Files
mscclpp/python/mscclpp/default_algos/send_recv.py
Ubuntu 57f7be6260 WIP
2026-04-11 05:28:29 +00:00

91 lines
4.3 KiB
Python

# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import argparse
from mscclpp.language.channel import *
from mscclpp.language.rank import *
from mscclpp.language.general import *
from mscclpp.language.program import *
from mscclpp.language.collectives import *
def send_recv_test(name, nnodes, gpus_per_node, split_mask):
gpu_size = nnodes * gpus_per_node
collective = SendRecv(gpu_size, 1, False)
with CollectiveProgram(
name,
collective,
gpu_size,
protocol="Simple",
num_threads_per_block=1024,
use_double_scratch_buffer=False,
min_message_size=0,
max_message_size=2**64 - 1,
instances=1
):
# Creating separate port channels for next and prev directions.
# When prev and next are the same peer (e.g., 2-node ring), both channels go to the same peer
# and get distinct tags. To ensure cross-rank tag matching (rank A's prev_channel signal
# arrives at rank B's next_channel wait), we create channels in opposite order for the
# "higher" rank so that tags cross-match:
# Lower rank: [next(tag0), prev(tag1)]
# Higher rank: [prev(tag0), next(tag1)]
# Then lower.prev(tag1) == higher.next(tag1) ✓ and higher.prev(tag0) == lower.next(tag0) ✓
# When prev != next (3+ nodes), each channel targets a different peer so each gets tag 0
# and this ordering doesn't matter.
group_size = split_mask + 1
num_groups = gpu_size // group_size
next_channels = {} # channel for sending to next rank
prev_channels = {} # channel for receiving from prev rank
prev_next_ids = {}
for node in range(nnodes):
for gpu in range(gpus_per_node):
global_rank_id = gpu + gpus_per_node * node
position_in_group = global_rank_id & split_mask
group_id = global_rank_id // group_size
next_group_id = (group_id + 1) % num_groups
next_global_rank_id = next_group_id * group_size + position_in_group
prev_group_id = (group_id - 1 + num_groups) % num_groups
prev_global_rank_id = prev_group_id * group_size + position_in_group
if prev_global_rank_id == next_global_rank_id and global_rank_id > prev_global_rank_id:
# Higher rank: create prev first, then next (swapped order)
prev_channels[global_rank_id] = PortChannel(prev_global_rank_id, global_rank_id)
next_channels[global_rank_id] = PortChannel(next_global_rank_id, global_rank_id)
else:
# Lower rank or different peers: create next first, then prev
next_channels[global_rank_id] = PortChannel(next_global_rank_id, global_rank_id)
prev_channels[global_rank_id] = PortChannel(prev_global_rank_id, global_rank_id)
prev_next_ids[global_rank_id] = (prev_global_rank_id, next_global_rank_id)
# sync with the next rank and the previous rank in the group
for node in range(nnodes):
for gpu in range(gpus_per_node):
global_rank_id = gpu + gpus_per_node * node
prev_global_rank_id, next_global_rank_id = prev_next_ids[global_rank_id]
prev_channels[global_rank_id].signal(tb=0, data_sync=SyncType.none)
next_channels[global_rank_id].wait(tb=0, data_sync=SyncType.after)
src_rank = Rank(global_rank_id)
src_buffer = src_rank.get_input_buffer()
dst_rank = Rank(next_global_rank_id)
dst_buffer = dst_rank.get_output_buffer()
next_channels[global_rank_id].put_with_signal(dst_buffer[:], src_buffer[:], tb=0)
prev_channels[global_rank_id].wait(tb=0, data_sync=SyncType.none)
print(JSON())
parser = argparse.ArgumentParser()
parser.add_argument("--name", type=str, help="name of the program")
parser.add_argument("--nnodes", type=int, default=1, help="number of nodes")
parser.add_argument("--gpus_per_node", type=int, help="number of gpus per node")
parser.add_argument("--split_mask", type=lambda x: int(x, 0), default=0x3, help="split mask (e.g. 0x3)")
args = parser.parse_args()
send_recv_test(
args.name, args.nnodes, args.gpus_per_node, args.split_mask
)