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#!/usr/bin/env python3
# RLX — versatile ML compiler + runtime.
# Copyright (C) 2026 Eugene Hauptmann, Nataliya Kosmyna.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, version 3.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
"""PyTorch / HuggingFace Gemma last-token logits for RLX parity (JSON lines on stdout)."""
from __future__ import annotations
import json
import sys
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
def main() -> int:
if len(sys.argv) < 3:
print(
"usage: gemma_parity_reference.py WEIGHTS.safetensors CONFIG.json",
file=sys.stderr,
)
return 2
weights_path = sys.argv[1]
config_path = sys.argv[2]
# Derive model directory from config path (HF layout).
import os
model_dir = os.path.dirname(os.path.abspath(config_path))
tok = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_dir,
torch_dtype=torch.float32,
device_map="cpu",
trust_remote_code=True,
)
model.eval()
prompt_ids = [2, 106, 164, 207, 417, 521, 897]
if tok.eos_token_id is not None:
prompt_ids.append(tok.eos_token_id)
input_ids = torch.tensor([prompt_ids], dtype=torch.long)
with torch.no_grad():
out = model(input_ids)
logits = out.logits[0, -1, :].float().cpu().tolist()
emit(
{
"prompt_ids": prompt_ids,
"logits": logits,
"top1": int(max(range(len(logits)), key=lambda i: logits[i])),
}
)
return 0
def emit(obj: dict) -> None:
print(json.dumps(obj), flush=True)
if __name__ == "__main__":
raise SystemExit(main())