Upload variant_a/scripts/setup_eval.sh with huggingface_hub

variant_a/scripts/setup_eval.sh

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+#!/bin/bash
+set -euo pipefail
+export PATH="/venv/main/bin:$PATH"
+export PYTHONDONTWRITEBYTECODE=1
+export CUDA_VISIBLE_DEVICES=0
+export WORK=/dev/shm/eval
+
+log() { echo "[$(date '+%Y-%m-%d %H:%M:%S')] $*"; }
+
+log "============================================"
+log "  Setup: Polish LLM Eval for QuIP# Bielik"
+log "============================================"
+
+mkdir -p "$WORK"
+cd "$WORK"
+
+# 1. HF login first (needed for private model download)
+log "HuggingFace login..."
+python -c "
+from huggingface_hub import login
+login(token='HF_TOKEN_REDACTED')
+print('Login OK')
+"
+
+# 2. Clone speakleash lm-eval fork with Polish tasks
+log "Cloning speakleash/lm-evaluation-harness (polish3 branch)..."
+if [ ! -d "$WORK/lm-evaluation-harness" ]; then
+    git clone https://github.com/speakleash/lm-evaluation-harness.git -b polish3
+fi
+cd "$WORK/lm-evaluation-harness"
+pip install -e . 2>&1 | tail -5
+log "lm-eval installed"
+
+# 3. Clone quip-sharp for model loading
+cd "$WORK"
+if [ ! -d "$WORK/quip-sharp" ]; then
+    git clone https://github.com/Cornell-RelaxML/quip-sharp.git
+fi
+cd "$WORK/quip-sharp"
+
+# 4. Build CUDA kernels
+log "Building quiptools CUDA kernels..."
+cd quiptools && python setup.py install 2>&1 | tail -5 && cd ..
+log "quiptools built"
+
+# 5. Install deps
+pip install glog primefac protobuf sentencepiece 2>&1 | tail -3
+
+# 6. Apply patches
+log "Applying quip-sharp patches..."
+
+# torch.load compat for PyTorch 2.10+
+python -c "
+for f in ['lib/utils/unsafe_import.py', 'eval/eval_zeroshot.py']:
+    try:
+        path = '$WORK/quip-sharp/' + f
+        with open(path, 'r') as fh:
+            c = fh.read()
+        if 'weights_only' not in c and 'torch.load' in c:
+            c = c.replace('import torch\n', 'import torch\n_orig_load = torch.load\ndef _compat_load(*a, **kw):\n    kw.setdefault(\"weights_only\", False)\n    return _orig_load(*a, **kw)\ntorch.load = _compat_load\n', 1)
+            with open(path, 'w') as fh:
+                fh.write(c)
+            print(f'Patched {f}')
+    except FileNotFoundError:
+        pass
+"
+
+# fast_hadamard_transform fallback
+python -c "
+path = '$WORK/quip-sharp/lib/utils/matmul_had.py'
+with open(path, 'r') as f:
+    content = f.read()
+if content.startswith('import fast_hadamard_transform'):
+    content = 'try:\n    import fast_hadamard_transform\n    HAS_FAST_HAD = True\nexcept ImportError:\n    HAS_FAST_HAD = False\n' + content.split('\n', 1)[1]
+    with open(path, 'w') as f:
+        f.write(content)
+    print('matmul_had.py patched')
+"
+
+# hadamard fallback implementation
+python -c "
+path = '$WORK/quip-sharp/lib/utils/matmul_had.py'
+with open(path, 'r') as f:
+    c = f.read()
+if 'HAS_FAST_HAD' in c and 'Walsh-Hadamard' not in c:
+    old = 'return fast_hadamard_transform.hadamard_transform(x, scale)'
+    new = '''if HAS_FAST_HAD:
+        return fast_hadamard_transform.hadamard_transform(x, scale)
+    else:
+        # Pure PyTorch Walsh-Hadamard fallback
+        n = x.shape[-1]
+        orig_shape = x.shape
+        x = x.contiguous().view(-1, n)
+        h = 1
+        while h < n:
+            x = x.view(-1, n // (2 * h), 2, h)
+            a = x[:, :, 0, :] + x[:, :, 1, :]
+            b = x[:, :, 0, :] - x[:, :, 1, :]
+            x = torch.stack([a, b], dim=2).view(-1, n)
+            h *= 2
+        return (x * scale).view(orig_shape)'''
+    if old in c:
+        c = c.replace(old, new)
+        with open(path, 'w') as f:
+            f.write(c)
+        print('hadamard fallback added')
+"
+
+# 7. Download QuIP# model from HuggingFace
+log "Downloading QuIP# model..."
+mkdir -p "$WORK/model"
+python -c "
+from huggingface_hub import snapshot_download
+path = snapshot_download('Jakubrd4/bielik-q2-variant-a', local_dir='$WORK/model')
+print(f'Model downloaded to: {path}')
+"
+log "Model downloaded"
+
+# 8. Download tokenizer (base model)
+log "Pre-downloading tokenizer..."
+python -c "
+from transformers import AutoTokenizer
+tok = AutoTokenizer.from_pretrained('speakleash/Bielik-11B-v2.3-Instruct')
+print(f'Tokenizer loaded: {tok.name_or_path}, vocab_size={tok.vocab_size}')
+"
+
+# 9. List available Polish tasks
+log "Available Polish tasks:"
+cd "$WORK/lm-evaluation-harness"
+python -c "
+from lm_eval import tasks
+mgr = tasks.TaskManager()
+polish = [t for t in mgr.all_tasks if 'polish' in t.lower() or 'polemo' in t.lower()]
+for t in sorted(polish):
+    print(f'  {t}')
+print(f'Total: {len(polish)} Polish tasks')
+" 2>/dev/null || echo "Task listing skipped"
+
+log "============================================"
+log "  Setup complete! Ready for eval."
+log "============================================"