109 lines
3.8 KiB
Python
109 lines
3.8 KiB
Python
"""
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Unified core module for Unsloth backend
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Imports are LAZY (via __getattr__) so that training subprocesses can
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import core.training.worker without pulling in heavy ML dependencies
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like unsloth, transformers, or torch before the version activation
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code has a chance to run.
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"""
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__all__ = [
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# Inference
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'InferenceBackend',
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'get_inference_backend',
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# Training
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'get_training_backend',
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'TrainingBackend',
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'TrainingProgress',
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# Config
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'ModelConfig',
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'is_vision_model',
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'scan_trained_loras',
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'load_model_defaults',
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'get_base_model_from_lora',
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# Utils
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'format_and_template_dataset',
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'normalize_path',
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'is_local_path',
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'is_model_cached',
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'without_hf_auth',
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'format_error_message',
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'get_gpu_memory_info',
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'log_gpu_memory',
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'get_device',
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'is_apple_silicon',
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'clear_gpu_cache',
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'DeviceType',
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]
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def __getattr__(name):
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# Inference
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if name in ('InferenceBackend', 'get_inference_backend'):
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from .inference import InferenceBackend, get_inference_backend
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globals()['InferenceBackend'] = InferenceBackend
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globals()['get_inference_backend'] = get_inference_backend
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return globals()[name]
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# Training
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if name in ('TrainingBackend', 'get_training_backend', 'TrainingProgress'):
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from .training import TrainingBackend, get_training_backend, TrainingProgress
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globals()['TrainingBackend'] = TrainingBackend
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globals()['get_training_backend'] = get_training_backend
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globals()['TrainingProgress'] = TrainingProgress
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return globals()[name]
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# Config (from utils.models)
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if name in ('is_vision_model', 'ModelConfig', 'scan_trained_loras',
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'load_model_defaults', 'get_base_model_from_lora'):
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from utils.models import (
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is_vision_model, ModelConfig, scan_trained_loras,
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load_model_defaults, get_base_model_from_lora,
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)
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globals()['is_vision_model'] = is_vision_model
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globals()['ModelConfig'] = ModelConfig
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globals()['scan_trained_loras'] = scan_trained_loras
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globals()['load_model_defaults'] = load_model_defaults
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globals()['get_base_model_from_lora'] = get_base_model_from_lora
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return globals()[name]
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# Paths
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if name in ('normalize_path', 'is_local_path', 'is_model_cached'):
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from utils.paths import normalize_path, is_local_path, is_model_cached
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globals()['normalize_path'] = normalize_path
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globals()['is_local_path'] = is_local_path
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globals()['is_model_cached'] = is_model_cached
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return globals()[name]
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# Utils
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if name in ('without_hf_auth', 'format_error_message'):
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from utils.utils import without_hf_auth, format_error_message
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globals()['without_hf_auth'] = without_hf_auth
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globals()['format_error_message'] = format_error_message
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return globals()[name]
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# Hardware
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if name in ('get_device', 'is_apple_silicon', 'clear_gpu_cache',
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'get_gpu_memory_info', 'log_gpu_memory', 'DeviceType'):
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from utils.hardware import (
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get_device, is_apple_silicon, clear_gpu_cache,
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get_gpu_memory_info, log_gpu_memory, DeviceType,
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)
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globals()['get_device'] = get_device
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globals()['is_apple_silicon'] = is_apple_silicon
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globals()['clear_gpu_cache'] = clear_gpu_cache
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globals()['get_gpu_memory_info'] = get_gpu_memory_info
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globals()['log_gpu_memory'] = log_gpu_memory
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globals()['DeviceType'] = DeviceType
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return globals()[name]
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# Datasets
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if name == 'format_and_template_dataset':
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from utils.datasets import format_and_template_dataset
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globals()['format_and_template_dataset'] = format_and_template_dataset
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return format_and_template_dataset
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raise AttributeError(f"module 'core' has no attribute {name!r}")
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