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Use the extended jax.experimental.colocated_python.colocated_cpu_devices API #1822

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Use the extended jax.experimental.colocated_python.colocated_cpu_devices API #1822

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12 changes: 4 additions & 8 deletions MaxText/multihost_dataloading.py
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Original file line number Diff line number Diff line change
Expand Up @@ -164,13 +164,9 @@ def _colocated_cpu_devices(
return colocated_python.colocated_cpu_devices(devices)


def _get_cpu_mesh(mesh: Mesh):
flat_devices = tuple(mesh.devices.flat)
flat_cpu_devices = _colocated_cpu_devices(flat_devices)
cpu_mesh = jax.sharding.Mesh(
np.array(flat_cpu_devices).reshape(mesh.devices.shape), mesh.axis_names, axis_types=mesh.axis_types
)
return cpu_mesh
def _colocated_cpu_mesh(mesh: Mesh) -> Mesh:
"""Returns a CPU mesh that has colocated CPU devices."""
return colocated_python.colocated_cpu_devices(mesh)


class RemoteIterator:
Expand All @@ -179,7 +175,7 @@ class RemoteIterator:
def __init__(self, get_ds_fn, preprocessing_fn, global_mesh, global_shape):
self.cpu_devices = _colocated_cpu_devices(jax.local_devices())
self.tpu_devices = jax.local_devices()
self.cpu_mesh = _get_cpu_mesh(global_mesh)
self.cpu_mesh = _colocated_cpu_mesh(global_mesh)
self.tpu_sharding = jax.sharding.NamedSharding(global_mesh, PartitionSpec(global_mesh.axis_names))
self.cpu_sharding = jax.sharding.NamedSharding(self.cpu_mesh, PartitionSpec(self.cpu_mesh.axis_names))
self.dummy_array = jnp.zeros((len(self.cpu_devices)))
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