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