# Spectra-Reason-GCD Implementation

This document details the implementation of the Spectra-Reason-GCD pipeline, which integrates a spectral encoder (DreaMS + Perceiver Resampler), an LLM reasoning engine (Llama-3-8B with LoRA), and grammar-constrained decoding (GCD) for valid SMILES generation.

## 1. Environment Setup

Use the provided `specrag` conda environment.

```bash
source /cluster/tufts/liulab/lib/anaconda3/etc/profile.d/conda.sh
conda activate specrag
```

## 2. Directory Structure

```
Spec-RAG/
├── data/                   # Dataset JSONL files
├── grammars/
│   └── smiles.ebnf         # SMILES grammar for GCD
├── scripts/
│   ├── train_stage1_bridge.py
│   ├── train_stage2_cot_lora.py
│   ├── generate_gcd.py
│   ├── create_test_spectra.py
│   └── ...
├── spec_rag/               # Python package
│   ├── cot_system2.py      # Chain-of-Thought logic
│   ├── perceiver_resampler.py
│   ├── spectra_reason_encoder.py
│   ├── gcd_inference.py
│   └── ...
└── out/                    # Checkpoints and logs
```

## 3. Training Pipeline

### Stage 1: Encoder Pre-training (Optional but Recommended)
Trains the Perceiver Resampler to bridge DreaMS embeddings to the LLM's embedding space.

```bash
accelerate launch scripts/train_stage1_bridge.py \
    --train-jsonl data/train.jsonl \
    --output-dir out/stage1 \
    --dreams-ckpt /cluster/tufts/liulab/yiwan01/SpecBridge/data/ssl_model.ckpt \
    --llm-name meta-llama/Meta-Llama-3-8B-Instruct \
    --epochs 5 \
    --batch-size 8 \
    --mixed_precision bf16
```

### Stage 2: End-to-End CoT Fine-tuning
Fine-tunes the LLM (LoRA) and Perceiver Resampler jointly using Chain-of-Thought data.

```bash
accelerate launch scripts/train_stage2_cot_lora.py \
    --train-jsonl data/train_cot.jsonl \
    --val-jsonl data/val_cot.jsonl \
    --output-dir out/stage2 \
    --dreams-ckpt /cluster/tufts/liulab/yiwan01/SpecBridge/data/ssl_model.ckpt \
    --stage1-perceiver out/stage1/perceiver.pt \
    --llm-name meta-llama/Meta-Llama-3-8B-Instruct \
    --epochs 3 \
    --batch-size 4 \
    --mixed_precision bf16
```

**Note:** Monitor the training loss. It should decrease significantly. If loss remains high or constant, check that the `dreams-ckpt` is loading correctly and data is valid.

## 4. Inference

Run inference on test spectra. We use Grammar-Constrained Decoding (GCD) to ensure valid SMILES output.

**Important:**
1.  Ensure `--encoder-dir` points to `out/stage2` to load the trained Perceiver.
2.  Use `--no-gcd` first to debug raw model output if you suspect issues.
3.  If output is garbage (e.g., repeating `0.0.0...`), check training loss and DreaMS loading.

```bash
python scripts/generate_gcd.py \
    --input-jsonl data/test_spectra.jsonl \
    --output-jsonl out/predictions.jsonl \
    --model-dir out/stage2 \
    --encoder-dir out/stage2 \
    --dreams-ckpt /cluster/tufts/liulab/yiwan01/SpecBridge/data/ssl_model.ckpt \
    --grammar grammars/smiles.ebnf \
    --max-peaks 60 \
    --max-new-tokens 200 \
    --no-gcd  # Remove this flag to enable Grammar Constraint
```

## 5. Troubleshooting

*   **Garbage Output ("0.0.0.0...", "** **..."):**
    *   This indicates the model is in a degenerate state.
    *   **Check Training:** Ensure training loss decreased. If loss was ~0.0 or constant, training failed.
    *   **Check Input:** Verify `test_spectra.jsonl` contains valid peaks (m/z, intensity).
    *   **Check DreaMS:** Ensure `dreams-ckpt` path is correct and accessible.
*   **Empty Output:**
    *   Can happen if `max_new_tokens` is too small or EOS token is generated immediately.
    *   The inference script has been patched to correctly handle `inputs_embeds` generation output.
*   **"No such file or directory":**
    *   Ensure you run scripts from the `Spec-RAG` root directory.
    *   Verify `data/` folder contains required JSONL files.

## 6. Evaluation

(Use `chemprop` or `rdkit` based scripts to evaluate validity, novelty, and exact match accuracy of `out/predictions.jsonl`).