chore(scripts): unify large graph render scripts into single parameterized script

Replace `run_250k.py` and `run_1M.py` with `render_large_graph.py` that
accepts any node count as a CLI argument, plus --format, --seed, and
--outdir options.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Author: johnmarktaylor91

scripts/CLAUDE.md

@@ -5,8 +5,7 @@
 | File | Purpose |
 |------|---------|
 | `check_flops_coverage.py` | Reports FLOPs module coverage against all decorated torch functions |
-| `run_250k.py` | Render a 250k-node graph with ELK layout |
-| `run_1M.py` | Render a 1M-node graph with ELK (phased: construct → log → render) |
+| `render_large_graph.py` | Render a large random graph with ELK layout (any node count) |
 
 ## check_flops_coverage.py
 
@@ -20,3 +19,12 @@ Run with:
 ```bash
 python scripts/check_flops_coverage.py
 ```
+
+## render_large_graph.py
+
+Renders a large random graph using the ELK layout engine. Accepts any target node count.
+
+```bash
+python scripts/render_large_graph.py 250000
+python scripts/render_large_graph.py 1000000 --format png --seed 123
+```

scripts/render_large_graph.py

@@ -0,0 +1,75 @@
+"""Render a large random graph with ELK layout.
+
+Usage:
+    python scripts/render_large_graph.py NUM_NODES [OPTIONS]
+
+Examples:
+    python scripts/render_large_graph.py 250000
+    python scripts/render_large_graph.py 1000000 --format png --seed 123
+    python scripts/render_large_graph.py 50000 --outdir /tmp/graphs
+"""
+
+import argparse
+import os
+import sys
+import time
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "tests"))
+
+import torch
+from example_models import RandomGraphModel
+from torchlens import log_forward_pass
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Render a large random graph with ELK layout.")
+    parser.add_argument("num_nodes", type=int, help="Target number of nodes in the graph")
+    parser.add_argument("--format", default="svg", help="Output format (default: svg)")
+    parser.add_argument("--seed", type=int, default=42, help="Random seed (default: 42)")
+    parser.add_argument(
+        "--outdir",
+        default=os.path.join("tests", "test_outputs", "visualizations", "large"),
+        help="Output directory",
+    )
+    args = parser.parse_args()
+
+    os.makedirs(args.outdir, exist_ok=True)
+    label = f"elk_{args.num_nodes // 1000}k"
+
+    # Phase 1: Model construction
+    t0 = time.time()
+    model = RandomGraphModel(target_nodes=args.num_nodes, seed=args.seed)
+    x = torch.randn(2, 64)
+    t1 = time.time()
+    print(f"Phase 1 — Model construction: {t1 - t0:.1f}s", flush=True)
+
+    # Phase 2: log_forward_pass (logging + postprocessing)
+    ml = log_forward_pass(model, x, layers_to_save=None, detect_loops=False, verbose=True)
+    t2 = time.time()
+    print(f"Phase 2 — log_forward_pass:    {t2 - t1:.1f}s ({len(ml)} layers)", flush=True)
+
+    # Phase 3: Render
+    ml.render_graph(
+        vis_opt="unrolled",
+        vis_nesting_depth=1000,
+        vis_outpath=os.path.join(args.outdir, label),
+        save_only=True,
+        vis_fileformat=args.format,
+        vis_node_placement="elk",
+    )
+    t3 = time.time()
+    print(f"Phase 3 — ELK render:          {t3 - t2:.1f}s", flush=True)
+
+    total = t3 - t0
+    print(f"Total: {total:.1f}s ({total / 60:.1f} min)", flush=True)
+
+    out_path = os.path.join(args.outdir, f"{label}.{args.format}")
+    if os.path.exists(out_path):
+        size_mb = os.path.getsize(out_path) / 1024 / 1024
+        print(f"Output: {out_path} ({size_mb:.1f} MB)")
+
+    ml.cleanup()
+
+
+if __name__ == "__main__":
+    main()