JAX 在类型提升方面有时不那么积极(请参阅类型提升语义)。 一些NumPy 例程具有依赖数据的输出形状(例如unique()和nonzero())。因为 XLA 编译器要求在编译时知道数组形状,这些操作与 JIT 不兼容。因此,JAX 在这些函数中添加了一个可选的size参数,可以在静态指定以便与 JIT 一起使用。 几乎所有适用的 NumPy 函数...
key = jax.random.PRNGKey(1)# As you can see, using host_local_array_to_global_array is not required since in_axis_resources says# that the input is fully replicated via P(None)pjit(f, in_shardings=None, out_shardings=None)(key)# Mixing inputsglobal_inp = multihost_utils.host_loca...
host_local_array_to_global_array是一种类型转换,它查看具有仅本地分片的值,并将其本地形状更改为在更改之前如果传递该值pjit会假定的形状。 支持完全复制的输入,即每个进程上具有相同形状,并且in_axis_resources为P(None)的情况。在这种情况下,您无需使用host_local_array_to_global_array,因为形状已经是全局的。
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如果在多进程环境中向pjit传递主机本地输入,请使用multihost_utils.host_local_array_to_global_array将批次转换为全局jax.Array,然后将其传递给pjit。 这种主机本地输入最常见的例子是输入数据批次。 这对任何主机本地输入都有效(不仅仅是输入数据批次)。
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一些NumPy 例程具有依赖数据的输出形状(例如unique()和nonzero())。因为 XLA 编译器要求在编译时知道数组形状,这些操作与 JIT 不兼容。因此,JAX 在这些函数中添加了一个可选的size参数,可以在静态指定以便与 JIT 一起使用。 几乎所有适用的 NumPy 函数都在jax.numpy命名空间中实现;它们如下所列。
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