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79 lines
2.5 KiB
ReStructuredText
79 lines
2.5 KiB
ReStructuredText
.. _unified_memory:
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Unified Memory
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==============
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.. currentmodule:: mlx.core
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Apple silicon has a unified memory architecture. The CPU and GPU have direct
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access to the same memory pool. MLX is designed to take advantage of that.
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Concretely, when you make an array in MLX you don't have to specify its location:
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.. code-block:: python
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a = mx.random.normal((100,))
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b = mx.random.normal((100,))
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Both ``a`` and ``b`` live in unified memory.
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In MLX, rather than moving arrays to devices, you specify the device when you
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run the operation. Any device can perform any operation on ``a`` and ``b``
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without needing to move them from one memory location to another. For example:
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.. code-block:: python
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mx.add(a, b, stream=mx.cpu)
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mx.add(a, b, stream=mx.gpu)
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In the above, both the CPU and the GPU will perform the same add
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operation. The operations can (and likely will) be run in parallel since
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there are no dependencies between them. See :ref:`using_streams` for more
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information the semantics of streams in MLX.
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In the above ``add`` example, there are no dependencies between operations, so
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there is no possibility for race conditions. If there are dependencies, the
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MLX scheduler will automatically manage them. For example:
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.. code-block:: python
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c = mx.add(a, b, stream=mx.cpu)
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d = mx.add(a, c, stream=mx.gpu)
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In the above case, the second ``add`` runs on the GPU but it depends on the
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output of the first ``add`` which is running on the CPU. MLX will
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automatically insert a dependency between the two streams so that the second
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``add`` only starts executing after the first is complete and ``c`` is
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available.
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A Simple Example
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~~~~~~~~~~~~~~~~
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Here is a more interesting (albeit slightly contrived example) of how unified
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memory can be helpful. Suppose we have the following computation:
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.. code-block:: python
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def fun(a, b, d1, d2):
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x = mx.matmul(a, b, stream=d1)
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for _ in range(500):
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b = mx.exp(b, stream=d2)
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return x, b
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which we want to run with the following arguments:
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.. code-block:: python
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a = mx.random.uniform(shape=(4096, 512))
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b = mx.random.uniform(shape=(512, 4))
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The first ``matmul`` operation is a good fit for the GPU since it's more
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compute dense. The second sequence of operations are a better fit for the CPU,
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since they are very small and would probably be overhead bound on the GPU.
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If we time the computation fully on the GPU, we get 2.8 milliseconds. But if we
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run the computation with ``d1=mx.gpu`` and ``d2=mx.cpu``, then the time is only
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about 1.4 milliseconds, about twice as fast. These times were measured on an M1
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Max.
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