File size: 7,576 Bytes
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import argparse
import os
import re
from dataclasses import dataclass, field
from datetime import datetime
from typing import List, Dict, Any
import matplotlib.pyplot as plt
import numpy as np
@dataclass
class RunRecord:
meta: Dict[str, Any] = field(default_factory=dict)
iters: List[int] = field(default_factory=list)
timestamps: List[str] = field(default_factory=list)
dual: List[float] = field(default_factory=list)
u: List[float] = field(default_factory=list)
uc: List[float] = field(default_factory=list)
grad: List[float] = field(default_factory=list)
temp: List[float] = field(default_factory=list)
active: List[float] = field(default_factory=list)
react_steps: List[int] = field(default_factory=list)
refresh_steps: List[int] = field(default_factory=list)
ARCH_RE = re.compile(
r"\[FCOT-SEP ARCH\] dim=(?P<dim>\d+), radius=(?P<radius>[\d\.]+), ny=(?P<ny>\d+)"
)
ITER_RE = re.compile(
r"\[(?P<ts>[^]]+)\] INFO:training: "
r"\[Iter (?P<iter>\d+)\] dual=(?P<dual>[-0-9.e+]+) "
r"u=(?P<u>[-0-9.e+]+) uc=(?P<uc>[-0-9.e+]+) "
r"grad=(?P<grad>[-0-9.e+]+).* temp=(?P<temp>[-0-9.e+]+) "
r"active=(?P<active>[-0-9.e+]+)%"
)
REACT_RE = re.compile(
r"\[FCOT-SEP\] Reactivated .* at step (?P<step>\d+)"
)
REFRESH_RE = re.compile(
r"\[FCOT-SEP\] Full refresh updated .* at step (?P<step>\d+)"
)
TRAIN_PATH_RE = re.compile(
r"tmp/FCOTSeparable_dim(?P<dim>\d+)_kernel-(?P<kernel>.+?)_"
)
def parse_training_log(path: str) -> List[RunRecord]:
"""
Scan `training.log` and extract iteration stats plus reactivation/refresh markers.
Parameters
----------
path : str
Path to the training log file generated by FCOT-Separable.
Returns
-------
List[RunRecord]
Parsed run records sorted in chronological order.
"""
runs: List[RunRecord] = []
current: RunRecord | None = None
with open(path, "r") as f:
for line in f:
line = line.strip()
if not line:
continue
# Start of a new run: architecture line
if "[FCOT-SEP ARCH]" in line:
m = ARCH_RE.search(line)
current = RunRecord()
if m:
current.meta["dim"] = int(m.group("dim"))
current.meta["radius"] = float(m.group("radius"))
current.meta["ny"] = int(m.group("ny"))
runs.append(current)
continue
if current is None:
# Skip lines before the first ARCH
continue
# Iteration line with dual/grad/etc.
if "[Iter " in line:
m = ITER_RE.search(line)
if not m:
continue
current.timestamps.append(m.group("ts"))
current.iters.append(int(m.group("iter")))
current.dual.append(float(m.group("dual")))
current.u.append(float(m.group("u")))
current.uc.append(float(m.group("uc")))
current.grad.append(float(m.group("grad")))
current.temp.append(float(m.group("temp")))
current.active.append(float(m.group("active")))
continue
# Reactivation events
if "[FCOT-SEP] Reactivated" in line:
m = REACT_RE.search(line)
if m:
current.react_steps.append(int(m.group("step")))
continue
# Full refresh events
if "[FCOT-SEP] Full refresh updated" in line:
m = REFRESH_RE.search(line)
if m:
current.refresh_steps.append(int(m.group("step")))
continue
# Lines that mention checkpoint paths (TRAIN/CACHE/SAVE) → extract kernel
if "[TRAIN] No checkpoint found at" in line or "[CACHE] Found saved" in line or "[SAVE] Writing checkpoint to" in line:
m = TRAIN_PATH_RE.search(line)
if m and current is not None:
current.meta["kernel"] = m.group("kernel")
continue
return runs
def plot_runs(
runs: List[RunRecord],
out_dir: str = "tmp/training_plots",
) -> None:
os.makedirs(out_dir, exist_ok=True)
for idx, run in enumerate(runs):
fig, axes = plot_run(run, idx=idx, show=False)
dim = run.meta.get("dim", "?")
fname = os.path.join(out_dir, f"run_{idx}_dim{dim}.png")
fig.savefig(fname, dpi=150)
plt.close(fig)
def plot_run(run: RunRecord, idx: int | None = None, show: bool = True):
"""
Plot a single RunRecord (dual, grad, active) and optionally show inline.
Returns (fig, axes) so it can be used easily from notebooks.
"""
if not run.iters:
raise ValueError("Run has no iteration data to plot.")
iters = np.array(run.iters)
dual = np.array(run.dual)
grad = np.array(run.grad)
active = np.array(run.active) # already in percent
dim = run.meta.get("dim", "?")
kernel = run.meta.get("kernel", "?")
fig, axes = plt.subplots(3, 1, sharex=True, figsize=(12, 8))
# Duration estimate from first/last timestamp (if available)
duration_str = ""
if run.timestamps:
try:
t0 = datetime.strptime(run.timestamps[0], "%Y-%m-%d %H:%M:%S,%f")
t1 = datetime.strptime(run.timestamps[-1], "%Y-%m-%d %H:%M:%S,%f")
dt_min = (t1 - t0).total_seconds() / 60.0
duration_str = f", Δt≈{dt_min:.1f} min"
except Exception:
duration_str = ""
# Dual
title_suffix = f" (run {idx})" if idx is not None else ""
axes[0].plot(iters, dual, label="dual")
axes[0].set_ylabel("dual")
axes[0].set_title(f"dim={dim}, kernel={kernel}{duration_str}{title_suffix}")
# Gradient norm
axes[1].plot(iters, grad, label="grad_norm", color="tab:orange")
axes[1].set_ylabel("grad norm")
# Active fraction
axes[2].plot(iters, active, label="active %", color="tab:green")
axes[2].set_ylabel("active (%)")
axes[2].set_xlabel("iteration")
# Reactivation / refresh markers
for ax in axes:
for s in run.react_steps:
ax.axvline(s, color="red", linewidth=0.6, alpha=0.6)
for s in run.refresh_steps:
ax.axvline(s, color="blue", linewidth=0.6, alpha=0.6)
axes[0].legend(loc="best")
axes[1].legend(loc="best")
axes[2].legend(loc="best")
fig.tight_layout()
if show:
plt.show()
return fig, axes
def main():
"""Entry point to parse `training.log` and dump summary plots to disk."""
parser = argparse.ArgumentParser(
description="Parse training.log and plot dual/grad/active with reactivation/refresh markers."
)
parser.add_argument(
"--log",
type=str,
default="training.log",
help="Path to training log (default: training.log)",
)
parser.add_argument(
"--out",
type=str,
default="tmp/training_plots",
help="Output directory for plots (default: tmp/training_plots)",
)
args = parser.parse_args()
runs = parse_training_log(args.log)
if not runs:
print(f"No runs found in {args.log}")
return
print(f"Parsed {len(runs)} runs from {args.log}")
plot_runs(runs, out_dir=args.out)
print(f"Saved plots to {args.out}")
if __name__ == "__main__":
main()
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