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db32e07 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | # 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`).
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