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[Nightly] Move ops to the correct path (#5642)

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### What this PR does / why we need it?
Move ops to the correct path where they belong

- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

Signed-off-by: wangli <wangli858794774@gmail.com>
This commit is contained in:
Li Wang
2026-01-09 09:23:36 +08:00
committed by GitHub
parent 1ff1c96d13
commit 595d3484c4
7 changed files with 6 additions and 7 deletions
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0
tests/e2e/nightly/single_node/ops/multicard_ops_a3/__init__.py Normal file
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tests/e2e/nightly/single_node/ops/multicard_ops_a3/test_dispatch_ffn_combine.py Normal file
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import random
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import torch_npu
from torch.distributed.distributed_c10d import _get_default_group
from vllm_ascend.utils import enable_custom_op
enable_custom_op()
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,
rank,
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 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 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(self.rank, 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_npu_out(self) -> bool:
torch_npu.npu.set_device(self.rank)
m = 2 # token-num 32
k = 4 # hidden_size 7168
n = 4 # mid-hidden-size 4096
topk = 2
e = 2 # expert-num-per-rank 16
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()
out = self.generate_random_tensor((m, k), dtype=torch.bfloat16).npu()
torch.ops._C_ascend.dispatch_ffn_combine(
x=x,
weight1=weight1,
weight2=weight2,
expert_idx=expert_idx,
scale1=scale1,
scale2=scale2,
probs=probs,
group=self.hcomm_info,
max_output_size=512,
out=out,
)
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()
out = op.run_npu_out()
q.put(out)
@torch.inference_mode()
def test_dispatch_ffn_combine_kernel():
world_size = 2
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)

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tests/e2e/nightly/single_node/ops/multicard_ops_a3/test_dispatch_gmm_combine_decode.py Normal file
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import gc
import os
import sys
from pathlib import Path
import numpy as np
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import torch_npu
import torchair
from vllm_ascend.utils import enable_custom_op
torch.manual_seed(42)
torch_npu.npu.config.allow_internal_format = True
enable_custom_op()
LOG_NAME = "dispatch_gmm_combine_decode_test_logs"
BASE_KWARGS = {
"batch_size": 64,
"token_hidden_size": 7168,
"moe_intermediate_size": 2048,
"ep_world_size": 16,
"moe_expert_num": 64,
"shared_expert_rank_num": 0,
"top_k": 8,
"test_bfloat16": True,
"enable_dynamic_bs": False,
"test_graph": False,
"with_mc2_mask": False,
"dynamic_eplb": False
}
def redirect_output(log_file_path):
log_path = Path(LOG_NAME) / log_file_path
log_path.parent.mkdir(parents=True, exist_ok=True)
f = open(LOG_NAME + "/" + log_file_path, "w")
os.dup2(f.fileno(), sys.stdout.fileno())
os.dup2(f.fileno(), sys.stderr.fileno())
return f
def permute_weight(w: torch.Tensor, tile_n):
*dims, n = w.shape
order = list(range(len(dims))) + [-2, -3, -1]
return w.reshape(*dims, 2, n // tile_n,
tile_n // 2).permute(order).reshape(*dims,
n).contiguous()
def output_to_file(rank_id):
return False
class DecodeMoeOps(torch.nn.Module):
def __init__(self,
gmm1_weight,
gmm1_weight_scale,
gmm2_weight,
gmm2_weight_scale,
ep_hcomm_info,
batch_size,
token_hidden_size,
moe_intermediate_size,
ep_world_size,
moe_expert_num,
global_rank_id,
shared_expert_rank_num=0,
dynamic_eplb=False):
super().__init__()
self.ep_hcomm_info = ep_hcomm_info
self.batch_size = batch_size
self.token_hidden_size = token_hidden_size
self.moe_intermediate_size = moe_intermediate_size
self.ep_world_size = ep_world_size
self.moe_expert_num = moe_expert_num
self.global_rank_id = global_rank_id
self.shared_expert_rank_num = shared_expert_rank_num
is_shared_expert = global_rank_id < shared_expert_rank_num
moe_expert_num_per_rank = moe_expert_num // (ep_world_size -
shared_expert_rank_num)
self.local_expert_num = 1 if is_shared_expert else moe_expert_num_per_rank
self.ep_recv_count_size = self.local_expert_num * ep_world_size
self.dynamic_eplb = dynamic_eplb
self.gmm1_weight = torch.empty([
self.local_expert_num, self.token_hidden_size,
self.moe_intermediate_size * 2
])
self.gmm1_weight_scale = torch.empty(
[self.local_expert_num, self.moe_intermediate_size * 2])
self.gmm2_weight = torch.empty([
self.local_expert_num, self.moe_intermediate_size,
self.token_hidden_size
])
self.gmm2_weight_scale = torch.empty(
[self.local_expert_num, self.token_hidden_size])
self._process_weights_after_loading(gmm1_weight, gmm1_weight_scale,
gmm2_weight, gmm2_weight_scale)
def _process_weights_after_loading(self, gmm1_weight, gmm1_weight_scale,
gmm2_weight, gmm2_weight_scale):
gmm1_weight = torch_npu.npu_format_cast(gmm1_weight,
torch_npu.Format.FRACTAL_NZ)
gmm2_weight = torch_npu.npu_format_cast(gmm2_weight,
torch_npu.Format.FRACTAL_NZ)
self.gmm1_weight = torch.nn.Parameter(gmm1_weight, requires_grad=False)
self.gmm1_weight_scale = torch.nn.Parameter(gmm1_weight_scale,
requires_grad=False)
self.gmm2_weight = torch.nn.Parameter(gmm2_weight, requires_grad=False)
self.gmm2_weight_scale = torch.nn.Parameter(gmm2_weight_scale,
requires_grad=False)
self.gmm1_weight_scale_fp32 = torch.nn.Parameter(
gmm1_weight_scale.float(), requires_grad=False)
self.gmm2_weight_scale_fp32 = torch.nn.Parameter(
gmm2_weight_scale.float(), requires_grad=False)
def _apply_ops(self, x, expert_ids, smooth_scales, expert_scales,
x_active_mask):
raise NotImplementedError("To be implemented in subclass")
def forward(self, x, expert_ids, smooth_scales, expert_scales,
x_active_mask):
return self._apply_ops(x, expert_ids, smooth_scales, expert_scales,
x_active_mask)
class SmallOps(DecodeMoeOps):
def __init__(self,
gmm1_weight,
gmm1_weight_scale,
gmm2_weight,
gmm2_weight_scale,
ep_hcomm_info,
batch_size,
token_hidden_size,
moe_intermediate_size,
ep_world_size,
moe_expert_num,
global_rank_id,
shared_expert_rank_num=0,
dynamic_eplb=False):
super().__init__(gmm1_weight, gmm1_weight_scale, gmm2_weight,
gmm2_weight_scale, ep_hcomm_info, batch_size,
token_hidden_size, moe_intermediate_size,
ep_world_size, moe_expert_num, global_rank_id,
shared_expert_rank_num, dynamic_eplb)
self.tp_hcomm_info = ""
def _apply_ops(self, x, expert_ids, smooth_scales, expert_scales,
x_active_mask):
outputs = torch_npu.npu_moe_distribute_dispatch_v2(
x=x,
expert_ids=expert_ids,
expert_scales=expert_scales,
x_active_mask=x_active_mask,
group_ep=self.ep_hcomm_info,
ep_world_size=self.ep_world_size,
ep_rank_id=self.global_rank_id,
moe_expert_num=self.moe_expert_num,
group_tp=self.tp_hcomm_info,
tp_world_size=1,
tp_rank_id=0,
expert_shard_type=0,
shared_expert_num=1,
shared_expert_rank_num=self.shared_expert_rank_num,
quant_mode=2,
global_bs=self.batch_size * self.ep_world_size,
expert_token_nums_type=1, # 0代表前缀和，1代表各自数量
)
expand_x, dynamic_scales, assist_info_for_combine, expert_token_nums, ep_send_counts, tp_send_counts, expand_scales = outputs
output_dtype = x.dtype
y1_int32 = torch_npu.npu_grouped_matmul(
x=[expand_x],
weight=[self.gmm1_weight],
split_item=3,
group_list_type=1, # 默认为0，代表前缀和形式
group_type=0, # 0代表m轴分组
group_list=expert_token_nums,
output_dtype=torch.int32)[0]
y1, y1_scale = torch_npu.npu_dequant_swiglu_quant(
x=y1_int32,
weight_scale=self.gmm1_weight_scale.to(torch.float32),
activation_scale=dynamic_scales,
bias=None,
quant_scale=None,
quant_offset=None,
group_index=expert_token_nums,
activate_left=True,
quant_mode=1,
)
y2 = torch_npu.npu_grouped_matmul(x=[y1],
weight=[self.gmm2_weight],
scale=[self.gmm2_weight_scale],
per_token_scale=[y1_scale],
split_item=2,
group_list_type=1,
group_type=0,
group_list=expert_token_nums,
output_dtype=output_dtype)[0]
combine_output = torch_npu.npu_moe_distribute_combine_v2(
expand_x=y2,
expert_ids=expert_ids,
assist_info_for_combine=assist_info_for_combine,
ep_send_counts=ep_send_counts,
expert_scales=expert_scales,
x_active_mask=x_active_mask,
group_ep=self.ep_hcomm_info,
ep_world_size=self.ep_world_size,
ep_rank_id=self.global_rank_id,
moe_expert_num=self.moe_expert_num,
tp_send_counts=tp_send_counts,
expand_scales=expand_scales,
group_tp=self.tp_hcomm_info,
tp_world_size=1,
tp_rank_id=0,
expert_shard_type=0,
shared_expert_num=1,
shared_expert_rank_num=self.shared_expert_rank_num,
global_bs=self.batch_size * self.ep_world_size)
return (combine_output, expert_token_nums)
class FusionOp(DecodeMoeOps):
def __init__(self,
gmm1_weight,
gmm1_weight_scale,
gmm2_weight,
gmm2_weight_scale,
ep_hcomm_info,
batch_size,
token_hidden_size,
moe_intermediate_size,
ep_world_size,
moe_expert_num,
global_rank_id,
shared_expert_rank_num=0,
dynamic_eplb=False):
super().__init__(gmm1_weight, gmm1_weight_scale, gmm2_weight,
gmm2_weight_scale, ep_hcomm_info, batch_size,
token_hidden_size, moe_intermediate_size,
ep_world_size, moe_expert_num, global_rank_id,
shared_expert_rank_num, dynamic_eplb)
def _apply_ops(self, x, expert_ids, smooth_scales, expert_scales,
x_active_mask):
output = torch.ops._C_ascend.dispatch_gmm_combine_decode(
x=x,
expert_ids=expert_ids,
gmm1_permuted_weight=self.gmm1_weight,
gmm1_permuted_weight_scale=self.gmm1_weight_scale_fp32,
gmm2_weight=self.gmm2_weight,
gmm2_weight_scale=self.gmm2_weight_scale_fp32,
expert_scales=expert_scales,
expert_smooth_scales=smooth_scales,
x_active_mask=x_active_mask,
group_ep=self.ep_hcomm_info,
ep_rank_size=self.ep_world_size,
ep_rank_id=self.global_rank_id,
moe_expert_num=self.moe_expert_num,
shared_expert_num=1,
shared_expert_rank_num=self.shared_expert_rank_num,
quant_mode=0,
global_bs=self.batch_size * self.ep_world_size)
return output
def _process_weights_after_loading(self, gmm1_weight, gmm1_weight_scale,
gmm2_weight, gmm2_weight_scale):
gmm1_weight = torch_npu.npu_format_cast(gmm1_weight,
torch_npu.Format.FRACTAL_NZ)
gmm2_weight = torch_npu.npu_format_cast(gmm2_weight,
torch_npu.Format.FRACTAL_NZ)
gmm1_weight_scale = gmm1_weight_scale.float()
gmm2_weight_scale = gmm2_weight_scale.float()
if self.dynamic_eplb:
self.gmm1_weight = [
weight.clone() for weight in gmm1_weight.unbind(dim=0)
]
self.gmm1_weight_scale_fp32 = [
weight.clone() for weight in gmm1_weight_scale.unbind(dim=0)
]
self.gmm2_weight = [
weight.clone() for weight in gmm2_weight.unbind(dim=0)
]
self.gmm2_weight_scale_fp32 = [
weight.clone() for weight in gmm2_weight_scale.unbind(dim=0)
]
else:
self.gmm1_weight = [gmm1_weight.clone()]
self.gmm1_weight_scale_fp32 = [gmm1_weight_scale.clone()]
self.gmm2_weight = [gmm2_weight.clone()]
self.gmm2_weight_scale_fp32 = [gmm2_weight_scale.clone()]
def generate_datas(batch_size,
token_hidden_size,
moe_intermediate_size,
ep_world_size,
moe_expert_num,
global_rank_id,
shared_expert_rank_num=0,
top_k=8,
test_bfloat16=True,
enable_dynamic_bs=False,
with_mc2_mask=False):
is_shared_expert = global_rank_id < shared_expert_rank_num
moe_expert_num_per_rank = moe_expert_num // (ep_world_size -
shared_expert_rank_num)
actual_bs = int(
torch.randint(2 if with_mc2_mask else 1, batch_size, [1]).item(
) if enable_dynamic_bs else batch_size)
local_expert_num = 1 if is_shared_expert else moe_expert_num_per_rank
gmm1_input_dim = token_hidden_size
gmm1_output_dim = moe_intermediate_size * 2
gmm2_input_dim = moe_intermediate_size
gmm2_output_dim = token_hidden_size
x = torch.rand([actual_bs, token_hidden_size]) * 10 - 5
expert_ids = torch.arange(
global_rank_id * batch_size * top_k,
global_rank_id * batch_size * top_k + actual_bs * top_k).to(
torch.int32).view(actual_bs, top_k)
expert_ids = expert_ids % moe_expert_num
if is_shared_expert:
gmm1_weight = torch.ones([
local_expert_num, gmm1_input_dim, gmm1_output_dim
]).to(torch.int8) * 4
gmm2_weight = torch.ones([
local_expert_num, gmm2_input_dim, gmm2_output_dim
]).to(torch.int8) * 4
gmm1_weight[:, :, ::2] = gmm1_weight[:, :, ::2] * -1
gmm2_weight[:, :, ::2] = gmm2_weight[:, :, ::2] * -1
gmm1_weight_scale = torch.ones([local_expert_num, gmm1_output_dim
]) * 0.0015
gmm2_weight_scale = torch.ones([local_expert_num, gmm2_output_dim
]) * 0.0015
else:
gmm1_weight = torch.randint(
-16, 16,
[local_expert_num, gmm1_input_dim, gmm1_output_dim]).to(torch.int8)
gmm2_weight = torch.randint(
-16, 16,
[local_expert_num, gmm2_input_dim, gmm2_output_dim]).to(torch.int8)
gmm1_weight_scale = torch.rand([local_expert_num, gmm1_output_dim
]) * 0.003 + 0.0015
gmm2_weight_scale = torch.rand([local_expert_num, gmm2_output_dim
]) * 0.003 + 0.0015
expert_scales = torch.rand(actual_bs, top_k)
if test_bfloat16:
x = x.bfloat16()
gmm1_weight_scale = gmm1_weight_scale.bfloat16()
gmm2_weight_scale = gmm2_weight_scale.bfloat16()
else:
x = x.half()
smooth_sales = None
x_active_mask = None
valid_token_num = actual_bs
if with_mc2_mask:
valid_token_num = int(torch.randint(1, actual_bs, [1]).item())
x_active_mask = torch.cat(
(torch.ones(valid_token_num),
torch.zeros(actual_bs - valid_token_num))).bool()
return (x, expert_ids, smooth_sales, expert_scales, x_active_mask), \
(gmm1_weight, gmm1_weight_scale, gmm2_weight, gmm2_weight_scale), \
actual_bs, valid_token_num
def run_once(local_rank_id,
batch_size,
token_hidden_size,
moe_intermediate_size,
ep_world_size,
moe_expert_num,
shared_expert_rank_num=0,
top_k=8,
test_bfloat16=True,
enable_dynamic_bs=False,
test_graph=False,
with_mc2_mask=False,
dynamic_eplb=False):
log_file = redirect_output(f"local_rank_{local_rank_id}.log"
) if output_to_file(local_rank_id) else None
global_rank_id = local_rank_id # 单机
device_id = local_rank_id % 16
torch_npu.npu.set_device(device_id)
# 初始化分布式环境
os.environ["MASTER_ADDR"] = "127.0.0.1"
os.environ["MASTER_PORT"] = "29500" # 端口号随意
dist.init_process_group(backend="hccl",
rank=local_rank_id,
world_size=ep_world_size)
ep_ranks_list = list(np.arange(0, ep_world_size))
ep_group = dist.new_group(backend="hccl", ranks=ep_ranks_list)
ep_group_small = dist.new_group(backend="hccl", ranks=ep_ranks_list)
ep_hcomm_info_fused = ep_group._get_backend(
torch.device("npu")).get_hccl_comm_name(local_rank_id)
ep_hcomm_info_small = ep_group_small._get_backend(
torch.device("npu")).get_hccl_comm_name(local_rank_id)
torch_npu.npu.synchronize(device_id)
parameter = (batch_size, token_hidden_size, moe_intermediate_size,
ep_world_size, moe_expert_num, global_rank_id,
shared_expert_rank_num)
input_datas, weight_datas, actual_bs, valid_token_num = generate_datas(
*parameter, top_k, test_bfloat16, enable_dynamic_bs, with_mc2_mask)
input_datas = [
data.npu() if data is not None else None for data in input_datas
]
weight_datas = [
data.npu() if data is not None else None for data in weight_datas
]
small_ops = SmallOps(*weight_datas, ep_hcomm_info_small, *parameter,
dynamic_eplb).npu() # type: ignore
fused_ops = FusionOp(*weight_datas, ep_hcomm_info_fused, *parameter,
dynamic_eplb).npu() # type: ignore
if test_graph:
config = torchair.CompilerConfig()
config.mode = "reduce-overhead"
npu_backend = torchair.get_npu_backend(compiler_config=config)
fused_ops = torch.compile(fused_ops, backend=npu_backend)
small_op_token_output, small_op_count_output = small_ops(*input_datas)
fused_op_token_output, fused_op_count_output = fused_ops(*input_datas)
torch_npu.npu.synchronize(device_id)
dist.destroy_process_group()
if log_file is not None:
log_file.close()
torch.testing.assert_close(small_op_token_output[0:valid_token_num].cpu(),
fused_op_token_output[0:valid_token_num].cpu(),
atol=2.0,
rtol=0.02)
torch.testing.assert_close(small_op_count_output.cpu(),
fused_op_count_output.cpu())
gc.collect()
torch.npu.empty_cache()
torch.npu.reset_peak_memory_stats()
@torch.inference_mode()
def test_dispatch_gmm_combine_decode_base():
custom_kwargs = BASE_KWARGS
ep_world_size = custom_kwargs["ep_world_size"]
custom_args = tuple(custom_kwargs.values())
mp.spawn(run_once, args=custom_args, nprocs=ep_world_size, join=True)
@torch.inference_mode()
def test_dispatch_gmm_combine_decode_with_mc2_mask():
custom_kwargs = BASE_KWARGS
custom_kwargs["with_mc2_mask"] = True
ep_world_size = custom_kwargs["ep_world_size"]
custom_args = tuple(custom_kwargs.values())
mp.spawn(run_once, args=custom_args, nprocs=ep_world_size, join=True)
@torch.inference_mode()
def test_dispatch_gmm_combine_decode_dynamic_eplb():
custom_kwargs = BASE_KWARGS
custom_kwargs["dynamic_eplb"] = True
ep_world_size = custom_kwargs["ep_world_size"]
custom_args = tuple(custom_kwargs.values())
mp.spawn(run_once, args=custom_args, nprocs=ep_world_size, join=True)