test: Add plot_memory_timeline.py (#548)

* Add matplotlib as a plot dependency
* Add paths.py file to store paths
* Add plot_memory_timeline.py

Author: ValerianRey

pyproject.toml

@@ -90,6 +90,7 @@ plot = [
     "plotly>=5.19.0",  # Recent version to avoid problems, could be relaxed
     "dash>=2.16.0",  # Recent version to avoid problems, could be relaxed
     "kaleido==0.2.1",  # Only works with locked version
+    "matplotlib>=3.10.0",  # Recent version to avoid problems, could be relaxed
 ]
 
 [project.optional-dependencies]

tests/paths.py

@@ -0,0 +1,4 @@
+from pathlib import Path
+
+TORCHJD_DIR = Path(__file__).parent.parent
+TRACES_DIR = TORCHJD_DIR / "traces"

tests/profiling/plot_memory_timeline.py

@@ -0,0 +1,97 @@
+"""
+Script to plot memory timeline evolution from profiling traces.
+Reads memory traces from json files and plots them on a single graph.
+"""
+
+import argparse
+import json
+from dataclasses import dataclass
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+from paths import TRACES_DIR
+
+
+@dataclass
+class MemoryFrame:
+    timestamp: int
+    total_allocated: int  # in bytes
+
+    @staticmethod
+    def from_event(event: dict):
+        args = event["args"]
+        return MemoryFrame(
+            timestamp=event["ts"],
+            total_allocated=args.get("Total Allocated"),
+        )
+
+
+def extract_memory_timeline(path: Path) -> np.ndarray:
+    with open(path, "r") as f:
+        data = json.load(f)
+
+    events = data["traceEvents"]
+    print(f"Total events in trace: {len(events):,}")
+    print("Extracting memory frames...")
+
+    frames = [MemoryFrame.from_event(e) for e in events if e["name"] == "[memory]"]
+    frames.sort(key=lambda frame: frame.timestamp)
+
+    print(f"Found {len(frames):,} memory frames")
+
+    timestamp_list = [frame.timestamp for frame in frames]
+    total_allocated_list = [frame.total_allocated for frame in frames]
+
+    return np.array([timestamp_list, total_allocated_list]).T
+
+
+def plot_memory_timelines(experiment: str, folders: list[str]) -> None:
+    timelines = list[np.ndarray]()
+    for folder in folders:
+        path = TRACES_DIR / folder / f"{experiment}.json"
+        timelines.append(extract_memory_timeline(path))
+
+    fig, ax = plt.subplots(figsize=(12, 6))
+    for folder, timeline in zip(folders, timelines):
+        time = (timeline[:, 0] - timeline[0, 0]) // 1000  # Make time start at 0 and convert to ms.
+        memory = timeline[:, 1]
+        ax.plot(time, memory, label=folder, linewidth=1.5)
+
+    ax.set_xlabel("Time (ms)", fontsize=12)
+    ax.set_ylabel("Total Allocated (bytes)", fontsize=12)
+    ax.set_title(f"Memory Timeline: {experiment}", fontsize=14, fontweight="bold")
+    ax.legend(loc="best", fontsize=11)
+    ax.grid(True, alpha=0.3)
+    ax.set_ylim(bottom=0)
+    fig.tight_layout()
+
+    output_dir = Path(TRACES_DIR / "memory_timelines")
+    output_dir.mkdir(parents=True, exist_ok=True)
+    output_path = output_dir / f"{experiment}.png"
+    print(f"\nSaving plot to: {output_path}")
+    fig.savefig(output_path, dpi=300, bbox_inches="tight")
+    print("Plot saved successfully!")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Plot memory timeline from profiling traces.")
+    parser.add_argument(
+        "experiment",
+        type=str,
+        help="Name of the experiment under profiling (e.g., 'WithTransformerLarge()-bs4-cpu')",
+    )
+    parser.add_argument(
+        "folders",
+        nargs="+",
+        type=str,
+        help="Folder names containing the traces (e.g., autojac_old autojac_new)",
+    )
+
+    args = parser.parse_args()
+
+    return plot_memory_timelines(args.experiment, args.folders)
+
+
+if __name__ == "__main__":
+    main()

tests/profiling/run_profiler.py

@@ -1,5 +1,4 @@
 import gc
-from pathlib import Path
 from typing import Callable
 
 import torch
@@ -22,6 +21,7 @@
 )
 from utils.tensors import make_inputs_and_targets
 
+from tests.paths import TRACES_DIR
 from torchjd.aggregation import UPGrad, UPGradWeighting
 from torchjd.autogram import Engine
 
@@ -93,10 +93,9 @@ def _save_and_print_trace(
     prof: profile, method_name: str, factory: ModuleFactory, batch_size: int
 ) -> None:
     filename = f"{factory}-bs{batch_size}-{DEVICE.type}.json"
-    torchjd_dir = Path(__file__).parent.parent.parent
-    traces_dir = torchjd_dir / "traces" / method_name
-    traces_dir.mkdir(parents=True, exist_ok=True)
-    trace_path = traces_dir / filename
+    output_dir = TRACES_DIR / method_name
+    output_dir.mkdir(parents=True, exist_ok=True)
+    trace_path = output_dir / filename
 
     prof.export_chrome_trace(str(trace_path))
     print(prof.key_averages().table(sort_by="self_cpu_memory_usage", row_limit=20))