File size: 4,648 Bytes
a402b9b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 | import multiprocessing as mp
import os
import socket
import unittest
from enum import IntEnum
from typing import Any
import sgl_kernel.allreduce as custom_ops
import torch
import torch.distributed as dist
class MscclContextSelection(IntEnum):
MSCCL1SHOT1NODELL = 1
MSCCL1SHOT2NODELL = 2
def _run_correctness_worker(world_size, rank, distributed_init_port, test_sizes):
device = torch.device(f"cuda:{rank % torch.cuda.device_count()}")
torch.cuda.set_device(device)
distributed_init_method = f"tcp://localhost:{distributed_init_port}"
dist.init_process_group(
backend="nccl",
init_method=distributed_init_method,
rank=rank,
world_size=world_size,
)
group = dist.group.WORLD
cpu_group = torch.distributed.new_group(list(range(world_size)), backend="gloo")
if rank == 0:
unique_id = [custom_ops.mscclpp_generate_unique_id()]
else:
unique_id = [None]
dist.broadcast_object_list(
unique_id, src=0, device=torch.device("cpu"), group=cpu_group
)
unique_id = unique_id[0]
rank_to_node, rank_to_ib = list(range(world_size)), list(range(world_size))
for r in range(world_size):
rank_to_node[r] = r // 8
rank_to_ib[r] = rank % 8
MAX_BYTES = 2**20
scratch = torch.empty(
MAX_BYTES * 8, dtype=torch.bfloat16, device=torch.cuda.current_device()
)
put_buffer = torch.empty(
MAX_BYTES, dtype=torch.bfloat16, device=torch.cuda.current_device()
)
print(f"[{rank}] start mscclpp_context init")
nranks_per_node = torch.cuda.device_count()
selection = int(MscclContextSelection.MSCCL1SHOT1NODELL)
mscclpp_context = custom_ops.mscclpp_init_context(
unique_id,
rank,
world_size,
scratch,
put_buffer,
nranks_per_node,
rank_to_node,
rank_to_ib,
selection,
)
try:
test_loop = 10
for sz in test_sizes:
for dtype in [torch.float32, torch.float16, torch.bfloat16]:
if sz * dtype.itemsize > MAX_BYTES:
continue
if rank == 0:
print(f"mscclpp allreduce test sz {sz}, dtype {dtype}")
for _ in range(test_loop):
inp1 = torch.randint(1, 16, (sz,), dtype=dtype, device=device)
inp1_ref = inp1.clone()
out1 = torch.empty_like(inp1)
custom_ops.mscclpp_allreduce(
mscclpp_context, inp1, out1, nthreads=512, nblocks=21
)
dist.all_reduce(inp1_ref, group=group)
torch.testing.assert_close(out1, inp1_ref)
finally:
dist.barrier(group=group)
dist.destroy_process_group(group=group)
def get_open_port() -> int:
try:
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
s.bind(("127.0.0.1", 0))
return s.getsockname()[1]
except OSError:
with socket.socket(socket.AF_INET6, socket.SOCK_STREAM) as s:
s.bind(("::1", 0))
return s.getsockname()[1]
def multi_process_parallel(
world_size: int, test_target: Any, target_args: tuple = ()
) -> None:
mp.set_start_method("spawn", force=True)
procs = []
distributed_init_port = get_open_port()
for i in range(world_size):
proc_args = (world_size, i, distributed_init_port) + target_args
proc = mp.Process(target=test_target, args=proc_args, name=f"Worker-{i}")
proc.start()
procs.append(proc)
for i in range(world_size):
procs[i].join()
assert (
procs[i].exitcode == 0
), f"Process {i} failed with exit code {procs[i].exitcode}"
class TestMSCCLAllReduce(unittest.TestCase):
test_sizes = [
512,
2560,
4096,
5120,
7680,
32768,
262144,
524288,
]
world_sizes = [8]
def test_correctness(self):
for world_size in self.world_sizes:
available_gpus = torch.cuda.device_count()
if world_size > available_gpus:
print(
f"Skipping world_size={world_size}, found {available_gpus} and now ray is not supported here"
)
continue
print(f"Running test for world_size={world_size}")
multi_process_parallel(
world_size, _run_correctness_worker, target_args=(self.test_sizes,)
)
print(f"custom allreduce tp = {world_size}: OK")
if __name__ == "__main__":
unittest.main()
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