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# SPDX-License-Identifier: OpenMDW-1.1
"""OFU (Operational FLOPs Utilization) callback for OmniMoT training.
Computes and logs OFU metrics by launching ``nvidia-smi dmon`` as a background
subprocess and parsing the Tensor Core activity (mmaact) and processor clock
(pclk) columns. OFU is defined as::
OFU = mmaact * (pclk / max_pclk)
where ``max_pclk`` is the max boost clock for the detected hardware (e.g.
1980 MHz for H100, 2062 MHz for GB200). The result is in the 0-100 range.
"""
from __future__ import annotations
import subprocess
import threading
from collections import defaultdict
from dataclasses import dataclass
import torch
import wandb
from cosmos_framework.model.attention.utils import is_blackwell_dc
from cosmos_framework.callbacks.every_n import EveryN
from cosmos_framework.model._base import ImaginaireModel
from cosmos_framework.trainer import ImaginaireTrainer
from cosmos_framework.utils import log
from cosmos_framework.utils.distributed import is_rank0, rank0_only
@dataclass
class HardwareTarget:
"""Hardware-specific constants for OFU normalisation.
Attributes:
name: Human-readable name (used as W&B tag, e.g. "H100").
max_pclk_mhz: Max boost SM clock in MHz used to normalise OFU.
"""
name: str
max_pclk_mhz: float
# Pre-defined hardware targets
H100 = HardwareTarget(name="H100", max_pclk_mhz=1980.0)
GB200 = HardwareTarget(name="GB200", max_pclk_mhz=2062.0)
class OFUCallback(EveryN):
"""Callback that computes and logs Operational FLOPs Utilization (OFU) to W&B.
OFU = mmaact * (pclk / max_pclk), where mmaact is the MMA activity
percentage and pclk is the current processor clock from ``nvidia-smi dmon``.
``max_pclk`` is determined from the detected hardware (H100 or GB200).
The result is in the 0-100 range.
The callback launches ``nvidia-smi dmon`` as a background subprocess on
``on_train_start`` and a daemon thread continuously reads its output.
At every logging interval, accumulated samples are consumed, averaged per GPU
and overall, and logged to W&B under ``ofu/{hardware_name}``.
Args:
hit_thres: Number of warm-up training iterations to skip before logging.
"""
def __init__(
self,
*args,
hit_thres: int = 5,
**kwargs,
) -> None:
super().__init__(*args, **kwargs)
self.hardware_target = GB200 if is_blackwell_dc() else H100
self.hit_thres = hit_thres
# Subprocess state
self._process: subprocess.Popen | None = None
self._reader_thread: threading.Thread | None = None
self._stop_event = threading.Event()
# Buffered samples protected by a lock: list of (gpu_idx, mmaact, pclk)
self._lock = threading.Lock()
self._samples: list[tuple[int, float, float]] = []
# Column indices parsed from the header (set by _reader_loop)
self._col_gpu: int | None = None
self._col_mmaact: int | None = None
self._col_pclk: int | None = None
# Warm-up counter
self._hit_counter: int = 0
# ------------------------------------------------------------------ #
# Background reader
# ------------------------------------------------------------------ #
def _parse_header(self, line: str) -> bool:
"""Parse a dmon header line to locate column indices.
Called on every ``#`` line because nvidia-smi dmon reprints the header
every few seconds. Returns True if ``gpu``, ``mmaact``, and ``pclk``
columns are all found; warns only when the column-names line (identified
by the presence of ``gpu``) lacks a required column. Silently ignores
the units line (``# Idx W C ...``) which does not contain ``gpu``.
"""
cols = line.lstrip("#").strip().split()
col_map = {name.lower(): idx for idx, name in enumerate(cols)}
gpu_idx = col_map.get("gpu")
mmaact_idx = col_map.get("mmaact")
pclk_idx = col_map.get("pclk")
if gpu_idx is not None and mmaact_idx is not None and pclk_idx is not None:
if self._col_mmaact is None:
log.info(f"OFUCallback: found mmaact at column {mmaact_idx}, pclk at column {pclk_idx}")
self._col_gpu = gpu_idx
self._col_mmaact = mmaact_idx
self._col_pclk = pclk_idx
return True
if gpu_idx is not None:
missing = [name for name, idx in [("mmaact", mmaact_idx), ("pclk", pclk_idx)] if idx is None]
log.warning(
f"OFUCallback: column(s) {missing} not found in nvidia-smi dmon header: {cols}. "
"OFU metrics will not be available."
)
return False
def _reader_loop(self) -> None:
"""Background thread that reads nvidia-smi dmon output line-by-line."""
assert self._process is not None and self._process.stdout is not None
for line in self._process.stdout:
if self._stop_event.is_set():
break
line = line.strip()
if not line:
continue
# Header lines repeat every few seconds — always re-parse so that a
# missed or failed first parse is recovered on the next occurrence.
if line.startswith("#"):
self._parse_header(line)
continue
# Skip data lines until we have column indices
if self._col_gpu is None or self._col_mmaact is None or self._col_pclk is None:
continue
parts = line.split()
try:
gpu_idx = int(parts[self._col_gpu])
mmaact = float(parts[self._col_mmaact])
pclk = float(parts[self._col_pclk])
except (ValueError, IndexError):
continue
with self._lock:
self._samples.append((gpu_idx, mmaact, pclk))
# ------------------------------------------------------------------ #
# Lifecycle hooks
# ------------------------------------------------------------------ #
def on_train_start(self, model: ImaginaireModel, iteration: int = 0) -> None:
if not is_rank0():
return
try:
# --gpm-metrics 5 means that we access Tensor Activity under mmaact column.
# -d 5 means that we sample the data every 5 seconds.
cmd = ["nvidia-smi", "dmon", "--gpm-metrics", "5", "-d", "5"]
self._process = subprocess.Popen(
cmd,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
text=True,
bufsize=1, # line-buffered
)
self._reader_thread = threading.Thread(target=self._reader_loop, daemon=True)
self._reader_thread.start()
log.info(f"OFUCallback: launched nvidia-smi dmon --gpm-metrics 5")
except FileNotFoundError:
log.warning("OFUCallback: nvidia-smi not found, OFU metrics will not be available")
except Exception as e:
log.warning(f"OFUCallback: failed to launch nvidia-smi dmon: {e}")
def on_train_end(self, model: ImaginaireModel, iteration: int = 0) -> None:
if not is_rank0():
return
self._stop_event.set()
if self._process is not None:
try:
self._process.terminate()
self._process.wait(timeout=5)
except ProcessLookupError:
pass # already exited
except subprocess.TimeoutExpired:
self._process.kill()
self._process = None
if self._reader_thread is not None:
self._reader_thread.join(timeout=5)
self._reader_thread = None
# ------------------------------------------------------------------ #
# Per-step gating
# ------------------------------------------------------------------ #
def on_training_step_end(
self,
model: ImaginaireModel,
data_batch: dict[str, torch.Tensor],
output_batch: dict[str, torch.Tensor],
loss: torch.Tensor,
iteration: int = 0,
) -> None:
# All ranks must enter super().on_training_step_end() so they reach the
# distributed barrier inside EveryN. Only rank 0 has samples to clear.
if self._hit_counter < self.hit_thres:
self._hit_counter += 1
if self._hit_counter == self.hit_thres:
# Discard samples collected during warm-up (compilation, allocation, etc.)
with self._lock:
self._samples.clear()
return
# Delegate to EveryN for the periodic reporting logic
super().on_training_step_end(model, data_batch, output_batch, loss, iteration)
# ------------------------------------------------------------------ #
# Periodic reporting
# ------------------------------------------------------------------ #
@rank0_only
def every_n_impl(
self,
trainer: ImaginaireTrainer,
model: ImaginaireModel,
data_batch: dict[str, torch.Tensor],
output_batch: dict[str, torch.Tensor],
loss: torch.Tensor,
iteration: int,
) -> None:
if self._process is None:
return
# Drain buffered samples
with self._lock:
samples = list(self._samples)
self._samples.clear()
if not samples:
log.warning(
f"OFUCallback: no nvidia-smi samples collected at iteration {iteration}. "
"Check that the dmon subprocess launched and that the mmaact column is present."
)
return
# Compute per-GPU OFU: mmaact * (pclk / max_pclk)
max_pclk = self.hardware_target.max_pclk_mhz
gpu_ofu: dict[int, list[float]] = defaultdict(list)
gpu_mmaact: dict[int, list[float]] = defaultdict(list)
gpu_pclk: dict[int, list[float]] = defaultdict(list)
for gpu_idx, mmaact, pclk in samples:
gpu_ofu[gpu_idx].append(mmaact * (pclk / max_pclk))
gpu_mmaact[gpu_idx].append(mmaact)
gpu_pclk[gpu_idx].append(pclk)
# Overall averages across all GPUs and samples
all_ofu = [v for vals in gpu_ofu.values() for v in vals]
all_mmaact = [v for vals in gpu_mmaact.values() for v in vals]
all_pclk = [v for vals in gpu_pclk.values() for v in vals]
log_info: dict[str, float] = {
f"ofu/{self.hardware_target.name}": sum(all_ofu) / len(all_ofu),
"ofu/mmaact": sum(all_mmaact) / len(all_mmaact),
"ofu/avg_pclk_mhz": sum(all_pclk) / len(all_pclk),
"ofu/num_samples": float(len(samples)),
}
if wandb.run is not None:
wandb.log(log_info, step=iteration)
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