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# SPDX-FileCopyrightText: 2025 Stanford University, ETH Zurich, and the project authors (see CONTRIBUTORS.md)
# SPDX-FileCopyrightText: 2025 This source file is part of the OpenTSLM open-source project.
#
# SPDX-License-Identifier: MIT
"""
Plot memory usage on simulation datasets from memory_simulation.csv.
- Only uses datasets starting with 'Simulation-'.
- Extracts time series length (L) and number of series (N).
- Computes total_length = N * L.
- Plots memory vs total_length per base model, comparing SoftPrompt vs Flamingo.
- OOM runs (> 180GB) are shown with a dashed line, red X, and "OOM" label.
- Always shows panels in order: gemma-270m, gemma-1b, llama-1b, llama-3b.
"""
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib
import re
OOM_THRESHOLD = 180 # GB
def parse_model_name(llm_id, model_type):
"""Return base_model, config (SoftPrompt or Flamingo)."""
if llm_id.startswith("meta-llama/"):
base_name = llm_id.replace("meta-llama/", "")
elif llm_id.startswith("google/"):
base_name = llm_id.replace("google/", "")
else:
base_name = llm_id
# Normalize base model names to match expected order
if "Llama-3.2-1B" in base_name:
base_name = "Llama-3.2-1B"
elif "Llama-3.2-3B" in base_name:
base_name = "Llama-3.2-3B"
elif "gemma-3-270m" in base_name:
base_name = "Gemma-3-270M"
elif "gemma-3-1b-pt" in base_name:
base_name = "Gemma-3-1B-pt"
if model_type == "OpenTSLMSP":
type_name = "SoftPrompt"
elif model_type == "OpenTSLMFlamingo":
type_name = "Flamingo"
else:
type_name = model_type
return base_name, type_name
def parse_simulation_dataset(name):
"""Parse Simulation dataset name like 'Simulation-L10-N5' → (L=10, N=5)."""
match = re.match(r"Simulation-L(\d+)-N(\d+)", name)
if match:
return int(match.group(1)), int(match.group(2))
return None, None
def plot_memory_usage_sim(csv_file="memory_simulation.csv"):
# --- Paper-style settings ---
plt.style.use("seaborn-v0_8-white")
matplotlib.rcParams.update(
{
"font.family": "serif",
"font.serif": ["Palatino", "Times New Roman", "DejaVu Serif"],
"font.size": 18,
"axes.labelsize": 20,
"axes.titlesize": 20,
"legend.fontsize": 17,
"xtick.labelsize": 17,
"ytick.labelsize": 17,
"axes.linewidth": 0.6,
"axes.edgecolor": "0.15",
}
)
df = pd.read_csv(csv_file)
# Replace -1 with NaN (ignore failed runs)
df["peak_cuda_reserved_gb"] = df["peak_cuda_reserved_gb"].replace(-1, pd.NA)
# Keep only simulation datasets
df = df[df["dataset"].str.startswith("Simulation-")]
# Parse model name and dataset details
df[["base_model", "config"]] = df.apply(
lambda row: pd.Series(parse_model_name(row["llm_id"], row["model"])), axis=1
)
df[["L", "N"]] = df["dataset"].apply(
lambda s: pd.Series(parse_simulation_dataset(s))
)
df = df.dropna(subset=["L", "N"])
df["L"] = df["L"].astype(int)
df["N"] = df["N"].astype(int)
# Compute total sequence length
df["total_length"] = df["L"] * df["N"]
# Sort
df = df.sort_values(by=["base_model", "config", "total_length"])
# Fixed base_model order
base_model_order = ["Gemma-3-270M", "Gemma-3-1B-pt", "Llama-3.2-1B", "Llama-3.2-3B"]
# One subplot per model (always 4)
n_models = len(base_model_order)
fig, axes = plt.subplots(1, n_models, figsize=(3.2 * n_models, 3.2), sharey=True)
if n_models == 1:
axes = [axes]
# Muted palette for configs - order matters for legend
palette = {"SoftPrompt": "#4477AA", "Flamingo": "#CC6677"}
config_order = ["SoftPrompt", "Flamingo"]
for ax, base_model in zip(axes, base_model_order):
subdf = df[df["base_model"] == base_model]
if subdf.empty:
ax.set_title(base_model, fontsize=13, fontweight="bold")
ax.set_facecolor("#F8F9FA")
ax.text(
0.5, 0.5, "No data", ha="center", va="center", fontsize=10, color="gray"
)
ax.set_xticks([])
ax.set_yticks([])
continue
for cfg in config_order:
cfg_df = subdf[subdf["config"] == cfg]
if cfg_df.empty:
continue
cfg_df = cfg_df.sort_values("total_length")
color = palette[cfg]
# Successful runs (≤ threshold)
ok_df = cfg_df[cfg_df["peak_cuda_reserved_gb"] <= OOM_THRESHOLD]
ax.plot(
ok_df["total_length"],
ok_df["peak_cuda_reserved_gb"],
label=cfg,
color=color,
linewidth=4.0,
alpha=0.9,
)
# First OOM run (if any)
oom_df = cfg_df[cfg_df["peak_cuda_reserved_gb"] > OOM_THRESHOLD]
if not oom_df.empty and not ok_df.empty:
first_oom = oom_df.iloc[0]
last_ok = ok_df.iloc[-1]
# dashed line up to OOM
ax.plot(
[last_ok["total_length"], first_oom["total_length"]],
[last_ok["peak_cuda_reserved_gb"], OOM_THRESHOLD * 1.05],
color=color,
linestyle="--",
linewidth=2.5,
alpha=0.8,
)
# red X marker
ax.scatter(
first_oom["total_length"],
OOM_THRESHOLD * 1.05,
color="red",
marker="x",
s=80,
linewidth=3,
zorder=5,
)
ax.text(
first_oom["total_length"],
OOM_THRESHOLD * 1.05,
"OOM",
color="red",
fontsize=9,
fontweight="bold",
ha="center",
va="bottom",
)
# Titles & labels
ax.set_title(base_model, fontsize=19, fontweight="bold")
# Only show axis labels on specific subplots
if ax == axes[0]: # Leftmost subplot
ax.set_ylabel("Peak VRAM Usage (GB)", fontsize=18, fontweight="bold")
ax.set_xlabel(
"Total Sequence Length (N × L)", fontsize=18, fontweight="bold"
)
else:
ax.set_ylabel("")
ax.set_xlabel("")
ax.set_facecolor("#F8F9FA")
ax.grid(True, which="major", linestyle="-", linewidth=0.4, alpha=0.5)
ax.grid(True, which="minor", linestyle=":", linewidth=0.3, alpha=0.3)
ax.minorticks_on()
ax.tick_params(axis="both", labelsize=17)
# Legend only in first subplot
if ax == axes[0]:
leg = ax.legend(
title=None,
fontsize=17,
loc="best",
frameon=True,
framealpha=0.95,
edgecolor="0.3",
)
for text in leg.get_texts():
text.set_fontweight("bold")
plt.tight_layout(pad=0.5)
for fmt in ["png", "pdf"]:
plt.savefig(
f"memory_usage_simulation.{fmt}",
dpi=300 if fmt == "png" else None,
bbox_inches="tight",
pad_inches=0,
facecolor="white",
format=fmt,
)
plt.show()
if __name__ == "__main__":
plot_memory_usage_sim()
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