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chore: import upstream snapshot with attribution
2026-07-13 12:38:16 +08:00

72 lines
2.0 KiB
Python

import logging
from typing import Any, Dict, List
import torch
import torch.distributed as dist
import triton
logger = logging.getLogger(__name__)
def execute():
if dist.get_rank() == 0:
logger.info(f"[slow_rank_detector] Start benchmarking...")
local_metrics = {
bench_name: _compute_local_metric(bench_name) for bench_name in _BENCH_NAMES
}
all_metrics = [None for _ in range(dist.get_world_size())]
dist.gather_object(local_metrics, all_metrics if dist.get_rank() == 0 else None)
if dist.get_rank() == 0:
_analyze_metrics(all_metrics)
class _GemmExecutor:
def __init__(self):
self.lhs = torch.randn((8192, 8192), dtype=torch.bfloat16, device="cuda")
self.rhs = torch.randn((8192, 8192), dtype=torch.bfloat16, device="cuda")
def __call__(self):
self.lhs @ self.rhs
class _ElementwiseExecutor:
def __init__(self):
self.value = torch.randint(
0, 10000, (128 * 1024**2,), dtype=torch.int32, device="cuda"
)
def __call__(self):
self.value += 1
_EXECUTOR_CLS_OF_BENCH = {
"gemm": _GemmExecutor,
"elementwise": _ElementwiseExecutor,
}
_BENCH_NAMES = list(_EXECUTOR_CLS_OF_BENCH.keys())
def _compute_local_metric(bench_name):
executor = _EXECUTOR_CLS_OF_BENCH[bench_name]()
ms = triton.testing.do_bench_cudagraph(executor, return_mode="mean", rep=20)
return ms
def _analyze_metrics(all_metrics: List[Dict[str, Any]]):
for bench_name in _BENCH_NAMES:
time_of_rank = torch.tensor([m[bench_name] for m in all_metrics])
speed_of_rank = 1 / time_of_rank
rel_speed_of_rank = speed_of_rank / speed_of_rank.max()
slowest_rel_speed = rel_speed_of_rank.min().item()
logger.info(
f"[slow_rank_detector] {bench_name=} {slowest_rel_speed=} {rel_speed_of_rank=} {time_of_rank=}"
)
if slowest_rel_speed < 0.9:
logger.warning(
"[slow_rank_detector] Some ranks are too slow compared with others"
)