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scripts/run_generation_arm.sh

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+#!/usr/bin/env bash
+# Runner script for GNN code generation (autoencoder) experiments.
+# Outputs METRICS:{json} for Ratiocinator fleet parsing.
+#
+# Environment variables (set by Ratiocinator fleet):
+#   DECODER_CONV_TYPE - Decoder conv type: GCN, SAGE, GAT, GIN, GraphConv (default: GAT)
+#   HIDDEN_DIM        - Hidden dimension (default: 256)
+#   NUM_LAYERS        - Number of decoder layers (default: 5)
+#   LEARNING_RATE     - Learning rate (default: 0.001)
+#   TYPE_WEIGHT       - Weight for node type loss (default: 2.0)
+#   PARENT_WEIGHT     - Weight for parent prediction loss (default: 1.0)
+#   LOSS_FN           - Loss function: simple, improved, comprehensive, original (default: improved)
+#   EPOCHS            - Training epochs (default: 30)
+#   DATASET_PATH      - Path to dataset dir (default: dataset/)
+
+set -uo pipefail
+
+DECODER_CONV_TYPE="${DECODER_CONV_TYPE:-GAT}"
+HIDDEN_DIM="${HIDDEN_DIM:-256}"
+NUM_LAYERS="${NUM_LAYERS:-5}"
+LEARNING_RATE="${LEARNING_RATE:-0.001}"
+TYPE_WEIGHT="${TYPE_WEIGHT:-2.0}"
+PARENT_WEIGHT="${PARENT_WEIGHT:-1.0}"
+LOSS_FN="${LOSS_FN:-improved}"
+EPOCHS="${EPOCHS:-30}"
+DATASET_PATH="${DATASET_PATH:-dataset/}"
+OUTPUT_PATH="models/experiment_decoder.pt"
+ENCODER_PATH="models/best_model.pt"
+
+echo "=== GNN Generation Arm ==="
+echo "DECODER=$DECODER_CONV_TYPE HIDDEN=$HIDDEN_DIM LAYERS=$NUM_LAYERS"
+echo "LR=$LEARNING_RATE TYPE_W=$TYPE_WEIGHT PARENT_W=$PARENT_WEIGHT LOSS=$LOSS_FN"
+
+# Pull LFS files if they are pointers (e.g., after shallow clone)
+if command -v git-lfs &>/dev/null || git lfs version &>/dev/null 2>&1; then
+    echo "Pulling LFS files..."
+    git lfs pull 2>&1 || echo "LFS pull returned non-zero (may be OK if files exist)"
+elif [ -f "${DATASET_PATH}/validation.jsonl" ] && head -1 "${DATASET_PATH}/validation.jsonl" | grep -q "^version https://git-lfs"; then
+    echo "ERROR: LFS pointer files detected but git-lfs not installed"
+    exit 1
+fi
+
+# Ensure train/val split exists
+if [ ! -f "${DATASET_PATH}/train.jsonl" ]; then
+    echo "Creating train/val split..."
+    python scripts/split_complexity_data.py \
+        --input "${DATASET_PATH}/validation.jsonl" \
+        --output-dir "${DATASET_PATH}"
+fi
+
+# Symlink validation.jsonl → val.jsonl for compatibility
+if [ -f "${DATASET_PATH}/val.jsonl" ]; then
+    ORIG_VAL="${DATASET_PATH}/validation.jsonl"
+    if [ -f "$ORIG_VAL" ] && ! [ -L "$ORIG_VAL" ]; then
+        mv "$ORIG_VAL" "${DATASET_PATH}/validation_full.jsonl"
+    fi
+    ln -sf val.jsonl "${DATASET_PATH}/validation.jsonl"
+fi
+
+# Need pre-trained encoder
+if [ ! -f "$ENCODER_PATH" ]; then
+    echo "Training encoder first..."
+    python train.py --epochs 20 --output_path "$ENCODER_PATH" --dataset_path "$DATASET_PATH" --num_workers 0
+fi
+
+mkdir -p models
+
+# Run autoencoder training — stream output directly
+TRAIN_LOG="/tmp/gen_train_$$.log"
+python train_autoencoder.py \
+    --dataset_path "$DATASET_PATH" \
+    --epochs "$EPOCHS" \
+    --output_path "$OUTPUT_PATH" \
+    --encoder_weights_path "$ENCODER_PATH" \
+    --hidden_dim "$HIDDEN_DIM" \
+    --num_layers "$NUM_LAYERS" \
+    --decoder_conv_type "$DECODER_CONV_TYPE" \
+    --learning_rate "$LEARNING_RATE" \
+    --type_weight "$TYPE_WEIGHT" \
+    --parent_weight "$PARENT_WEIGHT" \
+    --loss_fn "$LOSS_FN" \
+    2>&1 | tee "$TRAIN_LOG"
+
+TRAIN_RC=${PIPESTATUS[0]}
+if [ "$TRAIN_RC" -ne 0 ]; then
+    echo "ERROR: train_autoencoder.py exited with code $TRAIN_RC"
+    echo "METRICS:{\"error\": \"training_failed\", \"exit_code\": $TRAIN_RC}"
+    exit 1
+fi
+
+BEST_VAL_LOSS=$(grep "Best validation loss" "$TRAIN_LOG" | grep -oP '[\d.]+' | tail -1)
+
+# Run syntactic validity evaluation
+python -c "
+import sys, os, json, torch
+sys.path.insert(0, os.path.join(os.path.dirname('.'), 'src'))
+from models import ASTAutoencoder
+from data_processing import create_data_loaders
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+num_samples = 100
+valid_count = 0
+total = 0
+
+try:
+    model = ASTAutoencoder(
+        encoder_input_dim=74,
+        node_output_dim=74,
+        hidden_dim=$HIDDEN_DIM,
+        num_layers=$NUM_LAYERS,
+        conv_type='SAGE',
+        freeze_encoder=True,
+        encoder_weights_path='$ENCODER_PATH',
+        decoder_conv_type='$DECODER_CONV_TYPE',
+    ).to(device)
+
+    checkpoint = torch.load('$OUTPUT_PATH', map_location=device, weights_only=False)
+    model.decoder.load_state_dict(checkpoint['decoder_state_dict'])
+    model.eval()
+
+    # Load val data (JSONL or .pt)
+    val_path = os.path.join('${DATASET_PATH}', 'val.jsonl')
+    if not os.path.exists(val_path):
+        val_path = os.path.join('${DATASET_PATH}', 'validation.jsonl')
+    _, val_loader = create_data_loaders(val_path, val_path, batch_size=1, shuffle=False, num_workers=0)
+
+    with torch.no_grad():
+        for batch in val_loader:
+            if total >= num_samples:
+                break
+            batch = batch.to(device)
+            result = model(batch)
+            recon = result['reconstruction']
+            node_preds = recon.x if hasattr(recon, 'x') else None
+            if node_preds is not None:
+                pred_types = node_preds.argmax(dim=-1)
+                unique_types = len(pred_types.unique())
+                if unique_types > 2:
+                    valid_count += 1
+            total += 1
+
+    validity_pct = (valid_count / total * 100) if total > 0 else 0.0
+except Exception as e:
+    validity_pct = 0.0
+    total = num_samples
+    print(f'Eval error: {e}', file=sys.stderr)
+
+print('METRICS:' + json.dumps({
+    'syntactic_validity_pct': round(validity_pct, 2),
+    'val_loss': round(float('${BEST_VAL_LOSS:-0}'), 4),
+    'samples_evaluated': total,
+    'valid_samples': valid_count,
+    'decoder_conv_type': '$DECODER_CONV_TYPE',
+    'hidden_dim': $HIDDEN_DIM,
+    'num_layers': $NUM_LAYERS,
+    'loss_fn': '$LOSS_FN',
+    'type_weight': $TYPE_WEIGHT,
+    'parent_weight': $PARENT_WEIGHT,
+    'learning_rate': $LEARNING_RATE,
+    'epochs': $EPOCHS,
+}))
+" 2>&1
+
+rm -f "$TRAIN_LOG"