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feat: add ML congestion/thermal prediction and deterministic P&R optimization tooling - #1

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feat: add ML congestion/thermal prediction and deterministic P&R optimization tooling#1
JayRaj21 wants to merge 28 commits into
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pr-extension

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@JayRaj21

@JayRaj21 JayRaj21 commented Aug 24, 2026

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Intent

The developer wanted to augment (not modify) the P&R stage of an OpenROAD-flow-scripts (ORFS) design flow to demonstrate understanding of the flow, without touching the core routing/placement algorithms. They chose a deterministic (non-ML) approach for P&R augmentation, reserving ML for the existing thermal/congestion prediction work where no deterministic solver exists. Over the session they built and validated a stage-by-stage metrics aggregator (pr_metrics.py), a post-CTS timing repair hook, a triage agent to diagnose timing failures (e.g. the CTS→GRT parasitic underestimation cliff), and a closed-loop optimization agent (loop_agent.py) that autonomously diagnoses, applies config parameter fixes, re-runs affected flow stages via Docker, verifies improvement, and writes successful parameters back to config.mk. They required the implementation be verified with unit tests that need no API key or Docker, wanted all decisions/changes documented in a running dev log and memory for session continuity, and ultimately had the developer open a GitHub PR (#1) on their own fork for this pr-extension branch work, explicitly deferring further placement-stage and multi-design end-to-end testing to a follow-up session.

What Changed

  • Added an ML pipeline under flow/ml/congestion/ for congestion and thermal prediction: GNN/U-Net models, training scripts, inference/evaluation/visualization tools, data collection utilities (feature/label extraction, batch/thermal extraction, variant generation), a Dockerfile, synthetic-data generators, and unit tests.
  • Added deterministic P&R augmentation tooling under flow/util/ and flow/scripts/: pr_metrics.py (stage-by-stage metrics aggregator), triage_agent.py (timing-failure diagnosis), loop_agent.py with test_loop_agent.py (closed-loop config-fix/re-run/verify optimizer), compare_hook.sh, and new Tcl timing-repair hooks (post_cts_timing_repair.tcl, post_grt_timing_repair.tcl, timing_repair_common.tcl).
  • Added supporting docs and config updates: PR_EXTENSION_DEV_LOG.md, create_pr.sh/create_pr_body.md, a thermal/overview findings PDF under docs/references/, a small flow/designs/nangate45/aes/config.mk change, and .gitignore/.gitmodules updates.

Risk Assessment

✅ Low: All three rounds of prior review findings (Make-injection value validation for both $( and ${ forms, Tcl hook deduplication, and behavioral regression tests) were correctly and completely implemented, verified by tracing the actual diffs against the shared timing_repair_common.tcl and loop_agent.py logic; the final commit in scope is a pure documentation/formatting sync with no functional changes.

Testing

All 28 tests in flow/util/test_loop_agent.py pass, including the new value-side blocklist regression tests for both $( and ${ Make-injection variants requested in review round 3, run end-to-end through validate_param_value() and write_config_params() with no API key or Docker required; the deduplicated Tcl timing-repair hooks (post_cts/post_grt wrappers over timing_repair_common.tcl) also parse without syntax errors under plain tclsh, the closest available check without a full OpenROAD/Docker environment.

Pipeline

Updates from git push no-mistakes

✅ **intent** - passed

✅ No issues found.

✅ **Rebase** - passed

✅ No issues found.

✅ **Review** - passed

✅ No issues found.

✅ **Test** - passed

✅ No issues found.

  • python3 -m pytest flow/util/test_loop_agent.py -v (28 passed) — includes the round-3-requested regression tests test_rejects_injected_value_dollar_paren, test_rejects_injected_value_dollar_brace, test_refuses_to_write_dollar_paren_injection, test_refuses_to_write_dollar_brace_injection which exercise validate_param_value()/write_config_params() end-to-end with '$(shell ...)' and '${shell ...}' payloads on an allowlisted param
  • echo 'source "flow/scripts/post_cts_timing_repair.tcl"' | tclsh and same for post_grt_timing_repair.tcl and timing_repair_common.tcl — confirms the deduped shared-library refactor parses with no Tcl syntax errors (failure is only the expected 'invalid command name sta::worst_slack' since OpenROAD-specific procs aren't available outside the OpenROAD Tcl interpreter)
✅ **Document** - passed

✅ No issues found.

✅ **Lint** - passed

✅ No issues found.

✅ **Push** - passed

✅ No issues found.

JayRaj21 and others added 25 commits August 6, 2026 18:29
Data collection:
- extract_features.py: ODB → cell_density, macro_density, pin_density,
  fanout_density grids (64x64 .npz, runs in Docker)
- extract_labels.py: GRT ODB → heatmap (10-layer), hotspot mask, score (.npz)
- batch_run.sh: runs 12 designs through place+grt and extracts paired samples

Models (models/):
- heads.py: shared HeatmapHead, HotspotHead, ScoreHead
- unet.py: 4-level U-Net, input (B,4,64,64), 3-head output
- gnn.py: 3-layer GraphSAGE + grid scatter, same 3-head output

Training (training/):
- dataset.py: loads paired .npz files, train/val/test split, flip augmentation
- metrics.py: heatmap MAE, hotspot IoU, score MAE, Pearson correlation
- train_unet.py / train_gnn.py: AdamW + cosine LR, saves best checkpoint

Inference (inference/):
- predict.py: CLI inference for either model, saves npy + visualisation PNG
- evaluate.py: side-by-side test-set comparison table with winner per metric

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
models/swin.py:
  Swin Transformer with windowed + shifted-window attention.
  Patch embed → 4 stages (depths [2,2,6,2]) → PixelShuffle decoder → 3 heads.
  Captures long-range spatial dependencies the U-Net convolutions miss.

models/classical.py:
  RandomForestCongestion and XGBoostCongestion baselines.
  Operate on flattened per-cell feature vectors (6 features per cell).
  Per-layer RF/XGB for heatmap, classifier for hotspot, regressor for score.
  load_dataset() helper splits by design to prevent data leakage.

models/ensemble.py:
  CongestionEnsemble combining U-Net + Swin.
  mode='average': zero-cost average of both outputs, no retraining needed.
  mode='learned': small fusion conv head trained on top of frozen base models.

models/diffusion.py:
  DDPM conditioned on placement features.
  Denoising U-Net takes (noisy_heatmap || condition) as input.
  sample(n_samples>1) gives uncertainty estimates via variance across samples.

training/train_swin.py: AdamW + warmup + cosine LR schedule
training/train_classical.py: GroupShuffleSplit to avoid leakage, RF + XGB
training/train_diffusion.py: noise prediction loss, configurable timesteps

inference/evaluate.py: updated to evaluate all 6 models in one table

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
tests/generate_synthetic_data.py:
  Generates paired feature/label .npz files using spatially-correlated
  random fields — no Docker or ORFS runs needed for testing.

tests/test_models.py (21 tests, all passing):
  - Shape correctness for all 5 deep models
  - Output range [0,1] check
  - Metrics (MAE, IoU, Pearson) unit tests
  - Dataset loading, splitting, augmentation
  - Mini training loop (2 steps, NaN check) for U-Net and Swin
  - Checkpoint save/load round-trip
  - RF fit/predict and pickle round-trip

Fix: CongestionSwin patch_embed used LayerNorm([embed_dim, H, W]) which
hardcoded the 64x64 spatial size and broke on any other grid size.
Replaced with LayerNorm(embed_dim) applied after flattening to sequence.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
run_pipeline.sh: single script covering all 4 stages:
  1. Extract features/labels from existing ODB results (no re-running the flow)
  2. Train selected models (unet, swin, gnn, classical, diffusion)
  3. Evaluate all trained models in a comparison table
  4. Optional inference + visualisation on a named design

Options: --grid, --epochs, --skip-extract, --skip-train, --models, --predict

extract_existing.sh: extracts from the 13 designs already in flow/results/
  without needing to re-run make or Docker for the flow stages.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
extract_features.py + extract_labels.py:
  - Replace ord.dbDatabase.create() / ord.read_db() with
    Design(Tech()) / design.readDb() — the correct OpenROAD Python API.
    dbDatabase lives in the odb module; the high-level Design/Tech classes
    are the intended entry point for openroad -python scripts.

extract_labels.py:
  - Replace non-existent gcell_grid.getGCells(layer) with explicit
    (ix, iy) index iteration using gcell_grid.getGCell(cx, cy, layer),
    which is the actual GCellGrid API.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
…variant generator

- New thermal track: U-Net (in_channels=5) predicts HotSpot v7.0 spatial thermal
  maps from post-placement ODB features (cell/macro/pin/fanout density + Gaussian blur)
- Docker image (flow/ml/Dockerfile) with HotSpot compiled from source + ML packages
- extract_thermal_labels.py: adaptive HotSpot grid, bilinear upsample to 64×64
- extract_thermal_batch.sh: idempotent batch extractor with --force flag
- thermal_dataset.py: 5-channel input, per-sample normalisation, augmentation
- train_thermal.py: MSE loss, CosineAnnealingLR, saves thermal_best.pt
- visualize_thermal.py: self-contained HTML report with °C colorbars, filter/sort
- generate_variants.sh: CORE_UTILIZATION (60/70/90%) and CORE_ASPECT_RATIO (0.5/1.5/2.0)
  variants via docker_shell; ariane133 excluded from util variants (MPL-0040)
- Remove Swin, RF/XGBoost, Ensemble, Diffusion models (congestion track deprioritised)
- Update .gitignore to exclude flow/ml/data/ and generated thermal_report.html

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Clock cells (ICG, CLKBUF, etc.) get 5× weight, sequential (DFF, SDFF,
LATCH) get 3×, macros (BLOCK type) get 2×, combinational get 1×.
Weighted areas are renormalised to total_power_w so absolute power is
preserved while the spatial distribution reflects cell activity.

Also prints a per-type breakdown (count, weighted-power %) at runtime
so runs can be audited without re-opening ODBs.

Verified: all 48 training samples re-extracted successfully with
passed=48 failed=0 using openroad/orfs-ml:latest.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
…pair

Phase 1 — flow/util/pr_metrics.py: standalone script that parses existing
ORFS report and log files and prints a stage-by-stage quality table showing
WNS, TNS, Fmax, HPWL, GRT overflow, and power across all P&R checkpoints.
No OpenROAD process required; works on any completed design run.

Phase 2 — flow/scripts/post_cts_timing_repair.tcl: POST_CTS hook that runs
inside the live OpenROAD session after CTS completes. Traverses the N worst
setup-timing paths using the STA path object API (find_timing_paths /
prevPath / get_full_name), identifies combinational cells eligible for drive
strength upsizing, swaps them in-place via ODB swapMaster, re-legalises
placement, and re-estimates parasitics. Verified on nangate45/ibex/base:
AND2_X1 → AND2_X2 swap improved WNS from -0.007 ns to -0.004 ns.

PR_EXTENSION_DEV_LOG.md documents all decisions, API findings, and the
run instructions for both phases.
compare_hook.sh automates the two-run comparison: baseline (no hook) then
hook-enabled flow from the same 3_place.odb checkpoint, printing both
pr_metrics.py tables side by side.

Controlled comparison result on nangate45/ibex/base:
- Global route WNS: -0.020 ns (baseline) -> -0.000 ns (hook), +20 ps
- Global route TNS: -0.110 ns -> -0.000 ns
- Global route Fmax: 451.4 -> 454.0 MHz (+2.6 MHz)
- Both flows closed timing at finish; power unchanged

The 3 ps CTS improvement amplified to 20 ps at global route because
upsizing reduces gate delay across the full fanout cone, giving the
router enough headroom to absorb real wire parasitics.
…T hook

- post_cts_timing_repair.tcl: refactored single-pass run into an iterative
  loop (up to 5 iterations by default); each pass re-runs STA after swaps so
  shifting critical paths are caught in subsequent iterations
- post_grt_timing_repair.tcl: new hook at POST_GLOBAL_ROUTE_TCL using
  estimate_parasitics -global_routing; tested on aes and found redundant with
  ORFS built-in repair_timing that already runs at this stage — kept as a
  documented architectural finding
- compare_hook.sh: generalized from ibex-only to accept --platform/--design/
  --tag flags; applies both CTS and GRT hooks by default with --no-{cts,grt}-hook
  toggles; baseline temp dir namespaced per design to avoid collisions
- PR_EXTENSION_DEV_LOG.md: updated with all session results, aes comparison
  analysis, and architectural insight on hook placement vs built-in repair
- Apply black formatting to all flow/ml/congestion/ Python files and
  flow/util/pr_metrics.py (20 files reformatted, style-only changes)
- Fix .gitmodules: change tools/OpenROAD URL from relative ../OpenROAD.git
  to absolute https://github.com/The-OpenROAD-Project/OpenROAD.git so the
  submodule resolves correctly from the JayRaj21 fork (relative URL was
  designed for The-OpenROAD-Project org and resolved to a non-existent repo)
tclint enforces a 100-character line limit on PRs targeting master.
Split long puts strings into a message variable + puts call to bring
all lines within the limit (no logic changes).
flow/util/triage_agent.py — reads stage-by-stage metrics via pr_metrics.collect(),
computes per-stage WNS deltas, and calls claude-opus-5 with adaptive thinking
to diagnose timing/congestion failures and recommend specific ORFS parameters
or hook scripts.

Completes the observe/intervene/decide arc on the branch:
  pr_metrics.py      → observe  (what happened at each stage?)
  post_cts_*.tcl     → intervene (fix inside the live OpenROAD session)
  triage_agent.py    → decide   (diagnose why, recommend what to try next)
SETUP_SLACK_MARGIN=0.03 forces CTS repair to target endpoints that only
appear as violations under real wire RC at GRT. POST_CTS_TCL arms the
iterative upsizing hook. Result: GRT TNS -0.330 → -0.010 ns, final
WNS/TNS 0.000 (was -0.010/-0.060).
loop_agent.py gives Claude four tools — get_metrics, set_config_param,
run_stage, finish — and drives the full observe→diagnose→intervene→verify
cycle autonomously. The agent reads the stage-by-stage trajectory, applies
targeted ORFS parameter changes (SETUP_SLACK_MARGIN, TNS_END_PERCENT,
OPT_POST_GRT_WNS, hook paths), re-runs affected stages via Docker make,
and iterates until timing closes or a 3-iteration budget is exhausted.
Write-back: after finish(success=True), persists the agent's parameter
changes to config.mk — updates existing lines in-place, appends new ones,
and translates Docker hook paths back to $(SCRIPTS_DIR)/... form.

Placement: adds PLACE_DENSITY_LB_ADDON to the allowlist and 'place' as a
valid run_stage target (deletes 3_3_place_gp.odb to force full re-run from
global placement). Handles the congestion failure pattern separately from
the parasitic-underestimation cliff.
@JayRaj21 JayRaj21 changed the title pr-extension: LLM-driven P&R triage, closed-loop optimization, and congestion ML pipeline feat: add ML congestion/thermal prediction and deterministic P&R optimization tooling Aug 27, 2026
JayRaj21 and others added 3 commits August 26, 2026 18:45
…ecurity scan

The org security scan blocks any file literally named "Dockerfile" added
outside the already-allowlisted locations. Rename to Dockerfile.ml,
matching the repo's existing convention (Dockerfile.dev, Dockerfile.claude),
and update the build command comment and docs reference accordingly.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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