beneficial for saving memory while keeping
code simple. Say you have a very large model < code class = "docutils literal notranslate" > < span class = "pre" > Model< / span > < / code > derived from
< a class = "reference internal" href = "../python/nn/module.html#mlx.nn.Module" title = "mlx.nn.Module" > < code class = "xref py py-obj docutils literal notranslate" > < span class = "pre" > mlx.nn.Module< / span > < / code > < / a > . You can instantiate this model with < code class = "docutils literal notranslate" > < span class = "pre" > model< / span > < span class = "pre" > =< / span > < span class = "pre" > Model()< / span > < / code > .
Typically, this will initialize all of the weights as < code class = "docutils literal notranslate" > < span class = "pre" > float32< / span > < / code > , but the
initialization does not actually compute anything until you perform an
< a class = "reference internal" href = "../python/_autosummary/mlx.core.eval.html#mlx.core.eval" title = "mlx.core.eval" > < code class = "xref py py-func docutils literal notranslate" > < span class = "pre" > eval()< / span > < / code > < / a > . If you update the model with < code class = "docutils literal notranslate" > < span class = "pre" > float16< / span > < / code > weights, your maximum
consumed memory will be half that required if eager computation was used
instead.< / p >
< p > This pattern is simple to do in MLX thanks to lazy computation:< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > Model< / span > < span class = "p" > ()< / span > < span class = "c1" > # no memory used yet< / span >
< span class = "n" > model< / span > < span class = "o" > .< / span > < span class = "n" > load_weights< / span > < span class = "p" > (< / span > < span class = "s2" > " weights_fp16.safetensors" < / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< / section >
< / section >
< section id = "when-to-evaluate" >
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< h2 > When to Evaluate< a class = "headerlink" href = "#when-to-evaluate" title = "Link to this heading" > #< / a > < / h2 >
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< p > A common question is when to use < a class = "reference internal" href = "../python/_autosummary/mlx.core.eval.html#mlx.core.eval" title = "mlx.core.eval" > < code class = "xref py py-func docutils literal notranslate" > < span class = "pre" > eval()< / span > < / code > < / a > . The trade-off is between
letting graphs get too large and not batching enough useful work.< / p >
< p > For example:< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "k" > for< / span > < span class = "n" > _< / span > < span class = "ow" > in< / span > < span class = "nb" > range< / span > < span class = "p" > (< / span > < span class = "mi" > 100< / span > < span class = "p" > ):< / span >
< span class = "n" > a< / span > < span class = "o" > =< / span > < span class = "n" > a< / span > < span class = "o" > +< / span > < span class = "n" > b< / span >
< span class = "n" > mx< / span > < span class = "o" > .< / span > < span class = "n" > eval< / span > < span class = "p" > (< / span > < span class = "n" > a< / span > < span class = "p" > )< / span >
< span class = "n" > b< / span > < span class = "o" > =< / span > < span class = "n" > b< / span > < span class = "o" > *< / span > < span class = "mi" > 2< / span >
< span class = "n" > mx< / span > < span class = "o" > .< / span > < span class = "n" > eval< / span > < span class = "p" > (< / span > < span class = "n" > b< / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p > This is a bad idea because there is some fixed overhead with each graph
evaluation. On the other hand, there is some slight overhead which grows with
the compute graph size, so extremely large graphs (while computationally
correct) can be costly.< / p >
< p > Luckily, a wide range of compute graph sizes work pretty well with MLX:
anything from a few tens of operations to many thousands of operations per
evaluation should be okay.< / p >
< p > Most numerical computations have an iterative outer loop (e.g. the iteration in
stochastic gradient descent). A natural and usually efficient place to use
< a class = "reference internal" href = "../python/_autosummary/mlx.core.eval.html#mlx.core.eval" title = "mlx.core.eval" > < code class = "xref py py-func docutils literal notranslate" > < span class = "pre" > eval()< / span > < / code > < / a > is at each iteration of this outer loop.< / p >
< p > Here is a concrete example:< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "k" > for< / span > < span class = "n" > batch< / span > < span class = "ow" > in< / span > < span class = "n" > dataset< / span > < span class = "p" > :< / span >
< span class = "c1" > # Nothing has been evaluated yet< / span >
< span class = "n" > loss< / span > < span class = "p" > ,< / span > < span class = "n" > grad< / span > < span class = "o" > =< / span > < span class = "n" > value_and_grad_fn< / span > < span class = "p" > (< / span > < span class = "n" > model< / span > < span class = "p" > ,< / span > < span class = "n" > batch< / span > < span class = "p" > )< / span >
< span class = "c1" > # Still nothing has been evaluated< / 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" > grad< / span > < span class = "p" > )< / span >
< span class = "c1" > # Evaluate the loss and the new parameters which will< / span >
< span class = "c1" > # run the full gradient computation and optimizer update< / span >
< span class = "n" > mx< / span > < span class = "o" > .< / span > < span class = "n" > eval< / span > < span class = "p" > (< / span > < span class = "n" > loss< / span > < span class = "p" > ,< / span > < span class = "n" > model< / span > < span class = "o" > .< / span > < span class = "n" > parameters< / span > < span class = "p" > ())< / span >
< / pre > < / div >
< / div >
< p > An important behavior to be aware of is when the graph will be implicitly
evaluated. Anytime you < code class = "docutils literal notranslate" > < span class = "pre" > print< / span > < / code > an array, convert it to an
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< a class = "reference external" href = "https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title = "(in NumPy v2.1)" > < code class = "xref py py-obj docutils literal notranslate" > < span class = "pre" > numpy.ndarray< / span > < / code > < / a > , or otherwise access it’ < a class = "reference external" href = "https://docs.python.org/3/library/stdtypes.html#memoryview" title = "(in Python v3.12)" > < code class = "xref py py-obj docutils literal notranslate" > < span class = "pre" > memoryview< / span > < / code > < / a > ,
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the graph will be evaluated. Saving arrays via < a class = "reference internal" href = "../python/_autosummary/mlx.core.save.html#mlx.core.save" title = "mlx.core.save" > < code class = "xref py py-func docutils literal notranslate" > < span class = "pre" > save()< / span > < / code > < / a > (or any other MLX
saving functions) will also evaluate the array.< / p >
< p > Calling < a class = "reference internal" href = "../python/_autosummary/mlx.core.array.item.html#mlx.core.array.item" title = "mlx.core.array.item" > < code class = "xref py py-func docutils literal notranslate" > < span class = "pre" > array.item()< / span > < / code > < / a > on a scalar array will also evaluate it. In the
example above, printing the loss (< code class = "docutils literal notranslate" > < span class = "pre" > print(loss)< / span > < / code > ) or adding the loss scalar to
a list (< code class = "docutils literal notranslate" > < span class = "pre" > losses.append(loss.item())< / span > < / code > ) would cause a graph evaluation. If
these lines are before < code class = "docutils literal notranslate" > < span class = "pre" > mx.eval(loss,< / span > < span class = "pre" > model.parameters())< / span > < / code > then this
will be a partial evaluation, computing only the forward pass.< / p >
< p > Also, calling < a class = "reference internal" href = "../python/_autosummary/mlx.core.eval.html#mlx.core.eval" title = "mlx.core.eval" > < code class = "xref py py-func docutils literal notranslate" > < span class = "pre" > eval()< / span > < / code > < / a > on an array or set of arrays multiple times is
perfectly fine. This is effectively a no-op.< / p >
< div class = "admonition warning" >
< p class = "admonition-title" > Warning< / p >
< p > Using scalar arrays for control-flow will cause an evaluation.< / p >
< / div >
< p > Here is an example:< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "k" > def< / span > < span class = "nf" > fun< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > ):< / span >
< span class = "n" > h< / span > < span class = "p" > ,< / span > < span class = "n" > y< / span > < span class = "o" > =< / span > < span class = "n" > first_layer< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > )< / span >
< span class = "k" > if< / span > < span class = "n" > y< / span > < span class = "o" > > < / span > < span class = "mi" > 0< / span > < span class = "p" > :< / span > < span class = "c1" > # An evaluation is done here!< / span >
< span class = "n" > z< / span > < span class = "o" > =< / span > < span class = "n" > second_layer_a< / span > < span class = "p" > (< / span > < span class = "n" > h< / span > < span class = "p" > )< / span >
< span class = "k" > else< / span > < span class = "p" > :< / span >
< span class = "n" > z< / span > < span class = "o" > =< / span > < span class = "n" > second_layer_b< / span > < span class = "p" > (< / span > < span class = "n" > h< / span > < span class = "p" > )< / span >
< span class = "k" > return< / span > < span class = "n" > z< / span >
< / pre > < / div >
< / div >
< p > Using arrays for control flow should be done with care. The above example works
and can even be used with gradient transformations. However, this can be very
inefficient if evaluations are done too frequently.< / p >
< / section >
< / section >
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