113 lines
4.8 KiB
Python
113 lines
4.8 KiB
Python
import argparse
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import inspect
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import os
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import pathlib
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import sys
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from typing import Tuple
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import torch
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import torch.distributed as dist
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REPO_ROOT = pathlib.Path(__file__).resolve().parents[2]
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if str(REPO_ROOT) not in sys.path:
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sys.path.insert(0, str(REPO_ROOT))
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import deep_gemm
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def init_test_dist(local_rank_arg: int = None) -> Tuple[int, int, dist.ProcessGroup]:
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local_rank = local_rank_arg if local_rank_arg is not None else int(os.environ.get('LOCAL_RANK', '0'))
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rank = int(os.environ.get('RANK', '0'))
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world_size = int(os.environ.get('WORLD_SIZE', '1'))
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master_addr = os.environ.get('MASTER_ADDR', '127.0.0.1')
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master_port = int(os.environ.get('MASTER_PORT', '8361'))
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torch.cuda.set_device(local_rank)
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sig = inspect.signature(dist.init_process_group)
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params = {
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'backend': 'nccl',
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'init_method': f'tcp://{master_addr}:{master_port}',
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'world_size': world_size,
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'rank': rank,
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}
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if 'device_id' in sig.parameters:
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params['device_id'] = torch.device(f'cuda:{local_rank}')
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dist.init_process_group(**params)
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torch.set_default_device('cuda')
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return rank, world_size, dist.new_group(list(range(world_size)))
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def make_weights(num_experts_per_rank: int, hidden: int, intermediate_hidden: int):
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l1_weights = torch.randn(
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(num_experts_per_rank, intermediate_hidden * 2, hidden),
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dtype=torch.float32, device='cuda').to(torch.float8_e4m3fn)
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l2_weights = torch.randn(
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(num_experts_per_rank, hidden, intermediate_hidden),
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dtype=torch.float32, device='cuda').to(torch.float8_e4m3fn)
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l1_weights_sf = torch.ones(
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(num_experts_per_rank, (intermediate_hidden * 2 + 127) // 128, hidden // 128),
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dtype=torch.float32, device='cuda')
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l2_weights_sf = torch.ones(
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(num_experts_per_rank, (hidden + 127) // 128, intermediate_hidden // 128),
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dtype=torch.float32, device='cuda')
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return deep_gemm.transform_weights_for_mega_moe(
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(l1_weights, l1_weights_sf), (l2_weights, l2_weights_sf))
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def main() -> None:
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parser = argparse.ArgumentParser(description='SM90 MegaMoE Phase 1 interface smoke')
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parser.add_argument('--num-tokens', type=int, default=8)
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parser.add_argument('--num-max-tokens-per-rank', type=int, default=384)
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parser.add_argument('--hidden', type=int, default=512)
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parser.add_argument('--intermediate-hidden', type=int, default=256)
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parser.add_argument('--num-experts', type=int, default=16)
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parser.add_argument('--num-topk', type=int, default=6)
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parser.add_argument('--local-rank', type=int, default=None)
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args = parser.parse_args()
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local_rank = args.local_rank if args.local_rank is not None else int(os.environ.get('LOCAL_RANK', '0'))
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rank_idx, num_ranks, group = init_test_dist(local_rank)
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assert torch.cuda.get_device_capability(torch.cuda.current_device())[0] == 9
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assert args.hidden % 128 == 0 and args.intermediate_hidden % 128 == 0
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assert args.num_experts % num_ranks == 0
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buffer = deep_gemm.get_symm_buffer_for_mega_moe(
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group, args.num_experts,
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args.num_max_tokens_per_rank, args.num_topk,
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args.hidden, args.intermediate_hidden)
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assert buffer.x.shape == (buffer.num_max_tokens_per_rank, args.hidden)
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assert buffer.x_sf.shape == (buffer.num_max_tokens_per_rank, args.hidden // 128)
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assert buffer.x_sf.dtype == torch.float32
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assert buffer.l1_acts.shape[1] == args.hidden
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assert buffer.l1_acts_sf.shape[1] == args.hidden // 128
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assert buffer.l1_acts_sf.dtype == torch.float32
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assert buffer.l2_acts.shape[1] == args.intermediate_hidden
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assert buffer.l2_acts_sf.shape[1] == args.intermediate_hidden // 64
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assert buffer.l2_acts_sf.dtype == torch.float32
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assert buffer.combine_acts.shape == (args.num_topk, buffer.num_max_tokens_per_rank, args.hidden)
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assert buffer.combine_acts.dtype == torch.bfloat16
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num_tokens = args.num_tokens
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buffer.x[:num_tokens].copy_(torch.randn((num_tokens, args.hidden), device='cuda').to(torch.float8_e4m3fn))
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buffer.x_sf[:num_tokens].fill_(1.0)
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buffer.topk_idx[:num_tokens].fill_(rank_idx * (args.num_experts // num_ranks))
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buffer.topk_weights[:num_tokens].fill_(1.0)
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weights = make_weights(args.num_experts // num_ranks, args.hidden, args.intermediate_hidden)
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y = torch.empty((num_tokens, args.hidden), dtype=torch.bfloat16, device='cuda')
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stats = torch.zeros((args.num_experts // num_ranks,), dtype=torch.int32, device='cuda')
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deep_gemm.fp8_mega_moe(y, weights[0], weights[1], buffer,
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cumulative_local_expert_recv_stats=stats)
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torch.cuda.synchronize()
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dist.barrier(group=group)
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buffer.destroy()
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dist.destroy_process_group()
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if rank_idx == 0:
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print('[PASSED] SM90 MegaMoE Phase 1 interface smoke', flush=True)
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if __name__ == '__main__':
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main()
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