def _get_rank_helper(group, backend):
"""
The Helper to do get_rank_id.
Args:
group (str): The communication group.
backend (str): The backend, like "hccl".
Raises:
ValueError: If backend is invalid.
Returns:
Integer. The local rank id of the calling process.
"""
rank_id = None
if _is_role_pserver() or _is_role_sched():
rank_id = 0
return rank_id
if backend == Backend.HCCL:
if group == HCCL_WORLD_COMM_GROUP:
rank_id = hccl.get_rank_id()
else:
rank_id = hccl.get_rank_id(group)
elif backend == Backend.NCCL:
rank_id = mpi.get_rank_id(group)
else:
raise ValueError("Invalid backend: '{}'".format(backend))
return rank_id
def _get_rank_helper(group, backend):
"""
The Helper to do get_rank_id.
Args:
group (str): The communication group.
backend (str): The backend, like "hccl".
Raises:
ValueError: If backend is invalid.
Returns:
Integer. The local rank id of the calling process.
"""
rank_id = None
if MS_ROLE in ("MS_PSERVER", "MS_SCHED"):
rank_id = 0
return rank_id
if backend == Backend.HCCL:
if group == HCCL_WORLD_COMM_GROUP:
rank_id = hccl.get_rank_id()
else:
rank_id = hccl.get_rank_id(group)
elif backend == Backend.NCCL:
if group == NCCL_WORLD_COMM_GROUP:
rank_id = mpi.get_rank_id()
else:
raise RuntimeError(
"Nccl doesn't support get_rank_id by user group now.")
else:
raise ValueError("Invalid backend: '{}'".format(backend))
return rank_id
def test_net_reduce_scatter():
x = np.arange(12).astype(np.float32) * 0.1
reducescatter = Net()
rankid = mpi.get_rank_id()
print("self rankid:", rankid)
output = reducescatter(Tensor(x, mstype.float32))
print("output:\n", output)
if rankid == 0:
expect_result = np.arange(4).astype(np.float32) * 0.3
if rankid == 1:
expect_result = np.arange(4, 8).astype(np.float32) * 0.3
if rankid == 2:
expect_result = np.arange(8, 12).astype(np.float32) * 0.3
diff = abs(output.asnumpy() - expect_result)
error = np.ones(shape=expect_result.shape) * 1.0e-6
assert np.all(diff < error)
allgather = AllGatherNet()
allgather_output = allgather(output)
print("allgather result:\n", allgather_output)
expect_allgather_result = np.arange(12).astype(np.float32) * 0.3
diff = abs(allgather_output.asnumpy() - expect_allgather_result)
error = np.ones(shape=expect_allgather_result.shape) * 1.0e-6
assert np.all(diff < error)
