Commit 969e30f8 authored by lishen's avatar lishen
Browse files

完善低延迟模式int8类型的测试

parent f08e5bf1
......@@ -7,7 +7,7 @@ from functools import partial
from typing import Literal, Set
import deep_ep
from utils import init_dist, bench, bench_kineto, calc_diff, hash_tensor, per_token_cast_back, per_token_cast_back_int8
from utils import init_dist, bench, bench_kineto, calc_diff, hash_tensor, per_token_cast_back_int8
def test_main(num_tokens: int,
......@@ -68,6 +68,7 @@ def test_main(num_tokens: int,
hook() if return_recv_hook else event.current_stream_wait()
packed_recv_x = (packed_recv_x[0], packed_recv_x[1].contiguous()) if dispatch_use_fp8 else packed_recv_x
simulated_gemm_x = per_token_cast_back_int8(packed_recv_x[0].view(-1, hidden), packed_recv_x[1].view(-1, 1)).view(packed_recv_x[0].shape)
for i in range(num_local_experts if do_check else 0):
expert_id = rank * num_local_experts + i
......@@ -133,10 +134,16 @@ def test_main(num_tokens: int,
use_fp8=True, round_scale=False, use_ue8m0=False, use_int8=True,
async_finish=False, return_recv_hook=return_recv_hook)
large_gemm_with_hook(hook) if return_recv_hook else None
combined_x, event, hook = buffer.low_latency_combine(simulated_gemm_x,
topk_idx,
topk_weights,
handle,
return_recv_hook=return_recv_hook)
large_gemm_with_hook(hook) if return_recv_hook else None
# Calculate bandwidth
num_fp8_bytes, num_bf16_bytes = (hidden + hidden / 128 * 4 + 16), hidden * 2
num_logfmt10_bytes = hidden * 10 / 8 + hidden / 128 * 4
scale_size = 1 # hidden / 128
num_fp8_bytes, num_bf16_bytes = (hidden + scale_size * 4 + 16), hidden * 2
num_dispatch_comm_bytes, num_combine_comm_bytes = 0, 0
for i in range(num_tokens):
num_selections = (topk_idx[i] != -1).sum().item()
......@@ -144,18 +151,20 @@ def test_main(num_tokens: int,
num_combine_comm_bytes += num_bf16_bytes * num_selections
# Separate profiling
for return_recv_hook in (True, ):
for return_recv_hook in (True, False):
group.barrier()
dispatch_t = bench_kineto(partial(test_func, return_recv_hook=return_recv_hook),
kernel_names='dispatch',
dispatch_t, combine_t = bench_kineto(partial(test_func, return_recv_hook=return_recv_hook),
kernel_names=('dispatch', 'combine'),
barrier_comm_profiling=True,
suppress_kineto_output=True,
num_kernels_per_period=2 if return_recv_hook else 1)
if not return_recv_hook:
print(f'[rank {rank}] Dispatch bandwidth: {num_dispatch_comm_bytes / 1e9 / dispatch_t:.2f} GB/s, avg_t={dispatch_t * 1e6:.2f} us',
print(f'[rank {rank}] Dispatch bandwidth: {num_dispatch_comm_bytes / 1e9 / dispatch_t:.2f} GB/s, avg_t={dispatch_t * 1e6:.2f} us | '
f'Combine bandwidth: {num_combine_comm_bytes / 1e9 / combine_t:.2f} GB/s, avg_t={combine_t * 1e6:.2f} us',
flush=True)
else:
print(f'[rank {rank}] Dispatch send/recv time: {dispatch_t[0] * 1e6:.2f} + {dispatch_t[1] * 1e6:.2f} us',
print(f'[rank {rank}] Dispatch send/recv time: {dispatch_t[0] * 1e6:.2f} + {dispatch_t[1] * 1e6:.2f} us | '
f'Combine send/recv time: {combine_t[0] * 1e6:.2f} + {combine_t[1] * 1e6:.2f} us',
flush=True)
return hash_value
......@@ -178,30 +187,6 @@ def test_loop(local_rank: int, num_local_ranks: int, args: argparse.Namespace):
allow_mnnvl=args.allow_mnnvl)
test_main(num_tokens, hidden, num_experts, num_topk, rank, num_ranks, group, buffer, seed=1)
# do_pressure_test = args.pressure_test
# for seed in range(int(1e9) if do_pressure_test else 0):
# if local_rank == 0:
# print(f'Testing with seed {seed} ...', flush=True)
# ref_hash = test_main(num_tokens,
# hidden,
# num_experts,
# num_topk,
# rank,
# num_ranks,
# group,
# buffer,
# seed=seed)
# for _ in range(20):
# assert test_main(num_tokens,
# hidden,
# num_experts,
# num_topk,
# rank,
# num_ranks,
# group,
# buffer,
# seed=seed) == ref_hash, f'Error: seed={seed}'
# Destroy the buffer runtime and communication group
buffer.destroy()
dist.barrier()
......@@ -214,7 +199,7 @@ if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Test low-latency EP kernels')
parser.add_argument('--num-processes', type=int, default=8, help='Number of processes to spawn (default: 8)')
parser.add_argument('--num-tokens', type=int, default=128, help='Number of tokens (default: 128)')
parser.add_argument('--hidden', type=int, default=2560, help='Hidden dimension size (default: 7168)')
parser.add_argument('--hidden', type=int, default=7168, help='Hidden dimension size (default: 7168)')
parser.add_argument('--num-topk', type=int, default=8, help='Number of top-k experts (default: 8)')
parser.add_argument('--num-experts', type=int, default=256, help='Number of experts (default: 288)')
parser.add_argument('--allow-mnnvl', action="store_true", help='Allow MNNVL for communication')
......
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