Fix import formatting issues

Author: codewithdark-git

.github/workflows/docs.yml

@@ -1,23 +1,12 @@
-from .data import LoadDataset, DatasetPreprocessor, DatasetSplitter, DataLoader
-from .trainer import FineTuningTrainer, ModelEvaluator
-from .hub import HubManager, CheckpointManager
-from .utils import (
-    get_optimal_training_settings,
-    configure_logging,
-    enable_logging,
-    QuantizationBenchmark,
-)
 from .api import QuantLLM
-
-from .quant import (
-    QuantizationConfig,
-    QuantizationEngine,
-    QuantizedLinear,
-    GGUFQuantizer,
-)
-
-
-from .config import ModelConfig, DatasetConfig, TrainingConfig
+from .config import DatasetConfig, ModelConfig, TrainingConfig
+from .data import DataLoader, DatasetPreprocessor, DatasetSplitter, LoadDataset
+from .hub import CheckpointManager, HubManager
+from .quant import (GGUFQuantizer, QuantizationConfig, QuantizationEngine,
+                    QuantizedLinear)
+from .trainer import FineTuningTrainer, ModelEvaluator
+from .utils import (QuantizationBenchmark, configure_logging, enable_logging,
+                    get_optimal_training_settings)
 
 # Configure package-wide logging
 configure_logging()

quantllm/__init__.py

@@ -1,9 +1,12 @@
-from typing import Optional, Dict, Any, Union, Tuple
+from typing import Any, Dict, Optional, Tuple, Union
+
 import torch
-from transformers import PreTrainedModel, AutoTokenizer
-from ..quant.gguf import GGUFQuantizer, SUPPORTED_GGUF_BITS, SUPPORTED_GGUF_TYPES
-from ..utils.logger import logger
+from transformers import AutoTokenizer, PreTrainedModel
+
+from ..quant.gguf import (SUPPORTED_GGUF_BITS, SUPPORTED_GGUF_TYPES,
+                          GGUFQuantizer)
 from ..utils.benchmark import QuantizationBenchmark
+from ..utils.logger import logger
 
 
 class QuantLLM:

quantllm/api/high_level.py

@@ -1,6 +1,6 @@
 """Command-line interface for QuantLLM package."""
 
-from .commands import train, evaluate, quantize, serve
+from .commands import evaluate, quantize, serve, train
 from .parser import create_parser
 
 __all__ = ["main", "train", "evaluate", "quantize", "serve"]

quantllm/cli/__init__.py

@@ -1,9 +1,10 @@
 from argparse import Namespace
-from ..models import ModelLoader, QuantConfig
+
+from ..config import DatasetConfig, ModelConfig, TrainingConfig
 from ..data import DatasetLoader
-from ..training import FineTuningTrainer, ModelEvaluator
-from ..config import ModelConfig, TrainingConfig, DatasetConfig
+from ..models import ModelLoader, QuantConfig
 from ..runtime import DeviceManager
+from ..training import FineTuningTrainer, ModelEvaluator
 from ..utils.logger import logger
 
 

quantllm/cli/commands.py

@@ -1,6 +1,7 @@
 import argparse
 from typing import Any
-from .commands import train, evaluate, quantize, serve
+
+from .commands import evaluate, quantize, serve, train
 
 
 def create_parser() -> argparse.ArgumentParser:

quantllm/cli/parser.py

@@ -1,6 +1,6 @@
 from .config_manager import ConfigManager
-from .training_config import TrainingConfig
-from .model_config import ModelConfig
 from .dataset_config import DatasetConfig
+from .model_config import ModelConfig
+from .training_config import TrainingConfig
 
 __all__ = ["ConfigManager", "TrainingConfig", "ModelConfig", "DatasetConfig"]

quantllm/config/__init__.py

@@ -1,7 +1,9 @@
-import yaml
 import json
-from typing import Dict, Any, Optional
 from pathlib import Path
+from typing import Any, Dict, Optional
+
+import yaml
+
 from ..utils.logger import logger
 
 

quantllm/config/config_manager.py

@@ -1,6 +1,6 @@
 from dataclasses import dataclass
-from typing import Optional, Dict, Any, List, Union
 from pathlib import Path
+from typing import Any, Dict, List, Optional, Union
 
 
 @dataclass

quantllm/config/dataset_config.py

@@ -1,6 +1,6 @@
 from dataclasses import dataclass
-from typing import Optional, Dict, Any, List
 from pathlib import Path
+from typing import Any, Dict, List, Optional
 
 
 @dataclass

quantllm/config/model_config.py

@@ -1,6 +1,6 @@
 from dataclasses import dataclass
-from typing import Optional, List, Dict, Any
 from pathlib import Path
+from typing import Any, Dict, List, Optional
 
 
 @dataclass

quantllm/config/training_config.py

@@ -1,6 +1,6 @@
-from .load_dataset import LoadDataset
+from .dataloader import DataLoader
 from .dataset_preprocessor import DatasetPreprocessor
 from .dataset_splitter import DatasetSplitter
-from .dataloader import DataLoader
+from .load_dataset import LoadDataset
 
 __all__ = ["LoadDataset", "DatasetPreprocessor", "DatasetSplitter", "DataLoader"]

quantllm/data/__init__.py

@@ -1,7 +1,10 @@
-from typing import Optional, Union, Dict, Any
+from typing import Any, Dict, Optional, Union
+
 import torch
-from torch.utils.data import DataLoader as TorchDataLoader, Dataset, TensorDataset
 from datasets import Dataset as HFDataset
+from torch.utils.data import DataLoader as TorchDataLoader
+from torch.utils.data import Dataset, TensorDataset
+
 from .dataset_preprocessor import DatasetPreprocessor
 
 

quantllm/data/dataloader.py

@@ -1,10 +1,12 @@
+import logging
+import warnings
+from typing import Any, Callable, Dict, Optional, Tuple
+
 from datasets import Dataset
-from typing import Optional, Dict, Any, Callable, Tuple
+from tqdm.auto import tqdm
 from transformers import PreTrainedTokenizer
+
 from ..utils.logger import logger
-from tqdm.auto import tqdm
-import logging
-import warnings
 
 # Disable unnecessary logging
 logging.getLogger("tokenizers").setLevel(logging.ERROR)

quantllm/data/dataset_preprocessor.py

@@ -1,9 +1,11 @@
-from datasets import Dataset, DatasetDict
+import logging
 from typing import Optional, Tuple, Union
+
 import numpy as np
-from ..utils.logger import logger
+from datasets import Dataset, DatasetDict
 from tqdm.auto import tqdm
-import logging
+
+from ..utils.logger import logger
 
 # Configure logging
 logging.getLogger("datasets").setLevel(logging.WARNING)

quantllm/data/dataset_splitter.py

@@ -1,10 +1,12 @@
-from datasets import load_dataset, Dataset
-from typing import Optional, Dict, Any, Union
-import os
-from ..utils.logger import logger
-from tqdm.auto import tqdm
 import logging
+import os
 import warnings
+from typing import Any, Dict, Optional, Union
+
+from datasets import Dataset, load_dataset
+from tqdm.auto import tqdm
+
+from ..utils.logger import logger
 
 # Disable unnecessary logging
 logging.getLogger("datasets").setLevel(logging.WARNING)

quantllm/data/load_dataset.py

@@ -1,4 +1,4 @@
-from .hub_manager import HubManager
 from .checkpoint_manager import CheckpointManager
+from .hub_manager import HubManager
 
 __all__ = ["HubManager", "CheckpointManager"]

quantllm/hub/__init__.py

@@ -1,7 +1,8 @@
 import os
 import shutil
-from typing import Optional, Dict, Any
 from datetime import datetime
+from typing import Any, Dict, Optional
+
 from ..utils.logger import logger
 
 

quantllm/hub/checkpoint_manager.py

@@ -1,7 +1,9 @@
-from huggingface_hub import login, HfApi, Repository
-from typing import Optional, Dict, Any
-from ..utils.logger import logger
 import os
+from typing import Any, Dict, Optional
+
+from huggingface_hub import HfApi, Repository, login
+
+from ..utils.logger import logger
 
 
 class HubManager:

quantllm/hub/hub_manager.py

@@ -1,7 +1,8 @@
 """GGUF Quantization for Large Language Models."""
 
-from .quantization_engine import QuantizationConfig, QuantizedLinear, QuantizationEngine
 from .gguf import GGUFQuantizer
+from .quantization_engine import (QuantizationConfig, QuantizationEngine,
+                                  QuantizedLinear)
 
 __all__ = [
     "QuantizationConfig",

quantllm/quant/__init__.py

@@ -1,7 +1,8 @@
 """Support for advanced quantization formats."""
 
 from dataclasses import dataclass
-from typing import Optional, Dict, Any, Literal
+from typing import Any, Dict, Literal, Optional
+
 import torch
 
 

quantllm/quant/formats.py

@@ -2,19 +2,17 @@
 
 import gc
 import math
+import time
+from typing import Any, Dict, List, Optional, Tuple, Union
+
 import torch
 import torch.nn as nn
-from typing import Optional, Dict, Any, List, Union, Tuple
+from tqdm.auto import tqdm
 from transformers import PreTrainedModel
-from .quantization_engine import (
-    move_to_device,
-    BaseQuantizer,
-    QuantizationConfig,
-    QuantizedLinear,
-)
+
 from ..utils.logger import logger
-import time
-from tqdm.auto import tqdm
+from .quantization_engine import (BaseQuantizer, QuantizationConfig,
+                                  QuantizedLinear, move_to_device)
 
 try:
     import ctransformers
@@ -376,9 +374,9 @@ def convert_to_gguf(self, output_path: str):
             self.model.save_pretrained(temp_dir, safe_serialization=True)
 
             # Prepare conversion command
+            import os
             import subprocess
             import sys
-            import os
 
             # Try to find convert.py from llama.cpp
             convert_script = None

quantllm/quant/gguf.py

@@ -1,11 +1,12 @@
 """Optimized Triton kernels for faster model operations."""
 
+import math
+from typing import Any, Dict, List, Optional, Union
+
 import torch
 import triton
 import triton.language as tl
-from typing import Dict, Any, Optional, List, Union
 from transformers import PreTrainedModel
-import math
 
 
 @triton.jit

quantllm/quant/kernels.py

@@ -1,17 +1,15 @@
 """Efficient model quantization engine."""
 
-from typing import Dict, Any, Optional, Union, List, Tuple
+import gc
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import numpy as np
 import torch
 import torch.nn as nn
-from transformers import (
-    PreTrainedModel,
-    AutoConfig,
-    AutoModelForCausalLM,
-    AutoTokenizer,
-)
-import numpy as np
+from transformers import (AutoConfig, AutoModelForCausalLM, AutoTokenizer,
+                          PreTrainedModel)
+
 from ..utils.logger import logger
-import gc
 
 
 def get_device_map(model: PreTrainedModel) -> Dict[str, torch.device]:

quantllm/quant/quantization_engine.py

@@ -1,6 +1,6 @@
 import torch
 import torch.nn as nn
-from transformers import PreTrainedModel, PretrainedConfig
+from transformers import PretrainedConfig, PreTrainedModel
 
 
 class DummyConfig(PretrainedConfig):