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Fix model test model loading #2008

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Oct 12, 2025
Merged

Fix model test model loading #2008

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55 changes: 46 additions & 9 deletions tests/models/model_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,6 +44,7 @@
from transformers import AutoProcessor, AutoTokenizer # noqa: E402

from gptqmodel import BACKEND, GPTQModel # noqa: E402
from gptqmodel.models.base import BaseQModel # noqa: E402
from gptqmodel.nn_modules.qlinear import BaseQuantLinear # noqa: E402
from gptqmodel.quantization import FORMAT, METHOD # noqa: E402
from gptqmodel.quantization.config import QuantizeConfig # noqa: E402
Expand All @@ -68,7 +69,7 @@ class ModelTest(unittest.TestCase):
TORCH_DTYPE = "auto"
EVAL_BATCH_SIZE = "auto"
QUANT_BATCH_SIZE = 1
LOAD_BACKEND = BACKEND.AUTO
LOAD_BACKEND = BACKEND.TORCH
QUANT_BACKEND = BACKEND.AUTO
USE_VLLM = False
INPUTS_MAX_LENGTH = 2048
Expand Down Expand Up @@ -223,7 +224,12 @@ def run_arc_challenge_eval(self, model, backend, trust_remote_code=False):
def perform_post_quant_validation(self, model_path, trust_remote_code=False):
inference_records = {}
arc_records = {}
compare_backends = (BACKEND.MARLIN, BACKEND.TORCH) if self.FORMAT is FORMAT.GPTQ else (BACKEND.MARLIN, BACKEND.GEMM)
reuse_candidates = {}

compare_backends = (BACKEND.TORCH,) if self.FORMAT is FORMAT.GPTQ else (BACKEND.MARLIN, BACKEND.GEMM)
target_backend = self.LOAD_BACKEND
can_reuse = target_backend not in (BACKEND.AUTO, BACKEND.AUTO_TRAINABLE)

for backend in compare_backends:
log.info(f"Loading post-quant model with backend `{backend.name}`")
model = self.loadQuantModel(
Expand All @@ -233,14 +239,23 @@ def perform_post_quant_validation(self, model_path, trust_remote_code=False):
)
tokenizer = model.tokenizer or self.load_tokenizer(model_path, trust_remote_code=trust_remote_code)
inference_records[backend] = self.run_generic_inference_checks(model, tokenizer, backend)

should_reuse = can_reuse and backend == target_backend and not self.USE_VLLM

try:
arc_records[backend] = self.run_arc_challenge_eval(model, backend, trust_remote_code=trust_remote_code)
finally:
del model
if should_reuse:
reuse_candidates[backend] = model
else:
del model
torch_empty_cache()

self.render_inference_summary(inference_records)
self.render_arc_summary(arc_records)

return reuse_candidates

@staticmethod
def _human_size(num_bytes: int) -> str:
step = 1024.0
Expand Down Expand Up @@ -563,9 +578,17 @@ def quantModel(self, model_id_or_path, trust_remote_code=False, dtype="auto", ne
tokenizer.save_pretrained(path)
self._print_post_quant_artifacts(path)
log.info(f"Quantized Model saved to tmp dir: {path}")
self.perform_post_quant_validation(path, trust_remote_code=trust_remote_code)
q_model = self.loadQuantModel(path, trust_remote_code=trust_remote_code)
q_tokenizer = q_model.tokenizer

target_backend = self.LOAD_BACKEND
reuse_candidates = self.perform_post_quant_validation(path, trust_remote_code=trust_remote_code)

q_model = reuse_candidates.pop(target_backend, None)
if q_model is None:
q_model = self.loadQuantModel(path, trust_remote_code=trust_remote_code)
else:
log.info(f"Reusing post-quant validation model for backend `{target_backend.name}`")

q_tokenizer = q_model.tokenizer or self.load_tokenizer(path, trust_remote_code=trust_remote_code)
if need_create_processor:
processor = AutoProcessor.from_pretrained(path)

Expand Down Expand Up @@ -609,9 +632,13 @@ def loadQuantModel(self, model_id_or_path, trust_remote_code=False, tokenizer_pa
def lm_eval(self, model, apply_chat_template=False, trust_remote_code=False, delete_quantized_model=False, extra_args:dict=None):
try:
with tempfile.TemporaryDirectory() as tmp_dir:
model_path = getattr(model, "model_local_path", None)
if isinstance(model, str):
model_path = model

if self.USE_VLLM:
model_args = {
"pretrained": model.model_local_path,
"pretrained": model_path,
"dtype": "auto", #"float16",
"gpu_memory_utilization": 0.8,
"tensor_parallel_size": 1,
Expand All @@ -630,9 +657,19 @@ def lm_eval(self, model, apply_chat_template=False, trust_remote_code=False, del

for framework, tasks in task_groups.items():
log.info(f"TEST: EVAL starting: backend = {self.LOAD_BACKEND}")
log.info(f"Inference from model path: {model.model_local_path}")
if model_path:
log.info(f"Inference from model path: {model_path}")

if isinstance(model, BaseQModel) and not self.USE_VLLM:
eval_target = model
else:
eval_target = model_path

if eval_target is None:
raise ValueError("Model evaluation target could not be determined.")

results = GPTQModel.eval(
model_or_id_or_path=model.model_local_path,
model_or_id_or_path=eval_target,
llm_backend="vllm" if self.USE_VLLM else "gptqmodel",
model_args=model_args,
output_path=tmp_dir,
Expand Down