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#!/usr/bin/env python3
"""Convert a local BF16 model into Marlin-supported quant formats via llm-compressor."""
from __future__ import annotations
import gc
import os
import sys
from typing import Optional
import torch
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
# Allow running against the local llm-compressor checkout without installing.
LLM_COMPRESSOR_SRC = "/home/quixi/marlin-cdna/llm-compressor/src"
if os.path.isdir(LLM_COMPRESSOR_SRC):
sys.path.insert(0, LLM_COMPRESSOR_SRC)
from llmcompressor import oneshot # noqa: E402
from llmcompressor.modifiers.awq import AWQModifier # noqa: E402
from llmcompressor.modifiers.quantization import ( # noqa: E402
GPTQModifier,
QuantizationModifier,
)
MODEL_PATH = "/home/quixi/models/Llama-3.2-1B"
OUTPUT_ROOT = "/home/quixi/models"
CALIB_DATASET_ID = "HuggingFaceH4/ultrachat_200k"
CALIB_DATASET_SPLIT = "train_sft"
NUM_CALIBRATION_SAMPLES = 128
MAX_SEQUENCE_LENGTH = 512
def _load_tokenized_dataset(tokenizer):
ds = load_dataset(
CALIB_DATASET_ID,
split=f"{CALIB_DATASET_SPLIT}[:{NUM_CALIBRATION_SAMPLES}]",
).shuffle(seed=42)
def preprocess(example):
return {
"text": tokenizer.apply_chat_template(
example["messages"],
tokenize=False,
)
}
ds = ds.map(preprocess)
def tokenize(sample):
return tokenizer(
sample["text"],
padding=False,
max_length=MAX_SEQUENCE_LENGTH,
truncation=True,
add_special_tokens=False,
)
return ds.map(tokenize, remove_columns=ds.column_names)
def _load_model_and_tokenizer():
model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
if torch.cuda.is_available():
model.to("cuda")
return model, tokenizer
def _cleanup(model, tokenizer):
del model
del tokenizer
gc.collect()
if torch.cuda.is_available():
torch.cuda.empty_cache()
def _run_recipe(
name: str,
recipe,
*,
save_compressed: bool,
use_calibration: bool,
) -> Optional[str]:
print(f"\n=== Quantizing {name} ===")
model, tokenizer = _load_model_and_tokenizer()
oneshot_kwargs = {"model": model, "recipe": recipe}
if use_calibration:
ds = _load_tokenized_dataset(tokenizer)
oneshot_kwargs.update(
dataset=ds,
max_seq_length=MAX_SEQUENCE_LENGTH,
num_calibration_samples=NUM_CALIBRATION_SAMPLES,
)
oneshot(**oneshot_kwargs)
base_name = os.path.basename(MODEL_PATH.rstrip("/"))
save_dir = os.path.join(OUTPUT_ROOT, f"{base_name}-{name}")
os.makedirs(save_dir, exist_ok=True)
if save_compressed:
model.save_pretrained(save_dir, save_compressed=True)
else:
model.save_pretrained(save_dir)
tokenizer.save_pretrained(save_dir)
_cleanup(model, tokenizer)
return save_dir
def main():
# GPTQ W4A16 (INT4 weight-only).
_run_recipe(
"W4A16-GPTQ",
GPTQModifier(targets="Linear", scheme="W4A16", ignore=["lm_head"]),
save_compressed=True,
use_calibration=True,
)
# AWQ W4A16 (INT4 weight-only).
_run_recipe(
"W4A16-AWQ",
AWQModifier(
targets=["Linear"],
scheme="W4A16_ASYM",
ignore=["lm_head"],
duo_scaling="both",
),
save_compressed=True,
use_calibration=True,
)
# GPTQ W8A16 (INT8 weight-only).
_run_recipe(
"W8A16-GPTQ",
GPTQModifier(targets="Linear", scheme="W8A16", ignore=["lm_head"]),
save_compressed=True,
use_calibration=True,
)
# FP8 dynamic (W8A8-FP8).
_run_recipe(
"FP8-Dynamic",
QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"]),
save_compressed=False,
use_calibration=False,
)
# NVFP4A16 (FP4 weights + FP16 activations).
_run_recipe(
"NVFP4A16",
QuantizationModifier(targets="Linear", scheme="NVFP4A16", ignore=["lm_head"]),
save_compressed=True,
use_calibration=False,
)
# MXFP4 (FP4 weights).
_run_recipe(
"MXFP4",
QuantizationModifier(targets="Linear", scheme="MXFP4", ignore=["lm_head"]),
save_compressed=True,
use_calibration=False,
)
if __name__ == "__main__":
main() |