diffusion_policy/docs/superpowers/plans/2026-03-27-pusht-dit-nocausal-compare.md

PushT DiT No-Causal Compare Implementation Plan

For agentic workers: REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (- [ ]) syntax for tracking.

Goal: Add a PushT image DiT no-causal config, rerun the two prior DiT baselines for 350 epochs, and compare max test_mean_score plus batch-1 inference latency.

Architecture: Keep the existing causal DiT baselines unchanged and add a separate no-causal config that only flips policy.causal_attn=false while preserving the SwanLab naming safeguards. Launch the default DiT (256x8) locally and the 256x18 DiT on 5880 GPU0, then parse logs.json.txt and benchmark both checkpoints on the same hardware.

Tech Stack: Hydra, Diffusion Policy transformer image workspace, SwanLab, uv Python env, local 5090 + trusted remote 5880.

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Task 1: Add no-causal DiT config and config regression test

Files:

• Create: image_pusht_diffusion_policy_dit_nocausal.yaml

• Modify: tests/test_pusht_swanlab_config.py

• Write a failing test asserting the new no-causal DiT config uses SwanLab-safe naming and policy.causal_attn == False.

• Run the targeted pytest command and verify it fails because the config does not exist yet.

• Add the minimal new config by composing from the existing PushT DiT config and overriding only policy.causal_attn=false.

• Re-run the targeted pytest command and verify it passes.

Task 2: Smoke-verify the new config

Files:

• Read: image_pusht_diffusion_policy_dit_nocausal.yaml

• Run train.py --help against the new config.

• Verify Hydra resolves the config without errors.

Task 3: Launch the two 350-epoch no-causal reruns

Files:

• Write runtime scripts/logs under data/run_logs/

• Write outputs under data/outputs/

• Launch local run: dit_nocausal_img_pusht_default_seed42_local with 350 epochs.

• Launch remote run: dit_nocausal_img_pusht_emb256_layer18_seed42_5880gpu0 with 350 epochs and policy.n_layer=18.

• Use explicit SwanLab overrides: unique logging.name, logging.resume=false, logging.id=null, shared group dit_pusht_nocausal_compare.

• Record pid files and launcher scripts.

Task 4: Monitor and summarize

Files:

• Read: per-run logs.json.txt

• Read: checkpoints directories

• Monitor until both runs reach epoch 349 completion.

• Extract max(test_mean_score) and final logged test_mean_score.

• Identify the best checkpoint for each run.

• Benchmark batch-1 policy.predict_action(obs) latency on the same hardware.

• Report the final comparison table and short conclusion.