Remove marlin(old) kernel codes & do ruff (#719)

* clean marlin

* do ruff

gptqmodel/models/auto.py

@@ -1,10 +1,10 @@
 from __future__ import annotations
 
 import os.path
-import torch
 from os.path import isdir, join
 from typing import Dict, List, Optional, Union
 
+import torch
 from gptqmodel.quantization import QUANT_CONFIG_FILENAME
 from huggingface_hub import list_repo_files
 from transformers import AutoConfig

gptqmodel/models/base.py

@@ -10,9 +10,6 @@
 from packaging import version
 from transformers import AutoModelForCausalLM, PreTrainedModel, PreTrainedTokenizerBase, modeling_utils
 
-from ._const import CPU, get_best_device
-from .loader import ModelLoader
-from .writer import QUANT_LOG_DAMP, QUANT_LOG_LAYER, QUANT_LOG_LOSS, QUANT_LOG_MODULE, QUANT_LOG_TIME, ModelWriter
 from ..quantization import GPTQ, QuantizeConfig
 from ..quantization.config import FORMAT, QUANTIZE_BLACK_LIST, AutoRoundQuantizeConfig
 from ..utils.backend import BACKEND
@@ -24,6 +21,9 @@
                            get_module_by_name_suffix, get_moe_layer_modules, move_to,
                            nested_move_to, pack_model, simple_dispatch_model)
 from ..utils.progress import ProgressBar
+from ._const import CPU, get_best_device
+from .loader import ModelLoader
+from .writer import QUANT_LOG_DAMP, QUANT_LOG_LAYER, QUANT_LOG_LOSS, QUANT_LOG_MODULE, QUANT_LOG_TIME, ModelWriter
 
 
 def check_support_param_buffer_assignment(*args, **kwargs):
@@ -203,7 +203,7 @@ def quantize(
 
         if self.quantize_config.format == FORMAT.MARLIN:
             raise ValueError(
-                f"FORMAT.MARLIN is deprecated for quantization. Please switch to FORMAT.GPTQ. GPTQMOdel will auto-use Marlin kernel for accelerated inference for FORMAT.GPTQ."
+                "FORMAT.MARLIN is deprecated for quantization. Please switch to FORMAT.GPTQ. GPTQMOdel will auto-use Marlin kernel for accelerated inference for FORMAT.GPTQ."
             )
 
         if self.quantize_config.lm_head and not isinstance(self.quantize_config, AutoRoundQuantizeConfig):

gptqmodel/nn_modules/qlinear/qlinear_marlin_inference.py

@@ -3,10 +3,10 @@
 
 from typing import Any, Dict, List, Optional, Tuple
 
+import numpy as np
 import torch
-from torch.nn.parameter import Parameter
-
 from gptqmodel.nn_modules.qlinear import BaseQuantLinear
+from torch.nn.parameter import Parameter
 
 marlin_import_exception = None
 try:

gptqmodel/utils/backend.py

@@ -1,5 +1,5 @@
 from enum import Enum
-import torch
+
 
 class BACKEND(Enum):
     AUTO = 0  # choose the fastest one based on quant model compatibility

gptqmodel/utils/importer.py

@@ -2,7 +2,6 @@
 
 import torch
 
-from .backend import BACKEND
 from ..nn_modules.qlinear.qlinear_bitblas import BitBLASQuantLinear
 from ..nn_modules.qlinear.qlinear_cuda import CudaQuantLinear
 from ..nn_modules.qlinear.qlinear_exllama import ExllamaQuantLinear
@@ -12,6 +11,7 @@
 from ..nn_modules.qlinear.qlinear_tritonv2 import TRITON_AVAILABLE, TRITON_INSTALL_HINT, TritonV2QuantLinear
 from ..quantization import FORMAT
 from ..utils.logger import setup_logger
+from .backend import BACKEND
 
 logger = setup_logger()
 
@@ -90,7 +90,7 @@ def select_quant_linear(
         if hasattr(torch, "xpu") and torch.xpu.is_available():
             return IPEXQuantLinear
 
-        # Fallback to IPEX/CPU if cpu supports AVX512 
+        # Fallback to IPEX/CPU if cpu supports AVX512
         from device_smi import Device
         if "avx512_vnni" not in Device("cpu").features:
             raise ValueError("IPEX/CPU requires minimum avx512_vnni support.")

gptqmodel/utils/marlin.py

@@ -4,12 +4,11 @@
 import torch
 from accelerate.utils import find_tied_parameters
 
-from .model import recurse_getattr, recurse_setattr
-from .progress import ProgressBar
-from ..nn_modules.qlinear.qlinear_marlin_inference import MarlinInferenceQuantLinear
-from ..nn_modules.qlinear.qlinear_marlin_inference import _get_perms, unpack_qzeros
+from ..nn_modules.qlinear.qlinear_marlin_inference import MarlinInferenceQuantLinear, _get_perms, unpack_qzeros
 from ..quantization import FORMAT, QuantizeConfig
 from ..utils.logger import setup_logger
+from .model import recurse_getattr, recurse_setattr
+from .progress import ProgressBar
 
 logger = setup_logger()
 

gptqmodel/utils/model.py

@@ -20,16 +20,16 @@
 from transformers import AutoConfig, PretrainedConfig
 from transformers.utils.hub import cached_file
 
-from .backend import BACKEND
-from .importer import select_quant_linear
-from .logger import setup_logger
-from .progress import ProgressBar
 from ..models._const import CPU, EXLLAMA_DEFAULT_MAX_INPUT_LENGTH, EXPERT_INDEX_PLACEHOLDER, SUPPORTED_MODELS
 from ..nn_modules.qlinear import BaseQuantLinear
 from ..nn_modules.qlinear.qlinear_exllama import ExllamaQuantLinear
 from ..nn_modules.qlinear.qlinear_exllamav2 import ExllamaV2QuantLinear
 from ..nn_modules.qlinear.qlinear_marlin_inference import MarlinInferenceQuantLinear
 from ..quantization import FORMAT, QuantizeConfig
+from .backend import BACKEND
+from .importer import select_quant_linear
+from .logger import setup_logger
+from .progress import ProgressBar
 
 logger = setup_logger()