| #!/bin/bash
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| set -e
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| WORKDIR=/workspace
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| HF_TOKEN="$1"
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| if [ -z "$HF_TOKEN" ]; then echo "Usage: bash full_cloud_eval.sh <HF_TOKEN>"; exit 1; fi
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| HF_REPO="Jakubrd4/bielik-quip-e8p12"
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| LIMIT=200
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| export HF_DATASETS_TRUST_REMOTE_CODE=1
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|
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| echo "========================================"
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| echo " QuIP# Bielik Eval - FULL AUTO SETUP"
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| echo " RTX 4090 / A100 / H100 (NOT Blackwell)"
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| echo "========================================"
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| echo "Start: $(date)"
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| echo "GPU: $(python3 -c 'import torch; print(torch.cuda.get_device_name(0))' 2>/dev/null || echo 'unknown')"
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| echo ""
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| echo "[1/8] Cloning QuIP#..."
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| cd $WORKDIR
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| if [ -d quip-sharp ]; then
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| echo " Already exists, skipping clone"
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| else
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| git clone https://github.com/Cornell-RelaxML/quip-sharp.git
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| fi
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| cd quip-sharp
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| echo "[2/8] Applying patches..."
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| sed -i 's/from \.lm_eval_adaptor import.*/# disabled for lm-eval 0.4.x/' lib/utils/__init__.py
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| echo " __init__.py patched"
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|
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| python3 << 'PATCHPY'
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| path = 'lib/utils/unsafe_import.py'
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| with open(path) as f:
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| code = f.read()
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| if 'from model.mistral' not in code:
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| code = code.replace(
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| 'from model.llama import LlamaForCausalLM',
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| 'from model.llama import LlamaForCausalLM\nfrom model.mistral import MistralForCausalLM'
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| )
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| if "model_type == 'mistral'" not in code:
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| old = " else:\n raise Exception"
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| new = " elif model_type == 'mistral':\n model_str = transformers.MistralConfig.from_pretrained(path)._name_or_path\n model_cls = MistralForCausalLM\n else:\n raise Exception"
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| code = code.replace(old, new)
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|
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|
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| code = code.replace("attn_implementation='sdpa'", "attn_implementation='eager'")
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|
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| with open(path, 'w') as f:
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| f.write(code)
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| print(' unsafe_import.py patched for Mistral')
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| PATCHPY
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|
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| python3 << 'PATCHPY2'
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| path = 'model/llama.py'
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| with open(path) as f:
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| code = f.read()
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| old_line = " causal_mask = AttentionMaskConverter._unmask_unattended("
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| if old_line in code:
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| new_block = """ if hasattr(AttentionMaskConverter, '_unmask_unattended'):
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| causal_mask = AttentionMaskConverter._unmask_unattended(
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| causal_mask, min_dtype
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| )"""
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| code = code.replace(
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| old_line + "\n causal_mask, min_dtype\n )",
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| new_block
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| )
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| with open(path, 'w') as f:
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| f.write(code)
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| print(' llama.py patched (_unmask_unattended)')
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| else:
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| print(' llama.py: patch not needed or already applied')
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| PATCHPY2
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| sed -i 's/self.rope_theta = config.rope_theta/self.rope_theta = getattr(config, "rope_theta", 1000000.0)/' model/mistral.py 2>/dev/null || true
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| echo " rope_theta patched"
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| echo "[3/8] Fixing Python dependencies..."
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| pip install glog primefac protobuf 2>&1 | tail -3
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| pip install 'transformers==4.38.0' 2>&1 | tail -3
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| pip install 'datasets==2.20.0' 2>&1 | tail -3
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|
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| pip install 'peft==0.9.0' 2>&1 | tail -3
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| echo " Dependencies fixed"
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| echo "[4/8] Compiling QuIP# CUDA kernels..."
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| cd $WORKDIR/quip-sharp/quiptools
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| pip install --no-build-isolation -e . 2>&1 | tail -5
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| echo " quiptools installed"
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| echo " Installing fast-hadamard-transform..."
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| pip install --no-build-isolation fast-hadamard-transform 2>&1 | tail -3 || {
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| echo " PyPI install failed, trying from git..."
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| pip install --no-build-isolation git+https://github.com/Dao-AILab/fast-hadamard-transform.git 2>&1 | tail -3
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| }
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| echo " fast-hadamard-transform installed"
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| echo "[5/8] Installing lm-evaluation-harness (Polish fork)..."
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| cd $WORKDIR
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| if [ -d lm-evaluation-harness ]; then
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| echo " Already exists, skipping clone"
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| else
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| git clone --branch polish4_shuf https://github.com/speakleash/lm-evaluation-harness.git
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| fi
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| cd lm-evaluation-harness
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| pip install -e . 2>&1 | tail -5
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| echo " lm-eval installed"
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| echo "[6/8] Downloading model from HuggingFace..."
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| python3 << DLPY
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| from huggingface_hub import snapshot_download
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| print(" Starting download...")
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| snapshot_download('${HF_REPO}', local_dir='${WORKDIR}/model', token='${HF_TOKEN}')
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| print(" Model downloaded!")
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| DLPY
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| echo " Model files:"
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| ls -lh $WORKDIR/model/
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| echo "[7/8] Checking model config..."
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| python3 << 'CFGPY'
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| import json
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| p = '/workspace/model/config.json'
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| c = json.load(open(p))
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| changed = False
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| if 'rope_theta' not in c:
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| c['rope_theta'] = 1000000.0
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| changed = True
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| if changed:
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| json.dump(c, open(p, 'w'), indent=2)
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| print(" Added rope_theta to config")
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| else:
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| print(" Config OK")
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| CFGPY
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| echo "[8/8] Creating eval script and running..."
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| cat > $WORKDIR/run_eval.py << 'PYEOF'
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| import sys, os, json, time, torch, argparse
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| sys.path.insert(0, "/workspace/quip-sharp")
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| torch.set_grad_enabled(False)
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| from transformers import AutoTokenizer
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| from lm_eval import evaluator
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| from lm_eval.models.huggingface import HFLM
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| from lib.utils.unsafe_import import model_from_hf_path
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|
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| MC_TASKS = [
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| "polemo2_in_multiple_choice", "polemo2_out_multiple_choice",
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| "polish_8tags_multiple_choice", "polish_belebele_mc",
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| "polish_dyk_multiple_choice", "polish_ppc_multiple_choice",
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| "polish_psc_multiple_choice", "polish_cbd_multiple_choice",
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| "polish_klej_ner_multiple_choice", "polish_polqa_reranking_multiple_choice",
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| ]
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| PPL_TASKS = ["polish_poleval2018_task3_test_10k"]
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| BASELINES = {
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| "polemo2_in_multiple_choice": 0.416, "polemo2_out_multiple_choice": 0.368,
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| "polish_8tags_multiple_choice": 0.143, "polish_belebele_mc": 0.279,
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| "polish_dyk_multiple_choice": 0.289, "polish_ppc_multiple_choice": 0.419,
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| "polish_psc_multiple_choice": 0.466, "polish_cbd_multiple_choice": 0.149,
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| "polish_klej_ner_multiple_choice": 0.343, "polish_polqa_reranking_multiple_choice": 0.534,
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| }
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|
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| parser = argparse.ArgumentParser()
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| parser.add_argument("--limit", type=int, default=None)
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| parser.add_argument("--batch_size", type=int, default=1)
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| parser.add_argument("--model_path", type=str, default="/workspace/model")
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| args = parser.parse_args()
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|
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| ALL_TASKS = MC_TASKS + PPL_TASKS
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| start = time.time()
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| lstr = str(args.limit) if args.limit else "FULL"
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| print("=" * 70)
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| print("Open PL LLM Leaderboard - QuIP# E8P12 2-bit Instruct")
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| print("Batch: %d | Limit: %s" % (args.batch_size, lstr))
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| print("GPU: %s" % torch.cuda.get_device_name(0))
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| print("=" * 70)
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|
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| print("Loading model...")
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| model, model_str = model_from_hf_path(args.model_path, use_cuda_graph=False, use_flash_attn=False)
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| tokenizer = AutoTokenizer.from_pretrained(model_str)
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| tokenizer.pad_token = tokenizer.eos_token
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| lm = HFLM(pretrained=model, tokenizer=tokenizer, backend="causal", batch_size=args.batch_size, max_length=4096, trust_remote_code=True)
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|
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| ekw = dict(model=lm, tasks=ALL_TASKS, num_fewshot=5, batch_size=args.batch_size, log_samples=False)
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| if args.limit:
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| ekw["limit"] = args.limit
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|
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| print("Running eval...")
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| results = evaluator.simple_evaluate(**ekw)
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|
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| elapsed = time.time() - start
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| print("\n" + "=" * 70)
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| print("RESULTS (5-shot, limit=%s)" % lstr)
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| print("=" * 70)
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| scores = {}
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| nscores = {}
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| for t in ALL_TASKS:
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| if t not in results.get("results", {}):
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| print(" %-45s MISSING" % t)
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| continue
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| tr = results["results"][t]
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| score = None
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| metric = "?"
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| for mk in ["acc,none", "f1,none", "word_perplexity,none"]:
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| if mk in tr:
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| score = tr[mk]
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| metric = mk.split(",")[0]
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| break
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| if score is None:
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| continue
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| bl = BASELINES.get(t, 0)
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| is_ppl = t in PPL_TASKS
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| if is_ppl:
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| norm = None
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| elif 0 < bl < 1.0:
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| norm = max(0, (score - bl) / (1.0 - bl))
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| else:
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| norm = max(0, score)
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| scores[t] = score
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| if norm is not None:
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| nscores[t] = norm
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| ns = "norm=%.4f" % norm if norm is not None else ""
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| print(" %-45s %s=%.4f %s" % (t, metric, score, ns))
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|
|
| print("-" * 70)
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| avg = sum(nscores.values()) / len(nscores) if nscores else 0
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| print(" %-45s %.4f (%.2f%%)" % ("Avg MC (normalized)", avg, avg * 100))
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| print("=" * 70)
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| print("Time: %.1f min" % (elapsed / 60))
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| print("\nComparison:")
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| print(" SpeakLeash IQ2_XXS = 61.34%%")
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| print(" FP16 baseline = 65.71%%")
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| print(" QuIP# E8P12 2-bit = %.2f%%" % (avg * 100))
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| os.makedirs("/workspace/eval_results", exist_ok=True)
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| fn = "/workspace/eval_results/results_limit%s.json" % (str(args.limit) if args.limit else "full")
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| json.dump({"avg_mc": float(avg), "scores": {k: float(v) for k,v in scores.items()}, "normalized": {k: float(v) for k,v in nscores.items()}, "full": results.get("results", {})}, open(fn, "w"), indent=2, default=str)
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| print("Saved to %s" % fn)
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| PYEOF
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| echo " Eval script created"
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|
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| echo "Running evaluation with limit=$LIMIT..."
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| echo "========================================"
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| cd $WORKDIR/quip-sharp
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| python3 $WORKDIR/run_eval.py --limit $LIMIT
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|
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| echo ""
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| echo "========================================"
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| echo " ALL DONE! $(date)"
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| echo "========================================"
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|
