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docs update
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Awni Hannun
CircleCI Docs
parent
17470bf630
commit
9e69a72b8c
2
docs/build/html/.buildinfo
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docs/build/html/.buildinfo
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@ -1,4 +1,4 @@
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# Sphinx build info version 1
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# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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config: df22fdae6eaa6299681f0aab7c5d6029
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config: b49cb089891263e82aedf5bc4cacbe8a
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tags: 645f666f9bcd5a90fca523b33c5a78b7
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|
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16
docs/build/html/_sources/dev/extensions.rst
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docs/build/html/_sources/dev/extensions.rst
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@ -677,9 +677,9 @@ Let's look at the overall directory structure first.
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Binding to Python
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^^^^^^^^^^^^^^^^^^
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We use PyBind11_ to build a Python API for the C++ library. Since bindings
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for all needed components such as `mlx.core.array`, `mlx.core.stream`, etc.
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are already provided, adding our :meth:`axpby` becomes very simple!
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We use PyBind11_ to build a Python API for the C++ library. Since bindings for
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components such as :class:`mlx.core.array`, :class:`mlx.core.stream`, etc. are
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already provided, adding our :meth:`axpby` is simple!
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.. code-block:: C++
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@ -927,18 +927,18 @@ Results:
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We see some modest improvements right away!
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This operation is now good to be used to build other operations,
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in :class:`mlx.nn.Module` calls, and also as a part of graph
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transformations like :meth:`grad`!
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This operation is now good to be used to build other operations, in
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:class:`mlx.nn.Module` calls, and also as a part of graph transformations like
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:meth:`grad`!
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Scripts
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-------
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.. admonition:: Download the code
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The full example code is available in `mlx-examples <code>`_.
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The full example code is available in `mlx <code>`_.
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.. code: `TODO_LINK/extensions`_
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.. code: `https://github.com/ml-explore/mlx/tree/main/examples/extensions/`_
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.. _Accelerate: https://developer.apple.com/documentation/accelerate/blas?language=objc
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.. _Metal: https://developer.apple.com/documentation/metal?language=objc
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1
docs/build/html/_sources/index.rst
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1
docs/build/html/_sources/index.rst
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@ -41,6 +41,7 @@ are the CPU and GPU.
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usage/indexing
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usage/saving_and_loading
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usage/function_transforms
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usage/compile
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usage/numpy
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usage/using_streams
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6
docs/build/html/_sources/python/_autosummary/mlx.core.compile.rst
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6
docs/build/html/_sources/python/_autosummary/mlx.core.compile.rst
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@ -0,0 +1,6 @@
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mlx.core.compile
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================
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.. currentmodule:: mlx.core
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.. autofunction:: compile
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6
docs/build/html/_sources/python/_autosummary/mlx.core.disable_compile.rst
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6
docs/build/html/_sources/python/_autosummary/mlx.core.disable_compile.rst
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mlx.core.disable\_compile
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=========================
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.. currentmodule:: mlx.core
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.. autofunction:: disable_compile
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6
docs/build/html/_sources/python/_autosummary/mlx.core.enable_compile.rst
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6
docs/build/html/_sources/python/_autosummary/mlx.core.enable_compile.rst
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mlx.core.enable\_compile
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========================
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.. currentmodule:: mlx.core
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.. autofunction:: enable_compile
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@ -1,6 +0,0 @@
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mlx.core.simplify
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=================
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.. currentmodule:: mlx.core
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.. autofunction:: simplify
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@ -14,5 +14,6 @@
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~AdaDelta.__init__
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~AdaDelta.apply_single
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~AdaDelta.init_single
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@ -14,5 +14,6 @@
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~Adafactor.__init__
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~Adafactor.apply_single
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~Adafactor.init_single
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@ -14,5 +14,6 @@
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~Adagrad.__init__
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~Adagrad.apply_single
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~Adagrad.init_single
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@ -14,5 +14,6 @@
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~Adam.__init__
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~Adam.apply_single
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~Adam.init_single
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@ -14,5 +14,6 @@
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~Adamax.__init__
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~Adamax.apply_single
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~Adamax.init_single
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@ -14,5 +14,6 @@
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~Lion.__init__
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~Lion.apply_single
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~Lion.init_single
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6
docs/build/html/_sources/python/_autosummary/mlx.optimizers.Optimizer.apply_gradients.rst
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6
docs/build/html/_sources/python/_autosummary/mlx.optimizers.Optimizer.apply_gradients.rst
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mlx.optimizers.Optimizer.apply\_gradients
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=========================================
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.. currentmodule:: mlx.optimizers
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.. automethod:: Optimizer.apply_gradients
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6
docs/build/html/_sources/python/_autosummary/mlx.optimizers.Optimizer.init.rst
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6
docs/build/html/_sources/python/_autosummary/mlx.optimizers.Optimizer.init.rst
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mlx.optimizers.Optimizer.init
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=============================
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.. currentmodule:: mlx.optimizers
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.. automethod:: Optimizer.init
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@ -1,20 +0,0 @@
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mlx.optimizers.Optimizer
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========================
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.. currentmodule:: mlx.optimizers
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.. autoclass:: Optimizer
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.. rubric:: Methods
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.. autosummary::
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~Optimizer.__init__
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~Optimizer.apply_gradients
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~Optimizer.apply_single
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~Optimizer.update
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6
docs/build/html/_sources/python/_autosummary/mlx.optimizers.Optimizer.state.rst
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6
docs/build/html/_sources/python/_autosummary/mlx.optimizers.Optimizer.state.rst
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mlx.optimizers.Optimizer.state
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==============================
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.. currentmodule:: mlx.optimizers
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.. autoproperty:: Optimizer.state
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6
docs/build/html/_sources/python/_autosummary/mlx.optimizers.Optimizer.update.rst
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6
docs/build/html/_sources/python/_autosummary/mlx.optimizers.Optimizer.update.rst
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mlx.optimizers.Optimizer.update
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===============================
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.. currentmodule:: mlx.optimizers
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.. automethod:: Optimizer.update
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@ -1,17 +0,0 @@
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mlx.optimizers.OptimizerState
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=============================
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.. currentmodule:: mlx.optimizers
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.. autoclass:: OptimizerState
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.. rubric:: Methods
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.. autosummary::
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~OptimizerState.get
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@ -14,5 +14,6 @@
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~RMSprop.__init__
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~RMSprop.apply_single
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~RMSprop.init_single
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@ -14,5 +14,6 @@
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~SGD.__init__
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~SGD.apply_single
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~SGD.init_single
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: ALiBi
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.. autoclass:: ALiBi
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: BatchNorm
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.. autoclass:: BatchNorm
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: Conv1d
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.. autoclass:: Conv1d
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: Conv2d
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.. autoclass:: Conv2d
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: Dropout
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.. autoclass:: Dropout
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: Dropout2d
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.. autoclass:: Dropout2d
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: Dropout3d
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.. autoclass:: Dropout3d
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: Embedding
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.. autoclass:: Embedding
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: GELU
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.. autoclass:: GELU
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: GroupNorm
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.. autoclass:: GroupNorm
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: InstanceNorm
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.. autoclass:: InstanceNorm
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: LayerNorm
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.. autoclass:: LayerNorm
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: Linear
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.. autoclass:: Linear
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@ -3,6 +3,4 @@
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|
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.. currentmodule:: mlx.nn
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.. autoclass:: Mish
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.. autoclass:: Mish
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6
docs/build/html/_sources/python/nn/_autosummary/mlx.nn.Module.state.rst
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6
docs/build/html/_sources/python/nn/_autosummary/mlx.nn.Module.state.rst
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@ -0,0 +1,6 @@
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mlx.nn.Module.state
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===================
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.. currentmodule:: mlx.nn
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.. autoproperty:: Module.state
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: MultiHeadAttention
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.. autoclass:: MultiHeadAttention
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: PReLU
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.. autoclass:: PReLU
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: QuantizedLinear
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.. autoclass:: QuantizedLinear
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: RMSNorm
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.. autoclass:: RMSNorm
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: ReLU
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.. autoclass:: ReLU
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: RoPE
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.. autoclass:: RoPE
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: SELU
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.. autoclass:: SELU
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: Sequential
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.. autoclass:: Sequential
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: SiLU
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.. autoclass:: SiLU
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: SinusoidalPositionalEncoding
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.. autoclass:: SinusoidalPositionalEncoding
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: Softshrink
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.. autoclass:: Softshrink
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: Step
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.. autoclass:: Step
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: Transformer
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.. autoclass:: Transformer
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@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: gelu
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|
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.. autofunction:: gelu
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||||
@ -3,6 +3,4 @@
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.. currentmodule:: mlx.nn
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.. autoclass:: gelu_approx
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||||
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.. autofunction:: gelu_approx
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@ -3,6 +3,4 @@
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||||
|
||||
.. currentmodule:: mlx.nn
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.. autoclass:: gelu_fast_approx
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||||
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||||
.. autofunction:: gelu_fast_approx
|
||||
@ -1,6 +0,0 @@
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||||
mlx.nn.init.constant
|
||||
====================
|
||||
|
||||
.. currentmodule:: mlx.nn.init
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||||
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||||
.. autofunction:: constant
|
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@ -1,6 +0,0 @@
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mlx.nn.init.glorot\_normal
|
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==========================
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||||
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.. currentmodule:: mlx.nn.init
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||||
|
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.. autofunction:: glorot_normal
|
||||
@ -1,6 +0,0 @@
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mlx.nn.init.glorot\_uniform
|
||||
===========================
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||||
|
||||
.. currentmodule:: mlx.nn.init
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||||
|
||||
.. autofunction:: glorot_uniform
|
||||
@ -1,6 +0,0 @@
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mlx.nn.init.he\_normal
|
||||
======================
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||||
|
||||
.. currentmodule:: mlx.nn.init
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||||
|
||||
.. autofunction:: he_normal
|
||||
@ -1,6 +0,0 @@
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mlx.nn.init.he\_uniform
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||||
=======================
|
||||
|
||||
.. currentmodule:: mlx.nn.init
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||||
.. autofunction:: he_uniform
|
||||
@ -1,6 +0,0 @@
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mlx.nn.init.identity
|
||||
====================
|
||||
|
||||
.. currentmodule:: mlx.nn.init
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||||
|
||||
.. autofunction:: identity
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@ -1,6 +0,0 @@
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mlx.nn.init.normal
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==================
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||||
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.. currentmodule:: mlx.nn.init
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||||
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.. autofunction:: normal
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@ -1,6 +0,0 @@
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mlx.nn.init.uniform
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===================
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.. currentmodule:: mlx.nn.init
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.. autofunction:: uniform
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@ -1,6 +0,0 @@
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mlx.nn.initializers.constant
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============================
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.. currentmodule:: mlx.nn.initializers
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.. autofunction:: constant
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@ -1,6 +0,0 @@
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mlx.nn.initializers.glorot\_normal
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==================================
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.. currentmodule:: mlx.nn.initializers
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.. autofunction:: glorot_normal
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@ -1,6 +0,0 @@
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mlx.nn.initializers.glorot\_uniform
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===================================
|
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.. currentmodule:: mlx.nn.initializers
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|
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.. autofunction:: glorot_uniform
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||||
@ -1,6 +0,0 @@
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mlx.nn.initializers.he\_normal
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==============================
|
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.. currentmodule:: mlx.nn.initializers
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|
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.. autofunction:: he_normal
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@ -1,6 +0,0 @@
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mlx.nn.initializers.he\_uniform
|
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===============================
|
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||||
.. currentmodule:: mlx.nn.initializers
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|
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.. autofunction:: he_uniform
|
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@ -1,6 +0,0 @@
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mlx.nn.initializers.identity
|
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============================
|
||||
|
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.. currentmodule:: mlx.nn.initializers
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.. autofunction:: identity
|
||||
@ -1,6 +0,0 @@
|
||||
mlx.nn.initializers.normal
|
||||
==========================
|
||||
|
||||
.. currentmodule:: mlx.nn.initializers
|
||||
|
||||
.. autofunction:: normal
|
||||
@ -1,6 +0,0 @@
|
||||
mlx.nn.initializers.uniform
|
||||
===========================
|
||||
|
||||
.. currentmodule:: mlx.nn.initializers
|
||||
|
||||
.. autofunction:: uniform
|
||||
@ -3,6 +3,4 @@
|
||||
|
||||
.. currentmodule:: mlx.nn.losses
|
||||
|
||||
.. autoclass:: binary_cross_entropy
|
||||
|
||||
|
||||
.. autofunction:: binary_cross_entropy
|
||||
@ -3,6 +3,4 @@
|
||||
|
||||
.. currentmodule:: mlx.nn.losses
|
||||
|
||||
.. autoclass:: cosine_similarity_loss
|
||||
|
||||
|
||||
.. autofunction:: cosine_similarity_loss
|
||||
@ -3,6 +3,4 @@
|
||||
|
||||
.. currentmodule:: mlx.nn.losses
|
||||
|
||||
.. autoclass:: cross_entropy
|
||||
|
||||
|
||||
.. autofunction:: cross_entropy
|
||||
@ -3,6 +3,4 @@
|
||||
|
||||
.. currentmodule:: mlx.nn.losses
|
||||
|
||||
.. autoclass:: gaussian_nll_loss
|
||||
|
||||
|
||||
.. autofunction:: gaussian_nll_loss
|
||||
@ -3,6 +3,4 @@
|
||||
|
||||
.. currentmodule:: mlx.nn.losses
|
||||
|
||||
.. autoclass:: hinge_loss
|
||||
|
||||
|
||||
.. autofunction:: hinge_loss
|
||||
@ -3,6 +3,4 @@
|
||||
|
||||
.. currentmodule:: mlx.nn.losses
|
||||
|
||||
.. autoclass:: huber_loss
|
||||
|
||||
|
||||
.. autofunction:: huber_loss
|
||||
@ -3,6 +3,4 @@
|
||||
|
||||
.. currentmodule:: mlx.nn.losses
|
||||
|
||||
.. autoclass:: kl_div_loss
|
||||
|
||||
|
||||
.. autofunction:: kl_div_loss
|
||||
@ -3,6 +3,4 @@
|
||||
|
||||
.. currentmodule:: mlx.nn.losses
|
||||
|
||||
.. autoclass:: l1_loss
|
||||
|
||||
|
||||
.. autofunction:: l1_loss
|
||||
@ -3,6 +3,4 @@
|
||||
|
||||
.. currentmodule:: mlx.nn.losses
|
||||
|
||||
.. autoclass:: log_cosh_loss
|
||||
|
||||
|
||||
.. autofunction:: log_cosh_loss
|
||||
@ -0,0 +1,6 @@
|
||||
mlx.nn.losses.margin\_ranking\_loss
|
||||
===================================
|
||||
|
||||
.. currentmodule:: mlx.nn.losses
|
||||
|
||||
.. autofunction:: margin_ranking_loss
|
||||
@ -3,6 +3,4 @@
|
||||
|
||||
.. currentmodule:: mlx.nn.losses
|
||||
|
||||
.. autoclass:: mse_loss
|
||||
|
||||
|
||||
.. autofunction:: mse_loss
|
||||
@ -3,6 +3,4 @@
|
||||
|
||||
.. currentmodule:: mlx.nn.losses
|
||||
|
||||
.. autoclass:: nll_loss
|
||||
|
||||
|
||||
.. autofunction:: nll_loss
|
||||
@ -3,6 +3,4 @@
|
||||
|
||||
.. currentmodule:: mlx.nn.losses
|
||||
|
||||
.. autoclass:: smooth_l1_loss
|
||||
|
||||
|
||||
.. autofunction:: smooth_l1_loss
|
||||
@ -3,6 +3,4 @@
|
||||
|
||||
.. currentmodule:: mlx.nn.losses
|
||||
|
||||
.. autoclass:: triplet_loss
|
||||
|
||||
|
||||
.. autofunction:: triplet_loss
|
||||
@ -3,6 +3,4 @@
|
||||
|
||||
.. currentmodule:: mlx.nn
|
||||
|
||||
.. autoclass:: mish
|
||||
|
||||
|
||||
.. autofunction:: mish
|
||||
@ -3,6 +3,4 @@
|
||||
|
||||
.. currentmodule:: mlx.nn
|
||||
|
||||
.. autoclass:: prelu
|
||||
|
||||
|
||||
.. autofunction:: prelu
|
||||
@ -3,6 +3,4 @@
|
||||
|
||||
.. currentmodule:: mlx.nn
|
||||
|
||||
.. autoclass:: relu
|
||||
|
||||
|
||||
.. autofunction:: relu
|
||||
@ -3,6 +3,4 @@
|
||||
|
||||
.. currentmodule:: mlx.nn
|
||||
|
||||
.. autoclass:: selu
|
||||
|
||||
|
||||
.. autofunction:: selu
|
||||
@ -3,6 +3,4 @@
|
||||
|
||||
.. currentmodule:: mlx.nn
|
||||
|
||||
.. autoclass:: silu
|
||||
|
||||
|
||||
.. autofunction:: silu
|
||||
@ -3,6 +3,4 @@
|
||||
|
||||
.. currentmodule:: mlx.nn
|
||||
|
||||
.. autoclass:: softshrink
|
||||
|
||||
|
||||
.. autofunction:: softshrink
|
||||
@ -3,6 +3,4 @@
|
||||
|
||||
.. currentmodule:: mlx.nn
|
||||
|
||||
.. autoclass:: step
|
||||
|
||||
|
||||
.. autofunction:: step
|
||||
@ -1,18 +0,0 @@
|
||||
.. _initializers:
|
||||
|
||||
.. currentmodule:: mlx.nn.initializers
|
||||
|
||||
Initializers
|
||||
--------------
|
||||
|
||||
.. autosummary::
|
||||
:toctree: _autosummary_functions
|
||||
|
||||
constant
|
||||
normal
|
||||
uniform
|
||||
identity
|
||||
glorot_normal
|
||||
glorot_uniform
|
||||
he_normal
|
||||
he_uniform
|
||||
@ -18,6 +18,7 @@ Loss Functions
|
||||
kl_div_loss
|
||||
l1_loss
|
||||
log_cosh_loss
|
||||
margin_ranking_loss
|
||||
mse_loss
|
||||
nll_loss
|
||||
smooth_l1_loss
|
||||
|
||||
@ -11,6 +11,7 @@ Module
|
||||
:toctree: _autosummary
|
||||
|
||||
Module.training
|
||||
Module.state
|
||||
|
||||
.. rubric:: Methods
|
||||
|
||||
|
||||
23
docs/build/html/_sources/python/optimizer.rst
vendored
Normal file
23
docs/build/html/_sources/python/optimizer.rst
vendored
Normal file
@ -0,0 +1,23 @@
|
||||
Optimizer
|
||||
=========
|
||||
|
||||
.. currentmodule:: mlx.optimizers
|
||||
|
||||
.. autoclass:: Optimizer
|
||||
|
||||
|
||||
.. rubric:: Attributes
|
||||
|
||||
.. autosummary::
|
||||
:toctree: _autosummary
|
||||
|
||||
Optimizer.state
|
||||
|
||||
.. rubric:: Methods
|
||||
|
||||
.. autosummary::
|
||||
:toctree: _autosummary
|
||||
|
||||
Optimizer.apply_gradients
|
||||
Optimizer.init
|
||||
Optimizer.update
|
||||
@ -29,14 +29,16 @@ model's parameters and the **optimizer state**.
|
||||
# Compute the new parameters but also the optimizer state.
|
||||
mx.eval(model.parameters(), optimizer.state)
|
||||
|
||||
.. toctree::
|
||||
|
||||
optimizer
|
||||
|
||||
.. currentmodule:: mlx.optimizers
|
||||
|
||||
.. autosummary::
|
||||
:toctree: _autosummary
|
||||
:template: optimizers-template.rst
|
||||
|
||||
OptimizerState
|
||||
Optimizer
|
||||
SGD
|
||||
RMSprop
|
||||
Adagrad
|
||||
|
||||
@ -9,6 +9,9 @@ Transforms
|
||||
:toctree: _autosummary
|
||||
|
||||
eval
|
||||
compile
|
||||
disable_compile
|
||||
enable_compile
|
||||
grad
|
||||
value_and_grad
|
||||
jvp
|
||||
|
||||
430
docs/build/html/_sources/usage/compile.rst
vendored
Normal file
430
docs/build/html/_sources/usage/compile.rst
vendored
Normal file
@ -0,0 +1,430 @@
|
||||
.. _compile:
|
||||
|
||||
Compilation
|
||||
===========
|
||||
|
||||
.. currentmodule:: mlx.core
|
||||
|
||||
MLX has a :func:`compile` function transformation which compiles computation
|
||||
graphs. Function compilation results in smaller graphs by merging common work
|
||||
and fusing certain operations. In many cases this can lead to big improvements
|
||||
in run-time and memory use.
|
||||
|
||||
Getting started with :func:`compile` is simple, but there are some edge cases
|
||||
that are good to be aware of for more complex graphs and advanced usage.
|
||||
|
||||
Basics of Compile
|
||||
-----------------
|
||||
|
||||
Let's start with a simple example:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
def fun(x, y):
|
||||
return mx.exp(-x) + y
|
||||
|
||||
x = mx.array(1.0)
|
||||
y = mx.array(2.0)
|
||||
|
||||
# Regular call, no compilation
|
||||
# Prints: array(2.36788, dtype=float32)
|
||||
print(fun(x, y))
|
||||
|
||||
# Compile the function
|
||||
compiled_fun = mx.compile(fun)
|
||||
|
||||
# Prints: array(2.36788, dtype=float32)
|
||||
print(compiled_fun(x, y))
|
||||
|
||||
The output of both the regular function and the compiled function is the same
|
||||
up to numerical precision.
|
||||
|
||||
The first time you call a compiled function, MLX will build the compute
|
||||
graph, optimize it, and generate and compile code. This can be relatively
|
||||
slow. However, MLX will cache compiled functions, so calling a compiled
|
||||
function multiple times will not initiate a new compilation. This means you
|
||||
should typically compile functions that you plan to use more than once.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
def fun(x, y):
|
||||
return mx.exp(-x) + y
|
||||
|
||||
x = mx.array(1.0)
|
||||
y = mx.array(2.0)
|
||||
|
||||
compiled_fun = mx.compile(fun)
|
||||
|
||||
# Compiled here
|
||||
compiled_fun(x, y)
|
||||
|
||||
# Not compiled again
|
||||
compiled_fun(x, y)
|
||||
|
||||
# Not compiled again
|
||||
mx.compile(fun)(x, y)
|
||||
|
||||
There are some important cases to be aware of that can cause a function to
|
||||
be recompiled:
|
||||
|
||||
* Changing the shape or number of dimensions
|
||||
* Changing the type of any of the inputs
|
||||
* Changing the number of inputs to the function
|
||||
|
||||
In certain cases only some of the compilation stack will be rerun (for
|
||||
example when changing the shapes) and in other cases the full compilation
|
||||
stack will be rerun (for example when changing the types). In general you
|
||||
should avoid compiling functions too frequently.
|
||||
|
||||
Another idiom to watch out for is compiling functions which get created and
|
||||
destroyed frequently. This can happen, for example, when compiling an anonymous
|
||||
function in a loop:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
a = mx.array(1.0)
|
||||
# Don't do this, compiles lambda at each iteration
|
||||
for _ in range(5):
|
||||
mx.compile(lambda x: mx.exp(mx.abs(x)))(a)
|
||||
|
||||
Example Speedup
|
||||
---------------
|
||||
|
||||
The :func:`mlx.nn.gelu` is a nonlinear activation function commonly used with
|
||||
Transformer-based models. The implementation involves several unary and binary
|
||||
element-wise operations:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
def gelu(x):
|
||||
return x * (1 + mx.erf(x / math.sqrt(2))) / 2
|
||||
|
||||
If you use this function with small arrays, it will be overhead bound. If you
|
||||
use it with large arrays it will be memory bandwidth bound. However, all of
|
||||
the operations in the ``gelu`` are fusible into a single kernel with
|
||||
:func:`compile`. This can speedup both cases considerably.
|
||||
|
||||
Let's compare the runtime of the regular function versus the compiled
|
||||
function. We'll use the following timing helper which does a warm up and
|
||||
handles synchronization:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
import time
|
||||
|
||||
def timeit(fun, x):
|
||||
# warm up
|
||||
for _ in range(10):
|
||||
mx.eval(fun(x))
|
||||
|
||||
tic = time.perf_counter()
|
||||
for _ in range(100):
|
||||
mx.eval(fun(x))
|
||||
toc = time.perf_counter()
|
||||
tpi = 1e3 * (toc - tic) / 100
|
||||
print(f"Time per iteration {tpi:.3f} (ms)")
|
||||
|
||||
|
||||
Now make an array, and benchmark both functions:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
x = mx.random.uniform(shape=(32, 1000, 4096))
|
||||
timeit(nn.gelu, x)
|
||||
timeit(mx.compile(nn.gelu), x)
|
||||
|
||||
On an M1 Max the times are 15.5 and 3.1 milliseconds. The compiled ``gelu`` is
|
||||
five times faster.
|
||||
|
||||
.. note::
|
||||
|
||||
As of the latest MLX, CPU functions are not fully compiled. Compiling CPU
|
||||
functions can still be helpful, but won't typically result in as large a
|
||||
speedup as compiling operations that run on the GPU.
|
||||
|
||||
|
||||
Debugging
|
||||
---------
|
||||
|
||||
When a compiled function is first called, it is traced with placeholder
|
||||
inputs. This means you can't evaluate arrays (for example to print their
|
||||
contents) inside compiled functions.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
@mx.compile
|
||||
def fun(x):
|
||||
z = -x
|
||||
print(z) # Crash
|
||||
return mx.exp(z)
|
||||
|
||||
fun(mx.array(5.0))
|
||||
|
||||
For debugging, inspecting arrays can be helpful. One way to do that is to
|
||||
globally disable compilation using the :func:`disable_compile` function or
|
||||
``MLX_DISABLE_COMPILE`` flag. For example the following is okay even though
|
||||
``fun`` is compiled:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
@mx.compile
|
||||
def fun(x):
|
||||
z = -x
|
||||
print(z) # Okay
|
||||
return mx.exp(z)
|
||||
|
||||
mx.disable_compile()
|
||||
fun(mx.array(5.0))
|
||||
|
||||
|
||||
Pure Functions
|
||||
--------------
|
||||
|
||||
Compiled functions are intended to be *pure*; that is they should not have side
|
||||
effects. For example:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
state = []
|
||||
|
||||
@mx.compile
|
||||
def fun(x, y):
|
||||
z = x + y
|
||||
state.append(z)
|
||||
return mx.exp(z)
|
||||
|
||||
fun(mx.array(1.0), mx.array(2.0))
|
||||
# Crash!
|
||||
print(state)
|
||||
|
||||
After the first call of ``fun``, the ``state`` list will hold a placeholder
|
||||
array. The placeholder does not have any data; it is only used to build the
|
||||
computation graph. Printing such an array results in a crash.
|
||||
|
||||
You have two options to deal with this. The first option is to simply return
|
||||
``state`` as an output:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
state = []
|
||||
|
||||
@mx.compile
|
||||
def fun(x, y):
|
||||
z = x + y
|
||||
state.append(z)
|
||||
return mx.exp(z), state
|
||||
|
||||
_, state = fun(mx.array(1.0), mx.array(2.0))
|
||||
# Prints [array(3, dtype=float32)]
|
||||
print(state)
|
||||
|
||||
In some cases returning updated state can be pretty inconvenient. Hence,
|
||||
:func:`compile` has a parameter to capture implicit outputs:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from functools import partial
|
||||
|
||||
state = []
|
||||
|
||||
# Tell compile to capture state as an output
|
||||
@partial(mx.compile, outputs=state)
|
||||
def fun(x, y):
|
||||
z = x + y
|
||||
state.append(z)
|
||||
return mx.exp(z), state
|
||||
|
||||
fun(mx.array(1.0), mx.array(2.0))
|
||||
# Prints [array(3, dtype=float32)]
|
||||
print(state)
|
||||
|
||||
This is particularly useful for compiling a function which includes an update
|
||||
to a container of arrays, as is commonly done when training the parameters of a
|
||||
:class:`mlx.nn.Module`.
|
||||
|
||||
Compiled functions will also treat any inputs not in the parameter list as
|
||||
constants. For example:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
state = [mx.array(1.0)]
|
||||
|
||||
@mx.compile
|
||||
def fun(x):
|
||||
return x + state[0]
|
||||
|
||||
# Prints array(2, dtype=float32)
|
||||
print(fun(mx.array(1.0)))
|
||||
|
||||
# Update state
|
||||
state[0] = mx.array(5.0)
|
||||
|
||||
# Still prints array(2, dtype=float32)
|
||||
print(fun(mx.array(1.0)))
|
||||
|
||||
In order to have the change of state reflected in the outputs of ``fun`` you
|
||||
again have two options. The first option is to simply pass ``state`` as input
|
||||
to the function. In some cases this can be pretty inconvenient. Hence,
|
||||
:func:`compile` also has a parameter to capture implicit inputs:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from functools import partial
|
||||
state = [mx.array(1.0)]
|
||||
|
||||
# Tell compile to capture state as an input
|
||||
@partial(mx.compile, inputs=state)
|
||||
def fun(x):
|
||||
return x + state[0]
|
||||
|
||||
# Prints array(2, dtype=float32)
|
||||
print(fun(mx.array(1.0)))
|
||||
|
||||
# Update state
|
||||
state[0] = mx.array(5.0)
|
||||
|
||||
# Prints array(6, dtype=float32)
|
||||
print(fun(mx.array(1.0)))
|
||||
|
||||
|
||||
Compiling Training Graphs
|
||||
-------------------------
|
||||
|
||||
This section will step through how to use :func:`compile` with a simple example
|
||||
of a common setup: training a model with :obj:`mlx.nn.Module` using an
|
||||
:obj:`mlx.optimizers.Optimizer` with state. We will show how to compile the
|
||||
full forward, backward, and update with :func:`compile`.
|
||||
|
||||
To start, here is the simple example without any compilation:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
import mlx.core as mx
|
||||
import mlx.nn as nn
|
||||
import mlx.optimizers as optim
|
||||
|
||||
# 4 examples with 10 features each
|
||||
x = mx.random.uniform(shape=(4, 10))
|
||||
|
||||
# 0, 1 targets
|
||||
y = mx.array([0, 1, 0, 1])
|
||||
|
||||
# Simple linear model
|
||||
model = nn.Linear(10, 1)
|
||||
|
||||
# SGD with momentum
|
||||
optimizer = optim.SGD(learning_rate=0.1, momentum=0.8)
|
||||
|
||||
def loss_fn(model, x, y):
|
||||
logits = model(x).squeeze()
|
||||
return nn.losses.binary_cross_entropy(logits, y)
|
||||
|
||||
loss_and_grad_fn = nn.value_and_grad(model, loss_fn)
|
||||
|
||||
# Perform 10 steps of gradient descent
|
||||
for it in range(10):
|
||||
loss, grads = loss_and_grad_fn(model, x, y)
|
||||
optimizer.update(model, grads)
|
||||
mx.eval(model.parameters(), optimizer.state)
|
||||
|
||||
To compile the update we can put it all in a function and compile it with the
|
||||
appropriate input and output captures. Here's the same example but compiled:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
import mlx.core as mx
|
||||
import mlx.nn as nn
|
||||
import mlx.optimizers as optim
|
||||
from functools import partial
|
||||
|
||||
# 4 examples with 10 features each
|
||||
x = mx.random.uniform(shape=(4, 10))
|
||||
|
||||
# 0, 1 targets
|
||||
y = mx.array([0, 1, 0, 1])
|
||||
|
||||
# Simple linear model
|
||||
model = nn.Linear(10, 1)
|
||||
|
||||
# SGD with momentum
|
||||
optimizer = optim.SGD(learning_rate=0.1, momentum=0.8)
|
||||
|
||||
def loss_fn(model, x, y):
|
||||
logits = model(x).squeeze()
|
||||
return nn.losses.binary_cross_entropy(logits, y)
|
||||
|
||||
# The state that will be captured as input and output
|
||||
state = [model.state, optimizer.state]
|
||||
|
||||
@partial(mx.compile, inputs=state, outputs=state)
|
||||
def step(x, y):
|
||||
loss_and_grad_fn = nn.value_and_grad(model, loss_fn)
|
||||
loss, grads = loss_and_grad_fn(model, x, y)
|
||||
optimizer.update(model, grads)
|
||||
return loss
|
||||
|
||||
# Perform 10 steps of gradient descent
|
||||
for it in range(10):
|
||||
loss = step(x, y)
|
||||
# Evaluate the model and optimizer state
|
||||
mx.eval(state)
|
||||
print(loss)
|
||||
|
||||
|
||||
.. note::
|
||||
|
||||
If you are using a module which performs random sampling such as
|
||||
:func:`mlx.nn.Dropout`, make sure you also include ``mx.random.state`` in the
|
||||
``state`` captured by :func:`compile`, i.e. ``state = [model.state,
|
||||
optimizer.state, mx.random.state]``.
|
||||
|
||||
|
||||
.. note::
|
||||
|
||||
For more examples of compiling full training graphs checkout the `MLX
|
||||
Examples <https://github.com/ml-explore/mlx-examples>`_ GitHub repo.
|
||||
|
||||
Transformations with Compile
|
||||
----------------------------
|
||||
|
||||
In MLX function transformations are composable. You can apply any function
|
||||
transformation to the output of any other function transformation. For more on
|
||||
this, see the documentation on :ref:`function transforms
|
||||
<function_transforms>`.
|
||||
|
||||
Compiling transformed functions works just as expected:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
grad_fn = mx.grad(mx.exp)
|
||||
|
||||
compiled_grad_fn = mx.compile(grad_fn)
|
||||
|
||||
# Prints: array(2.71828, dtype=float32)
|
||||
print(grad_fn(mx.array(1.0)))
|
||||
|
||||
# Also prints: array(2.71828, dtype=float32)
|
||||
print(compiled_grad_fn(mx.array(1.0)))
|
||||
|
||||
.. note::
|
||||
|
||||
In order to compile as much as possible, a transformation of a compiled
|
||||
function will not by default be compiled. To compile the transformed
|
||||
function simply pass it through :func:`compile`.
|
||||
|
||||
You can also compile functions which themselves call compiled functions. A
|
||||
good practice is to compile the outer most function to give :func:`compile`
|
||||
the most opportunity to optimize the computation graph:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
@mx.compile
|
||||
def inner(x):
|
||||
return mx.exp(-mx.abs(x))
|
||||
|
||||
def outer(x):
|
||||
inner(inner(x))
|
||||
|
||||
# Compiling the outer function is good to do as it will likely
|
||||
# be faster even though the inner functions are compiled
|
||||
fun = mx.compile(outer)
|
||||
@ -5,9 +5,12 @@ Function Transforms
|
||||
|
||||
.. currentmodule:: mlx.core
|
||||
|
||||
MLX uses composable function transformations for automatic differentiation and
|
||||
vectorization. The key idea behind composable function transformations is that
|
||||
every transformation returns a function which can be further transformed.
|
||||
MLX uses composable function transformations for automatic differentiation,
|
||||
vectorization, and compute graph optimizations. To see the complete list of
|
||||
function transformations check-out the :ref:`API documentation <transforms>`.
|
||||
|
||||
The key idea behind composable function transformations is that every
|
||||
transformation returns a function which can be further transformed.
|
||||
|
||||
Here is a simple example:
|
||||
|
||||
@ -36,10 +39,10 @@ Using :func:`grad` on the output of :func:`grad` is always ok. You keep
|
||||
getting higher order derivatives.
|
||||
|
||||
Any of the MLX function transformations can be composed in any order to any
|
||||
depth. To see the complete list of function transformations check-out the
|
||||
:ref:`API documentation <transforms>`. See the following sections for more
|
||||
information on :ref:`automatic differentiaion <auto diff>` and
|
||||
:ref:`automatic vectorization <vmap>`.
|
||||
depth. See the following sections for more information on :ref:`automatic
|
||||
differentiaion <auto diff>` and :ref:`automatic vectorization <vmap>`.
|
||||
For more information on :func:`compile` see the :ref:`compile documentation <compile>`.
|
||||
|
||||
|
||||
Automatic Differentiation
|
||||
-------------------------
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
var DOCUMENTATION_OPTIONS = {
|
||||
URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
|
||||
VERSION: '0.1.0',
|
||||
VERSION: '0.2.0',
|
||||
LANGUAGE: 'en',
|
||||
COLLAPSE_INDEX: false,
|
||||
BUILDER: 'html',
|
||||
|
||||
23
docs/build/html/cpp/ops.html
vendored
23
docs/build/html/cpp/ops.html
vendored
@ -9,7 +9,7 @@
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
|
||||
|
||||
<title>Operations — MLX 0.1.0 documentation</title>
|
||||
<title>Operations — MLX 0.2.0 documentation</title>
|
||||
|
||||
|
||||
|
||||
@ -134,8 +134,8 @@
|
||||
|
||||
|
||||
|
||||
<img src="../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.1.0 documentation - Home"/>
|
||||
<script>document.write(`<img src="../_static/mlx_logo.png" class="logo__image only-dark" alt="MLX 0.1.0 documentation - Home"/>`);</script>
|
||||
<img src="../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.2.0 documentation - Home"/>
|
||||
<script>document.write(`<img src="../_static/mlx_logo.png" class="logo__image only-dark" alt="MLX 0.2.0 documentation - Home"/>`);</script>
|
||||
|
||||
|
||||
</a></div>
|
||||
@ -153,6 +153,7 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../usage/indexing.html">Indexing Arrays</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../usage/saving_and_loading.html">Saving and Loading Arrays</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../usage/function_transforms.html">Function Transforms</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../usage/compile.html">Compilation</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../usage/numpy.html">Conversion to NumPy and Other Frameworks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../usage/using_streams.html">Using Streams</a></li>
|
||||
</ul>
|
||||
@ -348,6 +349,9 @@
|
||||
</li>
|
||||
<li class="toctree-l1 has-children"><a class="reference internal" href="../python/transforms.html">Transforms</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.eval.html">mlx.core.eval</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.compile.html">mlx.core.compile</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.disable_compile.html">mlx.core.disable_compile</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.enable_compile.html">mlx.core.enable_compile</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.grad.html">mlx.core.grad</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.value_and_grad.html">mlx.core.value_and_grad</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.jvp.html">mlx.core.jvp</a></li>
|
||||
@ -379,6 +383,7 @@
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.nn.value_and_grad.html">mlx.nn.value_and_grad</a></li>
|
||||
<li class="toctree-l2 has-children"><a class="reference internal" href="../python/nn/module.html">Module</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Module.training.html">mlx.nn.Module.training</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Module.state.html">mlx.nn.Module.state</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Module.apply.html">mlx.nn.Module.apply</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Module.apply_to_modules.html">mlx.nn.Module.apply_to_modules</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Module.children.html">mlx.nn.Module.children</a></li>
|
||||
@ -451,6 +456,7 @@
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.kl_div_loss.html">mlx.nn.losses.kl_div_loss</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.l1_loss.html">mlx.nn.losses.l1_loss</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.log_cosh_loss.html">mlx.nn.losses.log_cosh_loss</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.margin_ranking_loss.html">mlx.nn.losses.margin_ranking_loss</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.mse_loss.html">mlx.nn.losses.mse_loss</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.nll_loss.html">mlx.nn.losses.nll_loss</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.smooth_l1_loss.html">mlx.nn.losses.smooth_l1_loss</a></li>
|
||||
@ -471,8 +477,13 @@
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1 has-children"><a class="reference internal" href="../python/optimizers.html">Optimizers</a><input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-14"><i class="fa-solid fa-chevron-down"></i></label><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.OptimizerState.html">mlx.optimizers.OptimizerState</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.Optimizer.html">mlx.optimizers.Optimizer</a></li>
|
||||
<li class="toctree-l2 has-children"><a class="reference internal" href="../python/optimizer.html">Optimizer</a><input class="toctree-checkbox" id="toctree-checkbox-15" name="toctree-checkbox-15" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-15"><i class="fa-solid fa-chevron-down"></i></label><ul>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.Optimizer.state.html">mlx.optimizers.Optimizer.state</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.Optimizer.apply_gradients.html">mlx.optimizers.Optimizer.apply_gradients</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.Optimizer.init.html">mlx.optimizers.Optimizer.init</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.Optimizer.update.html">mlx.optimizers.Optimizer.update</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.SGD.html">mlx.optimizers.SGD</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.RMSprop.html">mlx.optimizers.RMSprop</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.Adagrad.html">mlx.optimizers.Adagrad</a></li>
|
||||
@ -484,7 +495,7 @@
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.Lion.html">mlx.optimizers.Lion</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1 has-children"><a class="reference internal" href="../python/tree_utils.html">Tree Utils</a><input class="toctree-checkbox" id="toctree-checkbox-15" name="toctree-checkbox-15" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-15"><i class="fa-solid fa-chevron-down"></i></label><ul>
|
||||
<li class="toctree-l1 has-children"><a class="reference internal" href="../python/tree_utils.html">Tree Utils</a><input class="toctree-checkbox" id="toctree-checkbox-16" name="toctree-checkbox-16" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-16"><i class="fa-solid fa-chevron-down"></i></label><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.utils.tree_flatten.html">mlx.utils.tree_flatten</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.utils.tree_unflatten.html">mlx.utils.tree_unflatten</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.utils.tree_map.html">mlx.utils.tree_map</a></li>
|
||||
|
||||
37
docs/build/html/dev/extensions.html
vendored
37
docs/build/html/dev/extensions.html
vendored
@ -9,7 +9,7 @@
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
|
||||
|
||||
<title>Developer Documentation — MLX 0.1.0 documentation</title>
|
||||
<title>Developer Documentation — MLX 0.2.0 documentation</title>
|
||||
|
||||
|
||||
|
||||
@ -133,8 +133,8 @@
|
||||
|
||||
|
||||
|
||||
<img src="../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.1.0 documentation - Home"/>
|
||||
<script>document.write(`<img src="../_static/mlx_logo.png" class="logo__image only-dark" alt="MLX 0.1.0 documentation - Home"/>`);</script>
|
||||
<img src="../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.2.0 documentation - Home"/>
|
||||
<script>document.write(`<img src="../_static/mlx_logo.png" class="logo__image only-dark" alt="MLX 0.2.0 documentation - Home"/>`);</script>
|
||||
|
||||
|
||||
</a></div>
|
||||
@ -152,6 +152,7 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="../usage/indexing.html">Indexing Arrays</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../usage/saving_and_loading.html">Saving and Loading Arrays</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../usage/function_transforms.html">Function Transforms</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../usage/compile.html">Compilation</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../usage/numpy.html">Conversion to NumPy and Other Frameworks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../usage/using_streams.html">Using Streams</a></li>
|
||||
</ul>
|
||||
@ -347,6 +348,9 @@
|
||||
</li>
|
||||
<li class="toctree-l1 has-children"><a class="reference internal" href="../python/transforms.html">Transforms</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.eval.html">mlx.core.eval</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.compile.html">mlx.core.compile</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.disable_compile.html">mlx.core.disable_compile</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.enable_compile.html">mlx.core.enable_compile</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.grad.html">mlx.core.grad</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.value_and_grad.html">mlx.core.value_and_grad</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.jvp.html">mlx.core.jvp</a></li>
|
||||
@ -378,6 +382,7 @@
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.nn.value_and_grad.html">mlx.nn.value_and_grad</a></li>
|
||||
<li class="toctree-l2 has-children"><a class="reference internal" href="../python/nn/module.html">Module</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Module.training.html">mlx.nn.Module.training</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Module.state.html">mlx.nn.Module.state</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Module.apply.html">mlx.nn.Module.apply</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Module.apply_to_modules.html">mlx.nn.Module.apply_to_modules</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Module.children.html">mlx.nn.Module.children</a></li>
|
||||
@ -450,6 +455,7 @@
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.kl_div_loss.html">mlx.nn.losses.kl_div_loss</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.l1_loss.html">mlx.nn.losses.l1_loss</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.log_cosh_loss.html">mlx.nn.losses.log_cosh_loss</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.margin_ranking_loss.html">mlx.nn.losses.margin_ranking_loss</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.mse_loss.html">mlx.nn.losses.mse_loss</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.nll_loss.html">mlx.nn.losses.nll_loss</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.smooth_l1_loss.html">mlx.nn.losses.smooth_l1_loss</a></li>
|
||||
@ -470,8 +476,13 @@
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1 has-children"><a class="reference internal" href="../python/optimizers.html">Optimizers</a><input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-14"><i class="fa-solid fa-chevron-down"></i></label><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.OptimizerState.html">mlx.optimizers.OptimizerState</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.Optimizer.html">mlx.optimizers.Optimizer</a></li>
|
||||
<li class="toctree-l2 has-children"><a class="reference internal" href="../python/optimizer.html">Optimizer</a><input class="toctree-checkbox" id="toctree-checkbox-15" name="toctree-checkbox-15" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-15"><i class="fa-solid fa-chevron-down"></i></label><ul>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.Optimizer.state.html">mlx.optimizers.Optimizer.state</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.Optimizer.apply_gradients.html">mlx.optimizers.Optimizer.apply_gradients</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.Optimizer.init.html">mlx.optimizers.Optimizer.init</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.Optimizer.update.html">mlx.optimizers.Optimizer.update</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.SGD.html">mlx.optimizers.SGD</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.RMSprop.html">mlx.optimizers.RMSprop</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.Adagrad.html">mlx.optimizers.Adagrad</a></li>
|
||||
@ -483,7 +494,7 @@
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.optimizers.Lion.html">mlx.optimizers.Lion</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1 has-children"><a class="reference internal" href="../python/tree_utils.html">Tree Utils</a><input class="toctree-checkbox" id="toctree-checkbox-15" name="toctree-checkbox-15" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-15"><i class="fa-solid fa-chevron-down"></i></label><ul>
|
||||
<li class="toctree-l1 has-children"><a class="reference internal" href="../python/tree_utils.html">Tree Utils</a><input class="toctree-checkbox" id="toctree-checkbox-16" name="toctree-checkbox-16" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-16"><i class="fa-solid fa-chevron-down"></i></label><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.utils.tree_flatten.html">mlx.utils.tree_flatten</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.utils.tree_unflatten.html">mlx.utils.tree_unflatten</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.utils.tree_map.html">mlx.utils.tree_map</a></li>
|
||||
@ -1326,9 +1337,9 @@ the python package</p></li>
|
||||
</ul>
|
||||
<section id="binding-to-python">
|
||||
<h3>Binding to Python<a class="headerlink" href="#binding-to-python" title="Permalink to this heading">#</a></h3>
|
||||
<p>We use <a class="reference external" href="https://pybind11.readthedocs.io/en/stable/">PyBind11</a> to build a Python API for the C++ library. Since bindings
|
||||
for all needed components such as <cite>mlx.core.array</cite>, <cite>mlx.core.stream</cite>, etc.
|
||||
are already provided, adding our <code class="xref py py-meth docutils literal notranslate"><span class="pre">axpby()</span></code> becomes very simple!</p>
|
||||
<p>We use <a class="reference external" href="https://pybind11.readthedocs.io/en/stable/">PyBind11</a> to build a Python API for the C++ library. Since bindings for
|
||||
components such as <a class="reference internal" href="../python/_autosummary/mlx.core.array.html#mlx.core.array" title="mlx.core.array"><code class="xref py py-class docutils literal notranslate"><span class="pre">mlx.core.array</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">mlx.core.stream</span></code>, etc. are
|
||||
already provided, adding our <code class="xref py py-meth docutils literal notranslate"><span class="pre">axpby()</span></code> is simple!</p>
|
||||
<div class="highlight-C++ notranslate"><div class="highlight"><pre><span></span><span class="n">PYBIND11_MODULE</span><span class="p">(</span><span class="n">mlx_sample_extensions</span><span class="p">,</span><span class="w"> </span><span class="n">m</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
|
||||
<span class="w"> </span><span class="n">m</span><span class="p">.</span><span class="n">doc</span><span class="p">()</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s">"Sample C++ and metal extensions for MLX"</span><span class="p">;</span><span class="w"></span>
|
||||
|
||||
@ -1552,16 +1563,16 @@ with the naive <code class="xref py py-meth docutils literal notranslate"><span
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>We see some modest improvements right away!</p>
|
||||
<p>This operation is now good to be used to build other operations,
|
||||
in <a class="reference internal" href="../python/nn/module.html#mlx.nn.Module" title="mlx.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">mlx.nn.Module</span></code></a> calls, and also as a part of graph
|
||||
transformations like <code class="xref py py-meth docutils literal notranslate"><span class="pre">grad()</span></code>!</p>
|
||||
<p>This operation is now good to be used to build other operations, in
|
||||
<a class="reference internal" href="../python/nn/module.html#mlx.nn.Module" title="mlx.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">mlx.nn.Module</span></code></a> calls, and also as a part of graph transformations like
|
||||
<code class="xref py py-meth docutils literal notranslate"><span class="pre">grad()</span></code>!</p>
|
||||
</section>
|
||||
</section>
|
||||
<section id="scripts">
|
||||
<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>
|
||||
<p>The full example code is available in <a class="reference external" href="code">mlx</a>.</p>
|
||||
</div>
|
||||
</section>
|
||||
</section>
|
||||
|
||||
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