quantization/hsaq/quantize_guardian_for_serving.py

# /// script
# requires-python = ">=3.11"
# dependencies = [
#   "torch>=2.1,<2.7",
#   "transformers>=4.46,<4.50",
#   "datasets",
#   "hqq>=0.2.8",
#   "accelerate",
#   "tqdm",
#   "huggingface_hub",
# ]
# ///
"""
HSAQ-quantize the LoRA-merged guardian model for swarm serving.

Source: mxguru1/master-chief-guardian-8b-v1 (bf16 merged, 16 GB)
Output: HSAQ-quantized state_dict + tokenizer + config in
        mxguru1/master-chief-guardian-8b-v1-hsaq (HF dataset)

This is Phase 1A of the wargame thesis-test. The artifact is then loaded
by a local FastAPI shim (Phase 1B) that mimics Ollama's endpoints, so the
wargame can route to it via LOCAL_HF_SHIMS in adversarial_wargame_swarm.py.

Cost: ~$1.50, ~30 min on A100 80GB.
"""
from __future__ import annotations
import json, logging, os, subprocess, sys, time
from datetime import UTC, datetime
from pathlib import Path
import torch

if not torch.cuda.is_available():
    subprocess.check_call([sys.executable, "-m", "pip", "install", "torch", "--force-reinstall",
                            "--index-url", "https://download.pytorch.org/whl/cu124"])
    import importlib; importlib.reload(torch)
    if not torch.cuda.is_available(): sys.exit(1)

logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s | %(message)s")
logger = logging.getLogger("GuardianHSAQ")

sys.path.insert(0, "/opt/hsaq")
from quantization.hsaq.config import HSAQConfig
from quantization.hsaq.pipeline import HSAQPipeline

MODEL_ID = "mxguru1/master-chief-guardian-8b-v1"
HF_TOKEN = os.environ.get("HF_TOKEN")
RUN_TAG = datetime.now(UTC).strftime("%Y%m%d_%H%M%S")
OUT = Path("/tmp/guardian_hsaq"); OUT.mkdir(parents=True, exist_ok=True)


def main():
    start = time.time()
    report = {"run_tag": RUN_TAG, "approach": "hsaq_quantize_for_serving", "model_id": MODEL_ID}
    try:
        cfg = HSAQConfig(
            model_id=MODEL_ID,
            output_dir=str(OUT.parent),
            hf_token=HF_TOKEN,
            gpu_budget_gb=11.2,
            enable_2bit=False,
            enable_pruning=False,
            train_lora=False,  # already LoRA-merged
            calibration_samples=128,
        )
        pipe = HSAQPipeline(cfg)

        logger.info("Stage 1: load + profile %s", MODEL_ID)
        model, tokenizer = pipe._load_model()
        model = model.to("cuda:0")
        if tokenizer.pad_token is None:
            tokenizer.pad_token = tokenizer.eos_token

        sensitivity = pipe.profiler.profile(model)
        candidates = pipe._build_layer_candidates(sensitivity, model)
        from quantization.hsaq.assignment import assign_bit_widths
        weight_budget_gb = pipe._compute_weight_budget()
        assignment = assign_bit_widths(candidates, weight_budget_gb)
        name_to_bits = {a.component: a.chosen.bits for a in assignment.assignments}

        logger.info("Stage 2: HQQ apply to all 281 Linears")
        n_hqq = pipe._apply_per_module_hqq(model, name_to_bits, device="cuda:0")
        logger.info("HQQ applied to %d Linears", n_hqq)

        # Save model artifact
        artifact_dir = OUT / "guardian-hsaq"
        artifact_dir.mkdir(parents=True, exist_ok=True)
        try:
            from hqq.models.hf.base import AutoHQQHFModel
            AutoHQQHFModel.save_quantized(model, str(artifact_dir))
            save_method = "AutoHQQHFModel.save_quantized"
        except Exception as exc:
            logger.warning("AutoHQQHFModel.save_quantized failed (%s); falling back to state_dict", exc)
            torch.save(model.state_dict(), artifact_dir / "pytorch_model.bin")
            save_method = "state_dict"
        tokenizer.save_pretrained(str(artifact_dir))
        model.config.save_pretrained(str(artifact_dir))

        # Per-layer bits manifest for the shim
        manifest = {
            "model_id": MODEL_ID,
            "save_method": save_method,
            "n_linears_quantized": n_hqq,
            "name_to_bits": name_to_bits,
            "group_size": 64,
            "axis": 0,
            "compute_dtype": "bfloat16",
            "total_weight_gb": round(assignment.total_weights_gb, 3),
        }
        (artifact_dir / "hsaq_manifest.json").write_text(json.dumps(manifest, indent=2))
        report["artifact"] = manifest
        report["artifact_size_bytes"] = sum(p.stat().st_size for p in artifact_dir.rglob("*") if p.is_file())
        logger.info("Artifact saved: %s (%.2f GB)", artifact_dir, report["artifact_size_bytes"] / 1e9)

        # Upload as dataset (HQQ artifacts aren't standard HF model format; dataset is fine)
        from huggingface_hub import HfApi
        repo_id = "mxguru1/master-chief-guardian-8b-v1-hsaq"
        api = HfApi(token=HF_TOKEN)
        api.create_repo(repo_id=repo_id, repo_type="dataset", exist_ok=True)
        for p in artifact_dir.rglob("*"):
            if p.is_file():
                api.upload_file(
                    path_or_fileobj=str(p),
                    path_in_repo=p.name,
                    repo_id=repo_id,
                    repo_type="dataset",
                )
        logger.info("Uploaded artifact to https://huggingface.co/datasets/%s", repo_id)
        report["status"] = "success"
        report["artifact_repo"] = repo_id

    except Exception as e:
        logger.exception("Run failed")
        report["status"] = "failed"
        report["error"] = repr(e)
    finally:
        report["elapsed_s"] = round(time.time() - start, 1)
        # Always upload the report
        from huggingface_hub import HfApi
        api = HfApi(token=HF_TOKEN)
        rid = f"mxguru1/guardian-hsaq-job-{RUN_TAG}"
        try:
            api.create_repo(repo_id=rid, repo_type="dataset", exist_ok=True)
            p = OUT / "report.json"; p.write_text(json.dumps(report, indent=2))
            api.upload_file(path_or_fileobj=str(p), path_in_repo="report.json", repo_id=rid, repo_type="dataset")
        except Exception:
            pass


if __name__ == "__main__":
    main()