def _to_xla_op_sharding(self, num_dimensions: int) -> xc.OpSharding:
from jax.experimental.pjit import get_array_mapping, _prepare_axis_resources
parsed_spec, _, _, _ = _prepare_axis_resources(self.spec, "spec")
array_mapping = get_array_mapping(parsed_spec)
# TODO(yashkatariya): Move away from sharding spec in MeshPspecSharding
# since we don't really need sharding spec.
sharding_spec = pxla.new_mesh_sharding_specs(
self.mesh.shape, self.mesh.axis_names)(num_dimensions, array_mapping)
return sharding_spec.sharding_proto()
def _get_array_mapping(mesh_axes):
# Import here to avoid cyclic import error when importing gda in pjit.py.
from jax.experimental.pjit import get_array_mapping, _prepare_axis_resources
if not isinstance(mesh_axes, PartitionSpec):
pspec = PartitionSpec(*mesh_axes)
else:
pspec = mesh_axes
parsed_pspec, _, _ = _prepare_axis_resources(pspec, "mesh_axes")
return get_array_mapping(parsed_pspec)
def _get_indices(global_shape: Shape, global_mesh: pxla.Mesh,
mesh_axes: MeshAxes) -> Tuple[Index, ...]:
# Import here to avoid cyclic import error when importing gda in pjit.py.
from jax.experimental.pjit import get_array_mapping, _prepare_axis_resources
pspec = _canonicalize_mesh_axes(mesh_axes)
parsed_pspec, _, _ = _prepare_axis_resources(pspec, "mesh_axes")
array_mapping = get_array_mapping(parsed_pspec)
# The dtype doesn't matter for creating sharding specs.
aval = core.ShapedArray(global_shape, np.float32)
sharding_spec = pxla.mesh_sharding_specs(
global_mesh.shape, global_mesh.axis_names)(aval, array_mapping)
indices = pxla.spec_to_indices(global_shape, sharding_spec)
return indices # type: ignore
def _get_indices(global_shape: Shape, global_mesh: pxla.Mesh,
mesh_axes: MeshAxes) -> Tuple[pxla.Index, ...]:
# Import here to avoid cyclic import error when importing gda in pjit.py.
from jax.experimental.pjit import get_array_mapping, _prepare_axis_resources
if not isinstance(mesh_axes, PartitionSpec):
pspec = PartitionSpec(*mesh_axes)
else:
pspec = mesh_axes
parsed_pspec, _, _ = _prepare_axis_resources(pspec, "mesh_axes")
array_mapping = get_array_mapping(parsed_pspec)
# The dtype doesn't matter for creating sharding specs.
aval = core.ShapedArray(global_shape, np.float32)
sharding_spec = pxla.mesh_sharding_specs(
global_mesh.shape, global_mesh.axis_names)(aval, array_mapping)
indices = pxla.spec_to_indices(global_shape, sharding_spec)
for index in indices:
assert isinstance(index, tuple)
for idx in index:
assert isinstance(idx, slice)
return indices
def get_shard_indices(global_shape: Shape, global_mesh: pxla.Mesh,
mesh_axes: MeshAxes) -> Mapping[Device, Index]:
# Import here to avoid cyclic import error when importing gda in pjit.py.
from jax.experimental.pjit import get_array_mapping, _prepare_axis_resources
if not isinstance(mesh_axes, PartitionSpec):
pspec = PartitionSpec(*mesh_axes)
else:
pspec = mesh_axes
parsed_pspec, _, _ = _prepare_axis_resources(pspec, "mesh_axes")
array_mapping = get_array_mapping(parsed_pspec)
# The dtype doesn't matter for creating sharding specs.
aval = core.ShapedArray(global_shape, np.float32)
sharding_spec = pxla.mesh_sharding_specs(
global_mesh.shape, global_mesh.axis_names)(aval, array_mapping)
indices = pxla.spec_to_indices(global_shape, sharding_spec)
for index in indices:
assert isinstance(index, tuple)
for idx in index:
assert isinstance(idx, slice)
# The type: ignore is to ignore the type returned by `spec_to_indices`.
return dict((d, i) for d, i in safe_zip(global_mesh.devices.flat,
indices)) # type: ignore
def _get_array_mapping(mesh_axes):
# Import here to avoid cyclic import error when importing gda in pjit.py.
from jax.experimental.pjit import get_array_mapping, _prepare_axis_resources
parsed_pspec, _, _ = _prepare_axis_resources(mesh_axes, "GDA mesh_axes")
return get_array_mapping(parsed_pspec)