111 lines
4.6 KiB
Python
111 lines
4.6 KiB
Python
import argparse
|
|
import inspect
|
|
import os
|
|
import pathlib
|
|
import sys
|
|
from typing import Tuple
|
|
|
|
import torch
|
|
import torch.distributed as dist
|
|
|
|
|
|
REPO_ROOT = pathlib.Path(__file__).resolve().parents[2]
|
|
if str(REPO_ROOT) not in sys.path:
|
|
sys.path.insert(0, str(REPO_ROOT))
|
|
|
|
import deep_gemm
|
|
|
|
|
|
def init_test_dist(local_rank_arg: int = None) -> Tuple[int, int, dist.ProcessGroup]:
|
|
local_rank = local_rank_arg if local_rank_arg is not None else int(os.environ.get('LOCAL_RANK', '0'))
|
|
rank = int(os.environ.get('RANK', '0'))
|
|
world_size = int(os.environ.get('WORLD_SIZE', '1'))
|
|
master_addr = os.environ.get('MASTER_ADDR', '127.0.0.1')
|
|
master_port = int(os.environ.get('MASTER_PORT', '8361'))
|
|
|
|
torch.cuda.set_device(local_rank)
|
|
sig = inspect.signature(dist.init_process_group)
|
|
params = {
|
|
'backend': 'nccl',
|
|
'init_method': f'tcp://{master_addr}:{master_port}',
|
|
'world_size': world_size,
|
|
'rank': rank,
|
|
}
|
|
if 'device_id' in sig.parameters:
|
|
params['device_id'] = torch.device(f'cuda:{local_rank}')
|
|
dist.init_process_group(**params)
|
|
torch.set_default_device('cuda')
|
|
return rank, world_size, dist.new_group(list(range(world_size)))
|
|
|
|
|
|
def make_weights(num_experts_per_rank: int, hidden: int, intermediate_hidden: int):
|
|
l1_weights = torch.randn(
|
|
(num_experts_per_rank, intermediate_hidden * 2, hidden),
|
|
dtype=torch.float32, device='cuda').to(torch.float8_e4m3fn)
|
|
l2_weights = torch.randn(
|
|
(num_experts_per_rank, hidden, intermediate_hidden),
|
|
dtype=torch.float32, device='cuda').to(torch.float8_e4m3fn)
|
|
l1_weights_sf = torch.ones(
|
|
(num_experts_per_rank, (intermediate_hidden * 2 + 127) // 128, hidden // 128),
|
|
dtype=torch.float32, device='cuda')
|
|
l2_weights_sf = torch.ones(
|
|
(num_experts_per_rank, (hidden + 127) // 128, intermediate_hidden // 128),
|
|
dtype=torch.float32, device='cuda')
|
|
return deep_gemm.transform_weights_for_mega_moe(
|
|
(l1_weights, l1_weights_sf), (l2_weights, l2_weights_sf))
|
|
|
|
|
|
def main() -> None:
|
|
parser = argparse.ArgumentParser(description='SM90 MegaMoE Phase 1 interface smoke')
|
|
parser.add_argument('--num-tokens', type=int, default=8)
|
|
parser.add_argument('--num-max-tokens-per-rank', type=int, default=384)
|
|
parser.add_argument('--hidden', type=int, default=512)
|
|
parser.add_argument('--intermediate-hidden', type=int, default=256)
|
|
parser.add_argument('--num-experts', type=int, default=16)
|
|
parser.add_argument('--num-topk', type=int, default=6)
|
|
parser.add_argument('--local-rank', type=int, default=None)
|
|
args = parser.parse_args()
|
|
|
|
local_rank = args.local_rank if args.local_rank is not None else int(os.environ.get('LOCAL_RANK', '0'))
|
|
rank_idx, num_ranks, group = init_test_dist(local_rank)
|
|
assert torch.cuda.get_device_capability(torch.cuda.current_device())[0] == 9
|
|
assert args.hidden % 128 == 0 and args.intermediate_hidden % 128 == 0
|
|
assert args.num_experts % num_ranks == 0
|
|
|
|
buffer = deep_gemm.get_symm_buffer_for_mega_moe(
|
|
group, args.num_experts,
|
|
args.num_max_tokens_per_rank, args.num_topk,
|
|
args.hidden, args.intermediate_hidden)
|
|
assert buffer.x.shape == (buffer.num_max_tokens_per_rank, args.hidden)
|
|
assert buffer.x_sf.shape == (buffer.num_max_tokens_per_rank, args.hidden // 128)
|
|
assert buffer.x_sf.dtype == torch.float32
|
|
assert buffer.l1_acts.shape[1] == args.hidden
|
|
assert buffer.l1_acts_sf.shape[1] == args.hidden // 128
|
|
assert buffer.l1_acts_sf.dtype == torch.float32
|
|
assert buffer.l2_acts.shape[1] == args.intermediate_hidden
|
|
assert buffer.l2_acts_sf.shape[1] == args.intermediate_hidden // 128
|
|
assert buffer.l2_acts_sf.dtype == torch.float32
|
|
|
|
num_tokens = args.num_tokens
|
|
buffer.x[:num_tokens].copy_(torch.randn((num_tokens, args.hidden), device='cuda').to(torch.float8_e4m3fn))
|
|
buffer.x_sf[:num_tokens].fill_(1.0)
|
|
buffer.topk_idx[:num_tokens].fill_(rank_idx * (args.num_experts // num_ranks))
|
|
buffer.topk_weights[:num_tokens].fill_(1.0)
|
|
|
|
weights = make_weights(args.num_experts // num_ranks, args.hidden, args.intermediate_hidden)
|
|
y = torch.empty((num_tokens, args.hidden), dtype=torch.bfloat16, device='cuda')
|
|
stats = torch.zeros((args.num_experts // num_ranks,), dtype=torch.int32, device='cuda')
|
|
deep_gemm.fp8_mega_moe(y, weights[0], weights[1], buffer,
|
|
cumulative_local_expert_recv_stats=stats)
|
|
torch.cuda.synchronize()
|
|
|
|
dist.barrier(group=group)
|
|
buffer.destroy()
|
|
dist.destroy_process_group()
|
|
if rank_idx == 0:
|
|
print('[PASSED] SM90 MegaMoE Phase 1 interface smoke', flush=True)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|