def _sharded_jit_translation_rule(ctx, avals_in, avals_out, *in_nodes,
in_parts, out_parts_thunk, nparts,
name, call_jaxpr, local_in_parts,
local_out_parts_thunk, local_nparts):
subc = xc.XlaBuilder(f"sharded_jit_{name}")
# We assume any extra leading in_nodes are constants and replicate them.
num_extra_nodes = len(in_nodes) - len(in_parts)
assert num_extra_nodes >= 0
in_parts = (None,) * num_extra_nodes + in_parts
args = []
for i, (n, sharding) in enumerate(safe_zip(in_nodes, in_parts)):
# We use xla.set_sharding instead of xla.with_sharding because inlined calls
# shouldn't have shardings set directly on the inputs or outputs.
arg = xla.parameter(subc, i, ctx.builder.GetShape(n))
args.append(xla.set_sharding(subc, arg, sharding))
sub_ctx = ctx.replace(
builder=subc,
name_stack=new_name_stack(wrap_name(name, "sharded_jit")))
out_nodes = xla.jaxpr_subcomp(sub_ctx, call_jaxpr, (), *args)
out_parts = out_parts_thunk()
assert len(out_parts) == len(out_nodes)
out_nodes = [xla.set_sharding(subc, out, sharding)
for out, sharding in safe_zip(out_nodes, out_parts)]
subc = subc.build(xops.Tuple(subc, out_nodes))
return xla.xla_destructure(ctx.builder,
xops.Call(ctx.builder, subc, list(in_nodes)))
def _sharding_constraint_translation_rule(ctx, avals_in, avals_out, x_node,
partitions):
return [xla.set_sharding(ctx.builder, x_node, partitions)]
