import random
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import torch_npu
import mie_ops
from torch.distributed.distributed_c10d import _get_default_group
class TestDisptachFFNCombine:
def __init__(self, rank, world_size, port):
self.rank = rank
self.world_size = world_size
self.master_ip = "127.0.0.1"
self.port = port
def get_hcomm(self, comm_group):
hcomm_info = None
if torch.__version__ > "2.0.1":
hcomm_info = comm_group._get_backend(
torch.device("npu")).get_hccl_comm_name(self.rank)
else:
hcomm_info = comm_group.get_hccl_comm_name(self.rank)
return hcomm_info
def setup_ep_tp(
self,
tp_size,
ep_size,
backend_type,
ep_ranks_list=None,
tp_ranks_list=None,
):
for i in range(tp_size):
if ep_ranks_list:
ep_ranks = ep_ranks_list[i]
else:
ep_ranks = [x + ep_size * i for x in range(ep_size)]
ep_group = dist.new_group(backend=backend_type, ranks=ep_ranks)
if self.rank in ep_ranks:
ep_group_tmp = ep_group
for i in range(ep_size):
if tp_ranks_list:
tp_ranks = tp_ranks_list[i]
else:
tp_ranks = [x * ep_size + i for x in range(tp_size)]
tp_group = dist.new_group(backend=backend_type, ranks=tp_ranks)
if self.rank in tp_ranks:
tp_group_tmp = tp_group
return ep_group_tmp, tp_group_tmp
def generate_hcom(self):
torch_npu.npu.set_device(self.rank)
dist.init_process_group(
backend="hccl",
rank=self.rank,
world_size=self.world_size,
init_method=f"tcp://127.0.0.1:{self.port}",
)
ep_size = 0
tp_size = self.world_size
hcomm_info_dist = {
"default_pg_info": None,
"ep_hcomm_info": None,
"group_ep": None,
"tp_hcomm_info": None,
"group_tp": None,
}
if ep_size and tp_size:
group_ep, group_tp = self.setup_ep_tp(tp_size, ep_size,
"hccl", None, None)
hcomm_info_dist["ep_hcomm_info"] = self.get_hcomm(group_ep)
hcomm_info_dist["tp_hcomm_info"] = self.get_hcomm(group_tp)
hcomm_info_dist["group_ep"] = group_ep
hcomm_info_dist["group_tp"] = group_tp
else:
if dist.is_available():
default_pg = _get_default_group()
hcomm_info_dist["default_pg_info"] = self.get_hcomm(default_pg)
hcomm_info = hcomm_info_dist["default_pg_info"]
self.hcomm_info = hcomm_info
def run_tensor_list(self) -> bool:
torch_npu.npu.set_device(self.rank)
m = 64
k = 1024
n = 1024
topk = 8
e = 8
k2 = n // 2
n2 = k
torch_npu.npu.config.allow_internal_format = True
x = self.generate_random_tensor((m, k), dtype=torch.bfloat16).npu()
weight1 = self.generate_random_tensor((e, k, n),
dtype=torch.int8).npu()
weight1 = torch_npu.npu_format_cast(weight1, 29)
weight2 = self.generate_random_tensor((e, k2, n2),
dtype=torch.int8).npu()
weight2 = torch_npu.npu_format_cast(weight2, 29)
expert_idx = torch.randint(0,
self.world_size * e, (m, topk),
dtype=torch.int32).npu()
scale1 = torch.randint(0, 1, (e, n), dtype=torch.int64).npu()
scale2 = torch.randint(0, 1, (e, n2), dtype=torch.int64).npu()
probs = torch.randn(size=(m, topk), dtype=torch.float32).npu()
weight1_nz_npu = []
weight2_nz_npu = []
scale1_npu = []
scale2_npu = []
for i in range(e):
weight1_nz_npu.append(
torch_npu.npu_format_cast(weight1[i].npu(), 29))
scale1_npu.append(scale1[i].npu())
weight2_nz_npu.append(
torch_npu.npu_format_cast(weight2[i].npu(), 29))
scale2_npu.append(scale2[i].npu())
out = self.generate_random_tensor((m, k), dtype=torch.bfloat16).npu()
expert_token_nums = self.generate_random_tensor((1, m), dtype=torch.int32).npu()
torch.ops.mie_ops.npu_dispatch_ffn_combine(
x=x,
weight1=weight1_nz_npu,
weight2=weight2_nz_npu,
expert_idx=expert_idx,
scale1=scale1_npu,
scale2=scale2_npu,
probs=probs,
group=self.hcomm_info,
max_output_size=512,
out=out,
expert_token_nums=expert_token_nums,
)
return True
def run_normal(self) -> bool:
torch_npu.npu.set_device(self.rank)
m = 64
k = 1024
n = 1024
topk = 8
e = 8
k2 = n // 2
n2 = k
torch_npu.npu.config.allow_internal_format = True
x = self.generate_random_tensor((m, k), dtype=torch.bfloat16).npu()
weight1 = self.generate_random_tensor((e, k, n),
dtype=torch.int8).npu()
weight1 = torch_npu.npu_format_cast(weight1, 29)
weight2 = self.generate_random_tensor((e, k2, n2),
dtype=torch.int8).npu()
weight2 = torch_npu.npu_format_cast(weight2, 29)
expert_idx = torch.randint(0,
self.world_size * e, (m, topk),
dtype=torch.int32).npu()
scale1 = torch.randint(0, 1, (e, n), dtype=torch.int64).npu()
scale2 = torch.randint(0, 1, (e, n2), dtype=torch.int64).npu()
probs = torch.randn(size=(m, topk), dtype=torch.float32).npu()
weight1_nz_npu = []
weight2_nz_npu = []
scale1_npu = []
scale2_npu = []
weight1_nz_npu.append(torch_npu.npu_format_cast(weight1.npu(), 29))
scale1_npu.append(scale1.npu())
weight2_nz_npu.append(torch_npu.npu_format_cast(weight2.npu(), 29))
scale2_npu.append(scale2.npu())
out = self.generate_random_tensor((m, k), dtype=torch.bfloat16).npu()
expert_token_nums = self.generate_random_tensor((1, m), dtype=torch.int32).npu()
torch.ops.mie_ops.npu_dispatch_ffn_combine(
x=x,
weight1=weight1_nz_npu,
weight2=weight2_nz_npu,
expert_idx=expert_idx,
scale1=scale1_npu,
scale2=scale2_npu,
probs=probs,
group=self.hcomm_info,
max_output_size=512,
out=out,
expert_token_nums=expert_token_nums,
)
return True
def generate_random_tensor(self, size, dtype):
if dtype in [torch.float16, torch.bfloat16, torch.float32]:
return torch.randn(size=size, dtype=dtype)
elif dtype is torch.int8:
return torch.randint(-16, 16, size=size, dtype=dtype)
elif dtype is torch.int32:
return torch.randint(-1024, 1024, size=size, dtype=dtype)
else:
raise ValueError(f"Invalid dtype: {dtype}")
def worker(rank: int, world_size: int, port: int, q: mp.SimpleQueue):
op = TestDisptachFFNCombine(rank, world_size, port)
op.generate_hcom()
out1 = op.run_tensor_list()
q.put(out1)
out2 = op.run_normal()
q.put(out2)
@torch.inference_mode()
def test_npu_dispatch_ffn_combine_kernel():
world_size = 16
mp.set_start_method("fork", force=True)
q = mp.SimpleQueue()
p_list = []
port = 29501 + random.randint(0, 10000)
for rank in range(world_size):
p = mp.Process(target=worker, args=(rank, world_size, port, q))
p.start()
p_list.append(p)
results = [q.get() for _ in range(world_size)]
for p in p_list:
p.join()
assert all(results)
if __name__ == "__main__":
test_npu_dispatch_ffn_combine_kernel()
print("Test npu_dispatch_ffn_combine passed!")
