forked from zhoulin/zl_bench
Aharrypotter
ee5f70fa89
A 6-round A/B here reports side noise at the 0.01% level. Clock throttling and
thermal drift move latency by far more than that and leave no trace in
run.log, so the report comes out looking exactly as clean as a run on a stable
machine. The NV run that reproduced PR763 sampled nvidia-smi every 5s
alongside the campaign; the MUSA campaigns had no equivalent, and the parallel
plan makes it worse by heating the whole box.
telemetry.py samples through the backend, so cuda and musa emit the same
columns (index, sm_clock_mhz, mem_clock_mhz, temp_c, power_w, util_pct) and
two machines' campaigns can be read side by side. mthreads-gmi offers no CSV
query mode, so the MUSA path parses --json and strips the units glued onto
each value ("36C", "1888MHz", "477.68W"); its "Power Draw " key carries a
trailing space, matched by stripping rather than by literal.
The device comes from the backend's own visibility variable when not pinned. A
list of devices resolves to no -i at all, sampling every one, rather than
silently picking the first. Dropping the placeholder drops its flag too: a
bare trailing -i fails the sampler instead of widening it.
Telemetry is evidence, not a gate. A missing or unpermitted sampler exits
quietly with its reason in telemetry.err and the campaign runs unchanged.
50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
from __future__ import annotations
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from typing import Dict, List
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from .base import DeviceBackend
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class CudaBackend(DeviceBackend):
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name = "cuda"
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visibility_env_keys = ("CUDA_VISIBLE_DEVICES",)
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torch_namespace = "cuda"
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flaggems_vendor = "nvidia"
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supports_cuda_graph = True
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supports_mm_cluster_fix = True
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supports_triton_hooks = True
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list_command = (("nvidia-smi", "--list-gpus"), "GPU ")
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# Unchanged from when this list lived in _cudagraph_plugin; CUDA numbers do
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# not move because of the classification refactor.
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fatal_error_markers = (
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"illegal memory access",
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"cudaerrorillegaladdress",
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"out of memory",
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"device-side assert triggered",
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"an illegal instruction",
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"misaligned address",
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"uncorrectable ecc",
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)
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# No CUDA wording is known to poison the context without also matching one
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# of the fatal markers above, so nothing is listed here rather than guessed.
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poisoning_error_markers = ()
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telemetry_argv = (
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"nvidia-smi",
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"--query-gpu=index,clocks.sm,clocks.mem,temperature.gpu,power.draw,utilization.gpu",
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"--format=csv,noheader,nounits",
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"-i", "{dev}",
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)
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def parse_telemetry(self, raw: str) -> List[Dict[str, str]]:
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out: List[Dict[str, str]] = []
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for line in raw.splitlines():
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parts = [p.strip() for p in line.split(",")]
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if len(parts) != 6 or not parts[0].isdigit():
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continue
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out.append(dict(zip(
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("index", "sm_clock_mhz", "mem_clock_mhz",
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"temp_c", "power_w", "util_pct"), parts)))
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return out
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