# 30B-A3B MoE — Training & Inference File Map

Authoritative reference for every training script, DeepSpeed config, modeling
file, and eval path used for **Qwen3-VL-30B-A3B-Instruct** (MoE). Mirrors the
dense (4B/8B) pipeline but uses separate modeling & launcher files so 4B/8B
work is never impacted by MoE changes.

Last verified: 2026-04-12.

---

## 1. Core code paths

| Role | Path |
|------|------|
| Training entry (shared) | `QWENVL-PRIVATE/qwen-vl-finetune/qwenvl/train/train_qwen.py` |
| MoE dispatch | `train_qwen.py:179-184` — loads `Qwen3VLMoeForConditionalGeneration` when `config.model_type == 'qwen3_vl_moe'`. Try/except guarded import at line 48-52 so dense-only env still works. |
| **Modeling (MoE)** | **`QWENVL-PRIVATE/qwen_src/qwen3_vl/modeling_qwen3_vl_moe_batch.py`** |
| IWA-aware attention | `modeling_qwen3_vl_moe_batch.py:40` — `_iwa_aware_attn_forward`, monkey-patches `Qwen3VLMoeTextAttention.forward` |
| Main class | `modeling_qwen3_vl_moe_batch.py:112` — `Qwen3VLMoeForConditionalGeneration(_BaseModel)` |
| Modeling (dense, for ref) | `QWENVL-PRIVATE/qwen_src/qwen3_vl/modeling_qwen3_vl_batch.py` — 4B/8B/32B |
| Shared utils (arch-agnostic) | `QWENVL-PRIVATE/qwen_src/mm_utils.py`, `qwen_src/qwen3_vl/iwa.py`, `configuration_qwen3_vl.py`, `processing_qwen3_vl.py` |

### Isolation guarantees (proven 2026-04-12)
- **Dense → MoE**: dense modeling never imports MoE. Safe.
- **MoE → Dense**: one controlled import in `modeling_qwen3_vl_moe_batch.py:848` pulls three utils (`get_singleturn_query_text_hs_mheads, apply_rotary_pos_emb, repeat_kv`) from dense. Arch-agnostic — safe.
- `git log 10070ed..5602a58 -- QWENVL-PRIVATE/qwen_src/qwen3_vl/modeling_qwen3_vl_batch.py` returns **empty** (the 5 MoE commits did NOT touch the dense file).

---

## 2. Training scripts (all in `QWENVL-PRIVATE/tools/`)

### 2.1 Pre-training / Stage 1 (twig training — learning to route ROI)

| Script | GPUs | Notes |
|--------|------|-------|
| `stage1_30b_8gpu.sh` | 1×8 | single-node baseline |
| `stage1_30b_6gpu.sh` | 1×6 | reduced-GPU variant |
| `stage1_30b_natural.sh` | — | natural-image pseudo-label variant |
| `stage1_stage2_30b.sh` | 1×8 | stage1+stage2 combined (full twig pipeline) |

### 2.2 Stage 3 — SD-RPN spatial-only (α=1.0, pure ROI loss, no KL)

| Script | GPUs | Status | Current result |
|--------|------|--------|----------------|
| `stage3_30b_4node_8gpus_spatial_only.sh` | 4×8=32 | ✓ used | 30B SD-RPN, OCR=826 (tied with vanilla baseline; no gain yet) |
| `stage3_30b_8node_8gpus_spatial_only.sh` | 8×8=64 | scale-out | — |

Key flags in spatial_only: `--enable_twig True --twig_K 39 --twig_T 3 --distill_weight 1.0 --enable_iwa False`.

### 2.3 Stage 3 — Confluent Distillation (α=0.667, ROI + KL + IWA)

| Script | GPUs | Version | Status |
|--------|------|---------|--------|
| `stage3_30b_confluent.sh` | single-node | legacy | superseded |
| `stage3_30b_4node_8gpus_confluent.sh` | 4×8=32 | **v1** (τ=2.0) | kept for reference |
| **`stage3_30b_2node_8gpus_confluent_v2.sh`** | 2×8=**16** | **v2** (τ=1.5) | **CURRENT smoke-test** |
| **`stage3_30b_4node_8gpus_confluent_v2.sh`** | 4×8=**32** | **v2** (τ=1.5) | **CURRENT production** |
| **`stage3_30b_8node_8gpus_confluent_v2.sh`** | 8×8=**64** | **v2** (τ=1.5) | **CURRENT scale-out** |

**v1 → v2 delta** (from `diff stage3_30b_4node_8gpus_confluent.sh stage3_30b_4node_8gpus_confluent_v2.sh`):
- Output dir tag: `confluent-fixed` → `confluent-v2-fixed`
- Default `TAU`: 2.0 → 1.5

**Shared confluent v2 flags:**
```
--enable_twig True     \
--twig_K 39            \
--twig_T 3             \
--distill_weight 0.667 \
--enable_iwa True      \
TAU=1.5                \
ALPHA=0.667
```

### 2.4 Stage 3 — Generic / legacy templates

| Script | Purpose |
|--------|---------|
| `stage3_30b_4node_8gpus.sh` | generic multi-node (no method tag) |
| `stage3_30b_8node_8gpus.sh` | generic 8-node |
| `stage3_30b_multinode.sh` | generic multi-node template |
| `stage3_30b_fsdp.sh` | FSDP-only template |
| `stage3_30b_deepspeed.sh` | tiny DeepSpeed wrapper |

These are **not** currently used for the Confluent pipeline — prefer v2 scripts above.

### 2.5 DeepSpeed configs

| File | ZeRO Stage | Notes |
|------|-----------|-------|
| `tools/ds_zero2_30b.json` | 2 | smaller memory ceiling |
| `tools/ds_zero3_30b.json` | 3 | used by confluent v2 together with FSDP activation checkpointing. **Don't enable HF `gradient_checkpointing` alongside FSDP's `activation_checkpointing: true` — double-wrap issues.** |

---

## 3. Analysis & data-prep scripts

| Path | Purpose |
|------|---------|
| `QWENVL-PRIVATE/make_pseudo_label_natural_30b.py` | natural-image ROI pseudo-labels |
| `QWENVL-PRIVATE/make_pseudo_label_textual_30b.py` | text-image ROI pseudo-labels |
| `QWENVL-PRIVATE/analysis/qwen3vl_debug_30b.py` | ROI pseudo-label map generator (placeholder heads) |
| `QWENVL-PRIVATE/analysis/qwen3vl_identify_heads_30b.py` | per-layer + per-head attention visualization (picks grounding heads) |
| `QWENVL-PRIVATE/analysis/gen_roi_map_30b.py` | ROI map gen helper |
| `QWENVL-PRIVATE/tools/identify_heads_30b.sh` | head-identification runner wrapper |
| `QWENVL-PRIVATE/tools/identify_heads_30b_generic.sh` | head-ID generic wrapper |

---

## 4. Inference / eval file map

| CLI `--model` | Source file | Used for |
|---------------|-------------|----------|
| `qwen3_vl` | `lmms-eval/lmms_eval/models/simple/qwen3_vl.py` | 30B MoE (auto-dispatches via `config.model_type == 'qwen3_vl_moe'`) — also handles 4B/8B fallback |
| `qwen3_vl_hybrid` | `.../simple/qwen3_vl_hybrid.py` | 4B/8B dense (run7b-golden, matches commit `6360f83` byte-for-byte) |
| **`qwen3_vl_hybrid_30b_moe`** | `.../simple/qwen3_vl_hybrid_30b_moe.py` | **30B MoE hybrid (NEW 2026-04-12, commits 76697057e + a560bd626)** — mirror of `qwen3_vl_hybrid.py` using MoE class |

### Canonical eval commands

**30B MoE via simple (auto-dispatch):**
```bash
export PYTHONPATH=/opt/tiger/thothvl_pretrain/QWENVL-PRIVATE:$PYTHONPATH
export HF_HOME=/mnt/bn/leonworkspace/HF_HOME
export HF_TOKEN=hf_REDACTED

CKPT=/mnt/bn/leonworkspace/terry/model/qwen3vl-30b-roi-K39T3-185k-confluent-v2-fixed-<RUN_ID>
python3 -m lmms_eval --model qwen3_vl --model_args \
    "pretrained=$CKPT,device_map=auto,two_stage_roi=True,roi_baseline=True,roi_conf_thresh=0.15,high_res_thresh=0.1,attn_implementation=flash_attention_2" \
    --tasks ocrbench --batch_size 1 --output_path ./results
```

**30B MoE via hybrid (isolated file):**
```bash
python3 -m lmms_eval --model qwen3_vl_hybrid_30b_moe --model_args "<same args>" \
    --tasks ocrbench --batch_size 1 --output_path ./results
```

---

## 5. Known issues & fixes (2026-04-12 saga)

| Commit | Fix |
|--------|-----|
| `10070ed9c` | OOM: KL distillation recomputed `lm_head` three times; reuse `twig_logits` |
| `e7428e791` | IWA was created but never wired in MoE training forward — added `compute_importance` + per-layer `_iwa_bias` |
| `67063ecb7` | Added `_compute_roi_maps_moe` helper |
| `506d1b666` | Critical: DeepStack visual features were being dropped (silent tuple `[0]` truncation). Added `get_image_features` / `get_video_features` path |
| `5602a581f` | Implemented two-stage inference in MoE forward (shared prefix → twig → ROI map → sub-image reroute → backbone) |
| `b8724e4f1` | Skip IWA in two-stage reroute path (matches dense) |
| `b914a711c` | Eval dispatch (MoE via hybrid) — later deleted |
| `dd18961ba` | Deleted `qwen3_vl_hybrid.py` — regression (4B/8B 852→845) |
| `4d9e06434` + `da60dfc29` | Recovered 4B/8B by porting 14 SD-RPN attrs into `qwen3_vl.py` simple |

### Current smoke-test status (as of 2026-04-12)

- 30B Confluent v2 training: not yet launched end-to-end after the 5-commit MoE alignment. Needs a run with `stage3_30b_2node_8gpus_confluent_v2.sh` to verify (a) no OOM, (b) DeepStack flows, (c) IWA gamma in optimizer, (d) loss decreases.
- 30B SD-RPN spatial_only: current result OCR=826 (= vanilla baseline) — may be missing DeepStack (see fix `506d1b666`). Retrain required.
- 30B MoE hybrid eval file (`qwen3_vl_hybrid_30b_moe.py`): registered, import-tested, not yet run against a real MoE checkpoint.

---

## 6. GPU / node requirements

| Scale | Total GPUs | GPU RAM needed | Script |
|-------|------------|----------------|--------|
| Smoke-test | 16 | each ~60GB during training | `stage3_30b_2node_8gpus_confluent_v2.sh` |
| Production | 32 | each ~45GB | `stage3_30b_4node_8gpus_confluent_v2.sh` |
| Scale-out | 64 | each ~30GB | `stage3_30b_8node_8gpus_confluent_v2.sh` |

ETA per FSDP pass of ~185k samples:
- 16 GPUs: ~44 h
- 32 GPUs: ~22 h
- 64 GPUs: ~11 h