# PushT iMF Full-Attention 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 separate full-attention PushT image iMF config, commit/push it on a new branch, and launch the 9-run 350-epoch architecture sweep across 3 GPUs.

**Architecture:** Keep the existing causal iMF path untouched and add a standalone full-attention config that only flips `policy.causal_attn=false` while retaining one-step iMF inference and SwanLab-safe naming. Reuse the previous 9-run architecture matrix and balanced 3-queue scheduling across local 5090 plus 5880 GPU0/GPU1.

**Tech Stack:** Hydra, Diffusion Policy iMF image workspace, SwanLab, uv env, local shell + trusted remote 5880 over SSH.

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### Task 1: Add full-attention iMF config with TDD

**Files:**
- Create: `image_pusht_diffusion_policy_dit_imf_fullattn.yaml`
- Modify: `tests/test_pusht_swanlab_config.py`

- [ ] Write a failing config regression test asserting the new 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 full-attention config by composing from the existing PushT image iMF config and overriding only `exp_name` and `policy.causal_attn=false`.
- [ ] Re-run the targeted pytest and verify it passes.

### Task 2: Verify the new config

**Files:**
- Read: `image_pusht_diffusion_policy_dit_imf_fullattn.yaml`

- [ ] Run `train.py --help` for the new config.
- [ ] Run a real `training.debug=true` smoke test locally to confirm the training path is valid.

### Task 3: Commit and push the new branch

**Files:**
- Commit only the new config/test/plan files needed for the full-attention experiment chain.

- [ ] Run verification commands again before commit.
- [ ] Commit with a focused message.
- [ ] Push `feat/pusht-imf-fullattn` to origin.

### Task 4: Launch the 9-run sweep

**Files:**
- Write queue scripts and logs under `data/run_logs/` locally and on 5880.
- Write outputs under `data/outputs/` locally and on 5880.

- [ ] Use the same matrix as the prior iMF sweep: `n_emb ∈ {128,256,384}`, `n_layer ∈ {6,12,18}`, `seed=42`.
- [ ] Set `training.num_epochs=350` for all 9 runs.
- [ ] Encode `fullattn` in every `exp_name`, `logging.name`, and run directory to avoid collisions.
- [ ] Balance the 9 runs across local 5090, 5880 GPU0, and 5880 GPU1 as three serial queues.
- [ ] Sync the new config to the remote smoke repo before launching remote queues.

### Task 5: Monitor and auto-summarize

**Files:**
- Read local and remote pid files, logs, outputs, checkpoints.

- [ ] Start an xhigh monitoring agent that polls all three queues.
- [ ] On completion, parse all 9 `logs.json.txt` files and rank by max `test_mean_score`.
- [ ] Report embedding/layer trends and the best configuration.